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Causal explanations for mathematics performance given by low socioeconomic status African American mothers and their children

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Causal explanations for mathematics performance given by low socioeconomic status African American mothers and their children
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Cyrus, Kenneth R., 1956-
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xiii, 123 leaves : ; 29 cm.

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African Americans ( jstor )
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Furniture tables ( jstor )
Low socioeconomic status ( jstor )
Mathematical tables ( jstor )
Mathematics ( jstor )
Mothers ( jstor )
Rating scales ( jstor )
Statistical interpretation errors ( jstor )
Dissertations, Academic -- Foundations of Education -- UF
Foundations of Education thesis, Ph. D
Alachua County ( local )
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Thesis (Ph. D.)--University of Florida, 1996.
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Includes bibliographical references (leaves 117-122).
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Typescript.
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Vita.
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by Kenneth R. Cyrus.

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Full Text









CAUSAL EXPLANATIONS FOR MATHEMATICS PERFORMANCE
GIVEN BY LOW SOCIOECONOMIC STATUS AFRICAN AMERICAN MOTHERS
AND THEIR CHILDREN















By
KENNETH R. CYRUS













A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA




CAUSAL EXPLANATIONS FOR MATHEMATICS PERFORMANCE
GIVEN BY LOW SOCIOECONOMIC STATUS AFRICAN AMERICAN MOTHERS
AND THEIR CHILDREN
By
KENNETH R. CYRUS
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
19%


Copyright 1996
by
Kenneth R. Cyrus


To my grandparents,
Edward and Elizabeth Cyrus,
and
Anderson and Camilla Catchings,
to my parents,
Frank and Ethel Cyrus,
to my daughter,
Yvette Moran Cyrus,
and to the rest of the immediate and extended
Cyrus family


ACKNOWLEDGMENTS
I would first like to thank my doctoral committee for their support of my efforts to
complete this formidable task. My chairman, Dr. Barry Guinagh, with his sympathetic
heart and mind gave invaluable guidance for this project. I thank Dr. James Algina, for
his continued confidence in my ability and for his statistical and editorial expertise. Drs.
Walter Busby, Woodrow "Max" Parker, and Mary Howard-Hamilton deserve very warm
and special thanks for their valuable insight and encouragement. Two of the original
members of my committee, Dr. Donald Avila, and Dr. Robert Jester, did not live to see
this dissertation come to fruition. They remained an inspiration and a shining light when
the process became dark. My hat is off to these two men. I would also like to thank Dr.
Patricia Ashton who initially gave me confidence while pursuing my doctoral program at
the University of Florida. The faculty in Foundations, Dr. Arthur Newman, Dr. Robert
Sherman, Dr. Rodman Webb, Dr. John Newell, Dr. Hannalore Wass, Dr. A. O. White and
Dr. David Miller, were friends whose courses inspired me.
I thank the current Dean of the College of Education, Dr. Roderick McDavis,
who was responsible for my recruitment to the University of Florida, and who provided
guidance and inspiration through the many years I have known him.
IV


Dr. Willa Wolcott, Director of the Reading and Writing Center, deserves special
thanks for her support, confidence, and the employment she provided for a number of
years. May God richly bless her. I would like to thank Diane Heaney, Dr. Diane
Stevenson, and Margaret Steptoe for their encouragement and support. They provided
enthusiasm when I did not have any. Ellen Burleson, a colleague and friend, gave
untiring support, confidence, loyalty, and belief in me. I believe that, without her
support, completing this task would have been unusually difficult.
To the Alachua County Housing Authority, I would like to say thank you for the
introduction to the participants in this study. Under the direction of Ms. Eula Williams, I
was able to have access to the Housing Projects in Gainesville. My deepest thanks go to
Mr. Terry Lee who provided masterful guidance in arranging interviews with low income
mothers and their children. Mr. Lee is a special friend, who assisted me in so many
ways. Sincerest thanks go to Mr. Larry Saunders, a dear friend and confidant who
provided enormous support for a number of years. Also, thanks go to Dr. James D.
Lockett.
Finally, I would like to thank my parents for their patience, confidence, and
undying devotion to my pursuit of the doctoral degree. I would also like to acknowledge
my siblings who would never let me quit. For my daughter, I pass on the truth that
enabled me to persevere: Through the grace and providence of God, all things are
possible.
v


TABLE OF CONTENTS
ACKNOWLEDGMENTS iv
LIST OF TABLES viii
ABSTRACT xii
CHAPTER 1 INTRODUCTION 1
Statement of the Problem 2
Purpose of the Study 6
Significance of the Study 7
CHAPTER 2 REVIEW OF THE LITERATURE 9
Weiners Attribution Theory 10
Attribution Measurement Issues 14
Attribution Patterns of Mothers and Their Children 17
Mothers Attributions and Gender Differences 23
Childrens Predictions of Their MothersAttributions 27
Summary 28
CHAPTER 3 METHODOLOGY 30
Hypotheses 31
vi


Methods 32
Variables Under Investigation 40
Research Design 40
Data Analysis 40
CHAPTER 4 DATA ANALYSIS 41
Mothers and Childrens Success and Failure Attributions for Mathematics
performance: Rating Scale Method 42
Mothers Predictions of Their Childrens Mathematics Success and Failure
Attributions: Rating Scale Method 53
Childrens Predictions of Their Mothers Mathematics Success and Failure
Attributions: Rating Scale Method 59
Mothers and childrens cussess and Failure Attributions for mathematics
Performance: Chip Distribution Method 69
Mothers Predictions of Their Childrens Mathematics Success and Failure
Attributions: Chip Distribution Method 78
Childrens Predictions of Their Mothers Mathematics Success and Failure
Attributions: Chip Distribution Method 83
CHAPTER 5 DISCUSSION 94
APPENDICES 114
APPENDIX A: Parental Consent Form 114
APPENDIX B: Student Assent Form 115
REFERENCES 117
BIOGRAPHICAL SKETCH 123
vii


LIST OF TABLES
Table 3-1 Sample Size for Rating Scale Respondents 33
Table 3-2 Sample Size for Chip Distribution Respondents 34
Table 4-1 Mother's and Children's Mathematics Success Attributions by Grade
and Sex 45
Table 4-2 Summary of ANOVA for Success Attributions 46
Table 4-3 Success Mean Ratings as a Function of Respondent and Type of
Attribution 47
Table 4-4 Success Mean Ratings as a Function of Sex and Type of Attribution 47
Table 4-5 Success Mean Ratings as a Function of Respondent, Child's Sex, and
Type of Attribution 48
Table 4-6 Success Attribution Means by Grade 48
Table 4-7 Pearson Correlation Coefficients Between Mother's and Children's
Success Attributions within the Family 49
Table 4-8 Mother's and Children's Mathematics Failure Attributions by Grade
and Sex 50
Table 4-9 Summary of ANOVA for Failure Attributions 51
Table 4-10 Failure Mean Ratings as a Function of Respondent and Type of
Attribution 52
Table 4-11 Pearson Correlation Coefficients Between Mothers's
and Children's Failure Attributions Within the Family 52
viii


Table 4-12 Pearson Correlation Coefficients Between Children's Success
Attributions and Mothers's Predictions of Their Childrens Success
Attributions Within the Family 54
Table 4-13 Children's Mathematics Success Attributions and Mother's
Predictions of Their Children's Mathematics Success Attributions by
Grade and Sex 56
Table 4-14 Summary of ANOVA for Children's Mathematics Success
Attributions and Mother's Predictions of Their Children's
Mathematics Success Attributions 57
Table 4-15 Predicted Success Mean Rating as a Function of Respondent
and Type of Attribution 58
Table 4-16 Pearson Correlation Coefficients Between Children's Failure Attributions
and Mothers Prediction of Children's Attributions Within the Family 58
Table 4-17 Children's Mathematics Failure Attributions and Mother's Predictions
of Their Children's Mathematics Failure Attributions by Grade and Sex 60
Table 4-18 Summary of ANOVA for Children's Mathematics Failure Attributions
and Mother's Predictions of their Childrens Mathematics Failure
Attributions 61
Table 4-19 Predicted Failure Mean Ratings as a Function of Respondent and
Type of Attribution 62
Table 4-20 Pearson Correlation Coefficients Between Mother's Success
Attributions and Children's Predictions of Motherss Success
Attributions Within the Family 62
Table 4-21 Mother's Mathematics Success Attributions and Children's
Predictions of Their Mother's Mathematics Success Attributions by Grade
and Sex 63
Table 4-22 Summary of ANOVA for Mother's Mathematics Success Attributions
and Children's Predictions of Their Mother's Mathematics Success
Attributions 64
Table 4-23 Predicted Success Means by Respondent 65
ix


Table 4-24 Pearson Correlation Coefficients Between Mother's Failure
Attributions and Children's Predictions of Mother's Failure
Attributions Within the Family 66
Table 4-25 Mother's Mathematics Failure Attributions and Children's Predictions
of Their Mother's Mathematics Failure Attributions by Grade and Sex 67
Table 4-26 Summary of ANOVA for Mother's Mathematics Failure Attributions
and Children's Predictions of Their Mother's Mathematics Failure
Attributions 68
Table 4-27 Predicted Failure Means as a Function of Respondent x Type of
Attribution 69
Table 4-28 Mother's and Children's Mathematics Success Attributions by Grade
and Sex 72
Table 4-29 Summary of ANOVA for Success Attributions 73
Table 4-30 Success Means as a Function of Respondent and Type of Attribution ... 73
Table 4-31 Pearson Correlation Coefficients Between Mothers' and Children's Success
Attributions Within the Family 74
Table 4-32 Mother's and Children's Mathematics Failure Attributions by Grade
and Sex 75
Table 4-33 Summary of ANOVA for Failure Attributions 76
Table 4-34 Failure Means as a Function of Respondent, Type of Attribution, and
Grade 77
Table 4-35 Pearson Correlation Coefficients Between Mothers' and Children's
Failure Attributions Within the Family 77
Table 4-36 Pearson Correlation Coefficients Between Children's Actual Success
and Mothers' Predicted Success Attributions Within the Family 79
Table 4-37 Children's Mathematics Success Attributions and Mother's
Predictions of Their Children's Mathematics Success Attributions by
Grade and Sex 81
x


Table 4-3B Summary of ANOVA for Childrens Mathematics Success
Attributions and Mother's Predictions of Their Children's
Mathematics Success Attributions 82
Table 4-39 Predicted Success Means as a function of Respondent and Type of
Attribution 82
Table 4-40 Pearson Correlation Coefficients Between Children's Actual Failure
and Mothers' Predicted Failure Attributions Within the Family 83
Table 4-41 Children's Mathematics Failure Attributions and Mother's Predictions
of Their Children's Mathematics Failure Attributions by Grade and Sex .... 84
Table 4-42 Summary of ANOVA for Children's Mathematics Failure Attributions
and Mother's Predictions of Their Children's Mathematics Failure
Attributions 85
Table 4-43 Pearson Correlation Coefficients Between Mother's Actual Success
and Children's Predicted Success Attributions Within the Family 86
Table 4-44 Mother's Mathematics Success Attributions and Children's
Predictions of Their Mother's Mathematics Success Attributions by Grade
and Sex 87
Table 4-45 Summary of ANOVA for Mother's Mathematics Success
Attributions and Children's Predictions of Their Mother's Mathematics
Success Attributions 88
Table 4-46 Predicted Success Means as a Function of Respondent and Type of
Attribution 89
Table 4-47 Pearson Correlation Coefficients Between Mother's Actual Failure
and Children's Predicted Failure Attributions Within the Family 89
Table 4-48 Mother's Mathematics Failure Attributions and Children's Predictions
of Their Mothers Mathematics Failure Attributions by Grade and Sex 91
Table 4-49 Summary of ANOVA for Mother's Mathematics Failure Attributions
and Children's Predictions of Their Mother's Mathematics Failure
Attributions 92
Table 4-50 Predicted Failure Means as a Function of Respondent, Type of
Attribution, Grade, and Sex 93
xi


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
CAUSAL EXPLANATIONS FOR MATHEMATICS PERFORMANCE GIVEN BY
LOW SOCIOECONOMIC STATUS AFRICAN AMERICAN MOTHERS AND THEIR
CHILDREN
By
Kenneth R. Cyrus
May, 1996
Chairman: Barry Guinagh
Major Department: Foundations of Education
The present study examines the attributional beliefs of low SES African American
mothers and their 6th, 7th, and 8th grade children for the childrens mathematics
performance. Using two attribution measures, the rating scale and chip distribution
methods, data from mothers and children were examined for (a) attributional agreement
about the causes of mathematics performance, (b) mothers' attributions and children's
gender, (c) attributional agreement within the family, (d) mothers' and children's
attributional predictions, and (e) whether results would vary depending on the method-
chip distribution or rating scale-used for data collection.
The results from the rating scale and chip distribution methods revealed similar
findings on all hypotheses except for mothers and gender-related attributions. Both
xii


methods showed that mothers and children generally explained mathematics success as a
result of effort, with some emphasis on school and home training. For mathematics
failure, mothers emphasized lack of effort, whereas children emphasized lack of home
training and lack of effort. Both methods showed that mothers and children within the
same household were not in agreement about the causes of mathematics performance.
The two methods differed on the issue of mothers and gender-related attributions. The
chip distribution method showed that mothers believed that boys and girls succeed and
fail in mathematics for the same reasons. The rating scale revealed mothers believed
boys have more ability in mathematics than girls and girls had to try harder than boys to
be successful in mathematics. For failure, mothers did not make distinctions related to
gender. Both methods also showed that mothers could not predict the attributions the
children would give for the children's mathematics performance and children could not
predict the attributions their mothers would give for the children's performance.
Therefore, generally, the rating scale and chip distribution methods provided comparable
results when examining the attributions of low SES African American mothers and
children, with the exception of gender-related attributions.
Attributional beliefs of the African American family and measurement of
academic attributions are discussed.
xiii


CHAPTER 1
INTRODUCTION
Attributional beliefs are causal explanations individuals hold for their successful
or unsuccessful performance at a task. According to attribution theory, these beliefs
influence motivation, which in turn influence achievement. Consequently, research on
attributions is valuable to the extent that it provides insights into student motivational
factors (Graham, 1991; 1994). Attribution theory was first postulated by Weiner
(Weiner, 1979; 1985; 1986), who believed that attributions were one of the most
important influences on behavior and motivation in achievement settings. The causal
attributions individuals make for their performance will influence the individuals'
expectations for success on future tasks and their emotional response to the event. Causal
attributions most often given by individuals are ability, effort, task difficulty, and luck.
Sixty additional attributions have since been documented by other researchers (Hau &
Salili, 1993). Attributions can be classified on three attribution dimensions: locus of
causewhether the reason is internal or external to the individual; stability of cause
whether the reason is temporary or permanent; and controllability of causewhether the
reason is under the control of the individual or not.
Although individuals may use several attributions to explain success and failure,
causal dimensions help clarify the similarities and differences among the various causal
attributions (Weiner, 1985; 1986; Graham, 1991). For example, ability and effort are
1


2
similar attributions in that both attributions are internal to the individual. However,
ability and effort are different because ability is considered a stable attribution not subject
to change, whereas effort is an unstable attribution which may change from occasion to
occasion. Ability cannot be controlled by the individual but effort is controllable.
Further, task difficulty and luck are both classified in Weiner's (1985) attribution theory
as external attributions and, as such, are not under the control of the individual.
However, task difficulty and luck differ in that luck is an unstable attribution because it
varies from occasion to occasion, while task difficulty for a specific task is considered a
stable or permanent attribution.
Statement of the Problem
Causal attributions about school performance as they are held by a family unit and
the similarities and differences in attributional beliefs between parents and children have
been the focus of past empirical study (Cashmore & Goodnow, 1986; Hess, Chih-Mei, &
McDevitt, 1987; Holloway & Hess, 1982; Holloway, Kashiwagi, Hess, & Azuma, 1986;
Stevenson & Lee, 1990; Yamauchi, 1989). This research has revolved around five basic
issues: parents and children's agreement about attributions for school performance;
parent-child attributional agreement within individual families; comparisons of parent-
child agreement between cultures; the relation of gender of the child to causal attributions
of both parents and children; and children's accuracy in predicting their mother's
attributions about children's school performance.


3
Three basic problems have emerged from this research. First, there has been a
lack of consistency in the measurement of attributions across studies. Three methods
have been used: a ranking, a chip distribution, and a rating method. The ranking method
asks the subject to rank attributions (such as luck, effort, and ability) from most important
to least important. The chip method gives the subjects a limited number of chips (or
sometimes points -- e.g. Stevenson & Lee, 1990) that they use to designate the
importance of attributions. The rating scale method uses a scale that subjects use to
indicate the strength of each attribution. For example, Cashmore and Goodnow (1986)
employed a rank order measure when comparing Australian mothers' and children's
attributional beliefs. Holloway and Hess (1982) used a chip distribution measure to study
Caucasian American mothers and children while Parsons, Adler, and Kaczala (1982) used
a rating scale instrument with this population. A point distribution measure was used by
Stevenson and Lee (1990) to compare the attributional beliefs of Caucasian American,
Chinese, and Japanese mothers and children.
Studies of attributions have used one of these three methods, but not two or three
methods at the same time. Inconsistent findings in this area of attribution research may
have been caused by the way attributions were measured. Various measurement
techniques used in attribution studies have both advantages and disadvantages and
different results found in attribution research may vary according to the measurement
technique used. This raises questions of reliability and validity for attribution research
(Elig & Frieze 1979; Hau & Salili, 1993). The inconsistent use of methods and


4
subsequent results found in attribution research make predictability and generalization of
findings to other populations or situations difficult.
A second problem has been that results across various studies addressing parents'
and childrens attributions for academic performance were inconclusive. For example, for
parents' and children's attributional agreement, Caucasian American mothers and
children were in agreement about the causes for academic success and failure (Parsons,
Adler, & Kaczala, 1982); however, Holloway and Hess (1982) reported Caucasian
American mothers and children were not in agreement about the causes of school
performance. In another example, relating to gender, Japanese, Anglo-Australian, and
Italian Australian mothers did not make the distinction in attributions between their sons
and daughters for their mathematics performance (Cashmore & Goodnow, 1986;
Holloway, Kashiwagi, Hess, & Azuma, 1986). In contrast, Parsons, Adler, and Kaczala
(1982), Holloway and Hess (1982), and Holloway, Kashiwagi, Hess, and Azuma (1986)
found that Caucasian American mothers did make the distinction between attributions for
their sons' and daughters' mathematics performance.
The third problem found in the attribution literature has been that research
involving African Americans parents and children was quite limited. Some research has
been done with African American children that compares them to Caucasian American
children (Graham, 1991, 1994). However, no research with African American mothers
and children has been done (Graham, 1991; 1994). In reviews of minority participation
and achievement in mathematics, Matthews (1984), Reyes and Stanic (1988) identified
mothers as an important variable for their children's successful mathematics achievement.


5
They reported mothers' race, education, attitudes, expectations, and aspirations influenced
their children's mathematics achievement. However, both reviews cited a lack of research
in this area and recommended more research with minority populations. Although there
are few empirical studies, some early research involving minority women scientists (Hall
1981; Kenschaft, 1981; Malcolm, Hall, & Brown, 1976) found that the mother's attitudes
about, and a positive belief in their children's ability to achieve success in mathematics,
positively influenced the children's participation and performance. More research on
motivation is also needed because the African American population has consistently
achieved at lower levels than other groups (Jenkins, 1989), particularly in mathematics
and especially for the low socioeconomic status (SES) African American population
(Reyes, 1984; Reyes & Stanic, 1985; 1988). Therefore, an understanding of the
attributional beliefs of low SES African American mothers and children in relation to the
children's mathematics performance would seem to be important to understanding their
mathematics achievement.
This study examined the causal attributions of low SES African American
mothers and children for success and failure in mathematics. The following issues were
examined: (a) mothers and children's attributional beliefs about mathematics
performance; (b) agreement within the family about that performance; (c) gender
differences; (d) the ability of children to predict attributions given by their mothers; and
(e) the ability of mothers to predict the attributions given by their children. Additionally,
because the research in this field has used both a rating system and a chip distribution


system, both systems will be used to assess these beliefs and to examine the
comparability of these methods.
6
Purpose of the Study
The primary purpose of this study was to compare the causal attributions of low
SES African American mothers and children for mathematics performance. The
secondary purpose was to compare two attribution instruments in their ability to measure
the attributions of low SES African American mothers and children.
The research questions addressed by this study were as follows:
1. Are there significant differences in causal attributions of low SES African
American mothers and their children for mathematics success and failure?
2. Do low SES African American mothers give different explanations for
mathematics success and failure for their sons and daughters?
3. Can low SES African American mothers predict their children's causal
attributions for mathematics success and failure?
4. Can low SES African American children predict their mother's causal
attributions for the children's mathematics success and failure?
5. Are there significant differences in causal attributions of low SES African
American mothers and their children for mathematics success and failure within the
family unit?
6. Will low SES African American mothers' and children's causal attributions
differ when measured by separate attribution instruments?


7
Significance of the Study
Examining the causal attributions of African American children and parents is
important for several reasons. First, achievement in school is important. However,
African American children have not done well in mathematics, even though their mothers
believe they are doing well and will continue to do well (Alexander & Enwistle, 1988;
Matthews, 1984; Reyes & Stanic, 1988). Next, achievement is related to motivation and
motivation is related to attributions (Weiner, 1986). This connection between attributions
and motivation can be seen in other cultures. For example, Japanese and Chinese
mothers' belief in hard work or high effort has shown to be highly motivating for their
children's mathematics achievement (Holloway, Kashiwagi, Hess, & Azuma, 1986;
Stevenson & Lee, 1990). Parents influence the attitudes of children. Interviews of
African American parents find they want their children to do well in school (Alexander &
Enwistle, 1988; Stevenson, Chin, & Uttal, 1990). Do the attributions held by African
American children about motivation correlate to those attributions held by their parents,
or are there differences in attributions between mothers and children?
Additionally, this research is important because it attempts to clarify some
measurement concerns found in the parents and children's attribution literature. The past
research has used numerous methods to measure causal attributions. This may be the
cause of some of the inconsistent findings. The present study was designed to determine
whether results would be consistent when different methods are used to collect the
attribution data. Therefore, this study will determine whether two widely used
measurement instruments in attribution research are comparable for assessing the causal


g
attributions of low SES African American mothers and children for the children's
mathematics performance.
This study also provides further testing of attribution theory. Understanding the
attributional beliefs of African Americans may assist in understanding the nature of the
beliefs that help create the academic conditions experienced by this population.
Attribution theory could provide a model to investigate attribution patterns that hinder
achievement motivation in mathematics (Graham, 1991; 1994).
Finally this study will assist in establishing future research directions for the study
of motivation in the African American family. Graham (1994) has argued for more
research involving African American parents and children. She states,
Future studies conducted within a motivational
framework should systematically examine the sources of African-
American parents' beliefs about their children's ability and
prospects, whether and how these beliefs get communicated to the
child, and how they get played out in parenting practices that have
the potential to either undermine or enhance achievement strivings,
(p. 107)


CHAPTER 2
REVIEW OF THE LITERATURE
The primary purpose of this study was to compare the causal attributions of low
SES African American mothers and their children for mathematics performance. A
second purpose was to examine whether the rating scale instrument and the chip
distribution instrument yield similar and consistent results when measuring mathematics
attributions of African American mothers and children.
While mothers' and children's academic attributional beliefs of numerous cultures
have been studied (e.g., Bar-Tal & Guttmann, 1981; Cashmore & Goodnow, 1986; Hess,
Chih-Mei, & McDevitt, 1987; Holloway, & Hess, 1982; Stevenson & Lee, 1990), African
American mothers and children have not been the focus of any studies in the attribution
literature. Related literature was therefore chosen for review. The review will be
discussed as follows: first, a general overview of Weiner's attribution theory; second,
attribution measurement issues; third, attribution patterns of mothers and their children
for school performance; fourth, mothers' attributions and gender differences; fifth,
children's accuracy in predicting parent's causal attributions for the children's
mathematics performance. The chapter concludes with a summary of the review.
9


10
Weiner's Attribution Theory
"Attribution theory is a theory of motivation and emotion, with achievement
strivings as the theoretical focus (Weiner, 1985, p. 549). The attribution approach to
achievement motivation was originated by Heider (1958) and later elaborated by Weiner
(Weiner, 1979; 1986; Weiner & Kukla, 1970). "A central assumption of attribution
theory is that the search for understanding is the basic spring to action" (Weiner, 1979, p.
3). In a school setting the search for understanding of success and failure experiences
often leads to attributional questions such as "Why did 1 succeed?" or "Why did I fail?"
According to Weiner (1985), the individual's desire for mastery of his or her environment
and situations that are unexpected are contributors to searching for causal explanations.
Perceived Causality
In their initial statement regarding the perceived causes of success and failure
Weiner, Frieze, Kukla, Reed, Rest, and Rosenbaum (1971) argued that in achievement
related contexts the causes perceived as most responsible for success and failure are
ability, effort, task difficulty, and luck. They asked participants to rate the importance of
four causes of success and failure. The participants were to rate the relative contribution
of high or low ability, high or low effort, ease or difficulty of the task, and good or bad
luck to account for their success or failure on a task (see also, Weiner, 1986). However,
in later studies (Weiner, 1974; Weiner, Russell, & Lerman, 1978) suggested other factors
such as mood, fatigue, illness, and bias could serve as necessary and sufficient reasons for
achievement performance. Many investigations have been conducted that more
systematically examine causal perceptions, particularly the perceived causes of success


11
and failure (Weiner, 1985). Two research procedures were followed to determine if
subjects attributed success and failure to other factors. First, the free response method, in
which subjects were provided with only outcome information, namely a success or
failure, has taken place. Participants were asked to list all possibilities that come to mind
for the success or failure. In the second procedure, subjects were given large lists of
causes and were asked to rate the contribution of each cause to the hypothesized outcome
(Weiner, 1985,1986). From these two procedures, a variety of causes have been
documented (Weiner, 1986). Among the numerous causes, seven attributions received
the most attention including ability, immediate and long-term effort, task characteristics,
intrinsic motivation, teacher's competence, mood, and luck. It should be noted that the
most dominant causes were ability and effort. In most cases success was attributed to
high ability and hard work and failure was thought to be caused by low ability and poor
effort (Weiner, 1979; 1985; 1986). This also holds true for a number of cultural groups
(Triandis, 1972).
Structure of Perceived Causality
Weiner's (1979; 1985) theory classifies the types of attributions made by
individuals by using a three-dimensional taxonomy of causes of success and failure. The
first dimension, labeled locus of causality, determines whether a cause is within the
person or within the environment. Rotter's (1966) construct of locus of control was the
first to classify people as internals or externals. Ability and effort causal attributions are
internal to an individual, whereas task difficulty or luck are external.


12
The second dimension of causality, referred to as stability, characterizes causes on
a continuum. Causal attributions can be stable, or invariant, and unstable, or variant.
Weiner (1979; 1985) proposes that ability and task difficulty are likely to be perceived as
fixed, whereas luck and effort are more variable or unstable.
The third dimension in Weiner's three dimensional taxonomy is labeled
controllability. Effort is classified as controllable, while luck, ability, and task difficulty
are classified as uncontrollable.
Numerous investigations have provided support for Weiner's taxonomy (Meyer,
1980; Meyer & Koelbl, 1982; Passer, 1977; Stem, 1983; Wimer& Kelly, 1982). Meyer
(1980) presented questionnaires with 16 different situations and asked subjects to indicate
in each situation how much each of nine attributions was responsible for the outcome.
Using factor analysis, the results showed a correspondence between the three dimensions
of locus, stability, and controllability. Causal attributions theoretically can be classified
within one of eight cells: 2 levels of locus x 2 levels of stability x 2 levels of control
(Weiner, 1979).
According to Weiner's theory, each causal dimension has psychological
implications for thought and action. The locus of causality dimension has implications
for self-esteem. Self-esteem is higher when success is caused by internal factors (effort,
and ability) rather than external factors (luck or task difficulty). Self-esteem is lowered
when failure is caused by internal factors rather than external factors.
The causal dimension of stability is linked to expectancy for future success.
Failure that is caused by low ability or difficultly of task decreases the expectation of


13
future success more than failure attributed to bad luck, bad mood, and lack of effort
(Weiner, 1979). Weiner, Nirenburg, and Goldstein (1976) showed that American college
students who were successful at completing block designs because of ability and ease of
task anticipated a successful performance in the future, compared to students who were
successful because of effort and luck.
The dimension of control is linked to personal responsibility. The dimension of
control focuses on inferences about others and how beliefs about another's responsibility
for success and failure influence an individuals reactions toward that person. When a
person fails for controllable reasons, that person is blamed by others. Therefore, when a
person fails for uncontrollable reasons, that person does not receive blame.
Attributions. Expectations, and Consequences
Within Weiner's three-dimensional model, the stability dimension is posited to be
related to goal expectancy. According to Weiner (1985, p. 556), "the attributional
position is that the stability of a cause rather than its locus, determines expectancy shifts."
Therefore, when attributions are classified on the stability dimension they have a link to
expectations for future success. For example, if a subject attributed success on an
important examination to high ability, expectancy of future success will be high because
ability is a stable attribution which in most cases does not change from task to task. But,
if the reason for success was attributed to high effort one will not maintain such high
expectations for future success because effort may fluctuate from one task to the next.
Conversely, for the failure situation, if the cause for failure is one's low ability, the
expectancy of success might be decreased because one cannot change ability; but if


14
failure was attributed to inadequate effort, the positive expectancy for success might stay
high because the subject believes that trying hard will bring future success. Thus, if
failure is perceived as being caused by lack of effort rather than low ability, expectations
of future success will continue to stay high. Although ability and effort are both internal
attributions on the locus dimension (Weiner, 1985), ability and effort are classified
differently on the stability dimension. This means ability is a stable cause for success and
failure, while effort is perceived as unstable in most situations involving failure. Effort is
perceived as relatively stable in most situations of success. The attributions of effort may
bring about positive expectations in the future with both success and failure, whereas
ability attributions will create high expectations of failure.
Attributional theory, therefore,
is a theory of motivation and emotion in which causal attributions play a
key role. The perceived causes of success and failure share three common
properties: locus, stability, and controllability. The perceived stability of
causes influences changes in expectancy of success. All three dimensions
of causality affect a variety of common emotional experiences including
anger, gratitude, guilt, hopelessness, pity, pride, and shame. Expectancy
and affect, in turn, are presumed to guide motivated behavior. (Weiner,
1985, p. 548)
Attribution Measurement Issues
One of the problems in attribution research has been the lack of consistency in the
methods used to measure attributions (Elig & Frieze, 1979; Hau & Salili, 1993;
Maruyama, 1982; Whitley & Frieze, 1985). In a thorough review examining the
measurement of achievement attributions, Hau and Salili (1993) investigating methods,


15
question contents, and measurement formats. They concluded that no one method is
superior to another. They found that the appropriate use of any attribution measure
requires that the instrument specifically match the research question; yet they are not
clear on how this should be accomplished. Despite the authors' statement that the method
and the research question need to be matched, their review indicates that no one method
is superior to another. While the rating scale was the most popular structured method
used by attribution researchers, the authors found that all measurement methods are
equally satisfactory when used properly. However, they did not answer the question as to
whether all measurement instruments would be equally valid for the same research
question. Elig and Frieze (1979) also cautioned that various measurement instruments
can yield different results for the same research question.
Researchers who have been interested in study of parents' and children's causal
attributions for school performance have commonly used three structured measurement
techniques. The first type of measure is the chip distribution measure in which parents
and children have a limited number of plastic chips that they place on different choices
weighting the importance of causal attributions (Dunton, McDevitt & Hess, 1986; Hess,
Chih-Mei & McDevitt, 1987; Hess, Holloway & King, 1981; Hess & McDevitt, 1985;
Hess, McDevitt & Cheng, 1984; Holloway & Hess 1982; 1984; Holloway, Hess & King,
1981; King, Hess & Holloway, 1981). For example, a child would be asked to place 10
chips on 5 choices according to the importance of each attribution for that child. If 8
chips were placed on the ability attribution option only two chips would be left to place


16
on the remaining attribution options. A variation of this method uses a limited number of
points to indicate the importance of each attribution (Stevenson & Lee, 1990).
A second measurement instrument of parents' and children's attributions for
school success and failure is the structured rating scale format. Parents and children rate
on Likert-type scales (frequently 5-, 7-, or 9-point) the importance of each attribution
(Bar-Tal & Guttmann, 1981; Bird & Berman, 1985; Parsons, Adler & Kaczala, 1982;
Yamauchi, 1989; Yee, 1984; Yee & Eccles, 1988). The rating scale method allows
participants a chance to rate the importance of each attribution without one rating
effecting other ratings.
A third type of measure is the rank order format in which parents and children
rank the order of importance of each attribution in relation to other attributions
(Cashmore, 1980; 1982; Cashmore & Goodnow, 1986; Holloway, 1986). The rank order
method usually has a limited number of attributions and participants must decide which
attribution is first, second, or third (or so on) in its contribution for determining success or
failure.
It has been recommended (Hau & Salili, 1993) that there should be a match
between the research question and the measurement instrument. Attribution researchers
who study parents' and children's beliefs about school performance have continued to use
a variety of measures to investigate the same research question: "Do parents and children
in families hold similar beliefs about the children's success and failure in school?"
The results of this line of research have been inconsistent. Holloway and Hess
(1982), using a chip distribution method, found that mothers and their children did not


17
hold similar beliefs about the causes of children's academic success and failure.
However, Parsons, Adler, and Kaczala (1982), using a rating scale, found that parents and
children held similar beliefs about causes the children's academic performances. Further,
Cashmore and Goodnow (1986), using rank order method, did not find agreement
between parents and children. It is unknown as to whether these inconsistent findings
were due to the participants in these studies or to the variation in measurement
techniques. It is also unclear as to whether the researchers attempted to match the
measurement instrumentation with the research question. One weakness in this literature
is that no studies were found that simultaneously used two or more methods (chip, rating,
or ranking) in order to show the comparability of those measurement instruments.
Attributional Patterns of Mothers and Their Children
Because it is generally believed that parents transmit values, beliefs, or traits to
the next generation, one might expect to find agreement about these values, beliefs, or
traits between parents and their children (see Cashmore & Goodnow, 1986; Holloway &
Hess, 1982). Some attribution researchers examined the possibility that parents convey
attributional beliefs regarding academic success and failure directly to their children.
However, findings were inconsistent across studies.
Disagreement. In her study investigating parental belief systems about their
children's school performance, Cashmore (1980) found little agreement between parents'
and children's attributions of 100 Australian children and their parents. Parents and
children were asked to rank order three attributions: ability, effort, and teaching. Parents
viewed ability as the most important cause of success and effort the most important cause


18
of failure. Children believed that effort was the most important cause for success while
ability was given as the cause for failure. The quality of teaching at school did not seem
to be important for either parents or children.
In a follow-up study, Cashmore and Goodnow (1986) compared attributions of
mothers, fathers, and their children from an Anglo Saxon Australian sample and an
Italian Australian sample. They were asked to put in rank order the attributions of talent,
effort, and teaching for six skill areas. Results indicated parents and children's responses
varied across these skill areas. Children from both samples cited effort as the most
important cause for school performance, while parents in both samples cited talent
(ability).
Holloway and Hess (1982) investigated the causal attributions given by mothers
for their upper elementary school children to explain their reasons for high and low
academic performance. Participants were given chips to place on cards listing
attributions in order to weight the importance of ability, effort, personality, and training.
Mothers' cited children's ability as the main cause of success, while lack of effort was
viewed as the main reason for failure. Children held the opposite belief, that effort was
more important for success while lack of ability contributed to failure.
Bar-Tal and Guttmann (1981) compared teachers', students,' and
parents' attributions for the students' academic performance. Using a rating scale subjects
were asked to rate 10 causes: ability, interest, difficulty of material, effort, teacher's
explanation, home conditions, parent's help, luck, diligence, and test difficulty. Findings
indicated that parents attributed their children's success to home conditions, and teacher's


19
explanations. Failure was attributed to bad home conditions and children's low level of
interest and ability. Children attributed success to their own effort and teacher's
explanation and failure to lack of effort and low ability.
While it was thought by researchers that parents and children might be in
agreement, indicating the conveyance of values and beliefs, these studies show a lack of
agreement in the attributional beliefs of parents and children about academic
performance.
Agreement. In contrast to these findings, some researchers did find agreement
between parents and children regarding the attributional beliefs for academic success and
failure.
In a study done by Parsons, Adler, and Kaczala (1982), agreement between
parents and children in their causal attributions was documented. Using a rating scale
measuring attitudes and beliefs about mathematics achievement, children's attributions
matched those of their parents. The authors of this study concluded that children's
attributions were influenced more by their parents' beliefs about their abilities than by
their own previous mathematics achievement history.
Yamauchi (1989) conducted a study that compared the attributions of children
with mothers' attributions, and children's predictions of their mothers' attributions for
their child's mathematics performances in Japan. The results, from a rating scale
procedure, showed that more than half of the children and their mothers attributed school
performance to the effort attribution on both success and failure outcomes.


20
Numerous conclusions can be drawn from these studies. First, it is not clear from
the findings of studies in this literature if parents and children hold similar beliefs or
attributions within the same culture or vary across different cultures. Second, this
literature does not inform us on the best method to measure parents' and children's
attributions about school performance. No study explained why a method was chosen to
investigate the research question. Finally, this literature provides little guidance as to
why these findings have been so inconsistent.
Cultural Patterns.
When parents and children from different countries were compared to examine
their attributional beliefs about school performance, findings were different.
Stevenson (1983) conducted a study of parents and children in Japan, Taiwan, and
the United States. Participants were first asked to rank four attributions: effort, natural
ability, difficulty of school work, and luck regarding the children's achievement. Next,
they were asked to divide up 10 points across the 4 attributions. In all three cultures the
rank order of importance was the same: effort first, followed by ability, task difficulty,
and luck. However, the point distribution method (a variation of the chip distribution
method) indicated more emphasis on effort for the Asian populations (Taiwan and Japan)
than for the participants from the United States. This study provides some evidence that
attributional beliefs may have cultural variations.
In a comparison of Japanese mothers' and children's with American mother's and
children's attributions, Hess et al. (1986) found there was a contrast between the two
cultures. Using the chip distribution method (see Holloway and Hess, 1982), Japanese


21
mothers and children weighted lack of effort as the most important cause for mathematics
failure, while American mothers' and children's explanations were evenly divided among
ability, effort and training at school. Japanese mothers were less likely to blame training
at school as a cause of their children's low achievement.
In a further study of Japanese mothers and children who were compared to
American mothers and children for causal attributions about mathematics performance
cultural differences in attributions, transmission of beliefs from mothers to their children,
and the effect of gender of child on attributions were examined by Holloway, Kashiwagi,
Hess, and Azuma (1986). Using the chip distribution method, this study also found that
there were cultural differences between American and Japanese mothers and their
children. Japanese mothers and children tended to stress effort as a cause for success and
failure in mathematics, while American mothers and children tended to stress ability.
Similar results were reported by Stevenson, Lee, and Stigler (1986) when
comparing Chinese, Japanese and American mothers on beliefs about causes of their
children's mathematics success and failure. Using a point distribution system, findings
showed that while American mothers gave the largest number of points to ability for
explaining why their children succeed or fail in mathematics, the Asian mothers gave the
most points to effort.
In another study to investigate cultural variations in family beliefs, Hess, Chih-
Mei, and McDevitt (1987) examined beliefs about children's performance in mathematics
through interviews with mothers and their 6th grade children involving three cultures, the
Peoples' Republic of China, Chinese American, and American. The three groups showed


22
different attribution patterns with a chip distribution method for the attributions of ability,
effort, school training, home training, and luck. Mothers in the Peoples' Republic of
China viewed lack of effort as a major cause of low performance. The Chinese American
mothers also viewed lack of effort as important concerning failure, but assigned
responsibility to other sources. The American mothers distributed responsibility more
evenly across the attribution options. Children in the three cultural groups had patterns of
attributions similar to those of their parents.
Stevenson and Lee (1990) studied mothers from Japan, Taiwan, and the United
States. Participants were asked to rate the influences of the factors of studying hard,
intelligence, study habits, a good teacher, home environment, parents' assistance, the
curriculum, and luck on academic achievement. American mothers gave higher ratings to
study habits, good teacher, home environment, and curriculum than they did to effort. On
the other hand, Asian mothers rated effort highly and the only factor they rated higher
than effort was good teachers.
These studies provide support for differences in attributions made by Asian
parents and children and those made by American parents and children for the children's
academic performance. These studies show that Asian parents and children tend to
emphasize the effort attribution more strongly than American parents and children.
These studies may also lend support for the differences of meaning of effort and ability in
the two cultures. A limitation of these studies is that they do not address non-Asian
cultures or sub-cultures within the United States. Different attributions could be held by


23
South American parents and children or Hispanic or African American parents and
children.
Summary
In summary, several conclusions can be drawn from the parents' and children's
attribution literature regarding academic performance. First, several measurement
techniques have been used by researchers in studies that make-up this literature. The
methods used were primarily structured interviews. When rating causes, participants
were asked to rate on a 5- or 7-point scale. When weighting causes, subjects were asked
to place 10 chips or assign 10 or sometimes 100 points to 4 or 5 attributional cards. A
few studies asked subjects to rank order attributions. Both rating scale and chip
distribution methods were chosen for this study.
Second, researchers have examined and compared attributions of students,
teachers, parents, and numerous cultural groups. African American mothers and children
are considered to be a sub-culture in the United States. Additionally, they are a
population that had not been studied.
Third, a wide variety of attributions have been included in instruments. However,
all of the instrument included ability and effort. The instruments chosen therefore needed
to include these attributional elements.
Finally, it may be difficult to generalize from findings in this literature to other
groups because findings have not always been consistent. It was hoped that this study
would address this issue.


24
Mothers' Attributions and Gender Differences
Examining gender differences in mother attributions is important for two reasons.
Boys and girls have long been shown to have different attitudes and different
performance levels in mathematics (Fox, Tobin, & Brody, 1979; Holloway & Hess, 1984;
Reyes & Stanic, 1988).
Cashmore (1980) examined the causal attributions of 100 Australian mothers and
children across 6 achievement areas. Subjects were asked to rank order the attributions of
talent, effort, and teaching. Parents did not rate boys and girls differently. The findings
found no differences.
Bar-Tal and Guttmann (1981) examined the causal attributions for mathematics
performance of boys and girls by Israeli parents, teachers and students. Participants were
asked to rate 10 attributions for their contribution to mathematics success and failure.
Results indicated that fathers and mothers believed boys and girls succeed in mathematics
because of home conditions and teacher's explanations while they both fail because of
bad home conditions, low interest, and low ability. The author's concluded there were no
gender differences.
Hess, Holloway, and King (1981), Holloway, Hess and King (1981), and King,
Hess, and Holloway (1981) compared the causal attributions for 21 mothers and their 5th
and 6th grade children. Participants were given open-ended questionnaires to ascertain
the children's spontaneous attributions about doing well or poorly in school. Participants
were then asked to place 10 chips on 5 attributional cards. The attributions were ability,
effort, personality and good teaching. Results showed that mothers attributed both sons'


25
and daughters' academic successes to ability. Moreover, mothers thought that lack of
effort was a more important explanation for poor performance than lack of talent,
regardless of gender.
Holloway (1986) conducted a study of children's achievement in mathematics and
their motivation to achieve in mathematics. Holloway, found that mothers of girls made
more attributions to ability and task difficulty for success and were more satisfied with
past performance than were mothers of boys, but exerted less academic achievement
pressure. Mothers of boys were found to make more attributions to effort and luck for the
success of their sons than mothers of girls. However, mothers exerted more academic
pressure on their sons to achieve. These differences were, however, minor and the author
concluded that there were few differences overall.
In contrast to the studies showing no differences, there is research that indicates
that mothers do hold gender related beliefs about their children's school performance.
In a study by Holloway and Hess (1982), 21 mothers and children in 5th and 6th
grade were asked to place 10 chips on 5 attributional cards for success and failure in a
subject of high achievement and for failure in a subject of low achievement. Findings
indicated that mother of boys believed boys had more ability than girls while girls had to
exert more effort to be successful in school.
Parsons, Adler, and Kaczala (1982) compared the causal attributions for
mathematics performance of 5th through 11th grade students and their parents.
Participants were asked to rate on 7-point Likert scales their attitudes and beliefs about
mathematics performance. Results showed that parents held sex-differentiated


26
perception's of their children's mathematics aptitude despite the similarity of performance
of boys and girls. Parents of girls thought their daughters had to work harder to do well
in mathematics than did parents of sons. Parents of boys believed advanced mathematics
was more important for their sons when compared to their daughters.
In a study of Holloway and Hess (1984) investigating gender differences in
teacher and in parental beliefs, no significant sex differences on an achievement
assessment for mathematics performance were found for boys' and girls' mathematics
performance in 6th grade.
In a study by Yee and Eccles (1988), 7th grade students and parents were asked to
complete questionnaires at home about beliefs, expectations, and causal attributions for
their children's mathematics achievement. The results indicated that parents' perceptions
and expectations were commensurate with their childs level of mathematics ability.
Mothers attributions for mathematics successes differed for boys and girls.
Dunton, McDevitt, and Hess (1988) conducted a longitudinal study investigating
the origins of mothers' attributions about their daughters' and sons' academic performance
at 5 and 6 years of age and again at age 12. The findings indicated that there are both
similarities and differences in the sources of attributions given by mothers for their
children's performance. Mothers of boys attributed the source of their attributions to their
son's prior performance.
Mothers of son's attributed relative success to having ability and the relative
failure to lack of effort. The source of the mother's attributions for their daughters was
the affective relationship they have with them. Mothers of girls attributed their daughters'


27
relative success or failure to ability or effort depending on the type of affective
relationship.
Several conclusions can be drawn from the attribution literature related to
mother's attributions and gender differences. Evidence supporting mothers' contributions
to gender related attributions is mixed. Some studies show that mothers do not hold
gender related differences for their children's school performance, particularly
mathematics. Additionally, researchers have used several methods and cultural groups
when examining mothers' attributions and gender differences. There were no studies that
examined African American mothers and their gender related attributions. Finally,
generalizing findings from this literature to other cultural groups is difficult because the
findings are inconsistent.
Children's Prediction of Their Mother's Causal Attributions
Parental accuracy in predicting their children's causal attributions for success and
failure at school has not received much research attention. Yamauchi (1989) asked
Japanese children to predict their mother's attributions for the child's mathematics
performance. Using a rating scale methodology, results showed a discrepancy between
children's predictions of mothers' attributions and mothers actual attributions which
varied by children's school performance. The researcher concluded that children's
predictions of their mothers' mathematics attributions were not related to their mother's
actual attributions. Further research was recommended. As mothers' predictions of their
children's attributions about mathematics performance were not investigated, they might
yield more insight into this area.


28
Summary
While mothers' and children's attributional beliefs regarding mathematics
performance in numerous cultures have been studied, the attributional beliefs of African
American mothers and their children for the children's mathematics performance has been
neglected in the attribution literature. The primary purpose of this study was to compare
the causal attributions of low SES African American mothers and their children for
mathematics performance; a second purpose was to examine whether the rating scale
instrument and the chip distribution instrument yield similar and consistent results when
measuring mathematics attributions of African American mothers and children.
The review examined Weiner's attribution theory, attribution measurement issues,
attribution patterns of mothers and their children for school performance, mothers'
attributions and gender differences, and children's accuracy in predicting parent's causal
attributions for the children's mathematics performance.
Weiner's attribution theory can be used to study achievement motivation.
Attributions, or the reasons given by an individual to explain success and failure, are
dimensionalized in relationship to the individual (e.g. intemal/extemal;
controllable/uncontrollable; stable/unstable) and provide meaning that has expectations
and consequences for that person. Numerous achievement attributions have been
documented in the literature, but most prominent among them are ability, and effort,
usually in conjunction with additional attributions.
An examination of the literature indicated that a variety of attribution
measurement instruments have been employed by researchers. Common among them are


29
weighting instruments, rating scales, and rankings. The findings from studies have
frequently been compared to other studies without regard for the measurement instrument
used. This indicated a need for some studies that simultaneously used two or more
methods to see if the different instruments did, in fact, produce comparable results.
Studies of attribution patterns of mothers and their children for school
performance were examined to see if attributions were transmitted within the family.
While numerous studies using a variety of methods and looking at several cultures
examined this issue, the results were inconclusive. Ability and effort are often cited as
reasons for both success and failure, but mothers and children did not always give the
attributions the same importance.
Because gender differences have been found to exist in children's mathematics
performances, the literature regarding mothers' attributions and gender differences was
examined. Again, using a variety of methods and cultures the results were not consistent.
Children's accuracy in predicting parent's causal attributions for the children's
mathematics performance was examined. This was seen as an additional way it might be
determined that beliefs about academic achievement, especially for mathematics, were
transmitted. Further research was found to be needed in this area. In conclusion, the lack
of consistency found in the attribution literature supports the need for additional studies,
especially with a population that has received little attention, African American mothers
and their children, and to utilize two instruments to determine their comparability. It was
to this end that this study was undertaken.


CHAPTER 3
METHODOLOGY
In this study the causal attributions made by low SES African American mothers
and their children for the children's mathematics success and failure were compared. In
addition, two attribution instruments for measuring mothers' and children's causal
attributions were compared.
While a variety of methods and populations have been used in studies of parents
and children's attributions for the children's school performance, low SES African
American mothers and children have not been the focus of this research. Attribution
research with low SES African American mothers and children appears warranted,
especially in the area of mathematics, where low SES African American children achieve
poorly, and are under represented in mathematics courses in high school and in
mathematics related careers (Matthews, 1984; Reyes & Stanic, 1985, 1988).
Hypotheses
The causal attributions of African American mothers for their children's school
performance were compared to the causal attributions of their 6th, 7th, or 8th grade
African American children. Based on the parents' and children's attribution literature
30


31
involving school performance, the following hypotheses were expected to be supported
by this study:
1. Low SES African American mothers will not agree with their children about
causal attributions for mathematics success and failure.
2. Low SES African American mothers will not make different attributions for
boys and girls about their mathematics success and failure.
3. Low SES African American mothers will not be able to accurately predict
attributions their children give for the children's mathematics success and failure
performances.
4. Low SES African American children will not be able to accurately predict the
attributions their mother will give for the children's mathematics success and failure
performances.
5. Low SES African American mothers and children, within the same family, will
not be in agreement regarding the children's mathematics success and failure
performances.
6. Conclusions about the attributions will not depend on the method-chip
distribution instrument or rating scale instrument-used for data collection.
Methods
Participants
This study was conducted in government subsidized housing in Alachua County,
which is located in north central Florida. The participants were one hundred low SES


32
students from the 6th, 7th, and 8th grades, and their mothers. Approximate ages of the
students were 12, 13, and 14. This age group was chosen because there is evidence from
the developmental literature that children do not differentiate consistently between ability
and effort attributions until 12-13 years of age (Nicholls, 1978; Karabenick, & Heller,
1976; Kloosterman, 1983; Eccles, 1985). Although 6th, 7th and 8th grade students are
distinguished from each other in the design so that the middle school years could be
examined, there are no research questions related to the specific grade or age level.
Criteria for Selection
From a list of 325 mothers with children in the 6th, 7th, and 8th grades obtained
from the Alachua County Housing Authority, 100 volunteers were chosen. Fifty mothers
and their children were assigned to for the rating scale procedure and fifty mothers and
their children were assigned to for the chip distribution procedure. An attempt was made
to select an equal number of boys and girls from the volunteering families.
Pre-Selection Process
The researcher interviewed all potential mothers who volunteered for this study.
The interview consisted of a series of questions:
"Is your child in middle school6th, 7th, or 8th grade?"
"Is your child in special educations classes: this includes learning disabled,
mentally handicapped or emotionally handicapped classes?"
"Do you receive a copy of your child's report card, and if you are selected for the
study, can you locate, and bring with you, your child's most recent report card?"


33
Participating families lived in government subsidized housing. All children
selected for the study met the following criteria:
1. Students were in the 6th, 7th, or 8th grade regular education classes. Although
a large number of low SES African American children are in remedial or special
education classes, students who spent a majority of their day in regular classrooms were
classified as being in regular classes for this study.
2. Participating families had a copy of the student's most recent report card.
Student grades, however, were not a selection factor.
3. Only one child per family was selected.
Mothers and children not meeting selection criteria were thanked for their interest,
but were not selected.
The distribution of mothers and children by grade, gender, and method is shown
in Tables 3-1 and 3-2.
Table 3-1
Sample Size for Rating Scale Respondents
Grade
Child's Sex
Mothers
Children
6
M
9
9
F
8
8
7
M
9
9
F
7
7
8
M
10
10
F
7
7
TOTALS
50
50


34
Table 3-2
Samnle Size for Chin Distribution Respondents
Grade
Child's Sex
Mothers
Children
6
M
6
6
F
4
4
7
M
12
12
F
10
10
8
M
14
14
F
4
4
TOTALS
50
50
Instrumentation
In the present study, two methods, the chip distribution method and the rating
scale method, were selected from methods used in previous research (see Holloway &
Hess, 1982; 1984; Parsons, Adler, & Kaczala, 1982; and Yamauchi, 1989). For both
samples, the instruments consisted of questions about attributions and predictions for the
children and for the mothers.
Instrumentation for the children was identical to that used by mothers except for
word changes that make the instrument appropriate for children. The questionnaires were


35
designed to elicit 22 responses from the child and 22 responses from the mother in each
procedure (See Appendix C and D).
The Chip Distribution Method. The chip distribution procedure was used in the
research of Hess, Chih-Mei, and McDevitt (1987), Holloway and Hess (1982; 1984), and
Holloway, Kashiwagi, Hess, and Azuma (1986). Ten chips are given to the mother or
child, who is then asked to place the ten chips in 5 categories. The five categories
represent the five attributions in the study (ability, effort, training at school, training at
home, and luck). The ten chips can be distributed in any pattern. The chip distribution
instrument consisted of 5 choices for success, 5 choices for failure, 5 choices for success
prediction, and 5 choices for failure prediction. Each card had an attribution phrase on it.
For the success condition these phrases were used for the mothers:
My child has natural ability for math.
My child tries hard in math.
My child has had good training at school in math.
Ny child has had good training at home in math.
My child has been lucky in math.
Wording changes were made to adapt these phrases to the other conditions.
The Rating Scale Method. The rating scale procedure came from the research of
Bar-Tal and Guttmann (1981), Parsons, Adler, and Kaczala (1982), Yee and Eccles
(1988), and Yamauchi (1989).
For the rating scale instrument, the Holloway and Hess (1982; 1984) distribution
of chips was replaced by 9-point rating scales like those used by the Graham and Long


36
(1986) study. Every portion of the instrument was identical except instead of using chips
to weight the attributions, ratings scales were used to rate the attributions.
The prediction portion of the chip distribution and rating scale procedures were
used as an adaptation from the Yamauchi (1989) study that introduced the prediction
component in a study of Japanese mothers and their children about mathematics
performance. While the Yamauchi (1989) study asked only children to predict their
mother's causal attributions for the children's mathematics success and failure, in this
study mothers were asked to predict the attributions their children would give for the
children's mathematics success and failure, and children were asked to predict the
attributions their mothers would give for the children's mathematics success and failure.
Procedures
After volunteers agreed to participate, the studies were explained and informed
consent obtained, mothers were tested on the first visit and their children were tested on
the second visit in their homes. All study materials were read aloud while the subjects
read silently.
Mothers were not present when their child was "tested", and the child was not
present when their mother was "tested.
Sample A: The Rating Scales Procedure. While looking at their children's report
card, mothers were first asked to judge how well their children had performed in 6th, 7th
or 8th grade mathematics on their most recent report card relative to other members of
their class. (Therefore, success and failure are considered in relation to other class
members and is referred to as relative success and relative failureHolloway & Hess,


37
1982; 1984.) The mothers were to indicate the children's level of performance on a 6-
point scale.
Mothers: relative success condition. In this condition, the objective was to help
the mother think of her child as a "relative success." For the relative success condition
the interviewer first noted the response made by the mothers. If a mother rated her child's
performance as a 1 or 2, the interviewer said: "You rated your child's mathematics
performance as better than some of the other children in his/her class. Why do you think
your child did this well?"
If a mother rated her child's performance as a 3 or 4, "better than many" was
substituted into the above sentence. If a mother rated her child's performance as a 5,
"better than most was read in the sentence. If a mother rated her child's performance as
a 6, the interviewer stated: "You have rated your child's mathematics performance as the
very best in the class. Why do you think your child did this well?"
The interviewer then gives the mothers rating scales forms which ask them to rate
each attribution (reason) for their childrens performance by circling the number that best
indicates how much a reason causes their child's mathematics performance. The scale
ranged from 1 to 9; 1 indicated the reason was definitely not a cause to 9 indicating the
reason was definitely a cause.
Mothers: relative failure condition. An analogous procedure was used for
questions about relatively low performance (relative failure). In this condition the
question was, "You indicated that your child wasn't doing the very best in the class. Why


38
do you think he/she isn't doing even better in mathematics?" The purpose was to have the
mother think of her child as a "relative failure."
If the mothers reported that the children were doing the very best in the class, the
interviewer suggested that the children might not be the best in the school district and
asked why the children were not doing even better.
Prediction. In the prediction portion of the study, a mother was asked to predict how her
child would rate his or her mathematics performance based on the most recent report
card. The mothers were to indicate their predictions about their children's rating on a 6-
point scale.
Mothers: relative success condition. For the relative success condition the
interviewer first noted the response made by the mothers to the prediction question. If the
mothers predicted their children would rate their performance as a 1 or 2, the interviewer
said, "You predicted your child would rate his/her mathematics performance as better
than some of the other children in his/her class. Why do you think your child thought
he/she did this well?"
If the mothers predicted their children would rate their mathematics performance
as a 3 or 4, "better than many" would be substituted into the above sentence. If the
mothers predicted their children would rate their mathematics performance as a 5, "better
than most" was read in the sentence. If the mothers predicted their children would rate
the children's performance as a 6, the interviewer stated, "You have predicted that your
child would rate his/her mathematics performance as the very best in the class. Why do
you think your child thinks he/she did this well?"


39
The interviewer next gave the mothers the rating scale forms, indicating the
attributions (reasons) for the children's performance and then asked the mothers to
complete the 9-point scales by circling the number that best indicated the reason the child
would give for the child's mathematics performance.
Mothers: relative failure condition. An analogous procedure was used to ask
mothers to predict how their children would respond about relatively low performance.
In the Rating Scale procedures each mother provided a total of 22 responses.
Mothers rated their children's level of performance in mathematics (1 rating). Then the
mothers rated five success and five failure attributions (10 ratings). Mothers predicted
how their children would rate their level of mathematics performance (1 rating). They
predicted their children's attributions for their mathematics performance by rating their
prediction of their child's response giving five success and five failure attributions (10
ratings).
Rating Scale Procedures for children. In the Rating Scale Procedure, the
procedures designed for children were identical to those procedures for mothers except
for word changes that made the procedures appropriate for children. There were a total of
22 ratings for each child.
Sample B: Chip Distribution Procedure. The procedures for the Chip
Distribution portions of the study were identical to the Rating Scale procedures except
that the 5 attributions were written on 5 cards and 10 chips were given to subjects to
weight the importance of each attribution as a cause of their child's mathematics
performance.


40
Variables Under Investigation
There were three independent variables under investigation in this study: (1)
informant: mother/child, and (2) Sex: male/female and (3) grade (6th, 7th, and 8th), in
each outcome situation (success and failure). The five dependent variables in each
success and failure outcome were causal attribution scores for ability, effort, training at
school, training at home, and luck as measured by the rating scale procedure and the chip
distribution procedure.
Research Design
The design for this study is a 3 (grades) x 2 (male/female) x 2 (mother/child) x 5
(attributions) within subjects design with repeated measures on the last two factors. This
design was used for Sample A and Sample B.
Data Analysis
The data from this study in each outcome were analyzed using analysis of
variance (ANOVA). Analyses were computed separately for mothers' and children's
predictions. To determine attribution agreement within the family, correlations were
performed on parent and child pairs within the same family. Scores of parents and
children were correlated with Pearson product-moment correlations for each of the 5
success and 5 failure attributions in each sample.


CHAPTER 4
RESULTS
The primary purpose of this study was to compare the causal attributions that low
SES African American mothers and their children give for mathematics performance. A
secondary purpose was to examine whether the rating scale instrument and the chip
distribution instrument yield similar results when measuring causal attributions given by
low SES African American mothers and children for mathematics performance.
The results presented in this chapter are based on the data collected from 6th, 7th,
and 8th grade students and their mothers in six separate government-subsidized housing
projects in Alachua county, Florida. Attribution questionnaires were administered to 200
participants, including 100 parents and 100 children who volunteered to participate in the
study. The sample pool was divided in half. Fifty mothers and 50 children were
administered the rating scale instrument. Fifty mothers and 50 children were
administered the chip distribution instrument.
For both samples, the data analysis included a grade (3) x child sex (2) x
respondent (2) by type of attribution (5) with repeated measures on the last two factors.
Separate analyses were conducted for success and for failure attributions, and
additionally, to examine mothers' and children's success and failure predictions. Further,
Pearson-product moment correlations were used to explore the relationship of mothers
41


42
and children's beliefs within individual families and of mothers' and children's
performance evaluations of the children's mathematics grade.
Mother's and Childrens Success and Failure Attributions for Mathematics Performance:
Rating Scale Method
Mothers and children were asked to evaluate on a six-point scale the mathematics
grade from the children's most recent report card. The six-point scale ranged for 1 (not
doing as well as most) to 6 (doing the very best in the class). Mothers were then asked by
the interviewer "Why is your child doing this well?" and children were asked "Why are
you doing this well?" Mothers and children rated on 9-point scales the relative
importance of five attributional items. The scales ranged from 1 (definitely not a cause)
to 9 (definitely a cause). The items were ability, effort, training at school, training at
home, and luck for both success and failure conditions. Therefore, relative success in this
study is actually mothers' and children's evaluation of the children's current level of
mathematics performance. For relative failure, mothers and children were asked by the
interviewer, "Why did your child not do even better?" (for mothers), and "Why aren't you
doing even better?" (for the children). This condition examines relative failure as a
judgement in contrast to the current level of performance.
It was hypothesized that low SES African American mothers would not agree
with their children about the causes of the children's success and failure in mathematics.
For the success condition, means and standard deviations for each of the
attributional items are presented in Table 4-1. The results of an ANOVA of mothers' and
children's mathematics success attribution means are presented in Table 4-2. The results


43
in Table 4-2 include a significant Respondent x Type of Attribution interaction and a
significant main effect of attribution, indicating some disagreement between mothers and
children about the relative importance they place on the five types of attributions for
success in mathematics. Mean attributions as a function of respondent and type of
attribution are reported in Table 4-3. These means describe the nature of the
disagreement between mothers and children about the relative importance of the five
types of attributions. Mothers and children are in agreement about the relative
importance of the five types of attributions as causes of success. On average, both
mothers and children rate effort as the most important, followed by school, home, ability,
and luck. However, mothers put less emphasis than do their children on each type of
attribution. The most striking difference between mothers and children is the luck
attribution. Children rate luck almost as important as ability; mothers rate luck as less
important than do their children and as less important than ability. Therefore the largest
difference between mother's and children's beliefs about the causes of success in
mathematics is the way luck is perceived.
In Table 4-2, there is a significant Child's Gender x Type of Attribution
interaction. Means for interpreting this interaction are reported in Table 4-4. Each mean
in Table 4-4 is an average of a mean for mothers and the mean for children, with the
means calculated across grades. Thus the means in Table 4-4 reflect both children's and
mothers' attributions for success. The results in Table 4-4 indicate that mother-son
dyads tend to agree with mother-daughter dyads about the relative importance of the five
causes: effort is most important followed by school, home, ability and luck. Mother-


44
daughter dyads rate effort, school, and luck as more important than do mother-son dyads,
whereas motherson dyads rate ability and home as more important than do mother-
daughter dyads.
Examination of Table 4-4 analysis shows that in comparison to mother-daughter
dyads, motherson dyads believe success in mathematics depends more heavily on
ability. Motherson dyads also believe effort is less responsible for success than do
mother-daughter dyads in mathematics. Mother-daughter dyads believe that school
training is more important for mathematics success than do mother-son dyads. Mother-
son dyads believe home training is more important for mathematics success than do
mother-daughter dyads. Finally, mother-daughter dyads depend more on the luck
attribution for mathematics success than do mother-son dyads. For mother-daughter
dyads, luck and ability are of equal importance for successful mathematics achievement.
The absence of a Respondent x Child's Sex x Type of Attribution interaction
implies that the pattern of differences between boys' and girls' mean success attribution is
similar to the pattern of gender related difference between the mean success attributions
made by boys' and girls' mothers. As a result the means in Table 4-4 represent the pattern
of responding by both children and mothers. However, to avoid relying unduly on the
nonsignificant interaction, the means by child's sex for mothers and children are reported
separately in Table 4-5. Most of the generalizations that emerge from an inspection of
Table 4-4 also emerge from an inspection of Table 4-5. The exceptions are that in this


45
Table 4-1 Ratings
Mather's and Children's Mathematics Success Attributions bv Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.1
8.9
8.6
6.5
5.1
SD
2.4
0.4
0.7
1.5
3.1
Mother
M
5.4
8.3
8.3
7.1
3.4
SD
2.8
1.4
1.5
1.9
3.1
Male
Child
M
6.2
8.1
8.4
8.0
3.4
SD
2.1
2.0
1.3
1.7
3.7
Mother
M
5.8
8.4
5.9
5.8
1.6
SD
3.2
0.9
2.4
2.1
1.7
7
Female
Child
M
2.1
8.9
5.6
3.9
3.4
SD
2.0
0.4
1.7
2.5
2.4
Mother
M
1.6
8.0
7.9
4.4
2.0
SD
1.5
1.9
2.0
3.6
1.9
Male
Child
M
6.6
7.1
6.9
5.6
4.3
SD
3.0
2.8
1.8
2.3
3.4
Mother
M
5.2
7.2
6.7
6.0
1.9
SD
2.7
1.6
2.1
1.6
1.8
8
Female
Child
M
4.7
8.7
8.3
5.7
5.3
SD
3.4
0.8
1.9
3.3
4.1
Mother
M
4.4
8.1
6.6
5.6
3.3
SD
2.6
2.3
3.0
2.8
3.1
Male
Child
M
4.7
7.2
7.1
6.5
5.0
SD
2.8
2.1
2.3
2.0
3.6
Mother
M
4.3
6.6
5.9
6.3
1.7
SD
3.0
2.3
2.6
2.2
1.3


46
Table 4-2 Ratings
Summary .of ANQ.VA. for Success .Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
100.06
50.03
5.55
0.0071
Sex (S)
1
0.02
0.02
0.00
0.9651
GxS
2
55.98
27.99
3.10
0.0549
Error
44
396.91
9.02
Within
Respondent(R)
1
63.14
63.14
9.28
0.0039
RxG
2
9.89
4.95
0.73
0.4889
RxS
1
14.57
14.57
2.14
0.1504
Rx G x S
2
5.05
2.52
0.37
0.6921
Error
44
299.22
6.80
Attribution (A)
4
1368.73
342.18
57.82
0.0001
A x G
S
40.19
5.02
0.85
0.5611
A x S
4
143.24
35.81
6.05
0.0001
A x G x S
8
58.34
7.29
1.23
0.2828
Error
176
1041.65
5.92
Rx A
4
64.68
16.17
3.85
0.0050
Rx A x G
8
42.03
5.25
1.25
0.2726
R x A x S
4
14.66
3.67
0.87
0.4816
R x A x G x S
8
22.26
2.78
0.66
0.7241
Error
176
739.46
4.20


47
Table 4-3 Ratings
Success Means as a Function of Respondent and Type of Attribution
Respondent Ability Effort School Home Luck
Child 4.7 8.1 7.5 6.0 4.4
Mother 4.4 7.8 6.8 5.8 2.3
Table 4-4 Ratings
Success Mean Ratines as a Function of Sex and Tvne of Attribution
Sex
Ability
Effort
School
Home
Luck
Boy
5.4
7.4
6.8
6.3
3.0
Girl
3.7
8.4
7.5
5.5
3.7
sample, girls mothers' rate school as more important than do boys' mothers, but girls do
not rate school as more important than do boys.
Table 4-6 represents a main effect for grade. Examination of Table 4-6 means
indicate that mothers and children in sixth grade place more emphasis on the five types of
attributions than do mothers and children in either the seventh or the eighth grade.
Seventh grade mothers and children tend to place less emphasis on the five types of
attributions.
Pearson-product moment correlation coefficients presented in Table 4-7 indicate
that mothers and children within the same family do not agree about the causes of
mathematics success. Mothers and children were also not in agreement about the


48
Table 4-5 Ratings
Success Mean Ratings as a Function of Respondent, Child's Sex, and Type of Attribution
Respondent
Sex
Ability
Effort
School
Home
Luck
Child
Boy
5.8
7.5
7.4
6.7
4.2
Girl
3.6
8.8
7.4
5.3
4.6
Mother
Boy
5.1
7.4
6.1
6.0
1.7
Girl
3.7
8.1
7.5
5.7
2.8
Table 4-6 Ratings
Success Attribution Means by Grade
Grade
Means
6
6.3
7
5.2
8
5.8
children's performance in mathematics. Of the six correlations, only two coefficients
were significant: home training, r = .42; luck, r = .29. Even these were fairly small.
Overall, mothers and children within the same family were in disagreement about the
causes of the children's successful mathematics performance and about how well the
children were actually performing in the class.
For the failure condition, means and standard deviations for each of the
attributional items are presented in Table 4-8. The results of an ANOVA of mothers' and
n's mathematics failure attribution means are presented in Table 4-9. The results for


49
Table 4-7 Ratings
Pearson Correlation Coefficients Between Mother's and Children's Success Attributions
Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.19
.21
-.01
.42*
.29*
.11
* = p<.05
children's mathematics failure attribution means are presented in Table 4-9. The results
in Table 4-9 include a significant Respondent x Type of Attribution interaction and a
significant main effect of attribution. These results indicate disagreement between
mothers and children about the relative importance they place on the five types of
attributions for mathematics failure. Mean attributions as a function of respondent and
type of attribution are reported in Table 4-10. These means show the nature of the
disagreement between mothers and children about the relative importance of the five
types of attributions as causes of mathematics failure. Mothers and children were not in
agreement about the relative importance of the five types of attributions. On average,
mothers rated effort as most important followed by school, home, ability, and luck.
However, children rated home training as most important followed by effort, luck, and
school training (which were rated equally,) then ability.
The largest discrepancy between mothers' and children's attributions for
mathematics failure is how they view effort. Mothers put more emphasis on the effort


50
Table 4-8 Ratings
Mother's and Children's Mathematics Failure Attributions bv Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.0
5.1
2.6
3.5
2.5
SD
2.9
3.6
2.4
2.1
2.5
Mother
M
3.4
6.6
2.3
3.5
2.3
SD
2.7
3.5
2.3
2.9
2.4
Male
Child
M
2.9
3.4
3.9
4.3
4.4
SD
2.8
3.4
3.6
3.8
3.6
Mother
M
2.6
5.4
3.7
3.6
1.2
SD
2.0
3.8
3.0
2.1
0.7
7
Female
Child
M
2.0
2.3
3.4
4.4
3.0
SD
1.9
2.2
3.6
1.9
2.2
Mother
M
1.6
7.0
1.6
3.9
1.0
SD
1.5
3.1
1.5
3.0
0.0
Male
Child
M
2.4
3.6
3.6
3.1
3.3
SD
1.9
3.0
2.7
2.8
2.7
Mother
M
3.2
5.0
5.4
3.1
1.9
SD
2.5
3.8
3.3
2.2
1.5
8
Female
Child
M
2.3
3.3
3.9
3.6
3.3
SD
2.2
3.0
3.7
3.8
3.9
Mother
M
2.9
7.1
2.9
1.9
1.0
SD
3.1
3.0
3.1
1.6
0.0
Male
Child
M
4.5
5.3
3.1
5.0
4.1
SD
3.4
3.1
2.7
3.0
3.2
Mother
M
3.3
6.8
4.1
2.8
1.4
SD
2.3
2.7
3.4
1.9
1.0


51
Table 4-9 Ratings
Summary of ANOVA for Failure Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
13.34
6.67
0.42
0.6624
Sex (S)
1
21.72
21.72
1.35
0.2508
GxS
2
15.90
7.95
0.50
0.6126
Error
44
705.90
16.04
Within
Respondent (R)
1
2.13
2.13
0.19
0.6636
RxG
2
9.57
4.79
0.43
0.6530
RxS
1
1.25
1.25
0.11
0.7392
R x G x S
2
8.78
4.39
0.39
0.6761
Error
44
489.27
11.12
Attribution (A)
4
389.59
97.40
14.38
0.0001
A x G
8
34.59
4.32
0.64
0.7445
A x S
4
29.90
7.48
1.10
0.3562
A x G x S
8
65.01
8.13
1.20
0.3014
Error
176
1191.71
6.77
Rx A
4
268.49
67.12
11.81
0.0001
Rx AxG
8
18.06
2.26
0.40
0.9211
R x A x S
4
46.27
11.57
2.03
0.0916
Rx A x G x S
8
45.57
5.70
1.00
0.4364
Error
176
1000.60
5.69


52
Table 4-10 Ratings
Failure Mean; as a Functi.on.Qf Respondent and Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
3.0
3.8
3.4
4.0
3.4
Mother
2.8
6.3
3.3
3.1
1.4
Table 4-11 Ratings
Pearson Correlation Coefficients Between Mothers and Children's Failure Attributions
Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.20
.19
.14
-.01
.09
.11
* = p<.05
attribution than do children. Although children view effort as important in mathematics
failure, they tend to place slightly more emphasis on home training. Another major
difference between mothers and children regards the luck attribution. Children rate luck
equally as important as school training while mothers rate luck as the least important
cause of their children's mathematics failure.
Pearson-product moment correlation coefficients presented in Table 4-11 indicate
that mothers and children within the same family were not in agreement when evaluating
the children's mathematics performance and were also not in agreement about the causes
of mathematics failure. Of the six correlations, none of the coefficients were significant.


53
Mothers' Predictions of Their Children's Mathematics Success and Failure Attributions:
Rating Scale Method
Mothers were asked to predict the attributions their children would choose for
mathematics success and failure. Mothers predicted on a 6-point scale how their children
would evaluate the mathematics grade on their most recent report card. For predicted
success, the interviewer asked mothers "Why do you think your child thinks he or she is
doing this well?" For predicted failure, the interviewer asked "Why do you think that
your child thinks he or she is not doing even better?" Mothers predicted on 9-point scales
the relative importance their children would place on the five attributional items. The 9-
point scales ranged from 1 (definitely not a cause) to 9 (definitely a cause). The
attributional items were: ability, effort, school training, home training, and luck. It was
hypothesized that African American mothers will not accurately predict their children's
mathematics success and failure attributions. For the predicted success condition,
Pearson product-moment correlation coefficients between children's actual mathematics
success attributions and mothers' predictions of their children's mathematics success
attributions within the family are presented in Table 4-12. The results of Table 4-12
indicate that mothers cannot accurately predict their children's mathematics success
attributions within the family. Of the six correlations, four of the coefficients were not
significant. The two coefficients that were significant were home training, r= .39; and
luck, r= .29, and these were relatively small. Therefore, mothers were not very accurate
in predicting their children's mathematics success attributions within the family. Mothers


54
were also unable to accurately predict how their children would evaluate their
mathematics performance.
Means and standard deviations for children's actual mathematics success
attributions and mothers' predicted success attributions are presented in Table 4-13. The
results of an ANOVA of children's actual mathematics success attributions and mothers'
predicted success attributions are reported in Table 4-14.
Table 4-12 Ratings
Pearson Correlation Coefficients Between Children's Success Attributions and Mothers's
rreaicuons o i neir innaren s success Aiinouiions wnmn me family
Ability
Effort
School
Home
Luck
Evaluation
.13
-.01
.23
.37*
.29*
.16
' = p<.05
There are two types of effects in Table 4-14, effects involving respondent and
effects that do not involve respondent. The latter are analyses of the sum of a mother's
predictions and a child's ratings, and consequently, are not meaningful effects. The
former are comparisons of mothers' predictions and children's ratings, and are meaningful
effects. Consequently, this section will focus on effects involving respondent.
The results in Table 4-14 include a significant Respondent x Type of Attributions
interaction and a significant main effect of attribution suggesting some disagreement
about the relative importance the children place on the five types of attributions and the
mothers' predictions of their children's attributions. Mean attributions as a function of
respondent and type of attribution are reported in Table 4-15. These means show the


55
nature of the disagreement between mother's predicted success attributions and children's
actual success attributions when rating the importance of the five types of attributions.
Mothers average predicted success attributions tended to agree with children's average
actual success attributions about the relative importance of the five types of attributions
as causes of mathematics success. On average, both mothers and children rate effort as
the most important, followed by school training, home training, ability, and luck.
However, mothers average predicted success attributions were not accurate in predicting
the emphasis their children would place on the five types of attributions. The most
obvious difference between mothers' average predicted success attributions and children's
actual success attributions involved the ability and luck attributions. Mothers tended to
overestimate the importance their children would place on ability, and underestimate the
importance their children would place on luck. Nevertheless, on average, mothers'
predictions about the emphasis their children would place on effort, school training, and
home training were relatively close to their children's actual mathematics success
attributions.
For the predicted failure condition, Pearson product-moment correlation
coefficients between childrens actual mathematics failure attributionsand mothers
predicted mathematics failure attributions within the family. Only two coefficients were
significant. These were school training, r = .32; home training, r = .43 The four
remaining coefficients, ability, effort, luck, and the evaluation were not significant.


Table 4-13 Ratings
Children's Mathematics Success Attributions and Mother's Predictions of Their Children's
Mathematics Success Attributions bv Grade and Sex
56
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.1
8.9
8.6
6.5
5.1
SD
2.4
0.4
0.7
1.5
3.1
Mother
M
4.8
8.0
6.8
6.5
3.4
SD
3.8
2.8
3.0
2.8
2.6
Male
Child
M
6.2
8.1
8.4
8.0
3.4
SD
2.1
2.0
1.3
1.7
3.7
Mother
M
5.9
8.3
6.7
6.7
4.1
SD
3.2
1.4
2.5
1.7
3.1
7
Female
Child
M
2.1
8.9
5.6
3.9
3.4
SD
2.0
0.4
1.7
2.5
2.4
Mother
M
4.0
7.4
6.9
5.0
2.7
SD
3.2
2.1
3.1
3.2
2.4
Male
Child
M
6.6
7.1
6.9
5.6
4.3
SD
3.0
2.8
1.8
2.3
3.4
Mother
M
6.2
7.8
5.6
5.6
2.8
SD
1.9
1.3
3.0
1.0
2.2
8
Female
Child
M
4.7
8.7
8.3
5.7
5.3
SD
3.4
0.8
1.9
3.3
4.1
Mother
M
6.7
7.9
6.3
6.9
3.3
SD
3.0
2.2
3.0
2.5
3.9
Male
Child
M
4.7
7.2
7.1
6.5
5.0
SD
2.8
2.1
2.3
2.0
3.6
Mother
M
4.4
7.2
5.6
6.3
2.5
SD
2.6
1.5
2.9
2.0
1.9


57
Table 4-14 Ratings
Summary of ANOVA for Children's Mathematics Success Attributions and Mother's
Predictions of Their Children's Mathematics Success Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
84.89
42.44
3.65
0.0340
Sex (S)
1
2.79
2.79
0.24
0.6262
GxS
2
51.74
25.87
2.23
0.1199
Error
44
511.15
11.61
Within
Respondent (R)
1
23.26
23.26
3.66
0.0623
RxG
2
9.36
4.68
0.74
0.4848
RxS
1
5.19
5.19
0.82
0.3715
Rx Gx S
2
7.79
3.89
0.61
0.5467
Error
44
279.81
6.36
Attribution (A)
4
1026.74
256.68
40.02
0.0001
AxG
8
33.18
4.15
0.65
0.7376
A x S
4
62.48
15.62
2.44
0.0490
AxGxS
8
48.12
6.01
0.94
0.4868
Error
176
1128.88
6.41
Rx A
4
66.52
16.63
3.40
0.0105
Rx A x G
8
30.22
3.78
0.77
0.6276
Rx A x S
4
39.78
9.95
2.03
0.0917
R x A x G x S
8
21.16
2.64
0.54
0.8246
Error
176
860.77
4.90


58
Table 4-15 Ratings
Predicted Success Mean Ratings as a Function of Respondent and Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
4.7
8.1
7.5
6.0
4.4
Mother
5.3
7.8
7.8
6.1
3.1
Table 4-16 Ratings
Pearson Correlation Coefficients Between Children's Failure Attributions and Mothers
Prediction of Children's Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
-.19
.26
.32*
.43*
.12
.16
* = p<.05
Means and standard deviations for children's actual mathematics failure
attributions and mothers predicted failure attributions are presented in Table 4-17. The
results of an ANOVA of children's actual failure attributions and mothers' predicted
failure attributions are reported in Table 4-18. There are two types of effects in Table 4-
18, effects involving Respondent and effects that do not involve respondent. The only
meaningful effects are those involving respondent. Consequently this section will focus
on effects involving only the respondent. Examination of Table 4-18 indicates a
significant Respondent x Type of Attribution interaction indicating some discrepancy
about the relative importance children place on the five types of attributions and the
mothers' predictions of their children's attributions. Mean attributions as a function of


59
Respondent and Type of Attribution are reported in Table 4-19. Table 4-19 shows that
mothers' predicted failure attributions of their children's mathematics performance were
not in agreement for the relative importance of the five types of attribution. Also,
mothers predictions were not in agreement with the emphasis their children placed on
each of the five types of attributions. The only attribution mothers came close to
predicting about their children's mathematics failure was ability. It seems mothers knew
their children would not rate their ability as a cause of mathematics failure.
Children's Prediction's of Their Mother's Mathematics Success and Failure Attributions:
Rating Scale Method
Children were asked to predict the attributions their mothers would choose for the
children's mathematics success and failure. Children predicted on a 6-point scale how
their mothers would rate the mathematics grade on their most recent report card. For
predicted success, the interviewer asked children, "Why do you think your mother thinks
you are doing this well?" For predicted failure, the interviewer asked "Why do you think,
your mother thinks you are not doing even better?" Children predicted on 9-point scales
the relative importance their mothers would place on the five attributional items. The 9-
point scales ranged from 1 (definitely not a cause) to 9 (definitely a cause). The
attributional items were ability, effort, training at school, training at home and luck. It
was hypothesized that African American children will not accurately predict their
mother's mathematics success and failure attributions.
For the predicted success condition, Pearson product-moment correlation
coefficients between mothers' actual mathematics success attributions and children's


Table 4-17 Ratings
Children'? Mathematic? Failure Attributions and Mother's Predictions of Their Children's
Mathematics Failnre Attributions hv Grade and Sex
60
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.0
5.1
2.6
3.5
2.5
SD
2.9
3.6
2.4
2.1
2.5
Mother
M
2.5
4.6
2.4
3.9
2.3
SD
2.1
3.5
2.7
3.0
2.4
Male
Child
M
2.9
3.4
3.9
4.3
4.4
SD
2.8
3.4
3.6
3.8
3.6
Mother
M
3.2
4.7
3.7
3.4
3.0
SD
2.5
3.4
3.1
2.0
2.8
7
Female
Child
M
2.0
2.3
3.4
4.4
3.0
SD
1.9
2.2
3.4
1.9
2.2
Mother
M
3.9
5.3
3.9
2.4
1.0
SD
3.8
4.0
3.8
1.9
0.0
Male
Child
M
2.4
3.6
3.6
3.1
3.3
SD
1.9
3.0
2.7
2.8
2.7
Mother
M
3.4
4.8
5.4
3.6
2.4
SD
2.0
3.3
3.0
2.1
1.9
8
Female
Child
M
2.3
3.3
3.9
3.6
3.3
SD
2.2
3.0
3.7
3.8
3.9
Mother
M
4.1
4.4
4.9
3.7
2.7
SD
3.6
3.6
3.4
2.8
3.1
Male
Child
M
4.5
5.3
3.1
5.0
4.1
SD
3.3
3.1
2.7
3.0
3.2
Mother
M
2.7
6.3
5.0
3.0
3.3
SD
2.5
3.0
3.5
2.0
2.6


61
Table 4-18 Ratings
Summary of ANOVA for Children's Mathematics Failure Attributions and Mother's
Predictions of their Childrens Mathematics Failure Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
27.13
13.57
0.66
0.5203
Sex (S)
1
26.28
26.28
1.28
0.2632
GxS
2
1.50
0.75
0.04
0.9640
Error
44
900.05
20.46
Within
Respondent (R)
1
1.87
1.87
0.22
0.6393
RxG
2
13.65
6.82
0.81
0.4496
RxS
1
0.43
0.43
0.05
0.8228
RxGxS
2
13.74
6.87
0.82
0.4471
Error
44
368.81
8.38
Attribution (A)
4
131.45
32.86
4.21
0.0028
AxG
8
28.21
3.53
0.45
0.8882
A x S
4
13.24
3.32
0.42
0.7910
A x G x S
8
45.82
5.73
0.73
0.6614
Error
176
1373.57
7.80
RxA
4
84.55
21.14
3.29
0.0124
Rx A x G
8
34.37
4.30
0.67
0.7181
R x A x S
4
9.09
2.27
0.35
0.8409
Rx A x G x S
8
61.62
7.70
1.20
0.3017
Error
176
1129.99
6.42


Table 4-19 Ratings
Predicted Failure Mean Ratings as a Function of Respondent and Type of Attribution
62
Respondent
Ability
Effort
School
Home
Luck
Child
3.0
3.8
3.4
4.0
3.4
Mother
3.3
5.0
4.2
3.3
2.4
Table 4-20 Ratings
Pearson Correlation Coefficients Between Mother's Success Attributions and Children's
Predictions of Mothers's Success Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.23
.26
.06
.17
.16
.31*
*=p<.05
predictions of their mothers' mathematics success attributions within the family are
presented in Table 4-20. The results of Table 4-20 indicate that children were not able to
predict accurately their mothers' mathematics success attributions within the family. Of
the six correlations, five were not significant. However, a small significant correlation
was found when children predicted how their mothers would evaluate their mathematics
performance.
Means and standard deviations for mothers' actual mathematics success
attributions and children's predicted success attributions are presented in Table 4-21. The
results of an ANOVA of mothers' actual mathematics success attributions and children's
predicted success attributions are reported in Table 4-22. As in the preceding analyses


63
Table 4-21 Ratings
Mother's Mathematics Success Attributions and Children's Predictions of Their Mother's
Mathematics Success Attributions bv Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.9
8.0
8.3
6.9
5.8
SD
3.3
1.9
1.8
1.6
3.5
Mother
M
5.4
8.3
8.3
7.1
3.4
SD
2.8
1.4
1.5
1.9
3.1
Male
Child
M
6.8
7.7
7.6
8.1
3.0
SD
1.9
2.5
2.8
1.5
3.2
Mother
M
5.8
8.4
5.9
5.8
1.6
SD
3.2
0.9
2.4
2.1
1.7
7
Female
Child
M
4.0
7.7
7.0
6.0
3.1
SD
2.1
1.7
3.1
2.0
2.5
Mother
M
1.6
8.0
7.9
4.4
2.0
SD
1.5
1.9
2.0
3.6
1.9
Male
Child
M
5.3
7.0
7.7
5.6
2.4
SD
.33
2.0
1.5
3.2
2.2
Mother
M
5.2
7.2
6.7
6.0
1.9
SD
2.7
1.6
2.1
1.6
1.8
8
Female
Child
M
4.0
8.6
8.3
7.6
2.9
SD
3.6
1.1
1.9
2.5
2.3
Mother
M
4.4
8.1
6.6
5.6
3.3
SD
2.6
2.3
3.0
2.8
3.1
Male
Child
M
5.2
6.4
7.7
6.8
4.5
SD
2.8
2.3
1.4
2.8
3.5
Mother
M
4.3
6.6
5.9
6.3
1.7
SD
3.0
2.3
2.6
2.2
1.3


Table 4-22 Ratings
Summary of ANQVA for Mother's Mathematics Success Attributions and Children's
Predictions of Their Mother's Mathematics Success Attributions
64
Source
df
SS
MS
F
P
Between
Grade (G)
2
82.89
41.44
3.65
0.0341
Sex (S)
1
5.19
5.19
0.46
0.5022
GxS
2
17.83
8.91
0.79
0.4622
Error
44
499.24
11.35
Within
Respondent(R)
1
60.90
60.90
8.82
0.0048
RxG
2
3.37
1.68
0.24
0.7851
RxS
1
1.98
1.98
0.29
0.5954
RxGxS
2
11.67
5.84
0.85
0.4364
Error
44
303.89
6.91
Attribution (A)
4
1496.43
374.11
70.14
0.0001
A x G
8
33.91
4.24
0.79
0.6080
A x S
4
97.91
24.48
4.59
0.0015
A x G x S
8
46.01
5.76
1.08
0.3805
Error
176
938.77
5.33
R x A
4
32.71
8.18
1.81
0.1290
R x A x G
8
22.26
2.78
0.62
0.7639
R x A x S
4
10.49
2.62
0.58
0.6772
Rx A x G x S
8
59.74
7.47
1.65
0.1133
Error
176
795.55
4.52


65
Table 4-23 Ratings
Predicted Success Means bv Respondent
Respondent
Means
Child
6.1
Mother
5.4
involving predictions, only the effects involving respondent are meaningful, and of these
effects, only the respondent effect was significant.
Table 4-23 shows a main effect for respondent indicating a discrepancy between
children's predicted mathematics success attributions and mothers' actual mathematics
success attributions. Children tended to overestimate the emphasis their mothers would
place on the five types of attributions about the children's mathematics success.
For the predicted failure condition, Pearson product-moment coefficients between
mothers' actual mathematics failure attributions and children's predictions of their
mother's mathematics failure attributions within the family are presented in Table 4-24.
The results of Table 4-24 indicate that children were not able to accurately predict their
mothers' mathematics failure attributions nor were they able to predict how their mothers
would evaluate their mathematics performance. Only one of the six correlations was
significant: effort, r = .29, which does not show a strong relationship.


66
Table 4-24 Ratings
Pearson Correlation Coefficients Between Mother's Failure Attributions and Children's
Predictions of Mother's Failure Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.06
.29*
.20
.13
.24
.16
* = p<.05
Means and standard deviations for mothers' actual mathematics failure attributions
and children's predicted failure attributions are presented in Table 4-25. The results of an
ANOVA of mothers' actual mathematics failure attributions and children's predicted
failure attributions are reported in Table 4-26. Again, the only meaningful effects are
those involving respondent.
The results of Table 4-26 include a significant Respondent x Type of attribution
interaction indicating some disagreement about the relative importance the children place
on the five types of attributions and the mothers' actual mathematics attributions. Mean
attributions as a function of respondent and type of attribution are reported in Table 4-27.
These means describe the nature of the disagreement between mothers' actual
mathematics failure attributions and children's predicted failure attributions for the five
types of attributions. On average, mothers rated effort as most important, followed by
school, home, ability, and luck. However, children predicted their mothers, on average,
would rate effort as most important, followed by home, ability, luck, and school.
Therefore, children's predictions were not consistent with mothers actual mathematics


67
Table 4-25 Ratings
Mother's Mathematics Failure Attributions and Children's Predictions of Their Mother's
Mathematics Failure Attributions bv Grade and Sex
Grade Sex Subject Statistic
6 Female Child M
SD
Mother M
SD
Male Child M
SD
Mother M
SD
7 Female Child M
SD
Mother M
SD
Male Child M
SD
Mother M
SD
8 Female Child M
SD
Mother M
SD
Male Child M
SD
M
SD
Ability
Effort
School
Home
Luck
3.5
4.8
2.4
3.4
2.4
2.1
3.4
2.8
3.1
1.8
3.4
6.6
2.3
3.5
2.3
2.7
3.5
2.3
2.8
2.4
3.4
2.8
3.1
4.0
3.2
3.2
2.8
3.3
3.2
3.5
2.6
5.4
3.7
3.6
1.2
2.0
3.8
3.0
2.1
0.7
3.0
2.0
2.0
2.3
3.7
2.8
1.7
1.7
1.7
3.0
1.6
7.0
1.6
3.9
1.0
1.5
3.1
1.5
3.1
0.0
2.0
2.7
3.5
3.0
3.0
1.7
2.7
2.8
2.7
2.8
3.2
5.0
5.4
2.1
1.9
2.5
3.8
3.3
2.2
1.5
1.4
5.0
1.9
2.6
1.6
0.8
3.6
2.3
3.0
1.5
2.9
7.1
2.9
1.9
1.0
3.1
3.0
3.1
1.6
0.0
3.8
6.4
3.8
4.8
2.8
2.7
2.5
3.0
3.0
2.4
3.3
6.8
4.1
2.8
1.4
2.3
2.7
3.4
1.9
1.0
Mother


68
Table 4-26 Ratings
Summary of ANOVA for Mothers Mathematics Failure Attributions and Children's
Predictions of Their Mother's Mathematics Failure Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
13.14
6.57
0.38
0.6884
Sex (S)
1
31.77
31.77
1.32
0.1842
GxS
2
36.59
18.30
1.05
0.3592
Error
44
768.03
17.46
Within
Respondent (R)
1
9.07
9.07
1.22
0.2760
RxG
2
9.26
4.63
0.62
0.5422
RxS
1
4.39
4.39
0.59
0.4476
R x G x S
2
16.48
8.24
1.10
0.3402
Error
44
328.20
7.46
Attribution (A)
4
494.86
123.71
21.74
0.0001
AxG
8
93.83
11.73
2.06
0.0420
A x S
4
70.04
17.51
3.08
0.0176
A x G x S
8
22.19
2.77
0.49
0.8642
Error
176
1001.68
5.69
Rx A
4
184.29
46.07
8.66
0.0001
R x A x G
8
43.22
5.40
1.02
0.4259
RxAxS
4
16.83
4.21
0.79
0.5323
R x A x G x S
8
43.14
5.39
1.02
0.4273
Error
176
936.16
5.32


69
failure attributions in the relative importance they place on the five types of attributions.
Further, children tended to underestimate the emphasis mothers would place on effort for
mathematics failure and overestimate the emphasis their mothers would place on the luck
attribution.
Table 4-27 Ratings
Predicted Failure Means as a Function of Respondent x Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
2.9
3.9
2.7
3.3
2.8
Mother
2.8
6.3
3.3
3.1
1.4
Mothers' and Children's Success and Failure Attributions for Mathematics Performance:
Chip Distribution Method
Mothers and children were asked to evaluate on a six-point scale the mathematics
grade from the children's most recent report card. The six point scale ranged from 1 (not
doing as well as most) to 6 (doing the very best in the class). Mothers were then asked
by the interviewer "Why is your child doing this well?, and children were asked "Why
are you doing this well?" Mothers and children distributed 10 plastic chips on five cards
labeled with five attributions to determine the relative importance of each attribution.
The five attributions were ability, effort, training at school, training at home, and luck.
This procedure resulted in five attribution variables, each with possible ranges of 0 to 10


70
for both the success and failure conditions. For example, if all ten chips are placed on
one attribution there will be no chips left to place on any of the other attributions.
Relative success in this study is actually mothers' and children's evaluation of the
children's current level of performance. For relative failure, mothers and children were
asked by the interviewer, "Why did your child not do even better?" (for mothers), and
"Why aren't you doing even better?" (for the children). This condition examines relative
failure as a judgment in contrast to the current level of performance.
It was hypothesized that low SES African American mothers will not agree with
their children about the causes of their children's success and failure in mathematics.
For the success condition means and standard deviations for each of the
attributional items are presented in Table 4-28. The results of an ANOVA of mothers'
and children's mathematics success attribution means are presented in Table 4-29. The
chip distribution method has no between subject effects because there were only 10 chips
available for distribution. Therefore, only within subject effects will be reported in this
section. The results in Table 4-29 include a significant main effect of attribution and a
significant Respondent x Type of Attribution interaction indicating some discrepancies
between mothers and children about the relative importance they place on the five types
of attributions for success in mathematics.
Mean attributions as a function of respondent and type of attribution are reported
in Table 4-30. These means show the nature of disagreement between mothers and
children about the relative importance of the five types of attributions. Mothers and
children differed slightly about the relative importance of the five types of attributions as


71
causes of mathematics success. On average, mothers weighted effort as most important,
followed by school and home training which were weighted equally, followed by ability,
then luck. Children on average, weighted effort as most important, followed by school
training, home training, luck and ability. Mothers and children also differed in the
emphasis they placed on the five types of attributions. Mothers placed more weight than
the children on the effort attribution, while children placed more emphasis on the luck
attribution for mathematics success. Also, children believed that school training was
more important for mathematics success than their mothers. Mothers and children were
similar in their weightings of ability and home training as causes of mathematics success.
Overall, mothers and children agreed that effort was most important for mathematics
success. However, mothers and children differed in the emphasis placed on effort. Also
mothers and children differed in their beliefs about the importance of school training and
luck as causes of mathematics success.
Pearson-product moment correlation coefficients presented in Table 4-31 indicate
that mothers and children within the family do not agree about the causes of mathematics
success. Mothers and children were also not in agreement about the childrens
performance in mathematics. Of the six correlations, only one coefficient was
significant: ability r = .50, indicating a moderate relationship. However, there was very
little agreement for the remaining attributions.


Table 4-28 Weightings
Mother's and Children's Mathematics Success Attributions by Grade and Sex
72
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.0
3.3
2.5
1.8
1.5
SD
0.8
1.0
0.6
1.3
1.3
Mother
M
0.5
5.3
1.5
2.8
0.0
SD
1.0
2.2
1.7
1.5
0.0
Male
Child
M
1.0
3.3
2.8
1.2
1.7
SD
1.7
1.5
0.8
1.2
1.5
Mother
M
1.7
5.2
1.5
1.2
0.5
SD
3.1
2.9
1.4
1.5
1.2
7
Female
Child
M
0.8
4.0
3.0
1.0
1.2
SD
1.1
1.3
1.5
1.7
1.6
Mother
M
0.7
5.0
2.2
1.8
0.3
SD
1.2
2.4
1.6
1.5
0.8
Male
Child
M
1.2
4.2
1.8
2.0
0.9
SD
2.0
2.4
1.7
1.9
1.1
Mother
M
1.8
3.7
2.2
2.1
0.3
SD
3.1
2.7
1.6
2.1
0.8
8
Female
Child
M
0.5
4.8
2.3
1.5
1.0
SD
1.0
1.3
1.7
1.7
2.0
Mother
M
0.8
8.0
0.8
0.5
0.0
SD
1.5
2.4
1.5
1.0
0.0
Male
Child
M
1.2
3.9
2.9
1.3
0.7
SD
1.3
2.3
1.5
1.3
1.1
Mother
M
1.4
3.2
2.6
2.6
0.1
SD
1.7
1.7
1.4
1.7
0.4


73
Table 4-29 Weightings
Summary of ANOVA for Success Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
697.97
174.49
40.74
0.0001
A x G
8
14.34
1.79
0.42
0.9089
A x S
4
35.30
8.82
2.06
0.0880
Ax Gx S
8
54.41
6.80
1.59
0.1313
Error
176
753.85
4.28
R x A
4
57.77
14.44
5.17
0.0006
R x A x G
8
16.01
2.00
0.72
0.6768
R x A x S
4
22.23
5.56
1.99
0.0980
Rx A x G x S
8
25.16
3.14
1.13
0.3480
Error
176
491.61
2.80
Table 4-30 Weightings
Success Means as a Function of Resixmdent and Type of Attribution
Respondent
Ability
Effort
School Home
Luck
Child
0.9
3.9
2.5 1.4
1.2
Mother
1.1
5.0
1.8 1.8
0.2


74
Table 4-31 Weightings
Pearson Correlation Coefficients Between Mothers' and Children's Success Attributions
Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.50*
.19
.05
.12
.10
.18
* =p<.05
For the failure condition, means and standard deviations for each of the
attributional items are presented in Table 4-32. The results of an ANOVA of mothers'
and children's attribution means are presented in Table 4-33. The results in Table 4-33
include a significant Respondent x Type of Attribution x Grade interaction and a main
effect for attribution, indicating disagreement between mothers and children in each grade
level about the relative importance of the five types of attributions as causes of
mathematics failure. Means as a function of respondent, type of attribution, and grade are
reported in Table 4-34. These means describe the nature of the disagreement between
mothers and children in each grade level. On average, sixth grade children believed the
home was the most important cause of mathematics failure, while seventh and eighth
grade children believed effort was the most important cause. Sixth, seventh, and eighth
grade mothers, on average, all agreed that effort was the most important cause of their
children's mathematics failure. However, these mothers disagreed about the importance
of the remaining types of attributions. Eighth grade mothers and children were more in
agreement about the causes of mathematics failure than were sixth and seventh grade
mothers and children. Sixth and seventh grade mothers tended to weight home training


75
Table 4-32 Weightings
Mother's and Children's Mathematics Failure Attributions by Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.8
2.3
2.5
2.5
1.0
SD
1.3
2.6
1.0
0.6
1.2
Mother
M
1.3
5.0
2.8
0.0
1.0
SD
1.3
0.8
2.1
0.0
1.4
Male
Child
M
2.2
1.7
1.7
1.8
2.7
SD
4.0
2.3
2.0
2.5
2.5
Mother
M
1.0
4.3
1.7
1.3
1.7
SD
1.7
4.6
4.1
2.8
2.6
7
Female
Child
M
1.7
2.8
1.1
3.0
1.4
SD
1.6
3.3
1.4
4.0
2.0
Mother
M
3.1
3.3
1.8
1.1
0.7
SD
2.8
3.2
1.8
1.4
1.3
Male
Child
M
1.5
4.0
2.0
1.3
1.2
SD
3.0
3.8
2.1
2.0
1.9
Mother
M
0.9
6.0
1.5
1.2
0.4
SD
1.7
3.0
1.9
1.9
1.4
8
Female
Child
M
1.5
5.0
2.5
0.0
1.0
SD
3.0
5.8
5.0
0.0
2.0
Mother
M
1.8
0.3
3.5
2.0
2.5
SD
1.7
0.5
3.9
1.8
2.5
Male
Child
M
0.6
4.7
1.2
1.9
1.6
SD
1.0
3.0
1.7
1.9
2.8
Mother
M
1.3
5.6
1.5
1.4
0.2
SD
1.8
3.1
1.5
1.6
0.6


76
Table 4-33 Weightings
Summary of ANOVA for Failure Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
318.53
79.63
9.01
0.0001
AxG
8
26.76
3.34
0.38
0.9310
A x S
4
50.99
12.75
1.44
0.2219
A x G x S
8
73.16
9.15
1.04
0.4115
Error
176
1555.04
8.84
Rx A
4
20.53
5.13
0.93
0.4502
Rx A x G
8
92.03
11.50
2.08
0.0405
Rx Ax S
4
41.73
10.43
1.88
0.1156
Rx A x G x S
8
61.50
7.69
1.39
0.2050
Error
176
975.61
5.54
as the least important cause while eighth grade mothers believed home training was an
important cause of their childrens mathematics failure. Eighth grade children believed
home training was the least important cause of mathematics failure but sixth grade
children believed home training was the most important cause. The most striking
difference between mothers' and children's mathematics failure attributions is the
emphasis on effort. The children's emphasis on effort increases from sixth to eighth
grade while their mothers' emphasis decreases between the sixth and eighth grade.
In summary, mothers and children at all grade levels agreed that effort was the
most important cause of mathematics failure except for sixth grade children. However,


77
Table 4-34 Weightings
Failure Means as a Function of Respondent. Type of Attribution, and Grade
Grade
Respondent
Ability
Effort
School
Home
Luck
6
Child
1.9
1.9
2.1
2.2
1.8
Mother
1.1
4.7
2.2
0.7
1.3
7
Child
1.6
3.4
1.6
2.2
1.3
Mother
2.1
4.7
1.7
1.1
0.6
8
Child
1.1
4.9
1.9
0.9
1.3
Mother
1.5
2.9
2.5
1.7
1.4
mothers' endorsement of effort decreased as their children progressed through the middle
grades while their children's endorsement of effort increased.
Pearson-product moment correlation coefficients presented in Table 4-35 indicate
that mothers and children within the same family do not agree about the causes of
mathematics failure and were also not in agreement about the evaluation of the children's
mathematics performance. Of the six correlations, only one correlation was significant:
effort, r = .32, which is a relatively small relationship.
Table 4-35 Weightings
Pearson Correlation Coefficients Between Mothers' and Children's Failure Attributions
Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.19
.32*
.25
.13
-.06
.18
* = p<.05


78
Mother's Predictions of Their Children's Mathematics Success and Failure Attributions:
Chip Distribution Method
Mothers were asked to predict the attributions their children would choose for
mathematics success and failure. Mothers predicted on a 6-point scale how their children
would evaluate the mathematics grade on their most recent report card. For predicted
success, the interviewer asked mothers "Why do you think your child thinks he or she is
doing this well?" For predicted failure, the interviewer asked "Why do you think your
child thinks he or she is not doing even better?" Mothers predicted by distributing 10
plastics chips on five cards labeled with five attributions to determine the relative
importance of each attribution. The five attributions were ability, effort, training at
school, training at home, and luck. This procedure resulted in five attribution variables,
each with possible ranges of 0 to 10 for both success and failure conditions.
It was hypothesized that African American mothers will not accurately predict
their children's mathematics success and failure attributions. For the predicted success
condition, Pearson-product moment correlation coefficients between children's actual
mathematics success attributions and mothers' predictions of their children's mathematics
success attributions within the family are presented in Table 4-36. The results in Table 4-
36 indicate that mothers cannot accurately predict their children's mathematics success
attributions within the family. Mothers were also unable to accurately predict how their
children would evaluate their mathematics performance. Of the six correlations, none of
them were significant.


Table 4-36 Weightings
Pearson Correlation Coefficients Between Childrens Actual Success and Mothers'
Predicted Success Attributions Within the Family
79
Ability
Effort
School
Home
Luck
Evaluation
.33
.19
-.04
.02
-.10
.10
* = p<.05
Means and standard deviations for children's actual mathematics success
attributions and mothers' predicted success attributions are presented in Table 4-37. The
results of an ANOVA of children's actual mathematics success attributions and mothers'
predicted success attributions are reported in Table 4-38.
The results in Table 4-38 include a significant Respondent x Type of Attribution
interaction and a significant main effect of attribution suggesting disagreement about the
relative importance the children place on the five types of attributions and the mothers'
predictions about their children's attributions. Mean attributions as a function of
respondent and type of attribution are reported in Table 4-39. These means show the
nature of the disagreement between mothers' predicted success attributions and children's
actual success attributions when weighting the importance of the five types of
attributions. On average, mothers' predictions that children will place the most emphasis
on effort and school is correct but mothers overestimate the amount of emphasis their
children will place on effort.
On average, mothers predicted their children would weight effort as most
important followed by school, ability, home, and luck. Children's actual success


80
attributions on average were effort as most important, followed by school, home, luck,
then ability.
Although mothers predicted correctly on the importance their children would give
effort and school training, mothers did not predict accurately on the emphasis their
children would place on these two attributions. Mothers underestimated the emphasis
their children placed on school training and overestimated how their children would
weight effort. Overall, mothers were not very accurate in predicting their children's
success attributions.
For the predicted failure condition, Pearson-product moment correlation
coefficients between children's actual mathematics failure attributions and mothers'
predictions of their children's mathematics failure attributions within the family are
presented in Table 4-40. The results in Table 4-40 show that mothers cannot accurately
predict how their children would evlauate their mathematics performance nor could they
predict their children's mathematics failure attributions within the family. Of the six
correlations, only two correlations were significant: effort, r = .52, indicating a moderate
relationship, and home training, r = .38, indicating a small relationship.
Means and standard deviations of children's actual failure attributions and
mothers' predictions are presented in Table 4-41. The results of an ANOVA of children's
actual mathematics failure attributions and mother's predictions of their children's
mathematics failure attributions presented in Table 4-42 indicate no significant
interactions.


81
Table 4-37 Weightings
Children's Mathematics Success Attributions and Mother's Predictions of Their Children's
Mathematics Success Attributions by Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.0
3.3
2.5
1.8
1.5
SD
0.8
1.0
0.6
1.3
1.3
Mother
M
0.8
6.3
1.8
1.3
0.0
SD
1.5
2.9
1.3
1.9
0.0
Male
Child
M
1.0
3.3
2.8
1.2
1.7
SD
1.7
1.5
0.8
1.2
1.5
Mother
M
1.7
4.3
2.3
0.8
.83
SD
4.1
3.3
2.1
1.6
1.3
7
Female
Child
M
0.8
4.0
3.0
1.0
1.2
SD
1.1
1.3
1.5
1.7
1.6
Mother
M
0.9
4.8
3.4
0.7
.20
SD
1.5
2.1
2.5
1.2
.63
Male
Child
M
1.2
4.2
1.8
2.0
.92
SD
2.0
2.4
1.7
1.9
1.1
Mother
M
1.4
4.5
2.1
1.3
.67
SD
2.1
3.0
1.6
2.3
1.2
8
Female
Child
M
0.5
4.8
2.3
1.5
1.0
SD
1.0
1.3
1.7
1.7
2.0
Mother
M
2.5
6.3
0.5
0.5
.25
SD
5.0
4.8
1.0
1.0
.50
Male
Child
M
1.2
3.9
2.9
1.3
.71
SD
1.3
2.3
1.5
1.3
1.1
Mother
M
1.6
3.7
2.1
1.8
.86
SD
1.7
1.9
1.6
1.6
1.6


82
Table 4-38 Weightings
Summary of ANOVA for Children's Mathematics Success Attributions and Mothers
Predictions of their Children's Mathematics Success Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
672.50
168.13
35.64
0.0001
AxG
8
11.85
1.48
0.31
0.9600
A x S
4
19.70
4.93
1.04
0.3859
A x G x S
8
43.55
5.44
1.15
0.3298
Error
176
830.15
4.72
Rx A
4
45.11
11.28
3.00
0.0198
Rx A x G
8
25.26
3.16
0.84
0.5677
Rx A x S
4
14.19
3.55
0.94
0.4393
Rx Ax G x S
8
12.27
1.53
0.41
0.9147
Error
176
660.82
3.75
Table 4-39 Weightings
Predicted Success Means as a Function of Respondent and Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
0.9
3.9
2.5
1.4
1.2
Mother
1.4
5.0
2.0
1.0
0.5


83
Table 4-40 Weightings
Pearson Correlation Coefficients Between Children's Actual Failure and Mothers'
Predicted Failure Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.13
.52*
.05
.38*
.19
.10
* = p<.05
Children's Predictions of Their Mother's Mathematics Success and Failure Attributions:
Chip Distribution Method
Children were asked to predict the attributions their mothers would choose for the
children's mathematics success and failure. Children predicted on a 6-point scale how
their mothers would evaluate the mathematics grade on their most recent report card. For
predicted success, the interviewer asked children, "Why do you think your mother thinks
you are doing this well?" For predicted failure, the interviewer asked "Why do you think
your mother thinks you are not doing even better?" Children predicted by distributing 10
plastic chips on five cards labeled with five attributions to determine the relative
importance of each attribution. The five attributions were ability, effort, training at
school, training at home, and luck. This procedure resulted in five attribution variables,
each with possible ranges of 0 to 10 for both success and failure conditions.
It was hypothesized that African American children will not accurately predict
their mothers mathematics success and failure attributions. For the predicted success


84
Table 4-41 Weightings
Children's Mathematics Failure Attributions and Mother's Predictions of Their Children's
Mathematics Failure Attributions by Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.8
2.3
2.5
2.5
1.0
SD
1.3
2.6
1.0
0.6
1.2
Mother
M
1.8
3.5
2.3
1.8
0.8
SD
1.7
2.6
2.2
2.4
1.5
Male
Child
M
2.2
1.7
1.7
1.8
2.7
SD
4.0
2.3
2.0
2.5
2.5
Mother
M
1.5
2.7
2.3
2.0
1.5
SD
1.6
2.2
3.9
2.3
1.8
7
Female
Child
M
1.7
2.8
1.1
3.0
1.4
SD
1.6
3.3
1.4
4.0
2.0
Mother
M
2.6
2.1
1.5
2.3
1.5
SD
2.8
2.6
1.8
2.7
1.6
Male
Child
M
1.5
4.0
2.0
1.3
1.2
SD
3.0
3.8
2.1
2.0
1.9
Mother
M
0.9
3.6
3.3
1.6
0.7
SD
1.4
2.6
3.0
1.8
1.4
8
Female
Child
M
1.5
5.0
2.5
0.0
1.0
SD
3.0
5.8
5.0
0.0
2.0
Mother
M
1.8
5.5
0.3
1.0
1.5
SD
2.4
5.3
0.5
1.2
1.7
Male
Child
M
0.6
4.7
1.2
1.9
1.6
SD
1.0
3.0
1.7
1.9
2.8
Mother
M
1.6
4.2
1.8
1.8
0.6
SD
2.4
2.7
2.0
1.6
1.2


85
Table 4-42 Weightings
Summary of ANOVA for Children's Mathematics Failure Attributions and Mother's
Predictions of Their Children's Mathematics Failure Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
234.80
58.70
6.29
0.0001
A x G
8
94.88
11.86
1.27
0.2612
A x S
4
7.01
1.75
0.19
0.9445
A x G x S
8
74.64
9.33
1.00
0.4377
Error
176
1641.81
9.33
Rx A
4
4.14
1.03
0.19
0.9415
R x A x G
8
26.10
3.26
0.61
0.7681
R x A x S
4
18.27
4.57
0.86
0.4921
R x A x G x S
8
16.13
2.02
0.38
0.9315
Error
176
940.21
5.34
condition, Pearson-product moment correlation coefficients between mother's actual
success attributions and children's predictions of their mothers' mathematics success
attributions within the family are presented in Table 4-43. The results in Table 4-43
indicate that children cannot accurately predict their mothers' mathematics success
attributions within the family nor could the children predict how their mothers would
evaluate their mathematics performance. Of the six correlations, none of them were
significant.


Table 4-43 Weightings
Pearson Correlation Coefficients Between Mother's Actual Success and Children's
Predicted Success Attributions Within the Family
86
Ability
Effort
School
Home
Luck
Evaluation
.09
.12
.12
.02
-.03
.23
* = p<.05
Means and standard deviations for mothers actual mathematics success
attributions and children's predictions of their mother's mathematics success attributions
are presented in Table 4-44. The results of an ANOVA of mothers' actual mathematics
success attributions and children's predicted success attributions are reported in Table 4-
45.
The results in Table 4-45 include a significant Respondent x Type of Attribution
interaction and a significant main effect of attribution indicating disagreement about the
relative importance the mothers place on the five types of attributions and children's
predictions about their mothers' attributions. There are two types of effects in Table 4-45,
effects involving respondent and effects not involving respondent. Only the effects
involving respondent will be reported in this section.
Mean attributions as a function of respondent and type of attribution are presented
in Table 4-46. These means show the nature of the disagreement between mothers' actual
success attributions and children's predicted success attributions when weighting the
importance of the five types of attributions. Children on average predicted correctly on
the importance their mothers would place on the five types of attributions for predicted


Full Text
93
Table 4-50 Weightings
Predicted Failure Means as a function of Respondent. Type of Attribution. Grade, and
Sex
Grade
Respondent
Sex
Ability
Effort
School
Home
Luck
6
Child
Girl
1.0
4.8
1.0
2.0
1.3
Mother
1.2
5.0
2.8
0.0
1.0
Child
Boy
2.2
2.2
2.8
1.3
1.5
Mother
1.0
4.3
1.7
1.3
1.7
7
Child
Girl
1.5
3.0
1.6
3.3
0.6
Mother
3.1
3.3
1.8
1.1
0.7
Child
Boy
1.8
4.3
1.8
0.8
1.3
Mother
0.9
6.0
1.5
1.2
0.4
8
Child
Girl
1.3
7.5
1.0
0.3
0.0
Mother
1.8
0.3
3.5
2.0
2.5
Child
Boy
1.3
5.0
1.4
1.4
1.0
Mother
1.3
5.6
1.5
1.4
0.2


63
Table 4-21 Ratings
Mother's Mathematics Success Attributions and Children's Predictions of Their Mother's
Mathematics Success Attributions bv Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.9
8.0
8.3
6.9
5.8
SD
3.3
1.9
1.8
1.6
3.5
Mother
M
5.4
8.3
8.3
7.1
3.4
SD
2.8
1.4
1.5
1.9
3.1
Male
Child
M
6.8
7.7
7.6
8.1
3.0
SD
1.9
2.5
2.8
1.5
3.2
Mother
M
5.8
8.4
5.9
5.8
1.6
SD
3.2
0.9
2.4
2.1
1.7
7
Female
Child
M
4.0
7.7
7.0
6.0
3.1
SD
2.1
1.7
3.1
2.0
2.5
Mother
M
1.6
8.0
7.9
4.4
2.0
SD
1.5
1.9
2.0
3.6
1.9
Male
Child
M
5.3
7.0
7.7
5.6
2.4
SD
.33
2.0
1.5
3.2
2.2
Mother
M
5.2
7.2
6.7
6.0
1.9
SD
2.7
1.6
2.1
1.6
1.8
8
Female
Child
M
4.0
8.6
8.3
7.6
2.9
SD
3.6
1.1
1.9
2.5
2.3
Mother
M
4.4
8.1
6.6
5.6
3.3
SD
2.6
2.3
3.0
2.8
3.1
Male
Child
M
5.2
6.4
7.7
6.8
4.5
SD
2.8
2.3
1.4
2.8
3.5
Mother
M
4.3
6.6
5.9
6.3
1.7
SD
3.0
2.3
2.6
2.2
1.3


25
and daughters' academic successes to ability. Moreover, mothers thought that lack of
effort was a more important explanation for poor performance than lack of talent,
regardless of gender.
Holloway (1986) conducted a study of children's achievement in mathematics and
their motivation to achieve in mathematics. Holloway, found that mothers of girls made
more attributions to ability and task difficulty for success and were more satisfied with
past performance than were mothers of boys, but exerted less academic achievement
pressure. Mothers of boys were found to make more attributions to effort and luck for the
success of their sons than mothers of girls. However, mothers exerted more academic
pressure on their sons to achieve. These differences were, however, minor and the author
concluded that there were few differences overall.
In contrast to the studies showing no differences, there is research that indicates
that mothers do hold gender related beliefs about their children's school performance.
In a study by Holloway and Hess (1982), 21 mothers and children in 5th and 6th
grade were asked to place 10 chips on 5 attributional cards for success and failure in a
subject of high achievement and for failure in a subject of low achievement. Findings
indicated that mother of boys believed boys had more ability than girls while girls had to
exert more effort to be successful in school.
Parsons, Adler, and Kaczala (1982) compared the causal attributions for
mathematics performance of 5th through 11th grade students and their parents.
Participants were asked to rate on 7-point Likert scales their attitudes and beliefs about
mathematics performance. Results showed that parents held sex-differentiated


CHAPTER 3
METHODOLOGY
In this study the causal attributions made by low SES African American mothers
and their children for the children's mathematics success and failure were compared. In
addition, two attribution instruments for measuring mothers' and children's causal
attributions were compared.
While a variety of methods and populations have been used in studies of parents
and children's attributions for the children's school performance, low SES African
American mothers and children have not been the focus of this research. Attribution
research with low SES African American mothers and children appears warranted,
especially in the area of mathematics, where low SES African American children achieve
poorly, and are under represented in mathematics courses in high school and in
mathematics related careers (Matthews, 1984; Reyes & Stanic, 1985, 1988).
Hypotheses
The causal attributions of African American mothers for their children's school
performance were compared to the causal attributions of their 6th, 7th, or 8th grade
African American children. Based on the parents' and children's attribution literature
30


19
explanations. Failure was attributed to bad home conditions and children's low level of
interest and ability. Children attributed success to their own effort and teacher's
explanation and failure to lack of effort and low ability.
While it was thought by researchers that parents and children might be in
agreement, indicating the conveyance of values and beliefs, these studies show a lack of
agreement in the attributional beliefs of parents and children about academic
performance.
Agreement. In contrast to these findings, some researchers did find agreement
between parents and children regarding the attributional beliefs for academic success and
failure.
In a study done by Parsons, Adler, and Kaczala (1982), agreement between
parents and children in their causal attributions was documented. Using a rating scale
measuring attitudes and beliefs about mathematics achievement, children's attributions
matched those of their parents. The authors of this study concluded that children's
attributions were influenced more by their parents' beliefs about their abilities than by
their own previous mathematics achievement history.
Yamauchi (1989) conducted a study that compared the attributions of children
with mothers' attributions, and children's predictions of their mothers' attributions for
their child's mathematics performances in Japan. The results, from a rating scale
procedure, showed that more than half of the children and their mothers attributed school
performance to the effort attribution on both success and failure outcomes.


78
Mother's Predictions of Their Children's Mathematics Success and Failure Attributions:
Chip Distribution Method
Mothers were asked to predict the attributions their children would choose for
mathematics success and failure. Mothers predicted on a 6-point scale how their children
would evaluate the mathematics grade on their most recent report card. For predicted
success, the interviewer asked mothers "Why do you think your child thinks he or she is
doing this well?" For predicted failure, the interviewer asked "Why do you think your
child thinks he or she is not doing even better?" Mothers predicted by distributing 10
plastics chips on five cards labeled with five attributions to determine the relative
importance of each attribution. The five attributions were ability, effort, training at
school, training at home, and luck. This procedure resulted in five attribution variables,
each with possible ranges of 0 to 10 for both success and failure conditions.
It was hypothesized that African American mothers will not accurately predict
their children's mathematics success and failure attributions. For the predicted success
condition, Pearson-product moment correlation coefficients between children's actual
mathematics success attributions and mothers' predictions of their children's mathematics
success attributions within the family are presented in Table 4-36. The results in Table 4-
36 indicate that mothers cannot accurately predict their children's mathematics success
attributions within the family. Mothers were also unable to accurately predict how their
children would evaluate their mathematics performance. Of the six correlations, none of
them were significant.


106
accurately the attributions their mothers would give for the children's mathematics
performance. Unlike Yamauchi (1989), the present study examined whether mothers
could predict their children's mathematics attributions. Means from both the rating scale
and chip distribution procedures found that mothers were not accurate predictors of the
emphasis their children would place on the mathematics attributions.
Hypothesis Five: Attributional Agreement Within Families for Mathematics Performance
The fifth hypothesis asserted that mothers and children within the same household
would not agree about the causes of mathematics performance. Unlike the previous
analysis of groups, this study examined the relationship between mothers and children
within a home. The results from this study supported this hypothesis. Using either a
rating scale or chip distribution procedure, correlations revealed that mothers and children
living in the same household were not in agreement about the causes of mathematics
success and failure. The majority of the correlation coefficients in the success and failure
conditions were not significant, indicating little agreement on the five attributions. Those
correlation coefficients in both the success and failure conditions that were significant
tended to be small.
Support for these results were found in prior research by Holloway, Kashiwagi,
Hess, and Azuma (1986) who compared the attributions of Japanese mothers and children
to the attributions of American mothers and children for mathematics performance using
a chip distribution procedure. One major issue in their study concerned attributional
agreement among mothers and children living in the same household. It was believed


105
sons and daughters. Holloway, Kashiwagi, Hess, and Azuma (1986) who used a chip
distribution procedure and reported that Japanese mothers did not discriminate in their
mathematics attributions for their sons and daughters.
The studies in the attribution literature examining gender issues have investigated
a variety of cultural groups and used several measurement procedures (Cashmore &
Goodnow, 1986; Holloway, Kashiwagi, Hess, & Azuma, 1986; Parsons, Adler, &
Kaczala, 1982). Their findings have not been consistent. In this study, two measurement
procedures were used to measure African American mothers' mathematics attributions
and gender differences, and the findings were also inconsistent. Therefore, this study did
not clarify mothers' gender-related attributions for mathematics.
Hypotheses Three and Four: Mothers' Attributional Predictions and Children's
Attributional Predictions for Mathematics Performance
The third and fourth hypotheses asserted that mothers would not be able to
accurately predict the attributions their children would give, and children would not be
able to predict the attributions their mothers would give for the children's mathematics
performance. These hypotheses were both generally supported using both the rating scale
and chip distribution methodologies. Generally mothers and children could predict the
order of importance of the attributions that the other would give, however, the prediction
of the emphasis placed on each attribution was not accurate. The finding that children
could not predict the attributions their mothers would give is similar to Yamauchi's
(1989) study which used a rating scale in which Japanese children could not predict



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84
Table 4-41 Weightings
Children's Mathematics Failure Attributions and Mother's Predictions of Their Children's
Mathematics Failure Attributions by Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.8
2.3
2.5
2.5
1.0
SD
1.3
2.6
1.0
0.6
1.2
Mother
M
1.8
3.5
2.3
1.8
0.8
SD
1.7
2.6
2.2
2.4
1.5
Male
Child
M
2.2
1.7
1.7
1.8
2.7
SD
4.0
2.3
2.0
2.5
2.5
Mother
M
1.5
2.7
2.3
2.0
1.5
SD
1.6
2.2
3.9
2.3
1.8
7
Female
Child
M
1.7
2.8
1.1
3.0
1.4
SD
1.6
3.3
1.4
4.0
2.0
Mother
M
2.6
2.1
1.5
2.3
1.5
SD
2.8
2.6
1.8
2.7
1.6
Male
Child
M
1.5
4.0
2.0
1.3
1.2
SD
3.0
3.8
2.1
2.0
1.9
Mother
M
0.9
3.6
3.3
1.6
0.7
SD
1.4
2.6
3.0
1.8
1.4
8
Female
Child
M
1.5
5.0
2.5
0.0
1.0
SD
3.0
5.8
5.0
0.0
2.0
Mother
M
1.8
5.5
0.3
1.0
1.5
SD
2.4
5.3
0.5
1.2
1.7
Male
Child
M
0.6
4.7
1.2
1.9
1.6
SD
1.0
3.0
1.7
1.9
2.8
Mother
M
1.6
4.2
1.8
1.8
0.6
SD
2.4
2.7
2.0
1.6
1.2


4
subsequent results found in attribution research make predictability and generalization of
findings to other populations or situations difficult.
A second problem has been that results across various studies addressing parents'
and childrens attributions for academic performance were inconclusive. For example, for
parents' and children's attributional agreement, Caucasian American mothers and
children were in agreement about the causes for academic success and failure (Parsons,
Adler, & Kaczala, 1982); however, Holloway and Hess (1982) reported Caucasian
American mothers and children were not in agreement about the causes of school
performance. In another example, relating to gender, Japanese, Anglo-Australian, and
Italian Australian mothers did not make the distinction in attributions between their sons
and daughters for their mathematics performance (Cashmore & Goodnow, 1986;
Holloway, Kashiwagi, Hess, & Azuma, 1986). In contrast, Parsons, Adler, and Kaczala
(1982), Holloway and Hess (1982), and Holloway, Kashiwagi, Hess, and Azuma (1986)
found that Caucasian American mothers did make the distinction between attributions for
their sons' and daughters' mathematics performance.
The third problem found in the attribution literature has been that research
involving African Americans parents and children was quite limited. Some research has
been done with African American children that compares them to Caucasian American
children (Graham, 1991, 1994). However, no research with African American mothers
and children has been done (Graham, 1991; 1994). In reviews of minority participation
and achievement in mathematics, Matthews (1984), Reyes and Stanic (1988) identified
mothers as an important variable for their children's successful mathematics achievement.


75
Table 4-32 Weightings
Mother's and Children's Mathematics Failure Attributions by Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.8
2.3
2.5
2.5
1.0
SD
1.3
2.6
1.0
0.6
1.2
Mother
M
1.3
5.0
2.8
0.0
1.0
SD
1.3
0.8
2.1
0.0
1.4
Male
Child
M
2.2
1.7
1.7
1.8
2.7
SD
4.0
2.3
2.0
2.5
2.5
Mother
M
1.0
4.3
1.7
1.3
1.7
SD
1.7
4.6
4.1
2.8
2.6
7
Female
Child
M
1.7
2.8
1.1
3.0
1.4
SD
1.6
3.3
1.4
4.0
2.0
Mother
M
3.1
3.3
1.8
1.1
0.7
SD
2.8
3.2
1.8
1.4
1.3
Male
Child
M
1.5
4.0
2.0
1.3
1.2
SD
3.0
3.8
2.1
2.0
1.9
Mother
M
0.9
6.0
1.5
1.2
0.4
SD
1.7
3.0
1.9
1.9
1.4
8
Female
Child
M
1.5
5.0
2.5
0.0
1.0
SD
3.0
5.8
5.0
0.0
2.0
Mother
M
1.8
0.3
3.5
2.0
2.5
SD
1.7
0.5
3.9
1.8
2.5
Male
Child
M
0.6
4.7
1.2
1.9
1.6
SD
1.0
3.0
1.7
1.9
2.8
Mother
M
1.3
5.6
1.5
1.4
0.2
SD
1.8
3.1
1.5
1.6
0.6


24
Mothers' Attributions and Gender Differences
Examining gender differences in mother attributions is important for two reasons.
Boys and girls have long been shown to have different attitudes and different
performance levels in mathematics (Fox, Tobin, & Brody, 1979; Holloway & Hess, 1984;
Reyes & Stanic, 1988).
Cashmore (1980) examined the causal attributions of 100 Australian mothers and
children across 6 achievement areas. Subjects were asked to rank order the attributions of
talent, effort, and teaching. Parents did not rate boys and girls differently. The findings
found no differences.
Bar-Tal and Guttmann (1981) examined the causal attributions for mathematics
performance of boys and girls by Israeli parents, teachers and students. Participants were
asked to rate 10 attributions for their contribution to mathematics success and failure.
Results indicated that fathers and mothers believed boys and girls succeed in mathematics
because of home conditions and teacher's explanations while they both fail because of
bad home conditions, low interest, and low ability. The author's concluded there were no
gender differences.
Hess, Holloway, and King (1981), Holloway, Hess and King (1981), and King,
Hess, and Holloway (1981) compared the causal attributions for 21 mothers and their 5th
and 6th grade children. Participants were given open-ended questionnaires to ascertain
the children's spontaneous attributions about doing well or poorly in school. Participants
were then asked to place 10 chips on 5 attributional cards. The attributions were ability,
effort, personality and good teaching. Results showed that mothers attributed both sons'


69
failure attributions in the relative importance they place on the five types of attributions.
Further, children tended to underestimate the emphasis mothers would place on effort for
mathematics failure and overestimate the emphasis their mothers would place on the luck
attribution.
Table 4-27 Ratings
Predicted Failure Means as a Function of Respondent x Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
2.9
3.9
2.7
3.3
2.8
Mother
2.8
6.3
3.3
3.1
1.4
Mothers' and Children's Success and Failure Attributions for Mathematics Performance:
Chip Distribution Method
Mothers and children were asked to evaluate on a six-point scale the mathematics
grade from the children's most recent report card. The six point scale ranged from 1 (not
doing as well as most) to 6 (doing the very best in the class). Mothers were then asked
by the interviewer "Why is your child doing this well?, and children were asked "Why
are you doing this well?" Mothers and children distributed 10 plastic chips on five cards
labeled with five attributions to determine the relative importance of each attribution.
The five attributions were ability, effort, training at school, training at home, and luck.
This procedure resulted in five attribution variables, each with possible ranges of 0 to 10


113
(1994) pointed out the need for more research in this area. Researchers have tended to
examine African American children's attributions in a racial-comparative context and did
not isolate African American mothers and children for study. Therefore, replications of
this study are highly recommended.
Second, this study focused on attribution elements and not the attribution process
(see Graham & Long, 1986; Benson, 1989). Future studies could compare mothers and
children on how they perceive attribution elements on the attributional dimensions to
determine the relationship between attributions and the psychological consequences
linked to those dimensions. For example, do African American mothers and children see
effort as internal or external, temporary or permanent, controllable or uncontrollable
attribution?
Third, in the area of attribution retraining, future studies might attempt to train
students to the ability attribution (in combination with the effort attribution) and retrain
students away from the use of the luck attribution. Although luck was seen by mothers
and children as one of the least important attributions, children in this study placed
considerable emphasis on the luck attribution compared to their mothers, whether it
involved mathematics success or failure.


20
Numerous conclusions can be drawn from these studies. First, it is not clear from
the findings of studies in this literature if parents and children hold similar beliefs or
attributions within the same culture or vary across different cultures. Second, this
literature does not inform us on the best method to measure parents' and children's
attributions about school performance. No study explained why a method was chosen to
investigate the research question. Finally, this literature provides little guidance as to
why these findings have been so inconsistent.
Cultural Patterns.
When parents and children from different countries were compared to examine
their attributional beliefs about school performance, findings were different.
Stevenson (1983) conducted a study of parents and children in Japan, Taiwan, and
the United States. Participants were first asked to rank four attributions: effort, natural
ability, difficulty of school work, and luck regarding the children's achievement. Next,
they were asked to divide up 10 points across the 4 attributions. In all three cultures the
rank order of importance was the same: effort first, followed by ability, task difficulty,
and luck. However, the point distribution method (a variation of the chip distribution
method) indicated more emphasis on effort for the Asian populations (Taiwan and Japan)
than for the participants from the United States. This study provides some evidence that
attributional beliefs may have cultural variations.
In a comparison of Japanese mothers' and children's with American mother's and
children's attributions, Hess et al. (1986) found there was a contrast between the two
cultures. Using the chip distribution method (see Holloway and Hess, 1982), Japanese


43
in Table 4-2 include a significant Respondent x Type of Attribution interaction and a
significant main effect of attribution, indicating some disagreement between mothers and
children about the relative importance they place on the five types of attributions for
success in mathematics. Mean attributions as a function of respondent and type of
attribution are reported in Table 4-3. These means describe the nature of the
disagreement between mothers and children about the relative importance of the five
types of attributions. Mothers and children are in agreement about the relative
importance of the five types of attributions as causes of success. On average, both
mothers and children rate effort as the most important, followed by school, home, ability,
and luck. However, mothers put less emphasis than do their children on each type of
attribution. The most striking difference between mothers and children is the luck
attribution. Children rate luck almost as important as ability; mothers rate luck as less
important than do their children and as less important than ability. Therefore the largest
difference between mother's and children's beliefs about the causes of success in
mathematics is the way luck is perceived.
In Table 4-2, there is a significant Child's Gender x Type of Attribution
interaction. Means for interpreting this interaction are reported in Table 4-4. Each mean
in Table 4-4 is an average of a mean for mothers and the mean for children, with the
means calculated across grades. Thus the means in Table 4-4 reflect both children's and
mothers' attributions for success. The results in Table 4-4 indicate that mother-son
dyads tend to agree with mother-daughter dyads about the relative importance of the five
causes: effort is most important followed by school, home, ability and luck. Mother-


91
Table 4-48 Weightings
Mother's Mathematics Failure Attributions and Children's Predictions of Their Mother's
Mathematics Failure Attributions bv Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.0
4.8
1.0
2.0
1.3
SD
0.8
3.5
1.4
1.8
1.0
Mother
M
1.2
5.0
2.8
0.0
1.0
SD
1.0
0.8
2.1
0.0
1.4
Male
Child
M
2.2
2.2
2.8
1.3
1.5
SD
3.9
2.1
1.7
1.5
2.3
Mother
M
1.0
4.3
1.7
1.3
1.7
SD
1.7
4.6
4.1
2.8
2.6
7
Female
Child
M
1.5
3.0
1.6
3.3
0.6
SD
1.8
3.1
1.6
3.0
1.0
Mother
M
3.1
3.3
1.8
1.1
0.7
SD
2.8
3.2
1.8
1.4
1.3
Male
Child
M
1.8
4.3
1.8
0.8
1.3
SD
2.3
3.5
2.2
1.4
1.7
Mother
M
0.9
6.0
1.5
1.2
0.4
SD
1.7
3.0
1.9
1.9
1.4
8
Female
Child
M
1.3
7.5
1.0
0.3
0.0
SD
2.5
2.9
2.0
0.5
0.0
Mother
M
1.8
0.3
3.5
2.0
2.5
SD
1.7
0.5
3.9
1.8
2.5
Male
Child
M
1.3
5.0
1.4
1.4
1.0
SD
1.7
3.1
1.9
1.7
1.4
Mother
M
1.3
5.6
1.5
1.4
0.2
SD
1.8
3.1
1.5
1.6
0.6


35
designed to elicit 22 responses from the child and 22 responses from the mother in each
procedure (See Appendix C and D).
The Chip Distribution Method. The chip distribution procedure was used in the
research of Hess, Chih-Mei, and McDevitt (1987), Holloway and Hess (1982; 1984), and
Holloway, Kashiwagi, Hess, and Azuma (1986). Ten chips are given to the mother or
child, who is then asked to place the ten chips in 5 categories. The five categories
represent the five attributions in the study (ability, effort, training at school, training at
home, and luck). The ten chips can be distributed in any pattern. The chip distribution
instrument consisted of 5 choices for success, 5 choices for failure, 5 choices for success
prediction, and 5 choices for failure prediction. Each card had an attribution phrase on it.
For the success condition these phrases were used for the mothers:
My child has natural ability for math.
My child tries hard in math.
My child has had good training at school in math.
Ny child has had good training at home in math.
My child has been lucky in math.
Wording changes were made to adapt these phrases to the other conditions.
The Rating Scale Method. The rating scale procedure came from the research of
Bar-Tal and Guttmann (1981), Parsons, Adler, and Kaczala (1982), Yee and Eccles
(1988), and Yamauchi (1989).
For the rating scale instrument, the Holloway and Hess (1982; 1984) distribution
of chips was replaced by 9-point rating scales like those used by the Graham and Long


31
involving school performance, the following hypotheses were expected to be supported
by this study:
1. Low SES African American mothers will not agree with their children about
causal attributions for mathematics success and failure.
2. Low SES African American mothers will not make different attributions for
boys and girls about their mathematics success and failure.
3. Low SES African American mothers will not be able to accurately predict
attributions their children give for the children's mathematics success and failure
performances.
4. Low SES African American children will not be able to accurately predict the
attributions their mother will give for the children's mathematics success and failure
performances.
5. Low SES African American mothers and children, within the same family, will
not be in agreement regarding the children's mathematics success and failure
performances.
6. Conclusions about the attributions will not depend on the method-chip
distribution instrument or rating scale instrument-used for data collection.
Methods
Participants
This study was conducted in government subsidized housing in Alachua County,
which is located in north central Florida. The participants were one hundred low SES


66
Table 4-24 Ratings
Pearson Correlation Coefficients Between Mother's Failure Attributions and Children's
Predictions of Mother's Failure Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.06
.29*
.20
.13
.24
.16
* = p<.05
Means and standard deviations for mothers' actual mathematics failure attributions
and children's predicted failure attributions are presented in Table 4-25. The results of an
ANOVA of mothers' actual mathematics failure attributions and children's predicted
failure attributions are reported in Table 4-26. Again, the only meaningful effects are
those involving respondent.
The results of Table 4-26 include a significant Respondent x Type of attribution
interaction indicating some disagreement about the relative importance the children place
on the five types of attributions and the mothers' actual mathematics attributions. Mean
attributions as a function of respondent and type of attribution are reported in Table 4-27.
These means describe the nature of the disagreement between mothers' actual
mathematics failure attributions and children's predicted failure attributions for the five
types of attributions. On average, mothers rated effort as most important, followed by
school, home, ability, and luck. However, children predicted their mothers, on average,
would rate effort as most important, followed by home, ability, luck, and school.
Therefore, children's predictions were not consistent with mothers actual mathematics


33
Participating families lived in government subsidized housing. All children
selected for the study met the following criteria:
1. Students were in the 6th, 7th, or 8th grade regular education classes. Although
a large number of low SES African American children are in remedial or special
education classes, students who spent a majority of their day in regular classrooms were
classified as being in regular classes for this study.
2. Participating families had a copy of the student's most recent report card.
Student grades, however, were not a selection factor.
3. Only one child per family was selected.
Mothers and children not meeting selection criteria were thanked for their interest,
but were not selected.
The distribution of mothers and children by grade, gender, and method is shown
in Tables 3-1 and 3-2.
Table 3-1
Sample Size for Rating Scale Respondents
Grade
Child's Sex
Mothers
Children
6
M
9
9
F
8
8
7
M
9
9
F
7
7
8
M
10
10
F
7
7
TOTALS
50
50


92
Table 4-49 Weightings
Summary of ANOVA for Mother's Mathematics Failure Attributions and Children's
Predictions of Their Mother's Mathematics Failure Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
533.75
133.44
18.41
0.0001
A x G
8
18.66
2.33
0.32
0.9570
AxS
4
9.93
2.48
0.34
0.8491
A x G x S
8
73.20
9.15
1.26
0.2662
Error
176
1275.90
7.25
R x A
4
10.80
2.70
0.55
0.6961
R x A x G
8
115.11
14.39
2.95
0.0040
R x A x S
4
108.87
27.22
5.59
0.0003
R x A x G x S
8
85.65
10.71
2.20
0.0297
Error
176
857.19
4.87
Overall, boys and girls in each grade level were not good predictors of their
mothers' attributions about the children's mathematics failure. Eighth grade boys
appeared to be the exception in that they seem to understand how their mothers would
explain the causes of their mathematics failure.


54
were also unable to accurately predict how their children would evaluate their
mathematics performance.
Means and standard deviations for children's actual mathematics success
attributions and mothers' predicted success attributions are presented in Table 4-13. The
results of an ANOVA of children's actual mathematics success attributions and mothers'
predicted success attributions are reported in Table 4-14.
Table 4-12 Ratings
Pearson Correlation Coefficients Between Children's Success Attributions and Mothers's
rreaicuons o i neir innaren s success Aiinouiions wnmn me family
Ability
Effort
School
Home
Luck
Evaluation
.13
-.01
.23
.37*
.29*
.16
' = p<.05
There are two types of effects in Table 4-14, effects involving respondent and
effects that do not involve respondent. The latter are analyses of the sum of a mother's
predictions and a child's ratings, and consequently, are not meaningful effects. The
former are comparisons of mothers' predictions and children's ratings, and are meaningful
effects. Consequently, this section will focus on effects involving respondent.
The results in Table 4-14 include a significant Respondent x Type of Attributions
interaction and a significant main effect of attribution suggesting some disagreement
about the relative importance the children place on the five types of attributions and the
mothers' predictions of their children's attributions. Mean attributions as a function of
respondent and type of attribution are reported in Table 4-15. These means show the



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/' 81,9(56,7< 2) )/25,'$


115
CONSENT FORM
STUDENT'S NAME:
Please print
STUDENT'S GRADE:
PARENT'S NAME:
Please print
PROJECT TITLE: Causal Explanations for Mathematics Performance Given by
Low Socioeconomic Status African American Mothers and
Their Children
1 have read, and I understand the procedure described on the previous pages.
I agree to allow myself and my child to participate in the attribution study, and I
have received a copy of the procedures involved.
Parent's signature
Date
Kenneth R. Cyrus, Principle Investigator
Date


87
Table 4-44 Weightings
Mother's Mathematics Success Attributions and Children's Predictions of Their Mother's
Mathematics Success Attributions by Grade and Sex
Grade Sex Subject Statistic
6 Female Child M
SD
Mother M
SD
Male Child M
SD
Mother M
SD
7 Female Child M
SD
Mother M
SD
Male Child M
SD
Mother M
SD
8 Female Child M
SD
Mother M
SD
Male Child M
SD
M
SD
Ability
Effort
School
Home
Luck
1.5
3.5
2.8
1.0
1.3
1.0
1.3
0.5
1.2
1.0
0.5
5.3
1.5
2.8
0.0
1.0
2.2
1.7
1.5
0.0
2.3
3.7
2.0
1.8
0.2
1.5
1.2
1.7
1.0
0.4
1.7
5.2
1.5
1.2
0.5
3.1
2.9
1.4
1.5
1.2
0.9
3.9
3.6
1.2
0.4
1.3
1.4
1.7
1.8
1.0
0.7
5.0
2.2
1.8
0.3
1.2
2.4
1.6
1.5
0.7
0.9
4.3
2.8
1.8
0.1
2.1
2.1
2.1
1.7
0.3
1.8
3.7
2.2
2.1
0.3
3.1
2.7
1.6
2.1
0.8
0.3
5.5
3.3
1.0
0.0
0.5
1.3
1.3
2.0
0.0
0.8
8.0
0.8
0.5
0.0
1.5
2.4
1.5
1.0
0.0
1.8
3.2
2.8
1.5
0.6
1.3
1.3
1.3
0.9
1.1
1.4
3.2
2.6
2.6
0.1
1.7
1.7
1.4
1.7
0.4
Mother


49
Table 4-7 Ratings
Pearson Correlation Coefficients Between Mother's and Children's Success Attributions
Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.19
.21
-.01
.42*
.29*
.11
* = p<.05
children's mathematics failure attribution means are presented in Table 4-9. The results
in Table 4-9 include a significant Respondent x Type of Attribution interaction and a
significant main effect of attribution. These results indicate disagreement between
mothers and children about the relative importance they place on the five types of
attributions for mathematics failure. Mean attributions as a function of respondent and
type of attribution are reported in Table 4-10. These means show the nature of the
disagreement between mothers and children about the relative importance of the five
types of attributions as causes of mathematics failure. Mothers and children were not in
agreement about the relative importance of the five types of attributions. On average,
mothers rated effort as most important followed by school, home, ability, and luck.
However, children rated home training as most important followed by effort, luck, and
school training (which were rated equally,) then ability.
The largest discrepancy between mothers' and children's attributions for
mathematics failure is how they view effort. Mothers put more emphasis on the effort


90
mothers would evaluate their mathematics performance. Of the six correlations, none of
them were significant.
Means and standard deviations of mothers' actual failure attributions and
children's mathematics failure predictions are presented in Table 4-48. The results of an
ANOVA of mothers actual mathematics failure attributions and children's mathematics
failure predictions are presented in Table 4-49. Again, only the effect involving
respondent are meaningful. The results in Table 4-49 include a significant Respondent x
Type of Attribution x Grade x Sex interaction indicating disagreement between mothers
and children of both sexes in each grade level about the relative importance of the five
types of attributions.
Mean attributions as a function of Respondent x Type of Attribution x Grade x
Sex are reported in Table 4-50. These means show the nature of the disagreement
between mothers' actual mathematics failure attributions and children's predicted
mathematics failure attributions.
In Table 4-50 the most striking results are for the effort attribution. In the sixth
and seventh grade, females on average, are accurate predictors of their mothers' effort
attributions. Eighth grade females substantially overestimate their mothers' effort
attributions. Boys, on the other hand, underestimate their mothers' effort attributions in
all three grades, but less so in eighth grade than in either grades six or seven.


40
Variables Under Investigation
There were three independent variables under investigation in this study: (1)
informant: mother/child, and (2) Sex: male/female and (3) grade (6th, 7th, and 8th), in
each outcome situation (success and failure). The five dependent variables in each
success and failure outcome were causal attribution scores for ability, effort, training at
school, training at home, and luck as measured by the rating scale procedure and the chip
distribution procedure.
Research Design
The design for this study is a 3 (grades) x 2 (male/female) x 2 (mother/child) x 5
(attributions) within subjects design with repeated measures on the last two factors. This
design was used for Sample A and Sample B.
Data Analysis
The data from this study in each outcome were analyzed using analysis of
variance (ANOVA). Analyses were computed separately for mothers' and children's
predictions. To determine attribution agreement within the family, correlations were
performed on parent and child pairs within the same family. Scores of parents and
children were correlated with Pearson product-moment correlations for each of the 5
success and 5 failure attributions in each sample.


Table 4-43 Weightings
Pearson Correlation Coefficients Between Mother's Actual Success and Children's
Predicted Success Attributions Within the Family
86
Ability
Effort
School
Home
Luck
Evaluation
.09
.12
.12
.02
-.03
.23
* = p<.05
Means and standard deviations for mothers actual mathematics success
attributions and children's predictions of their mother's mathematics success attributions
are presented in Table 4-44. The results of an ANOVA of mothers' actual mathematics
success attributions and children's predicted success attributions are reported in Table 4-
45.
The results in Table 4-45 include a significant Respondent x Type of Attribution
interaction and a significant main effect of attribution indicating disagreement about the
relative importance the mothers place on the five types of attributions and children's
predictions about their mothers' attributions. There are two types of effects in Table 4-45,
effects involving respondent and effects not involving respondent. Only the effects
involving respondent will be reported in this section.
Mean attributions as a function of respondent and type of attribution are presented
in Table 4-46. These means show the nature of the disagreement between mothers' actual
success attributions and children's predicted success attributions when weighting the
importance of the five types of attributions. Children on average predicted correctly on
the importance their mothers would place on the five types of attributions for predicted


52
Table 4-10 Ratings
Failure Mean; as a Functi.on.Qf Respondent and Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
3.0
3.8
3.4
4.0
3.4
Mother
2.8
6.3
3.3
3.1
1.4
Table 4-11 Ratings
Pearson Correlation Coefficients Between Mothers and Children's Failure Attributions
Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.20
.19
.14
-.01
.09
.11
* = p<.05
attribution than do children. Although children view effort as important in mathematics
failure, they tend to place slightly more emphasis on home training. Another major
difference between mothers and children regards the luck attribution. Children rate luck
equally as important as school training while mothers rate luck as the least important
cause of their children's mathematics failure.
Pearson-product moment correlation coefficients presented in Table 4-11 indicate
that mothers and children within the same family were not in agreement when evaluating
the children's mathematics performance and were also not in agreement about the causes
of mathematics failure. Of the six correlations, none of the coefficients were significant.


APPENDIX A
PARENTAL INFORMED CONSENT
PROJECT TITLE: Causal Explanations for Mathematics Performance Given by Low
Socioeconomic Status African American Mothers and Their
Children
PRINCIPLE INVESTIGATOR: Kenneth R. Cyrus, Ph.D. Candidate,
University of Florida
Phone: (904) 392-0723 Ext. 257
Foundations of Education
Home Address: 808 N. W. 7th Ave
Gainesville, Florida 32601
Home Phone: (904) 376-6004
I agree to allow my child and myself to participate in this study as explained below:
The aim of this study is to assess the reason (causal attributions) that African American
students give for their math success and failure in school. We also want to compare those
reasons to the reasons mothers give for their child's math success and failure. The information
collected will be used to better understand achievement motivation in African American
students. The names of those who participate in the study will not be included in any report.
To participate in the study, you and your child will be asked to respond to one of two
situations.
In the first type of situation, you and your child will be asked to rate your child's
performance and then place ten plastic chips on five attributions which are written on five cards.
This will weight how important the reasons are for your child's performance.
OR. in the second situation, you and your child will be asked to evaluate your child's
math performance. Then you and your child will be asked to rate five causal attributions that
explain math success and failure. Also, you and you child will be asked to predict how the other
would rate the five attributions.
You will need to bring the child's most recent report card from school. The study
required thirty minutes to complete and will be given in your home or other location that is
convenient for you. No discomfort is anticipated in completing the questionnaires and no
monetary compensation is provided. Although no immediate benefits are expected for you or
your child by participating in this study, you and your child's participation will aid in the
increased understating about minority motivation.
Please feel free to ask any questions you may have about this study at any time. You
may drop out to the study at any time, as your participation it totally voluntary. You do not have
to answer any question you do not wish to answer.
The nature and purpose of this study, as printed above has been explained to me. Any
information given to the researcher will remain confidential. You and your child will be
assigned a number which will appear on all study materials. Only the researcher will know what
number you have been given. Your name will never be used. At the conclusion of the study,
lists of participants, or anything that might identify a participant, will be destroyed.
You should keep this page for your records after signing the consent on the next page.
114


To my grandparents,
Edward and Elizabeth Cyrus,
and
Anderson and Camilla Catchings,
to my parents,
Frank and Ethel Cyrus,
to my daughter,
Yvette Moran Cyrus,
and to the rest of the immediate and extended
Cyrus family


17
hold similar beliefs about the causes of children's academic success and failure.
However, Parsons, Adler, and Kaczala (1982), using a rating scale, found that parents and
children held similar beliefs about causes the children's academic performances. Further,
Cashmore and Goodnow (1986), using rank order method, did not find agreement
between parents and children. It is unknown as to whether these inconsistent findings
were due to the participants in these studies or to the variation in measurement
techniques. It is also unclear as to whether the researchers attempted to match the
measurement instrumentation with the research question. One weakness in this literature
is that no studies were found that simultaneously used two or more methods (chip, rating,
or ranking) in order to show the comparability of those measurement instruments.
Attributional Patterns of Mothers and Their Children
Because it is generally believed that parents transmit values, beliefs, or traits to
the next generation, one might expect to find agreement about these values, beliefs, or
traits between parents and their children (see Cashmore & Goodnow, 1986; Holloway &
Hess, 1982). Some attribution researchers examined the possibility that parents convey
attributional beliefs regarding academic success and failure directly to their children.
However, findings were inconsistent across studies.
Disagreement. In her study investigating parental belief systems about their
children's school performance, Cashmore (1980) found little agreement between parents'
and children's attributions of 100 Australian children and their parents. Parents and
children were asked to rank order three attributions: ability, effort, and teaching. Parents
viewed ability as the most important cause of success and effort the most important cause


36
(1986) study. Every portion of the instrument was identical except instead of using chips
to weight the attributions, ratings scales were used to rate the attributions.
The prediction portion of the chip distribution and rating scale procedures were
used as an adaptation from the Yamauchi (1989) study that introduced the prediction
component in a study of Japanese mothers and their children about mathematics
performance. While the Yamauchi (1989) study asked only children to predict their
mother's causal attributions for the children's mathematics success and failure, in this
study mothers were asked to predict the attributions their children would give for the
children's mathematics success and failure, and children were asked to predict the
attributions their mothers would give for the children's mathematics success and failure.
Procedures
After volunteers agreed to participate, the studies were explained and informed
consent obtained, mothers were tested on the first visit and their children were tested on
the second visit in their homes. All study materials were read aloud while the subjects
read silently.
Mothers were not present when their child was "tested", and the child was not
present when their mother was "tested.
Sample A: The Rating Scales Procedure. While looking at their children's report
card, mothers were first asked to judge how well their children had performed in 6th, 7th
or 8th grade mathematics on their most recent report card relative to other members of
their class. (Therefore, success and failure are considered in relation to other class
members and is referred to as relative success and relative failureHolloway & Hess,


& Goodnow, 1986; Holloway, 1986; Holloway & Hess, 1984; Holloway, Kashiwagi,
Hess & Azuma, 1986; Parsons, Adler, & Kaczala, 1982; Stevenson & Lee, 1990; Yee &
Eccles, 1988). A second problem has been the variation in types of attribution measures
used across studies. The chip distribution, rating scales, and rank ordering methods all
have been used in attribution studies which raises issues regarding whether the methods
used matched the research question (Hau & Salili, 1993), and whether various
instruments produce the same results (Elig & Frieze, 1979). For example, if a researcher
wants to know the most important attribution among several for a given population, a
chip distribution methodology may be appropriate because it forces the participant to
allocate a limited number of chips among the attributions. If four chips out of ten are
placed on one attribution, only six chips are left for the remaining attributions. The rating
scale could produce different results because the participant has an opportunity to rate all
attributions as high or low as the individual chooses within the limits of the likert scale.
Researchers have not always explained why they chose their methods, the strengths and
weaknesses of those methods and how those methods relate to the purpose of their
research.
An additional problem has been that attribution research examining African
American parents and children has been quite limited. Some attribution research has
been done with African American children comparing their attributions to Caucasian
American children's attributions about academic performance (Graham, 1991; 1994).
However, there has been no research that examines African American mothers' and their
children's attributions for the children's school performance (Graham, 1994).


APPENDIX B
STUDENT ASSENT FORM
Hello, my name is Kenneth R. Cyrus. I am a graduate student at the University of
Florida. I am conducting a study about motivation of African American students in the
6th, 7th, and 8th grades. I am very interested in the reasons African American students
give for success and failure in math at school. I would also like to know what your
mother thinks about the reasons you give for success and failure in math.
Your mother has agreed to participate in the study. I would like to know if you
would also like to participate. You don't have to be in the study if you don't want to. If
you agree to participate, you can get out of the study at any time. You do not have to
answer any question you do not wish to. You will be asked to place chips on cards that
have reasons on them for why you succeed or fail. Or, you will be asked to fill out rating
scales for the reasons you give. It will take 10 or 15 minutes to place the chips or to
complete the rating scales.
Your mother and teachers will not see the rating scales or the chip placement. I
will keep the information you give me confidential, which means I will have a number
that will be on your materials and I will not use your name. When the study is finished, I
will destroy anything that has your name on it. Your mother will not be in the room with
us when participate in this study.
We will meet together in your home or at another location that is convenient for
you to do this study.
You will receive no money for your participation.
Would you like to participate?
YES NO
YOUR NAME
TODAY'S DATE
116


77
Table 4-34 Weightings
Failure Means as a Function of Respondent. Type of Attribution, and Grade
Grade
Respondent
Ability
Effort
School
Home
Luck
6
Child
1.9
1.9
2.1
2.2
1.8
Mother
1.1
4.7
2.2
0.7
1.3
7
Child
1.6
3.4
1.6
2.2
1.3
Mother
2.1
4.7
1.7
1.1
0.6
8
Child
1.1
4.9
1.9
0.9
1.3
Mother
1.5
2.9
2.5
1.7
1.4
mothers' endorsement of effort decreased as their children progressed through the middle
grades while their children's endorsement of effort increased.
Pearson-product moment correlation coefficients presented in Table 4-35 indicate
that mothers and children within the same family do not agree about the causes of
mathematics failure and were also not in agreement about the evaluation of the children's
mathematics performance. Of the six correlations, only one correlation was significant:
effort, r = .32, which is a relatively small relationship.
Table 4-35 Weightings
Pearson Correlation Coefficients Between Mothers' and Children's Failure Attributions
Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.19
.32*
.25
.13
-.06
.18
* = p<.05


99
mothers were asked "Why do you think your child is doing this well?" Success means
from the rating scale method revealed that mothers rated the attributions in the following
order of importance: 1) effort, 2) training at school, 3) training at home, 4) ability,
followed by 5) luck. Mothers' success means on the chip distribution method showed
mothers emphasized 1) effort, 2) training at school and training at home weighted
equally, followed by 4) ability, and 5) luck. Mothers' responses from both measures
appear similar. Both measures show that mothers believe their children do well in
mathematics because of hard work or effort, with some emphasis on school and home
training.
Children were also asked to evaluate their mathematics grade from their most
recent report card. They were then asked, "Why do you think you are doing this well?"
Success means from the rating scale method indicated that children rated the attributions
in the following order of importance: 1) effort, 2) school training, 3) home training, 4)
ability, and 5) luck. Children's success means from the chip distribution method showed
children emphasized 1) effort, 2) school training, 3) home training, 4) luck, then 5)
ability. This indicates that both measures show children held similar attributions for
mathematics success with the exception of the luck and ability attributions. The children
responding on the rating scale method believed that ability was more important than luck.
However, the chip distribution method showed children believed luck was more
important than ability as an explanation for mathematics success.


16
on the remaining attribution options. A variation of this method uses a limited number of
points to indicate the importance of each attribution (Stevenson & Lee, 1990).
A second measurement instrument of parents' and children's attributions for
school success and failure is the structured rating scale format. Parents and children rate
on Likert-type scales (frequently 5-, 7-, or 9-point) the importance of each attribution
(Bar-Tal & Guttmann, 1981; Bird & Berman, 1985; Parsons, Adler & Kaczala, 1982;
Yamauchi, 1989; Yee, 1984; Yee & Eccles, 1988). The rating scale method allows
participants a chance to rate the importance of each attribution without one rating
effecting other ratings.
A third type of measure is the rank order format in which parents and children
rank the order of importance of each attribution in relation to other attributions
(Cashmore, 1980; 1982; Cashmore & Goodnow, 1986; Holloway, 1986). The rank order
method usually has a limited number of attributions and participants must decide which
attribution is first, second, or third (or so on) in its contribution for determining success or
failure.
It has been recommended (Hau & Salili, 1993) that there should be a match
between the research question and the measurement instrument. Attribution researchers
who study parents' and children's beliefs about school performance have continued to use
a variety of measures to investigate the same research question: "Do parents and children
in families hold similar beliefs about the children's success and failure in school?"
The results of this line of research have been inconsistent. Holloway and Hess
(1982), using a chip distribution method, found that mothers and their children did not


39
The interviewer next gave the mothers the rating scale forms, indicating the
attributions (reasons) for the children's performance and then asked the mothers to
complete the 9-point scales by circling the number that best indicated the reason the child
would give for the child's mathematics performance.
Mothers: relative failure condition. An analogous procedure was used to ask
mothers to predict how their children would respond about relatively low performance.
In the Rating Scale procedures each mother provided a total of 22 responses.
Mothers rated their children's level of performance in mathematics (1 rating). Then the
mothers rated five success and five failure attributions (10 ratings). Mothers predicted
how their children would rate their level of mathematics performance (1 rating). They
predicted their children's attributions for their mathematics performance by rating their
prediction of their child's response giving five success and five failure attributions (10
ratings).
Rating Scale Procedures for children. In the Rating Scale Procedure, the
procedures designed for children were identical to those procedures for mothers except
for word changes that made the procedures appropriate for children. There were a total of
22 ratings for each child.
Sample B: Chip Distribution Procedure. The procedures for the Chip
Distribution portions of the study were identical to the Rating Scale procedures except
that the 5 attributions were written on 5 cards and 10 chips were given to subjects to
weight the importance of each attribution as a cause of their child's mathematics
performance.


67
Table 4-25 Ratings
Mother's Mathematics Failure Attributions and Children's Predictions of Their Mother's
Mathematics Failure Attributions bv Grade and Sex
Grade Sex Subject Statistic
6 Female Child M
SD
Mother M
SD
Male Child M
SD
Mother M
SD
7 Female Child M
SD
Mother M
SD
Male Child M
SD
Mother M
SD
8 Female Child M
SD
Mother M
SD
Male Child M
SD
M
SD
Ability
Effort
School
Home
Luck
3.5
4.8
2.4
3.4
2.4
2.1
3.4
2.8
3.1
1.8
3.4
6.6
2.3
3.5
2.3
2.7
3.5
2.3
2.8
2.4
3.4
2.8
3.1
4.0
3.2
3.2
2.8
3.3
3.2
3.5
2.6
5.4
3.7
3.6
1.2
2.0
3.8
3.0
2.1
0.7
3.0
2.0
2.0
2.3
3.7
2.8
1.7
1.7
1.7
3.0
1.6
7.0
1.6
3.9
1.0
1.5
3.1
1.5
3.1
0.0
2.0
2.7
3.5
3.0
3.0
1.7
2.7
2.8
2.7
2.8
3.2
5.0
5.4
2.1
1.9
2.5
3.8
3.3
2.2
1.5
1.4
5.0
1.9
2.6
1.6
0.8
3.6
2.3
3.0
1.5
2.9
7.1
2.9
1.9
1.0
3.1
3.0
3.1
1.6
0.0
3.8
6.4
3.8
4.8
2.8
2.7
2.5
3.0
3.0
2.4
3.3
6.8
4.1
2.8
1.4
2.3
2.7
3.4
1.9
1.0
Mother


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74
Table 4-31 Weightings
Pearson Correlation Coefficients Between Mothers' and Children's Success Attributions
Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.50*
.19
.05
.12
.10
.18
* =p<.05
For the failure condition, means and standard deviations for each of the
attributional items are presented in Table 4-32. The results of an ANOVA of mothers'
and children's attribution means are presented in Table 4-33. The results in Table 4-33
include a significant Respondent x Type of Attribution x Grade interaction and a main
effect for attribution, indicating disagreement between mothers and children in each grade
level about the relative importance of the five types of attributions as causes of
mathematics failure. Means as a function of respondent, type of attribution, and grade are
reported in Table 4-34. These means describe the nature of the disagreement between
mothers and children in each grade level. On average, sixth grade children believed the
home was the most important cause of mathematics failure, while seventh and eighth
grade children believed effort was the most important cause. Sixth, seventh, and eighth
grade mothers, on average, all agreed that effort was the most important cause of their
children's mathematics failure. However, these mothers disagreed about the importance
of the remaining types of attributions. Eighth grade mothers and children were more in
agreement about the causes of mathematics failure than were sixth and seventh grade
mothers and children. Sixth and seventh grade mothers tended to weight home training


methods showed that mothers and children generally explained mathematics success as a
result of effort, with some emphasis on school and home training. For mathematics
failure, mothers emphasized lack of effort, whereas children emphasized lack of home
training and lack of effort. Both methods showed that mothers and children within the
same household were not in agreement about the causes of mathematics performance.
The two methods differed on the issue of mothers and gender-related attributions. The
chip distribution method showed that mothers believed that boys and girls succeed and
fail in mathematics for the same reasons. The rating scale revealed mothers believed
boys have more ability in mathematics than girls and girls had to try harder than boys to
be successful in mathematics. For failure, mothers did not make distinctions related to
gender. Both methods also showed that mothers could not predict the attributions the
children would give for the children's mathematics performance and children could not
predict the attributions their mothers would give for the children's performance.
Therefore, generally, the rating scale and chip distribution methods provided comparable
results when examining the attributions of low SES African American mothers and
children, with the exception of gender-related attributions.
Attributional beliefs of the African American family and measurement of
academic attributions are discussed.
xiii


107
that mothers and children may hold similar attributions when explaining the children's
mathematics performance, suggesting attributional beliefs are passed directly from
parents to children. Their results indicated that mothers and children in the same
household did not hold similar attributions in either culture when explaining mathematics
performance. This suggests that attributional beliefs are developed from a variety of
sources such as teachers, peers, and the media (Holloway, Kashiwagi, Hess & Azuma,
1986). This appears to be the case with African American mothers and children.
One finding for which there was no hypothesis related to the evaluation of the
children' mathematics performance. Mothers and children were asked to evaluate the
children's mathematics grade from the most recent report card. Means from the rating
scale and chip distribution methods showed that mothers and children were not in
agreement about how well the children were performing in mathematics. Children
believed they were performing better in mathematics than their mothers did. This finding
may be important as low SES African American students historically have not achieved
as well in mathematics when compared to other groups (Reyes & Stanic, 1988).
In summary, although studies on African American mothers' and children'
attributions for school performance were not found in the attribution literature (Graham,
1994), this study found that African American mothers and children were generally in
agreement about the causes of mathematics success and failure. Means showed mothers
and children used three dominant attributions to explain mathematics performance:
effort, school training, and home training. Correlations showed little attributional
agreement between mothers and children within the same household. Means also


50
Table 4-8 Ratings
Mother's and Children's Mathematics Failure Attributions bv Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.0
5.1
2.6
3.5
2.5
SD
2.9
3.6
2.4
2.1
2.5
Mother
M
3.4
6.6
2.3
3.5
2.3
SD
2.7
3.5
2.3
2.9
2.4
Male
Child
M
2.9
3.4
3.9
4.3
4.4
SD
2.8
3.4
3.6
3.8
3.6
Mother
M
2.6
5.4
3.7
3.6
1.2
SD
2.0
3.8
3.0
2.1
0.7
7
Female
Child
M
2.0
2.3
3.4
4.4
3.0
SD
1.9
2.2
3.6
1.9
2.2
Mother
M
1.6
7.0
1.6
3.9
1.0
SD
1.5
3.1
1.5
3.0
0.0
Male
Child
M
2.4
3.6
3.6
3.1
3.3
SD
1.9
3.0
2.7
2.8
2.7
Mother
M
3.2
5.0
5.4
3.1
1.9
SD
2.5
3.8
3.3
2.2
1.5
8
Female
Child
M
2.3
3.3
3.9
3.6
3.3
SD
2.2
3.0
3.7
3.8
3.9
Mother
M
2.9
7.1
2.9
1.9
1.0
SD
3.1
3.0
3.1
1.6
0.0
Male
Child
M
4.5
5.3
3.1
5.0
4.1
SD
3.4
3.1
2.7
3.0
3.2
Mother
M
3.3
6.8
4.1
2.8
1.4
SD
2.3
2.7
3.4
1.9
1.0


55
nature of the disagreement between mother's predicted success attributions and children's
actual success attributions when rating the importance of the five types of attributions.
Mothers average predicted success attributions tended to agree with children's average
actual success attributions about the relative importance of the five types of attributions
as causes of mathematics success. On average, both mothers and children rate effort as
the most important, followed by school training, home training, ability, and luck.
However, mothers average predicted success attributions were not accurate in predicting
the emphasis their children would place on the five types of attributions. The most
obvious difference between mothers' average predicted success attributions and children's
actual success attributions involved the ability and luck attributions. Mothers tended to
overestimate the importance their children would place on ability, and underestimate the
importance their children would place on luck. Nevertheless, on average, mothers'
predictions about the emphasis their children would place on effort, school training, and
home training were relatively close to their children's actual mathematics success
attributions.
For the predicted failure condition, Pearson product-moment correlation
coefficients between childrens actual mathematics failure attributionsand mothers
predicted mathematics failure attributions within the family. Only two coefficients were
significant. These were school training, r = .32; home training, r = .43 The four
remaining coefficients, ability, effort, luck, and the evaluation were not significant.


119
Hess, R. D., Azuma, H., Kashiwagi, K., Dickson, W. P., Nagano, S., Holloway, S.
D., Miyake, K., Price, G., Hatano, G., & McDevitt, T. (1986). Family influences on
school readiness and achievement in Japan and the United States: An overview of a
longitudinal study. In H. Stevenson, H. Azuma, & K. Hakuta (Eds.), Child development
and education in Japan (pp. 147-185), New York: W. H. Freeman.
Hess, R. D., Chih-Mei, C., & McDevitt, T. M. (1987). Cultural variations in
family beliefs about children's performance in mathematics: comparisons among People's
Republic of China, Chinese-American, and Caucasian-American families. Journal of
Educational Psychology. 79. 179-188.
Hess, R. D., & Holloway, S. D., & King, D. R. (1981). Causal explanations for
low and high performance in school: Some contrasts between parents and children. Paper
presented at the biennial meeting of the Society of Research in Child Development,
Boston.
Hess, R. D., & McDevitt, T. M. (1985). Some antecedents of maternal attributions
about childrens performance in mathematics. In R. Ashmore & D. Brodzinsky (Eds.),
Perspectives on the family (pp 95-118).Hillsdale, NJ:Erlbaum.
Hess, R. D., McDevitt, T. M., & Chen, (1988). Causal attributions hv Chinese-
American and Caucasian-American mothers and children about children's performances
in mathematics. Unpublished paper presented at 23rd Congress of Psychology. Acapulco.
Holloway, S. D. (1986). The relationship of mothers' beliefs to children's
mathematics achievement: Some effects of sex differences. Merrill-Palmer Quarterly. 32.
231-250.
Holloway, S. D. (1988). Concepts of ability and effort in Japan and the United
States. Review of Educational Research. 58,327-345.
Holloway, S. D., & Hess, R. D. (1982). Causal explanations for school
performance: Contrasts between mothers and children. Journal of Annlied Developmental
Psychology. 3. 319-327.
Holloway, S. D., & Hess, R. D. (1984). Mothers's and teachers attributions about
children's math performance, In I. E. Sigel (Ed.), Parental belief systems: The
psychological consequences for children. Hillsdale, NJ: Erlbaum.
Holloway, S. D., Hess, R. D., & King, D. R. (1981). Mothers' and children's
explanations for school performance: Relation to academic achievement. Paper present at
the annual meeting of the American Educational Research Association, Los Angeles.


Table 4-22 Ratings
Summary of ANQVA for Mother's Mathematics Success Attributions and Children's
Predictions of Their Mother's Mathematics Success Attributions
64
Source
df
SS
MS
F
P
Between
Grade (G)
2
82.89
41.44
3.65
0.0341
Sex (S)
1
5.19
5.19
0.46
0.5022
GxS
2
17.83
8.91
0.79
0.4622
Error
44
499.24
11.35
Within
Respondent(R)
1
60.90
60.90
8.82
0.0048
RxG
2
3.37
1.68
0.24
0.7851
RxS
1
1.98
1.98
0.29
0.5954
RxGxS
2
11.67
5.84
0.85
0.4364
Error
44
303.89
6.91
Attribution (A)
4
1496.43
374.11
70.14
0.0001
A x G
8
33.91
4.24
0.79
0.6080
A x S
4
97.91
24.48
4.59
0.0015
A x G x S
8
46.01
5.76
1.08
0.3805
Error
176
938.77
5.33
R x A
4
32.71
8.18
1.81
0.1290
R x A x G
8
22.26
2.78
0.62
0.7639
R x A x S
4
10.49
2.62
0.58
0.6772
Rx A x G x S
8
59.74
7.47
1.65
0.1133
Error
176
795.55
4.52


TABLE OF CONTENTS
ACKNOWLEDGMENTS iv
LIST OF TABLES viii
ABSTRACT xii
CHAPTER 1 INTRODUCTION 1
Statement of the Problem 2
Purpose of the Study 6
Significance of the Study 7
CHAPTER 2 REVIEW OF THE LITERATURE 9
Weiners Attribution Theory 10
Attribution Measurement Issues 14
Attribution Patterns of Mothers and Their Children 17
Mothers Attributions and Gender Differences 23
Childrens Predictions of Their MothersAttributions 27
Summary 28
CHAPTER 3 METHODOLOGY 30
Hypotheses 31
vi


58
Table 4-15 Ratings
Predicted Success Mean Ratings as a Function of Respondent and Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
4.7
8.1
7.5
6.0
4.4
Mother
5.3
7.8
7.8
6.1
3.1
Table 4-16 Ratings
Pearson Correlation Coefficients Between Children's Failure Attributions and Mothers
Prediction of Children's Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
-.19
.26
.32*
.43*
.12
.16
* = p<.05
Means and standard deviations for children's actual mathematics failure
attributions and mothers predicted failure attributions are presented in Table 4-17. The
results of an ANOVA of children's actual failure attributions and mothers' predicted
failure attributions are reported in Table 4-18. There are two types of effects in Table 4-
18, effects involving Respondent and effects that do not involve respondent. The only
meaningful effects are those involving respondent. Consequently this section will focus
on effects involving only the respondent. Examination of Table 4-18 indicates a
significant Respondent x Type of Attribution interaction indicating some discrepancy
about the relative importance children place on the five types of attributions and the
mothers' predictions of their children's attributions. Mean attributions as a function of


108
indicated children were not accurate predictors of the attributions their mothers would
give for the children's mathematics performance, and mothers were not accurate
predictors of the attributions their children would give. Mothers and children did not
even agree on how well the children were actually performing in mathematics. These
findings were consistent using either a rating scale or a chip distribution methodology.
No other study has looked at all the issues that this study has examined. The only study
that is similar in some respects is Holloway, Kashiwagi, Hess, & Azuma (1986) which
explored parent-child agreement, gender and mothers' attributions, and parent-child
agreement within the family.
Discussion
Studies investigating various cultures have reported inconsistent findings about
parents' and children's attributions for academic performance, and have generally been
inconsistent in the methods chosen to measure attributions of parents and children.
Studies that compared Asian mothers and children (Chinese, Japanese, Taiwanese)
generally found that mothers and children agreed that the effort attribution was the
primary cause of mathematics performance (Hess, Chih-Mei, and McDevitt, 1987;
Stevenson & Lee, 1990; Yamauchi, 1989). In studies with Australian and Caucasian
American parents and children, the results generally showed that parents and children
gave different attributions for academic performance (Cashmore, 1980; Cashmore &
Goodnow, 1986; Holloway & Hess, 1982). For example, Cashmore and Goodnow
(1986) found that parents stressed talent (ability) for school success, while children


42
and children's beliefs within individual families and of mothers' and children's
performance evaluations of the children's mathematics grade.
Mother's and Childrens Success and Failure Attributions for Mathematics Performance:
Rating Scale Method
Mothers and children were asked to evaluate on a six-point scale the mathematics
grade from the children's most recent report card. The six-point scale ranged for 1 (not
doing as well as most) to 6 (doing the very best in the class). Mothers were then asked by
the interviewer "Why is your child doing this well?" and children were asked "Why are
you doing this well?" Mothers and children rated on 9-point scales the relative
importance of five attributional items. The scales ranged from 1 (definitely not a cause)
to 9 (definitely a cause). The items were ability, effort, training at school, training at
home, and luck for both success and failure conditions. Therefore, relative success in this
study is actually mothers' and children's evaluation of the children's current level of
mathematics performance. For relative failure, mothers and children were asked by the
interviewer, "Why did your child not do even better?" (for mothers), and "Why aren't you
doing even better?" (for the children). This condition examines relative failure as a
judgement in contrast to the current level of performance.
It was hypothesized that low SES African American mothers would not agree
with their children about the causes of the children's success and failure in mathematics.
For the success condition, means and standard deviations for each of the
attributional items are presented in Table 4-1. The results of an ANOVA of mothers' and
children's mathematics success attribution means are presented in Table 4-2. The results


LD
1780
1994
UNIVERSITY OF FLORIDA
3 1262 08555 0035


BIOGRAPHICAL SKETCH
Kenneth Ramon Cyrus was bom May 30,1956, in Crystal Springs, Mississippi,
and grew up in Tuskegee, Alabama. Kenneth attended Chicago State University from
1974 to 1978 on a basketball scholarship. Kenneth was named N.A.I.A. All-American in
basketball and received a bachelor's degree in psychology in 1978. In 1981, Kenneth
received his master's degree in counseling from Tuskegee University. He worked for
several years as a public school teacher in Georgia. Kenneth also worked as a counselor
and placement director at Stillman College in Tuscaloosa, Alabama, for two years before
pursuing his Ph.D. at the University of Florida.
123


Table 4-24 Pearson Correlation Coefficients Between Mother's Failure
Attributions and Children's Predictions of Mother's Failure
Attributions Within the Family 66
Table 4-25 Mother's Mathematics Failure Attributions and Children's Predictions
of Their Mother's Mathematics Failure Attributions by Grade and Sex 67
Table 4-26 Summary of ANOVA for Mother's Mathematics Failure Attributions
and Children's Predictions of Their Mother's Mathematics Failure
Attributions 68
Table 4-27 Predicted Failure Means as a Function of Respondent x Type of
Attribution 69
Table 4-28 Mother's and Children's Mathematics Success Attributions by Grade
and Sex 72
Table 4-29 Summary of ANOVA for Success Attributions 73
Table 4-30 Success Means as a Function of Respondent and Type of Attribution ... 73
Table 4-31 Pearson Correlation Coefficients Between Mothers' and Children's Success
Attributions Within the Family 74
Table 4-32 Mother's and Children's Mathematics Failure Attributions by Grade
and Sex 75
Table 4-33 Summary of ANOVA for Failure Attributions 76
Table 4-34 Failure Means as a Function of Respondent, Type of Attribution, and
Grade 77
Table 4-35 Pearson Correlation Coefficients Between Mothers' and Children's
Failure Attributions Within the Family 77
Table 4-36 Pearson Correlation Coefficients Between Children's Actual Success
and Mothers' Predicted Success Attributions Within the Family 79
Table 4-37 Children's Mathematics Success Attributions and Mother's
Predictions of Their Children's Mathematics Success Attributions by
Grade and Sex 81
x


ACKNOWLEDGMENTS
I would first like to thank my doctoral committee for their support of my efforts to
complete this formidable task. My chairman, Dr. Barry Guinagh, with his sympathetic
heart and mind gave invaluable guidance for this project. I thank Dr. James Algina, for
his continued confidence in my ability and for his statistical and editorial expertise. Drs.
Walter Busby, Woodrow "Max" Parker, and Mary Howard-Hamilton deserve very warm
and special thanks for their valuable insight and encouragement. Two of the original
members of my committee, Dr. Donald Avila, and Dr. Robert Jester, did not live to see
this dissertation come to fruition. They remained an inspiration and a shining light when
the process became dark. My hat is off to these two men. I would also like to thank Dr.
Patricia Ashton who initially gave me confidence while pursuing my doctoral program at
the University of Florida. The faculty in Foundations, Dr. Arthur Newman, Dr. Robert
Sherman, Dr. Rodman Webb, Dr. John Newell, Dr. Hannalore Wass, Dr. A. O. White and
Dr. David Miller, were friends whose courses inspired me.
I thank the current Dean of the College of Education, Dr. Roderick McDavis,
who was responsible for my recruitment to the University of Florida, and who provided
guidance and inspiration through the many years I have known him.
IV


89
would place on the ability attribution. Children also came very close to predicting the
emphasis their mothers would place on home training and luck for their children's
mathematics success.
Table 4-46 Weightings
Predicted Success Means as a function of Respondent and Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
1.1
4.0
2.4
1.4
0.4
Mother
1.1
5.0
1.8
1.8
0.2
For the predicted failure condition, Pearson-product moment correlation
coefficients between mothers actual mathematics failure attributions and children's
predictions of their mother's failure attributions within the family are presented in Table
4-47. Children were not very accurate in predicting the attributions their mothers would
give for the children's mathematics failure, nor were they able to predict how their
Table 4-47 Weightings
Pearson Correlation Coefficients Between Mother's Actual Failure and Children's
Predicted Failure Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.24
.23
-.05
.00
-.06
.18
= p<.05


76
Table 4-33 Weightings
Summary of ANOVA for Failure Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
318.53
79.63
9.01
0.0001
AxG
8
26.76
3.34
0.38
0.9310
A x S
4
50.99
12.75
1.44
0.2219
A x G x S
8
73.16
9.15
1.04
0.4115
Error
176
1555.04
8.84
Rx A
4
20.53
5.13
0.93
0.4502
Rx A x G
8
92.03
11.50
2.08
0.0405
Rx Ax S
4
41.73
10.43
1.88
0.1156
Rx A x G x S
8
61.50
7.69
1.39
0.2050
Error
176
975.61
5.54
as the least important cause while eighth grade mothers believed home training was an
important cause of their childrens mathematics failure. Eighth grade children believed
home training was the least important cause of mathematics failure but sixth grade
children believed home training was the most important cause. The most striking
difference between mothers' and children's mathematics failure attributions is the
emphasis on effort. The children's emphasis on effort increases from sixth to eighth
grade while their mothers' emphasis decreases between the sixth and eighth grade.
In summary, mothers and children at all grade levels agreed that effort was the
most important cause of mathematics failure except for sixth grade children. However,


Methods 32
Variables Under Investigation 40
Research Design 40
Data Analysis 40
CHAPTER 4 DATA ANALYSIS 41
Mothers and Childrens Success and Failure Attributions for Mathematics
performance: Rating Scale Method 42
Mothers Predictions of Their Childrens Mathematics Success and Failure
Attributions: Rating Scale Method 53
Childrens Predictions of Their Mothers Mathematics Success and Failure
Attributions: Rating Scale Method 59
Mothers and childrens cussess and Failure Attributions for mathematics
Performance: Chip Distribution Method 69
Mothers Predictions of Their Childrens Mathematics Success and Failure
Attributions: Chip Distribution Method 78
Childrens Predictions of Their Mothers Mathematics Success and Failure
Attributions: Chip Distribution Method 83
CHAPTER 5 DISCUSSION 94
APPENDICES 114
APPENDIX A: Parental Consent Form 114
APPENDIX B: Student Assent Form 115
REFERENCES 117
BIOGRAPHICAL SKETCH 123
vii


112
African American children, in this study, believed training at home to be an important
attribution for mathematics success and failure.
Researchers interested in retraining African American students to explain
mathematics performance as a consequence of effort instead of lack of ability should be
informed by this study that African American children are already using the effort
attribution to explain mathematics performance, at least in this study. This population
gave little importance to the ability attribution concerning mathematics success and
failure.
Limitations
Several limitations were present in this study. First, the causal attributions chosen
for this study were the ones present in the two instruments being compared (Holloway &
Hess, 1982; Parsons, Adler & Kaczala, 1982). This confined participants to a limited set
of attributions, an open-ended questionnaire may have elicited different attributions for
this population (Benson, 1989; Hau & Salili, 1993). Further research should determine
which attributions are spontaneously used by this population. Second, the participants
were volunteers. This could mean that the people who volunteered were not
representative of all low SES African American mothers and children. Finally, all
material were read to the participants by the researcher, as they read along because of
possible educational limitations of the participants.
Recommendations for Future Research.
There are several recommendations for future research. First this was an
exploratory study in an area that has not received much research attention. Graham


53
Mothers' Predictions of Their Children's Mathematics Success and Failure Attributions:
Rating Scale Method
Mothers were asked to predict the attributions their children would choose for
mathematics success and failure. Mothers predicted on a 6-point scale how their children
would evaluate the mathematics grade on their most recent report card. For predicted
success, the interviewer asked mothers "Why do you think your child thinks he or she is
doing this well?" For predicted failure, the interviewer asked "Why do you think that
your child thinks he or she is not doing even better?" Mothers predicted on 9-point scales
the relative importance their children would place on the five attributional items. The 9-
point scales ranged from 1 (definitely not a cause) to 9 (definitely a cause). The
attributional items were: ability, effort, school training, home training, and luck. It was
hypothesized that African American mothers will not accurately predict their children's
mathematics success and failure attributions. For the predicted success condition,
Pearson product-moment correlation coefficients between children's actual mathematics
success attributions and mothers' predictions of their children's mathematics success
attributions within the family are presented in Table 4-12. The results of Table 4-12
indicate that mothers cannot accurately predict their children's mathematics success
attributions within the family. Of the six correlations, four of the coefficients were not
significant. The two coefficients that were significant were home training, r= .39; and
luck, r= .29, and these were relatively small. Therefore, mothers were not very accurate
in predicting their children's mathematics success attributions within the family. Mothers


Table 4-45 Weightings
Summary of ANOVA for Mother's Mathematics Success Attributions and Children's
Predictions of Their Mother's Mathematics Success Attributions
88
Source
df
SS
MS
F
P
Within
Attribution (A)
4
792.27
198.07
58.63
0.0001
A x G
8
22.76
2.85
0.84
0.5666
A x S
4
53.20
13.30
3.94
0.0044
A x G x S
8
63.60
7.95
2.35
0.0199
Error
176
594.53
3.38
R x A
4
48.79
12.20
3.88
0.0048
Rx A x G
8
14.59
1.82
0.58
0.7933
Rx A x S
4
20.72
5.18
1.65
0.1643
R x A x G x S
8
23.90
2.99
0.95
0.4766
Error
176
553.15
3.14
mathematics success. On average, mothers' actual mathematics success attributions for
their children's mathematics performance were effort, weighted most important, followed
by school and home training which were weighted equally, then ability, and luck.
Children predicted on average, effort as most important, followed by school, home,
ability, the luck. However, children's predictions were not very accurate when weighting
the emphasis their mothers would place on effort and school training. Children
underestimated the emphasis their mothers would place on the effort attribution and
overestimated the emphasis their mother's would place on school training. Children on
average were very accurate in predicting the importance and emphasis their mothers


65
Table 4-23 Ratings
Predicted Success Means bv Respondent
Respondent
Means
Child
6.1
Mother
5.4
involving predictions, only the effects involving respondent are meaningful, and of these
effects, only the respondent effect was significant.
Table 4-23 shows a main effect for respondent indicating a discrepancy between
children's predicted mathematics success attributions and mothers' actual mathematics
success attributions. Children tended to overestimate the emphasis their mothers would
place on the five types of attributions about the children's mathematics success.
For the predicted failure condition, Pearson product-moment coefficients between
mothers' actual mathematics failure attributions and children's predictions of their
mother's mathematics failure attributions within the family are presented in Table 4-24.
The results of Table 4-24 indicate that children were not able to accurately predict their
mothers' mathematics failure attributions nor were they able to predict how their mothers
would evaluate their mathematics performance. Only one of the six correlations was
significant: effort, r = .29, which does not show a strong relationship.


12
The second dimension of causality, referred to as stability, characterizes causes on
a continuum. Causal attributions can be stable, or invariant, and unstable, or variant.
Weiner (1979; 1985) proposes that ability and task difficulty are likely to be perceived as
fixed, whereas luck and effort are more variable or unstable.
The third dimension in Weiner's three dimensional taxonomy is labeled
controllability. Effort is classified as controllable, while luck, ability, and task difficulty
are classified as uncontrollable.
Numerous investigations have provided support for Weiner's taxonomy (Meyer,
1980; Meyer & Koelbl, 1982; Passer, 1977; Stem, 1983; Wimer& Kelly, 1982). Meyer
(1980) presented questionnaires with 16 different situations and asked subjects to indicate
in each situation how much each of nine attributions was responsible for the outcome.
Using factor analysis, the results showed a correspondence between the three dimensions
of locus, stability, and controllability. Causal attributions theoretically can be classified
within one of eight cells: 2 levels of locus x 2 levels of stability x 2 levels of control
(Weiner, 1979).
According to Weiner's theory, each causal dimension has psychological
implications for thought and action. The locus of causality dimension has implications
for self-esteem. Self-esteem is higher when success is caused by internal factors (effort,
and ability) rather than external factors (luck or task difficulty). Self-esteem is lowered
when failure is caused by internal factors rather than external factors.
The causal dimension of stability is linked to expectancy for future success.
Failure that is caused by low ability or difficultly of task decreases the expectation of


120
Holloway, S. D., Kashiwagi, K., Hess, R. D., & Azuma, H. (1986). Causal
attributions by Japanese and American mothers and children about performance in
mathematics. International Journal of Psychology, 21,269-286.
Jenkins, L. E. (1989). The black family and academic achievement. In G. Berry
and J. Asamen (Eds.), Black students: Psychological issues and academic achievement
(pp 138-152). Newbury Park, CA: Sage.
Karabenick, J. D., & Heller, K. A. (1976). A developmental study of effort and
ability attributions. Developmental Psychology. 12. 559-560.
Kenschaft. P. (1981). Black women in mathematics. American Mathematical
Monthly. 81.592-604.
King, D. R., Hess, R. D., & Holloway, S. D. (1981). A study of mothers' and
children's performance in school subjects. Paper presented at the annual meeting of the
American Educational Research Association, Los Angeles.
Kloosterman, P. (1983). Attribution theory and achievement in mathematics.
Unpublished manuscript, University of Wisconsin, Madison, WI.
Malcolm, S. M., Hall, P. Q., & Brown, J. W. (1976). The double bind: The price
of being a minority woman in science (Report No. 76-R-3). Washington, DC: American
Association for the Advancement of Science.
Maruyama, G. (1982). How should attributions be measured? A reanalysis of data
from Elig and Frieze. American Educational Research Journal, 19. 522-528.
Matthews, W. (1984). The third national assessment: Minorities and mathematics.
Journal for Research in Mathematics Education. 15. 165-171.
Meyer, J. P. (1980). Causal attributions for success and failure; A multivariate
investigation of dimensionality, formation, and consequences. Journal of Personality and
Social Psychology. 38. 704-715.
Meyer, J. P., & Koebl, S. L. M. (1982). Dimensionality of students' causal
attributions for test performance. Personality and Social Psychology Bulletin. 8. 31-36.
Nicholls, J. G. (1978). The development of the concepts of effort and ability,
perception of academic attainment, and the understanding that difficult tasks require more
ability. Child Development. 49. 800-814.


14
failure was attributed to inadequate effort, the positive expectancy for success might stay
high because the subject believes that trying hard will bring future success. Thus, if
failure is perceived as being caused by lack of effort rather than low ability, expectations
of future success will continue to stay high. Although ability and effort are both internal
attributions on the locus dimension (Weiner, 1985), ability and effort are classified
differently on the stability dimension. This means ability is a stable cause for success and
failure, while effort is perceived as unstable in most situations involving failure. Effort is
perceived as relatively stable in most situations of success. The attributions of effort may
bring about positive expectations in the future with both success and failure, whereas
ability attributions will create high expectations of failure.
Attributional theory, therefore,
is a theory of motivation and emotion in which causal attributions play a
key role. The perceived causes of success and failure share three common
properties: locus, stability, and controllability. The perceived stability of
causes influences changes in expectancy of success. All three dimensions
of causality affect a variety of common emotional experiences including
anger, gratitude, guilt, hopelessness, pity, pride, and shame. Expectancy
and affect, in turn, are presumed to guide motivated behavior. (Weiner,
1985, p. 548)
Attribution Measurement Issues
One of the problems in attribution research has been the lack of consistency in the
methods used to measure attributions (Elig & Frieze, 1979; Hau & Salili, 1993;
Maruyama, 1982; Whitley & Frieze, 1985). In a thorough review examining the
measurement of achievement attributions, Hau and Salili (1993) investigating methods,


80
attributions on average were effort as most important, followed by school, home, luck,
then ability.
Although mothers predicted correctly on the importance their children would give
effort and school training, mothers did not predict accurately on the emphasis their
children would place on these two attributions. Mothers underestimated the emphasis
their children placed on school training and overestimated how their children would
weight effort. Overall, mothers were not very accurate in predicting their children's
success attributions.
For the predicted failure condition, Pearson-product moment correlation
coefficients between children's actual mathematics failure attributions and mothers'
predictions of their children's mathematics failure attributions within the family are
presented in Table 4-40. The results in Table 4-40 show that mothers cannot accurately
predict how their children would evlauate their mathematics performance nor could they
predict their children's mathematics failure attributions within the family. Of the six
correlations, only two correlations were significant: effort, r = .52, indicating a moderate
relationship, and home training, r = .38, indicating a small relationship.
Means and standard deviations of children's actual failure attributions and
mothers' predictions are presented in Table 4-41. The results of an ANOVA of children's
actual mathematics failure attributions and mother's predictions of their children's
mathematics failure attributions presented in Table 4-42 indicate no significant
interactions.


2
similar attributions in that both attributions are internal to the individual. However,
ability and effort are different because ability is considered a stable attribution not subject
to change, whereas effort is an unstable attribution which may change from occasion to
occasion. Ability cannot be controlled by the individual but effort is controllable.
Further, task difficulty and luck are both classified in Weiner's (1985) attribution theory
as external attributions and, as such, are not under the control of the individual.
However, task difficulty and luck differ in that luck is an unstable attribution because it
varies from occasion to occasion, while task difficulty for a specific task is considered a
stable or permanent attribution.
Statement of the Problem
Causal attributions about school performance as they are held by a family unit and
the similarities and differences in attributional beliefs between parents and children have
been the focus of past empirical study (Cashmore & Goodnow, 1986; Hess, Chih-Mei, &
McDevitt, 1987; Holloway & Hess, 1982; Holloway, Kashiwagi, Hess, & Azuma, 1986;
Stevenson & Lee, 1990; Yamauchi, 1989). This research has revolved around five basic
issues: parents and children's agreement about attributions for school performance;
parent-child attributional agreement within individual families; comparisons of parent-
child agreement between cultures; the relation of gender of the child to causal attributions
of both parents and children; and children's accuracy in predicting their mother's
attributions about children's school performance.


28
Summary
While mothers' and children's attributional beliefs regarding mathematics
performance in numerous cultures have been studied, the attributional beliefs of African
American mothers and their children for the children's mathematics performance has been
neglected in the attribution literature. The primary purpose of this study was to compare
the causal attributions of low SES African American mothers and their children for
mathematics performance; a second purpose was to examine whether the rating scale
instrument and the chip distribution instrument yield similar and consistent results when
measuring mathematics attributions of African American mothers and children.
The review examined Weiner's attribution theory, attribution measurement issues,
attribution patterns of mothers and their children for school performance, mothers'
attributions and gender differences, and children's accuracy in predicting parent's causal
attributions for the children's mathematics performance.
Weiner's attribution theory can be used to study achievement motivation.
Attributions, or the reasons given by an individual to explain success and failure, are
dimensionalized in relationship to the individual (e.g. intemal/extemal;
controllable/uncontrollable; stable/unstable) and provide meaning that has expectations
and consequences for that person. Numerous achievement attributions have been
documented in the literature, but most prominent among them are ability, and effort,
usually in conjunction with additional attributions.
An examination of the literature indicated that a variety of attribution
measurement instruments have been employed by researchers. Common among them are


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CHAPTER 1
INTRODUCTION
Attributional beliefs are causal explanations individuals hold for their successful
or unsuccessful performance at a task. According to attribution theory, these beliefs
influence motivation, which in turn influence achievement. Consequently, research on
attributions is valuable to the extent that it provides insights into student motivational
factors (Graham, 1991; 1994). Attribution theory was first postulated by Weiner
(Weiner, 1979; 1985; 1986), who believed that attributions were one of the most
important influences on behavior and motivation in achievement settings. The causal
attributions individuals make for their performance will influence the individuals'
expectations for success on future tasks and their emotional response to the event. Causal
attributions most often given by individuals are ability, effort, task difficulty, and luck.
Sixty additional attributions have since been documented by other researchers (Hau &
Salili, 1993). Attributions can be classified on three attribution dimensions: locus of
causewhether the reason is internal or external to the individual; stability of cause
whether the reason is temporary or permanent; and controllability of causewhether the
reason is under the control of the individual or not.
Although individuals may use several attributions to explain success and failure,
causal dimensions help clarify the similarities and differences among the various causal
attributions (Weiner, 1985; 1986; Graham, 1991). For example, ability and effort are
1


47
Table 4-3 Ratings
Success Means as a Function of Respondent and Type of Attribution
Respondent Ability Effort School Home Luck
Child 4.7 8.1 7.5 6.0 4.4
Mother 4.4 7.8 6.8 5.8 2.3
Table 4-4 Ratings
Success Mean Ratines as a Function of Sex and Tvne of Attribution
Sex
Ability
Effort
School
Home
Luck
Boy
5.4
7.4
6.8
6.3
3.0
Girl
3.7
8.4
7.5
5.5
3.7
sample, girls mothers' rate school as more important than do boys' mothers, but girls do
not rate school as more important than do boys.
Table 4-6 represents a main effect for grade. Examination of Table 4-6 means
indicate that mothers and children in sixth grade place more emphasis on the five types of
attributions than do mothers and children in either the seventh or the eighth grade.
Seventh grade mothers and children tend to place less emphasis on the five types of
attributions.
Pearson-product moment correlation coefficients presented in Table 4-7 indicate
that mothers and children within the same family do not agree about the causes of
mathematics success. Mothers and children were also not in agreement about the


21
mothers and children weighted lack of effort as the most important cause for mathematics
failure, while American mothers' and children's explanations were evenly divided among
ability, effort and training at school. Japanese mothers were less likely to blame training
at school as a cause of their children's low achievement.
In a further study of Japanese mothers and children who were compared to
American mothers and children for causal attributions about mathematics performance
cultural differences in attributions, transmission of beliefs from mothers to their children,
and the effect of gender of child on attributions were examined by Holloway, Kashiwagi,
Hess, and Azuma (1986). Using the chip distribution method, this study also found that
there were cultural differences between American and Japanese mothers and their
children. Japanese mothers and children tended to stress effort as a cause for success and
failure in mathematics, while American mothers and children tended to stress ability.
Similar results were reported by Stevenson, Lee, and Stigler (1986) when
comparing Chinese, Japanese and American mothers on beliefs about causes of their
children's mathematics success and failure. Using a point distribution system, findings
showed that while American mothers gave the largest number of points to ability for
explaining why their children succeed or fail in mathematics, the Asian mothers gave the
most points to effort.
In another study to investigate cultural variations in family beliefs, Hess, Chih-
Mei, and McDevitt (1987) examined beliefs about children's performance in mathematics
through interviews with mothers and their 6th grade children involving three cultures, the
Peoples' Republic of China, Chinese American, and American. The three groups showed


32
students from the 6th, 7th, and 8th grades, and their mothers. Approximate ages of the
students were 12, 13, and 14. This age group was chosen because there is evidence from
the developmental literature that children do not differentiate consistently between ability
and effort attributions until 12-13 years of age (Nicholls, 1978; Karabenick, & Heller,
1976; Kloosterman, 1983; Eccles, 1985). Although 6th, 7th and 8th grade students are
distinguished from each other in the design so that the middle school years could be
examined, there are no research questions related to the specific grade or age level.
Criteria for Selection
From a list of 325 mothers with children in the 6th, 7th, and 8th grades obtained
from the Alachua County Housing Authority, 100 volunteers were chosen. Fifty mothers
and their children were assigned to for the rating scale procedure and fifty mothers and
their children were assigned to for the chip distribution procedure. An attempt was made
to select an equal number of boys and girls from the volunteering families.
Pre-Selection Process
The researcher interviewed all potential mothers who volunteered for this study.
The interview consisted of a series of questions:
"Is your child in middle school6th, 7th, or 8th grade?"
"Is your child in special educations classes: this includes learning disabled,
mentally handicapped or emotionally handicapped classes?"
"Do you receive a copy of your child's report card, and if you are selected for the
study, can you locate, and bring with you, your child's most recent report card?"


46
Table 4-2 Ratings
Summary .of ANQ.VA. for Success .Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
100.06
50.03
5.55
0.0071
Sex (S)
1
0.02
0.02
0.00
0.9651
GxS
2
55.98
27.99
3.10
0.0549
Error
44
396.91
9.02
Within
Respondent(R)
1
63.14
63.14
9.28
0.0039
RxG
2
9.89
4.95
0.73
0.4889
RxS
1
14.57
14.57
2.14
0.1504
Rx G x S
2
5.05
2.52
0.37
0.6921
Error
44
299.22
6.80
Attribution (A)
4
1368.73
342.18
57.82
0.0001
A x G
S
40.19
5.02
0.85
0.5611
A x S
4
143.24
35.81
6.05
0.0001
A x G x S
8
58.34
7.29
1.23
0.2828
Error
176
1041.65
5.92
Rx A
4
64.68
16.17
3.85
0.0050
Rx A x G
8
42.03
5.25
1.25
0.2726
R x A x S
4
14.66
3.67
0.87
0.4816
R x A x G x S
8
22.26
2.78
0.66
0.7241
Error
176
739.46
4.20


Table 4-28 Weightings
Mother's and Children's Mathematics Success Attributions by Grade and Sex
72
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.0
3.3
2.5
1.8
1.5
SD
0.8
1.0
0.6
1.3
1.3
Mother
M
0.5
5.3
1.5
2.8
0.0
SD
1.0
2.2
1.7
1.5
0.0
Male
Child
M
1.0
3.3
2.8
1.2
1.7
SD
1.7
1.5
0.8
1.2
1.5
Mother
M
1.7
5.2
1.5
1.2
0.5
SD
3.1
2.9
1.4
1.5
1.2
7
Female
Child
M
0.8
4.0
3.0
1.0
1.2
SD
1.1
1.3
1.5
1.7
1.6
Mother
M
0.7
5.0
2.2
1.8
0.3
SD
1.2
2.4
1.6
1.5
0.8
Male
Child
M
1.2
4.2
1.8
2.0
0.9
SD
2.0
2.4
1.7
1.9
1.1
Mother
M
1.8
3.7
2.2
2.1
0.3
SD
3.1
2.7
1.6
2.1
0.8
8
Female
Child
M
0.5
4.8
2.3
1.5
1.0
SD
1.0
1.3
1.7
1.7
2.0
Mother
M
0.8
8.0
0.8
0.5
0.0
SD
1.5
2.4
1.5
1.0
0.0
Male
Child
M
1.2
3.9
2.9
1.3
0.7
SD
1.3
2.3
1.5
1.3
1.1
Mother
M
1.4
3.2
2.6
2.6
0.1
SD
1.7
1.7
1.4
1.7
0.4


68
Table 4-26 Ratings
Summary of ANOVA for Mothers Mathematics Failure Attributions and Children's
Predictions of Their Mother's Mathematics Failure Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
13.14
6.57
0.38
0.6884
Sex (S)
1
31.77
31.77
1.32
0.1842
GxS
2
36.59
18.30
1.05
0.3592
Error
44
768.03
17.46
Within
Respondent (R)
1
9.07
9.07
1.22
0.2760
RxG
2
9.26
4.63
0.62
0.5422
RxS
1
4.39
4.39
0.59
0.4476
R x G x S
2
16.48
8.24
1.10
0.3402
Error
44
328.20
7.46
Attribution (A)
4
494.86
123.71
21.74
0.0001
AxG
8
93.83
11.73
2.06
0.0420
A x S
4
70.04
17.51
3.08
0.0176
A x G x S
8
22.19
2.77
0.49
0.8642
Error
176
1001.68
5.69
Rx A
4
184.29
46.07
8.66
0.0001
R x A x G
8
43.22
5.40
1.02
0.4259
RxAxS
4
16.83
4.21
0.79
0.5323
R x A x G x S
8
43.14
5.39
1.02
0.4273
Error
176
936.16
5.32


122
Weiner, B. (1986). An attributional theory of motivation and emotion. New York:
Springer-Verlag.
Weiner, B., Frieze, I., Kukla, A., Reed, L., Rest, S., & Rosenbaum, R. (1971).
Perceiving the causes of success and failure. In E. E. Jones (Ed.), Attribution: Perceiving
the causes of behavior. New York: General Learning Press.
Weiner, B., Nirenberg, R., & Goldstein, M. (1976). Social learning locus of
control versus attributional (causal stability) interpretation of expectancy for success.
Journal of Personality, 44. 52-68.
Weiner, B., Russell, D., & Lerman, D. (1979). The cognition-emotion process in
achievement-related contexts. Journal of Personality and Social Psychology, 7, 1211-
1220.
Whitley, B. E., & Frieze, I. H. (1985). Children's causal attributions for success
and failure in achievement settings: A meta-analysis. Journal of Educational Psychology.
ZL 608-616.
Wimer, S., & Kelley, H. H. (1982). An investigation of the dimensions of causal
attribution. Journal of Personality and_S.pci.al. Psychology,. 43,1142-1162.
Yamauchi, FI. (1989). Congruence of causal attributions for school performance
given by children and mothers. Psychological Reports. 64. 359-363.
Yee, D. K. (1984). Parent perceptions and expectations for children's math
achievement. Unpublished paper, University of Michigan.
Yee, D. K., & Eccles, J. S. (1988). Parent perceptions and attributions for
children's math achievement. Sex Roles. 19. 317-333.


Table 4-3B Summary of ANOVA for Childrens Mathematics Success
Attributions and Mother's Predictions of Their Children's
Mathematics Success Attributions 82
Table 4-39 Predicted Success Means as a function of Respondent and Type of
Attribution 82
Table 4-40 Pearson Correlation Coefficients Between Children's Actual Failure
and Mothers' Predicted Failure Attributions Within the Family 83
Table 4-41 Children's Mathematics Failure Attributions and Mother's Predictions
of Their Children's Mathematics Failure Attributions by Grade and Sex .... 84
Table 4-42 Summary of ANOVA for Children's Mathematics Failure Attributions
and Mother's Predictions of Their Children's Mathematics Failure
Attributions 85
Table 4-43 Pearson Correlation Coefficients Between Mother's Actual Success
and Children's Predicted Success Attributions Within the Family 86
Table 4-44 Mother's Mathematics Success Attributions and Children's
Predictions of Their Mother's Mathematics Success Attributions by Grade
and Sex 87
Table 4-45 Summary of ANOVA for Mother's Mathematics Success
Attributions and Children's Predictions of Their Mother's Mathematics
Success Attributions 88
Table 4-46 Predicted Success Means as a Function of Respondent and Type of
Attribution 89
Table 4-47 Pearson Correlation Coefficients Between Mother's Actual Failure
and Children's Predicted Failure Attributions Within the Family 89
Table 4-48 Mother's Mathematics Failure Attributions and Children's Predictions
of Their Mothers Mathematics Failure Attributions by Grade and Sex 91
Table 4-49 Summary of ANOVA for Mother's Mathematics Failure Attributions
and Children's Predictions of Their Mother's Mathematics Failure
Attributions 92
Table 4-50 Predicted Failure Means as a Function of Respondent, Type of
Attribution, Grade, and Sex 93
xi


83
Table 4-40 Weightings
Pearson Correlation Coefficients Between Children's Actual Failure and Mothers'
Predicted Failure Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.13
.52*
.05
.38*
.19
.10
* = p<.05
Children's Predictions of Their Mother's Mathematics Success and Failure Attributions:
Chip Distribution Method
Children were asked to predict the attributions their mothers would choose for the
children's mathematics success and failure. Children predicted on a 6-point scale how
their mothers would evaluate the mathematics grade on their most recent report card. For
predicted success, the interviewer asked children, "Why do you think your mother thinks
you are doing this well?" For predicted failure, the interviewer asked "Why do you think
your mother thinks you are not doing even better?" Children predicted by distributing 10
plastic chips on five cards labeled with five attributions to determine the relative
importance of each attribution. The five attributions were ability, effort, training at
school, training at home, and luck. This procedure resulted in five attribution variables,
each with possible ranges of 0 to 10 for both success and failure conditions.
It was hypothesized that African American children will not accurately predict
their mothers mathematics success and failure attributions. For the predicted success


100
Failure condition
For the measurement of attributions in the failure condition, after responding to
the success question, the mothers were then asked, "Why is your child not doing even
better?" The failure means from the rating scale showed mothers rated the attributions in
the following order: 1) lack of effort, followed by 2) lack of school training, 3) lack of
home training, 4) lack of ability, and 5) bad luck. The failure means from the chip
distribution method showed mothers weighted the attributions differently based on the
children's grade in school. Mothers of sixth grade children weighted 1) lack of effort, 2)
lack of school training, 3) bad luck, 4) lack of ability, followed by 5) lack of home
training. Seventh grade mothers gave the following weightings: 1) lack of effort, 2) lack
of ability, 3) lack of school training, 4) lack of home training, and 5) bad luck. Eighth
grade mothers weighted 1) lack of effort, 2) lack of school training, 3) lack of home
training, 4) lack of ability, then 5) bad luck. This indicates that while lack of effort was
the primary attribution given for mathematics failure, using either measure, the ordering
of the remaining attributions differed. A grade effect was found for the chip distribution
instrument but the same effect was not found with the rating scale. Although differences
were found between the two measures for mathematics failure, mothers on both measures
did agree that lack of effort is the most important attribution.
Using the rating scale children rated 1) lack of home training, followed closely by
2) lack of effort. 3) Lack of school training and bad luck were rated equally, and finally,
5) lack of ability. The chip distribution method, the failure means indicated a grade
effect. Sixth grade children weighted 1) lack of home training, 2) lack of school training,


Table 4-12 Pearson Correlation Coefficients Between Children's Success
Attributions and Mothers's Predictions of Their Childrens Success
Attributions Within the Family 54
Table 4-13 Children's Mathematics Success Attributions and Mother's
Predictions of Their Children's Mathematics Success Attributions by
Grade and Sex 56
Table 4-14 Summary of ANOVA for Children's Mathematics Success
Attributions and Mother's Predictions of Their Children's
Mathematics Success Attributions 57
Table 4-15 Predicted Success Mean Rating as a Function of Respondent
and Type of Attribution 58
Table 4-16 Pearson Correlation Coefficients Between Children's Failure Attributions
and Mothers Prediction of Children's Attributions Within the Family 58
Table 4-17 Children's Mathematics Failure Attributions and Mother's Predictions
of Their Children's Mathematics Failure Attributions by Grade and Sex 60
Table 4-18 Summary of ANOVA for Children's Mathematics Failure Attributions
and Mother's Predictions of their Childrens Mathematics Failure
Attributions 61
Table 4-19 Predicted Failure Mean Ratings as a Function of Respondent and
Type of Attribution 62
Table 4-20 Pearson Correlation Coefficients Between Mother's Success
Attributions and Children's Predictions of Motherss Success
Attributions Within the Family 62
Table 4-21 Mother's Mathematics Success Attributions and Children's
Predictions of Their Mother's Mathematics Success Attributions by Grade
and Sex 63
Table 4-22 Summary of ANOVA for Mother's Mathematics Success Attributions
and Children's Predictions of Their Mother's Mathematics Success
Attributions 64
Table 4-23 Predicted Success Means by Respondent 65
ix


118
Eccles, J. (1985). Why doesn't Jane Run? Sex differences in educational and
occupational patterns. In F. D. Horowitz & M. O'Brien (Eds.), The gifted and talented: A
developmental perspective (pp 251-295). Washington, DC: American Psychological
Association.
Elig, T. W., & Frieze, I. H. (1979). Measuring causal attributions for success and
failure. Journal of Personality and Social Psychology. 37. 621-634.
Fox, L. H., Tobin, D., & Brody, L. (1979). Sex-role socialization and achievement
in mathematics. In M. S. Wittig & A. C. Peterson (Eds.), Sex-related differences in
cognitive functioning: Developmental issues (pp 303-332). New York: Academic Press.
Graham, S. (1988). Can attribution theory tell us something about motivation in
blacks? Educational Psychologist. 23. 3-21.
Graham. S. (1989). Motivation in Afro-Americans. In G. Berry and J. Asamen
(Eds.), Black students: Psychological issues and academic achievement (pp 40-68).
Newbury Park, CA: Sage.
Graham, S. (1991). A review of attribution theory in achievement contexts.
Educational Psychology Review, 3. 5-39.
Graham, S. (1994). Motivation in African Americans. Review of Educational
Research. 64. 55-117.
Graham, S., & Long, A. (1986). Race, class, and the attributional process. Journal
of Educational Psychology, 78.4-13.
Hall, P. Q. (1981). Problems and solutions in education, employment and personal
choices of minority women in science. Washington, DC: American Association for the
Advancement of Science, Office of Opportunities in Science.
Hall, V., Howe, A., Merkel, S., & Lederman, N. (1986). Behavior, motivation,
and achievement in black and white junior high school science classes. Journal of
Educational Psychology. 97, 108-115.
Hau, K., & Salili, F. (1993). Measurement of achievement attribution: A review
of instigation methods, question contents, and measurement formats. Educational
Psychology Review. 5. 377-422.
Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.


48
Table 4-5 Ratings
Success Mean Ratings as a Function of Respondent, Child's Sex, and Type of Attribution
Respondent
Sex
Ability
Effort
School
Home
Luck
Child
Boy
5.8
7.5
7.4
6.7
4.2
Girl
3.6
8.8
7.4
5.3
4.6
Mother
Boy
5.1
7.4
6.1
6.0
1.7
Girl
3.7
8.1
7.5
5.7
2.8
Table 4-6 Ratings
Success Attribution Means by Grade
Grade
Means
6
6.3
7
5.2
8
5.8
children's performance in mathematics. Of the six correlations, only two coefficients
were significant: home training, r = .42; luck, r = .29. Even these were fairly small.
Overall, mothers and children within the same family were in disagreement about the
causes of the children's successful mathematics performance and about how well the
children were actually performing in the class.
For the failure condition, means and standard deviations for each of the
attributional items are presented in Table 4-8. The results of an ANOVA of mothers' and
n's mathematics failure attribution means are presented in Table 4-9. The results for


Table 4-17 Ratings
Children'? Mathematic? Failure Attributions and Mother's Predictions of Their Children's
Mathematics Failnre Attributions hv Grade and Sex
60
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.0
5.1
2.6
3.5
2.5
SD
2.9
3.6
2.4
2.1
2.5
Mother
M
2.5
4.6
2.4
3.9
2.3
SD
2.1
3.5
2.7
3.0
2.4
Male
Child
M
2.9
3.4
3.9
4.3
4.4
SD
2.8
3.4
3.6
3.8
3.6
Mother
M
3.2
4.7
3.7
3.4
3.0
SD
2.5
3.4
3.1
2.0
2.8
7
Female
Child
M
2.0
2.3
3.4
4.4
3.0
SD
1.9
2.2
3.4
1.9
2.2
Mother
M
3.9
5.3
3.9
2.4
1.0
SD
3.8
4.0
3.8
1.9
0.0
Male
Child
M
2.4
3.6
3.6
3.1
3.3
SD
1.9
3.0
2.7
2.8
2.7
Mother
M
3.4
4.8
5.4
3.6
2.4
SD
2.0
3.3
3.0
2.1
1.9
8
Female
Child
M
2.3
3.3
3.9
3.6
3.3
SD
2.2
3.0
3.7
3.8
3.9
Mother
M
4.1
4.4
4.9
3.7
2.7
SD
3.6
3.6
3.4
2.8
3.1
Male
Child
M
4.5
5.3
3.1
5.0
4.1
SD
3.3
3.1
2.7
3.0
3.2
Mother
M
2.7
6.3
5.0
3.0
3.3
SD
2.5
3.0
3.5
2.0
2.6


22
different attribution patterns with a chip distribution method for the attributions of ability,
effort, school training, home training, and luck. Mothers in the Peoples' Republic of
China viewed lack of effort as a major cause of low performance. The Chinese American
mothers also viewed lack of effort as important concerning failure, but assigned
responsibility to other sources. The American mothers distributed responsibility more
evenly across the attribution options. Children in the three cultural groups had patterns of
attributions similar to those of their parents.
Stevenson and Lee (1990) studied mothers from Japan, Taiwan, and the United
States. Participants were asked to rate the influences of the factors of studying hard,
intelligence, study habits, a good teacher, home environment, parents' assistance, the
curriculum, and luck on academic achievement. American mothers gave higher ratings to
study habits, good teacher, home environment, and curriculum than they did to effort. On
the other hand, Asian mothers rated effort highly and the only factor they rated higher
than effort was good teachers.
These studies provide support for differences in attributions made by Asian
parents and children and those made by American parents and children for the children's
academic performance. These studies show that Asian parents and children tend to
emphasize the effort attribution more strongly than American parents and children.
These studies may also lend support for the differences of meaning of effort and ability in
the two cultures. A limitation of these studies is that they do not address non-Asian
cultures or sub-cultures within the United States. Different attributions could be held by


102
contrast, Parsons, Adler, and Kaczala (1982) used a rating scale, reporting attributional
agreement among parents and children for mathematics performance. This study
examined the mathematics attributions of mothers and children for the children's
mathematics performance using the rating scale and chip distribution procedures.
According to findings in the attribution literature, with the rating scale this researcher
should have found attributional agreement among mothers and children, while there
should have been little agreement found using the chip distribution method. However,
results of this study indicated that with either measure mothers and children generally
agreed about the causes of mathematics success and failure.
Elig and Frieze (1979) showed that findings can differ on the same research
question depending on the measurement instrument employed. Further, Hau and Salili
(1993) in a review addressing this problem, concluded that researchers have continued
research efforts without any concern for matching their research question to the
instrument that best serves their purpose. The findings from the current study suggest
that two different data collection methods do not yield different results. The findings of
this study suggest that both methods are equally useful in measuring attributions of
African American mothers and children.
Hypothesis One: Attributional Agreement Between Mothers and Their Children for
Mathematics Performance
The first hypothesis was concerned with agreement in attributions that African
American mothers and their children give for mathematics performance. It was
hypothesized that there would be no agreement between mothers and children and this


5
They reported mothers' race, education, attitudes, expectations, and aspirations influenced
their children's mathematics achievement. However, both reviews cited a lack of research
in this area and recommended more research with minority populations. Although there
are few empirical studies, some early research involving minority women scientists (Hall
1981; Kenschaft, 1981; Malcolm, Hall, & Brown, 1976) found that the mother's attitudes
about, and a positive belief in their children's ability to achieve success in mathematics,
positively influenced the children's participation and performance. More research on
motivation is also needed because the African American population has consistently
achieved at lower levels than other groups (Jenkins, 1989), particularly in mathematics
and especially for the low socioeconomic status (SES) African American population
(Reyes, 1984; Reyes & Stanic, 1985; 1988). Therefore, an understanding of the
attributional beliefs of low SES African American mothers and children in relation to the
children's mathematics performance would seem to be important to understanding their
mathematics achievement.
This study examined the causal attributions of low SES African American
mothers and children for success and failure in mathematics. The following issues were
examined: (a) mothers and children's attributional beliefs about mathematics
performance; (b) agreement within the family about that performance; (c) gender
differences; (d) the ability of children to predict attributions given by their mothers; and
(e) the ability of mothers to predict the attributions given by their children. Additionally,
because the research in this field has used both a rating system and a chip distribution


45
Table 4-1 Ratings
Mather's and Children's Mathematics Success Attributions bv Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.1
8.9
8.6
6.5
5.1
SD
2.4
0.4
0.7
1.5
3.1
Mother
M
5.4
8.3
8.3
7.1
3.4
SD
2.8
1.4
1.5
1.9
3.1
Male
Child
M
6.2
8.1
8.4
8.0
3.4
SD
2.1
2.0
1.3
1.7
3.7
Mother
M
5.8
8.4
5.9
5.8
1.6
SD
3.2
0.9
2.4
2.1
1.7
7
Female
Child
M
2.1
8.9
5.6
3.9
3.4
SD
2.0
0.4
1.7
2.5
2.4
Mother
M
1.6
8.0
7.9
4.4
2.0
SD
1.5
1.9
2.0
3.6
1.9
Male
Child
M
6.6
7.1
6.9
5.6
4.3
SD
3.0
2.8
1.8
2.3
3.4
Mother
M
5.2
7.2
6.7
6.0
1.9
SD
2.7
1.6
2.1
1.6
1.8
8
Female
Child
M
4.7
8.7
8.3
5.7
5.3
SD
3.4
0.8
1.9
3.3
4.1
Mother
M
4.4
8.1
6.6
5.6
3.3
SD
2.6
2.3
3.0
2.8
3.1
Male
Child
M
4.7
7.2
7.1
6.5
5.0
SD
2.8
2.1
2.3
2.0
3.6
Mother
M
4.3
6.6
5.9
6.3
1.7
SD
3.0
2.3
2.6
2.2
1.3


CAUSAL EXPLANATIONS FOR MATHEMATICS PERFORMANCE
GIVEN BY LOW SOCIOECONOMIC STATUS AFRICAN AMERICAN MOTHERS
AND THEIR CHILDREN
By
KENNETH R. CYRUS
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
19%


Ill
mathematics success only, that mothers believed boys had more ability in mathematics
than girls, and girls needed to try harder than boys to be successful in mathematics.
Practical Implications
It has been argued by Graham (1988, 1989, 1991, 1994) that Weiner's attribution
theory was a useful and appropriate framework for examining motivation patterns in
African Americans. The findings of this study show that Weiner's attribution model can
be used to identify and explain motivational patterns of low SES African American
mothers and children. This study found that effort in Weiner's attributional model is an
important attribution for African American mothers and children to explain mathematics
performance.
One implication of this study for practitioners (mathematics teachers who instruct
low SES African American children and have contact with their parents) is that they can
use an understanding of the attribution patterns of this population to better assist the
children into more successful mathematics performance. Further, they can assist the
parents by providing activities and strategies that are consistent with the attribution
patterns of the mothers: mothers believe for mathematics success their children must try
hard. Homework and other activities should be designed where the children have to try
hard to achieve success. Teachers should also realize that mothers and children also
believe school training is important for mathematics success. Therefore, this population
believes the teacher plays an important role in the children's mathematics success.
Teachers also can play a role in assisting parents in helping their children at home.


34
Table 3-2
Samnle Size for Chin Distribution Respondents
Grade
Child's Sex
Mothers
Children
6
M
6
6
F
4
4
7
M
12
12
F
10
10
8
M
14
14
F
4
4
TOTALS
50
50
Instrumentation
In the present study, two methods, the chip distribution method and the rating
scale method, were selected from methods used in previous research (see Holloway &
Hess, 1982; 1984; Parsons, Adler, & Kaczala, 1982; and Yamauchi, 1989). For both
samples, the instruments consisted of questions about attributions and predictions for the
children and for the mothers.
Instrumentation for the children was identical to that used by mothers except for
word changes that make the instrument appropriate for children. The questionnaires were


97
asked the same questions about their own mathematics performance. Participants using
the rating scale to measure mathematics attributions were then asked to rate on a 9-point
likert-type scale the importance of ability, effort, school training, home training, and luck
as attributions to explain their success or failure. Participants using the chip distribution
method to measure mathematics attributions were asked to weight the importance of each
attribution by distributing 10 plastic chips on 5 attributional cards labeled ability, effort,
school training, home training, and luck. For the prediction portion of the study, the same
procedure was used.
For both attribution methods, the data analysis included a Grade (3) x Gender (2)
x Respondent (2) x Type of attribution (5) with repeated measures on the last two factors.
Separate analyses were conducted for mothers' and children's success and failure
attributions, and their success and failure predictions. Additionally, Pearson-Product
moment correlations were used to explore the relationship of mothers' and children's
evaluations within individual families and of the children's mathematics grade.
Hypotheses important to understanding the application of attribution theory to the
mathematics achievement of low SES African American children were investigated.
Based on the parents' and children's attribution literature involving school performance,
the following six hypotheses were expected to be supported by this study:
1. Low SES African American mothers will not agree with their children about
causal attributions for mathematics success and failure.
2. Low SES African American mothers will not make different attributions for
boys and girls about their mathematics success and failure.


CHAPTER 5
DISCUSSION
In this study, the causal attributions given by low SES African American mothers
and children about the children's mathematics performance were examined. Two
methodologies, the rating scale and chip distribution methods, were used to measure
mothers' and children's attributional beliefs. Discussion of this research will involve an
examination of the findings from the rating scale and chip distribution measures
regarding six hypotheses. This chapter will also include implications of the findings for
current attribution research, theory, and practice. Finally, the discussion will conclude
with limitations of the study and recommendations for future research.
Causal attributions about school performance and the similarities and differences
in attributional beliefs between parents and children have been the focus of past empirical
study (Cashmore & Goodnow, 1986; Hess, Chih-Mei, & McDevitt, 1987; Holloway &
Hess, 1982; Holloway, Kashiwagi, Hess & Azuma, 1986; Stevenson & Lee, 1990;
Yamauchi,1989). Several problems have emerged from this research. Reviews of these
studies have found them inconclusive (Parsons, Adler, and Kaczala, 1982; Holloway and
Hess, 1982). In addition, there is a lack of agreement in studies about mothers'
perceptions related to gender-linked attributions (Bar-Tal & Guttmann, 1981; Cashmore
94


18
of failure. Children believed that effort was the most important cause for success while
ability was given as the cause for failure. The quality of teaching at school did not seem
to be important for either parents or children.
In a follow-up study, Cashmore and Goodnow (1986) compared attributions of
mothers, fathers, and their children from an Anglo Saxon Australian sample and an
Italian Australian sample. They were asked to put in rank order the attributions of talent,
effort, and teaching for six skill areas. Results indicated parents and children's responses
varied across these skill areas. Children from both samples cited effort as the most
important cause for school performance, while parents in both samples cited talent
(ability).
Holloway and Hess (1982) investigated the causal attributions given by mothers
for their upper elementary school children to explain their reasons for high and low
academic performance. Participants were given chips to place on cards listing
attributions in order to weight the importance of ability, effort, personality, and training.
Mothers' cited children's ability as the main cause of success, while lack of effort was
viewed as the main reason for failure. Children held the opposite belief, that effort was
more important for success while lack of ability contributed to failure.
Bar-Tal and Guttmann (1981) compared teachers', students,' and
parents' attributions for the students' academic performance. Using a rating scale subjects
were asked to rate 10 causes: ability, interest, difficulty of material, effort, teacher's
explanation, home conditions, parent's help, luck, diligence, and test difficulty. Findings
indicated that parents attributed their children's success to home conditions, and teacher's


121
Parsons, J. E., Adler, T. F., & Kaczala, C. K. (1982). Socialization of
achievement attitudes and beliefs: Parental influences. Child Develonment. 53. 310-321.
Passer, M. W. (1977). Perceiving the causes of success and failure revisited: A
multidimensional scaling approach. Doctoral dissertation, University of California at Los
Angeles. Dissertation Abstracts International, 38. (11), 5662B.
Reyes, L. H. (1984). Affective variables and mathematics education. The
Elementary School Journal. 84, 558-581.
Reyes, L. H., & Stanic, G. M. A. (1985). A review of literature on Blacks and
mathematics. Information Bulletin No. 1. 2-10.
Reyes. L. H., & Stanic, G. M. A. (1988). Race, sex, socioeconomic status and
mathematics. Journal for Research in Mathematics Education. 19, 26-43.
Rotter, J. B. (1966). Generalized expectancies for internal versus external control
of reinforcement. Psychological Monographs. 80. (1, Whole No. 609).
Stem, P. (1983). A multimethod analysis of student perceptions of causal
dimensions. Doctoral dissertation, University of California at Los Angeles. Dissertation
Abstracts International. 441121. 3664A.
Stevenson, H. W. (1983). Making the grade: School achievement in Japan.
Taiwan, and the United States. Stanford, CA: Center for Advanced Study in the
Behavioral Sciences.
Stevenson, H. W., Chen, C., & Uttal, D. (1990). Beliefs and achievement: A study
of Black, White, and Hispanic children. Child Development. 61. 508-523.
Stevenson, H. W., & Lee, S. (1990). Contexts of Achievement. Monographs of
the Society for Research in Child Development (221). 55, 59-67.
Stevenson, H. W., Lee, S., & Stigler, J. W. (1986). Mathematics achievement of
Chinese, Japanese, and American children. Science 231. 693-699.
Triandis, H. (1972). The analysis of subjective culture. New York: Wiley.
Weiner, B. (1979). A theory of motivation for some classroom experiences.
Journal of Educational Psychology, 71. 3-25.
Weiner, B. (1985). An attributional theory of achievement motivation and
emotion. Psychological Review. 92. 548-573.


I certify that I have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for
the degree of Doctor of Philosophy.
I certify that 1 have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for
the degree of Doctor of Philosophy.
llames Algina
nessor of Foundaltiorfs of Education
I certify that I have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for
the degree of Doctor of Philosophy.
Professor of Counselor Education
I certify that I have read this study and that in my opinion it conforms to acceptable
standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for
the degree of Doctor of Philosophy.
Assistant Professor of Counselor Education


37
1982; 1984.) The mothers were to indicate the children's level of performance on a 6-
point scale.
Mothers: relative success condition. In this condition, the objective was to help
the mother think of her child as a "relative success." For the relative success condition
the interviewer first noted the response made by the mothers. If a mother rated her child's
performance as a 1 or 2, the interviewer said: "You rated your child's mathematics
performance as better than some of the other children in his/her class. Why do you think
your child did this well?"
If a mother rated her child's performance as a 3 or 4, "better than many" was
substituted into the above sentence. If a mother rated her child's performance as a 5,
"better than most was read in the sentence. If a mother rated her child's performance as
a 6, the interviewer stated: "You have rated your child's mathematics performance as the
very best in the class. Why do you think your child did this well?"
The interviewer then gives the mothers rating scales forms which ask them to rate
each attribution (reason) for their childrens performance by circling the number that best
indicates how much a reason causes their child's mathematics performance. The scale
ranged from 1 to 9; 1 indicated the reason was definitely not a cause to 9 indicating the
reason was definitely a cause.
Mothers: relative failure condition. An analogous procedure was used for
questions about relatively low performance (relative failure). In this condition the
question was, "You indicated that your child wasn't doing the very best in the class. Why


10
Weiner's Attribution Theory
"Attribution theory is a theory of motivation and emotion, with achievement
strivings as the theoretical focus (Weiner, 1985, p. 549). The attribution approach to
achievement motivation was originated by Heider (1958) and later elaborated by Weiner
(Weiner, 1979; 1986; Weiner & Kukla, 1970). "A central assumption of attribution
theory is that the search for understanding is the basic spring to action" (Weiner, 1979, p.
3). In a school setting the search for understanding of success and failure experiences
often leads to attributional questions such as "Why did 1 succeed?" or "Why did I fail?"
According to Weiner (1985), the individual's desire for mastery of his or her environment
and situations that are unexpected are contributors to searching for causal explanations.
Perceived Causality
In their initial statement regarding the perceived causes of success and failure
Weiner, Frieze, Kukla, Reed, Rest, and Rosenbaum (1971) argued that in achievement
related contexts the causes perceived as most responsible for success and failure are
ability, effort, task difficulty, and luck. They asked participants to rate the importance of
four causes of success and failure. The participants were to rate the relative contribution
of high or low ability, high or low effort, ease or difficulty of the task, and good or bad
luck to account for their success or failure on a task (see also, Weiner, 1986). However,
in later studies (Weiner, 1974; Weiner, Russell, & Lerman, 1978) suggested other factors
such as mood, fatigue, illness, and bias could serve as necessary and sufficient reasons for
achievement performance. Many investigations have been conducted that more
systematically examine causal perceptions, particularly the perceived causes of success


85
Table 4-42 Weightings
Summary of ANOVA for Children's Mathematics Failure Attributions and Mother's
Predictions of Their Children's Mathematics Failure Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
234.80
58.70
6.29
0.0001
A x G
8
94.88
11.86
1.27
0.2612
A x S
4
7.01
1.75
0.19
0.9445
A x G x S
8
74.64
9.33
1.00
0.4377
Error
176
1641.81
9.33
Rx A
4
4.14
1.03
0.19
0.9415
R x A x G
8
26.10
3.26
0.61
0.7681
R x A x S
4
18.27
4.57
0.86
0.4921
R x A x G x S
8
16.13
2.02
0.38
0.9315
Error
176
940.21
5.34
condition, Pearson-product moment correlation coefficients between mother's actual
success attributions and children's predictions of their mothers' mathematics success
attributions within the family are presented in Table 4-43. The results in Table 4-43
indicate that children cannot accurately predict their mothers' mathematics success
attributions within the family nor could the children predict how their mothers would
evaluate their mathematics performance. Of the six correlations, none of them were
significant.


system, both systems will be used to assess these beliefs and to examine the
comparability of these methods.
6
Purpose of the Study
The primary purpose of this study was to compare the causal attributions of low
SES African American mothers and children for mathematics performance. The
secondary purpose was to compare two attribution instruments in their ability to measure
the attributions of low SES African American mothers and children.
The research questions addressed by this study were as follows:
1. Are there significant differences in causal attributions of low SES African
American mothers and their children for mathematics success and failure?
2. Do low SES African American mothers give different explanations for
mathematics success and failure for their sons and daughters?
3. Can low SES African American mothers predict their children's causal
attributions for mathematics success and failure?
4. Can low SES African American children predict their mother's causal
attributions for the children's mathematics success and failure?
5. Are there significant differences in causal attributions of low SES African
American mothers and their children for mathematics success and failure within the
family unit?
6. Will low SES African American mothers' and children's causal attributions
differ when measured by separate attribution instruments?


82
Table 4-38 Weightings
Summary of ANOVA for Children's Mathematics Success Attributions and Mothers
Predictions of their Children's Mathematics Success Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
672.50
168.13
35.64
0.0001
AxG
8
11.85
1.48
0.31
0.9600
A x S
4
19.70
4.93
1.04
0.3859
A x G x S
8
43.55
5.44
1.15
0.3298
Error
176
830.15
4.72
Rx A
4
45.11
11.28
3.00
0.0198
Rx A x G
8
25.26
3.16
0.84
0.5677
Rx A x S
4
14.19
3.55
0.94
0.4393
Rx Ax G x S
8
12.27
1.53
0.41
0.9147
Error
176
660.82
3.75
Table 4-39 Weightings
Predicted Success Means as a Function of Respondent and Type of Attribution
Respondent
Ability
Effort
School
Home
Luck
Child
0.9
3.9
2.5
1.4
1.2
Mother
1.4
5.0
2.0
1.0
0.5


23
South American parents and children or Hispanic or African American parents and
children.
Summary
In summary, several conclusions can be drawn from the parents' and children's
attribution literature regarding academic performance. First, several measurement
techniques have been used by researchers in studies that make-up this literature. The
methods used were primarily structured interviews. When rating causes, participants
were asked to rate on a 5- or 7-point scale. When weighting causes, subjects were asked
to place 10 chips or assign 10 or sometimes 100 points to 4 or 5 attributional cards. A
few studies asked subjects to rank order attributions. Both rating scale and chip
distribution methods were chosen for this study.
Second, researchers have examined and compared attributions of students,
teachers, parents, and numerous cultural groups. African American mothers and children
are considered to be a sub-culture in the United States. Additionally, they are a
population that had not been studied.
Third, a wide variety of attributions have been included in instruments. However,
all of the instrument included ability and effort. The instruments chosen therefore needed
to include these attributional elements.
Finally, it may be difficult to generalize from findings in this literature to other
groups because findings have not always been consistent. It was hoped that this study
would address this issue.


73
Table 4-29 Weightings
Summary of ANOVA for Success Attributions
Source
df
SS
MS
F
P
Within
Attribution (A)
4
697.97
174.49
40.74
0.0001
A x G
8
14.34
1.79
0.42
0.9089
A x S
4
35.30
8.82
2.06
0.0880
Ax Gx S
8
54.41
6.80
1.59
0.1313
Error
176
753.85
4.28
R x A
4
57.77
14.44
5.17
0.0006
R x A x G
8
16.01
2.00
0.72
0.6768
R x A x S
4
22.23
5.56
1.99
0.0980
Rx A x G x S
8
25.16
3.14
1.13
0.3480
Error
176
491.61
2.80
Table 4-30 Weightings
Success Means as a Function of Resixmdent and Type of Attribution
Respondent
Ability
Effort
School Home
Luck
Child
0.9
3.9
2.5 1.4
1.2
Mother
1.1
5.0
1.8 1.8
0.2


44
daughter dyads rate effort, school, and luck as more important than do mother-son dyads,
whereas motherson dyads rate ability and home as more important than do mother-
daughter dyads.
Examination of Table 4-4 analysis shows that in comparison to mother-daughter
dyads, motherson dyads believe success in mathematics depends more heavily on
ability. Motherson dyads also believe effort is less responsible for success than do
mother-daughter dyads in mathematics. Mother-daughter dyads believe that school
training is more important for mathematics success than do mother-son dyads. Mother-
son dyads believe home training is more important for mathematics success than do
mother-daughter dyads. Finally, mother-daughter dyads depend more on the luck
attribution for mathematics success than do mother-son dyads. For mother-daughter
dyads, luck and ability are of equal importance for successful mathematics achievement.
The absence of a Respondent x Child's Sex x Type of Attribution interaction
implies that the pattern of differences between boys' and girls' mean success attribution is
similar to the pattern of gender related difference between the mean success attributions
made by boys' and girls' mothers. As a result the means in Table 4-4 represent the pattern
of responding by both children and mothers. However, to avoid relying unduly on the
nonsignificant interaction, the means by child's sex for mothers and children are reported
separately in Table 4-5. Most of the generalizations that emerge from an inspection of
Table 4-4 also emerge from an inspection of Table 4-5. The exceptions are that in this


LIST OF TABLES
Table 3-1 Sample Size for Rating Scale Respondents 33
Table 3-2 Sample Size for Chip Distribution Respondents 34
Table 4-1 Mother's and Children's Mathematics Success Attributions by Grade
and Sex 45
Table 4-2 Summary of ANOVA for Success Attributions 46
Table 4-3 Success Mean Ratings as a Function of Respondent and Type of
Attribution 47
Table 4-4 Success Mean Ratings as a Function of Sex and Type of Attribution 47
Table 4-5 Success Mean Ratings as a Function of Respondent, Child's Sex, and
Type of Attribution 48
Table 4-6 Success Attribution Means by Grade 48
Table 4-7 Pearson Correlation Coefficients Between Mother's and Children's
Success Attributions within the Family 49
Table 4-8 Mother's and Children's Mathematics Failure Attributions by Grade
and Sex 50
Table 4-9 Summary of ANOVA for Failure Attributions 51
Table 4-10 Failure Mean Ratings as a Function of Respondent and Type of
Attribution 52
Table 4-11 Pearson Correlation Coefficients Between Mothers's
and Children's Failure Attributions Within the Family 52
viii


Dr. Willa Wolcott, Director of the Reading and Writing Center, deserves special
thanks for her support, confidence, and the employment she provided for a number of
years. May God richly bless her. I would like to thank Diane Heaney, Dr. Diane
Stevenson, and Margaret Steptoe for their encouragement and support. They provided
enthusiasm when I did not have any. Ellen Burleson, a colleague and friend, gave
untiring support, confidence, loyalty, and belief in me. I believe that, without her
support, completing this task would have been unusually difficult.
To the Alachua County Housing Authority, I would like to say thank you for the
introduction to the participants in this study. Under the direction of Ms. Eula Williams, I
was able to have access to the Housing Projects in Gainesville. My deepest thanks go to
Mr. Terry Lee who provided masterful guidance in arranging interviews with low income
mothers and their children. Mr. Lee is a special friend, who assisted me in so many
ways. Sincerest thanks go to Mr. Larry Saunders, a dear friend and confidant who
provided enormous support for a number of years. Also, thanks go to Dr. James D.
Lockett.
Finally, I would like to thank my parents for their patience, confidence, and
undying devotion to my pursuit of the doctoral degree. I would also like to acknowledge
my siblings who would never let me quit. For my daughter, I pass on the truth that
enabled me to persevere: Through the grace and providence of God, all things are
possible.
v


29
weighting instruments, rating scales, and rankings. The findings from studies have
frequently been compared to other studies without regard for the measurement instrument
used. This indicated a need for some studies that simultaneously used two or more
methods to see if the different instruments did, in fact, produce comparable results.
Studies of attribution patterns of mothers and their children for school
performance were examined to see if attributions were transmitted within the family.
While numerous studies using a variety of methods and looking at several cultures
examined this issue, the results were inconclusive. Ability and effort are often cited as
reasons for both success and failure, but mothers and children did not always give the
attributions the same importance.
Because gender differences have been found to exist in children's mathematics
performances, the literature regarding mothers' attributions and gender differences was
examined. Again, using a variety of methods and cultures the results were not consistent.
Children's accuracy in predicting parent's causal attributions for the children's
mathematics performance was examined. This was seen as an additional way it might be
determined that beliefs about academic achievement, especially for mathematics, were
transmitted. Further research was found to be needed in this area. In conclusion, the lack
of consistency found in the attribution literature supports the need for additional studies,
especially with a population that has received little attention, African American mothers
and their children, and to utilize two instruments to determine their comparability. It was
to this end that this study was undertaken.


11
and failure (Weiner, 1985). Two research procedures were followed to determine if
subjects attributed success and failure to other factors. First, the free response method, in
which subjects were provided with only outcome information, namely a success or
failure, has taken place. Participants were asked to list all possibilities that come to mind
for the success or failure. In the second procedure, subjects were given large lists of
causes and were asked to rate the contribution of each cause to the hypothesized outcome
(Weiner, 1985,1986). From these two procedures, a variety of causes have been
documented (Weiner, 1986). Among the numerous causes, seven attributions received
the most attention including ability, immediate and long-term effort, task characteristics,
intrinsic motivation, teacher's competence, mood, and luck. It should be noted that the
most dominant causes were ability and effort. In most cases success was attributed to
high ability and hard work and failure was thought to be caused by low ability and poor
effort (Weiner, 1979; 1985; 1986). This also holds true for a number of cultural groups
(Triandis, 1972).
Structure of Perceived Causality
Weiner's (1979; 1985) theory classifies the types of attributions made by
individuals by using a three-dimensional taxonomy of causes of success and failure. The
first dimension, labeled locus of causality, determines whether a cause is within the
person or within the environment. Rotter's (1966) construct of locus of control was the
first to classify people as internals or externals. Ability and effort causal attributions are
internal to an individual, whereas task difficulty or luck are external.


103
was not supported. For both the chip distribution and the rating scale procedures,
mothers and children agreed on attributions for successful mathematics performance.
Both groups agreed that effort, school training, and home training were the three
dominant causes of mathematics success. Ability and luck were not seen as important
causes. However, children believed luck was more important for mathematics success
than ability. When asked to give the attributions for the failure condition mothers and
children generally agreed that lack of effort, lack of school training, and lack of home
training were important causes of mathematics failure. Ability and luck were judged less
important.
For both the success and failure condition the pattern was generally the same: 1)
effort 2) school training 3) home training 4) ability and 5) luck. These results are similar
to the Parsons, Adler, and Kaczala (1982) study in that attributional agreement was found
between parents and children when measured with a rating scale, but differed from the
results of Holloway and Hess (1982) who used a chip distribution procedure and did not
find agreement. The discrepancies in the literature about parents and children's
agreement were apparently not due to the methodology employed considering the
findings of this study, because the results were similar for both methodologies. The
results from this study suggest that Holloway and Hess (1982) should have found similar
attributions reported for mothers and children.
Holloway and Hess (1982) and Parsons, Adler, & Kaczala (1982) used two
different attribution measureschip distribution and rating scaleon similar populations
white middle class students, ranging from fifth through eleventh grade, along with their


Table 4-36 Weightings
Pearson Correlation Coefficients Between Childrens Actual Success and Mothers'
Predicted Success Attributions Within the Family
79
Ability
Effort
School
Home
Luck
Evaluation
.33
.19
-.04
.02
-.10
.10
* = p<.05
Means and standard deviations for children's actual mathematics success
attributions and mothers' predicted success attributions are presented in Table 4-37. The
results of an ANOVA of children's actual mathematics success attributions and mothers'
predicted success attributions are reported in Table 4-38.
The results in Table 4-38 include a significant Respondent x Type of Attribution
interaction and a significant main effect of attribution suggesting disagreement about the
relative importance the children place on the five types of attributions and the mothers'
predictions about their children's attributions. Mean attributions as a function of
respondent and type of attribution are reported in Table 4-39. These means show the
nature of the disagreement between mothers' predicted success attributions and children's
actual success attributions when weighting the importance of the five types of
attributions. On average, mothers' predictions that children will place the most emphasis
on effort and school is correct but mothers overestimate the amount of emphasis their
children will place on effort.
On average, mothers predicted their children would weight effort as most
important followed by school, ability, home, and luck. Children's actual success


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy
CAUSAL EXPLANATIONS FOR MATHEMATICS PERFORMANCE GIVEN BY
LOW SOCIOECONOMIC STATUS AFRICAN AMERICAN MOTHERS AND THEIR
CHILDREN
By
Kenneth R. Cyrus
May, 1996
Chairman: Barry Guinagh
Major Department: Foundations of Education
The present study examines the attributional beliefs of low SES African American
mothers and their 6th, 7th, and 8th grade children for the childrens mathematics
performance. Using two attribution measures, the rating scale and chip distribution
methods, data from mothers and children were examined for (a) attributional agreement
about the causes of mathematics performance, (b) mothers' attributions and children's
gender, (c) attributional agreement within the family, (d) mothers' and children's
attributional predictions, and (e) whether results would vary depending on the method-
chip distribution or rating scale-used for data collection.
The results from the rating scale and chip distribution methods revealed similar
findings on all hypotheses except for mothers and gender-related attributions. Both
xii


98
3. Low SES African American mothers will not be able to accurately predict
attributions their children give for the children's mathematics success and failure
performances.
4. Low SES African American children will not be able to accurately predict the
attributions their mother will give for the children's mathematics success and failure
performances.
5. Low SES African American mothers and children, within the same family, will
not be in agreement regarding the children's mathematics success and failure
performances.
6. Conclusions about the attributions will not depend on the method-chip
distribution instrument or rating scale instrument-used for data collection.
The first five hypotheses were tested by both the rating scale and the chip
distribution methods generating two sets of data. Because the findings from the two
instruments will be used to guide the discussion of the remaining five hypotheses,
hypothesis six will be discussed first.
Hypothesis Six: Comparisons of the Chip Distribution and Rating Scale Methods
Success condition
Attribution measurement results from the success condition of hypothesis six held
that conclusions about attributions will not depend on the method used for data
collection. Support for this hypothesis was found. Mothers were asked to evaluate their
children's mathematics grade from the most recent report card. For the success condition,


15
question contents, and measurement formats. They concluded that no one method is
superior to another. They found that the appropriate use of any attribution measure
requires that the instrument specifically match the research question; yet they are not
clear on how this should be accomplished. Despite the authors' statement that the method
and the research question need to be matched, their review indicates that no one method
is superior to another. While the rating scale was the most popular structured method
used by attribution researchers, the authors found that all measurement methods are
equally satisfactory when used properly. However, they did not answer the question as to
whether all measurement instruments would be equally valid for the same research
question. Elig and Frieze (1979) also cautioned that various measurement instruments
can yield different results for the same research question.
Researchers who have been interested in study of parents' and children's causal
attributions for school performance have commonly used three structured measurement
techniques. The first type of measure is the chip distribution measure in which parents
and children have a limited number of plastic chips that they place on different choices
weighting the importance of causal attributions (Dunton, McDevitt & Hess, 1986; Hess,
Chih-Mei & McDevitt, 1987; Hess, Holloway & King, 1981; Hess & McDevitt, 1985;
Hess, McDevitt & Cheng, 1984; Holloway & Hess 1982; 1984; Holloway, Hess & King,
1981; King, Hess & Holloway, 1981). For example, a child would be asked to place 10
chips on 5 choices according to the importance of each attribution for that child. If 8
chips were placed on the ability attribution option only two chips would be left to place


101
3) lack of effort and lack of ability were equally weighted, and then 5) bad luck. Seventh
grade children weighted 1) lack of effort, 2) lack of home training, 3) lack of ability and
lack of school training were equally weighted, followed by 5) bad luck. Eighth grade
children weighted 1) lack of effort, 2) lack of school training, 3) bad luck, 4) lack of
ability and 5) lack of home training. Therefore, both measures revealed lack of home
training, and lack of effort were the dominant attributions given by children for their
mathematics failure. In the failure condition greater variation was found in children's
responses with the chip distribution method, however, no help at home with mathematics
and not putting forth appropriate effort were the primary explanations given by children
reported in both measures.
Returning to hypothesis six, one can conclude there is similarity in the
information provided by the two different methods of assessing attributions. However,
the two methods do not yield data that are identical. This study did not attempt to resolve
whether one method of assessing attributions is superior to another. The two methods
were used to assess attributions in a more robust fashion than would be possible if only
one method was used.
Prior studies in the attribution literature that involved mothers and children
attributional agreement for school performance have often reported inconsistent findings.
Some researchers have attributed these differences in findings to the differences in
methodologies used to collect the data (Elig & Frieze, 1979; Hau & Salili, 1993). For
example, Holloway and Hess (1982) used a chip distribution procedure and reported little
attributional agreement among mothers and children regarding school performance. In


61
Table 4-18 Ratings
Summary of ANOVA for Children's Mathematics Failure Attributions and Mother's
Predictions of their Childrens Mathematics Failure Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
27.13
13.57
0.66
0.5203
Sex (S)
1
26.28
26.28
1.28
0.2632
GxS
2
1.50
0.75
0.04
0.9640
Error
44
900.05
20.46
Within
Respondent (R)
1
1.87
1.87
0.22
0.6393
RxG
2
13.65
6.82
0.81
0.4496
RxS
1
0.43
0.43
0.05
0.8228
RxGxS
2
13.74
6.87
0.82
0.4471
Error
44
368.81
8.38
Attribution (A)
4
131.45
32.86
4.21
0.0028
AxG
8
28.21
3.53
0.45
0.8882
A x S
4
13.24
3.32
0.42
0.7910
A x G x S
8
45.82
5.73
0.73
0.6614
Error
176
1373.57
7.80
RxA
4
84.55
21.14
3.29
0.0124
Rx A x G
8
34.37
4.30
0.67
0.7181
R x A x S
4
9.09
2.27
0.35
0.8409
Rx A x G x S
8
61.62
7.70
1.20
0.3017
Error
176
1129.99
6.42


Table 4-19 Ratings
Predicted Failure Mean Ratings as a Function of Respondent and Type of Attribution
62
Respondent
Ability
Effort
School
Home
Luck
Child
3.0
3.8
3.4
4.0
3.4
Mother
3.3
5.0
4.2
3.3
2.4
Table 4-20 Ratings
Pearson Correlation Coefficients Between Mother's Success Attributions and Children's
Predictions of Mothers's Success Attributions Within the Family
Ability
Effort
School
Home
Luck
Evaluation
.23
.26
.06
.17
.16
.31*
*=p<.05
predictions of their mothers' mathematics success attributions within the family are
presented in Table 4-20. The results of Table 4-20 indicate that children were not able to
predict accurately their mothers' mathematics success attributions within the family. Of
the six correlations, five were not significant. However, a small significant correlation
was found when children predicted how their mothers would evaluate their mathematics
performance.
Means and standard deviations for mothers' actual mathematics success
attributions and children's predicted success attributions are presented in Table 4-21. The
results of an ANOVA of mothers' actual mathematics success attributions and children's
predicted success attributions are reported in Table 4-22. As in the preceding analyses


CHAPTER 4
RESULTS
The primary purpose of this study was to compare the causal attributions that low
SES African American mothers and their children give for mathematics performance. A
secondary purpose was to examine whether the rating scale instrument and the chip
distribution instrument yield similar results when measuring causal attributions given by
low SES African American mothers and children for mathematics performance.
The results presented in this chapter are based on the data collected from 6th, 7th,
and 8th grade students and their mothers in six separate government-subsidized housing
projects in Alachua county, Florida. Attribution questionnaires were administered to 200
participants, including 100 parents and 100 children who volunteered to participate in the
study. The sample pool was divided in half. Fifty mothers and 50 children were
administered the rating scale instrument. Fifty mothers and 50 children were
administered the chip distribution instrument.
For both samples, the data analysis included a grade (3) x child sex (2) x
respondent (2) by type of attribution (5) with repeated measures on the last two factors.
Separate analyses were conducted for success and for failure attributions, and
additionally, to examine mothers' and children's success and failure predictions. Further,
Pearson-product moment correlations were used to explore the relationship of mothers
41


104
mothers. This study used both procedures on a low income, African American students
in sixth through the eighth grades along with their mothers. It is not clear why Holloway
and Hess (1982) did not find agreement between mothers and children when Parsons,
Adler, & Kaczala (1982) and this study did find agreement.
Hvnothesis Two: Mothers' Attributions and Gender Differences
The second hypothesis asserted that low SES African American mothers would
not make gender differences when making causal attributions for their sons' and
daughter's mathematics performance. This hypothesis was partially supported. Findings
from this study revealed that results with respect to gender differences depended on the
method used to collect the attributions. Using the rating scale, mothers selected different
mathematics attributions based on the gender of their children for the success condition,
but not for the failure condition. Mothers believed that ability was more important for
boys' success than for girls success and effort was more important for girls than for boys.
However, mothers did not make this distinction when it came to mathematics failure. In
another study using a rating scale Yee and Eccles (1988) found that parents attributions
varied based on the gender of their children for successful mathematics performance but
not for the failure condition. In contrast to these findings, when the chip distribution
method was used mothers did not select different attributions in either the success or
failure condition for their sons and daughters. Therefore, the chip distribution method
found that mothers believe boys and girls succeed and fail in mathematics for the same
reasons. Other studies have also found that attributions used by mothers are similar for


59
Respondent and Type of Attribution are reported in Table 4-19. Table 4-19 shows that
mothers' predicted failure attributions of their children's mathematics performance were
not in agreement for the relative importance of the five types of attribution. Also,
mothers predictions were not in agreement with the emphasis their children placed on
each of the five types of attributions. The only attribution mothers came close to
predicting about their children's mathematics failure was ability. It seems mothers knew
their children would not rate their ability as a cause of mathematics failure.
Children's Prediction's of Their Mother's Mathematics Success and Failure Attributions:
Rating Scale Method
Children were asked to predict the attributions their mothers would choose for the
children's mathematics success and failure. Children predicted on a 6-point scale how
their mothers would rate the mathematics grade on their most recent report card. For
predicted success, the interviewer asked children, "Why do you think your mother thinks
you are doing this well?" For predicted failure, the interviewer asked "Why do you think,
your mother thinks you are not doing even better?" Children predicted on 9-point scales
the relative importance their mothers would place on the five attributional items. The 9-
point scales ranged from 1 (definitely not a cause) to 9 (definitely a cause). The
attributional items were ability, effort, training at school, training at home and luck. It
was hypothesized that African American children will not accurately predict their
mother's mathematics success and failure attributions.
For the predicted success condition, Pearson product-moment correlation
coefficients between mothers' actual mathematics success attributions and children's


Table 4-13 Ratings
Children's Mathematics Success Attributions and Mother's Predictions of Their Children's
Mathematics Success Attributions bv Grade and Sex
56
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
4.1
8.9
8.6
6.5
5.1
SD
2.4
0.4
0.7
1.5
3.1
Mother
M
4.8
8.0
6.8
6.5
3.4
SD
3.8
2.8
3.0
2.8
2.6
Male
Child
M
6.2
8.1
8.4
8.0
3.4
SD
2.1
2.0
1.3
1.7
3.7
Mother
M
5.9
8.3
6.7
6.7
4.1
SD
3.2
1.4
2.5
1.7
3.1
7
Female
Child
M
2.1
8.9
5.6
3.9
3.4
SD
2.0
0.4
1.7
2.5
2.4
Mother
M
4.0
7.4
6.9
5.0
2.7
SD
3.2
2.1
3.1
3.2
2.4
Male
Child
M
6.6
7.1
6.9
5.6
4.3
SD
3.0
2.8
1.8
2.3
3.4
Mother
M
6.2
7.8
5.6
5.6
2.8
SD
1.9
1.3
3.0
1.0
2.2
8
Female
Child
M
4.7
8.7
8.3
5.7
5.3
SD
3.4
0.8
1.9
3.3
4.1
Mother
M
6.7
7.9
6.3
6.9
3.3
SD
3.0
2.2
3.0
2.5
3.9
Male
Child
M
4.7
7.2
7.1
6.5
5.0
SD
2.8
2.1
2.3
2.0
3.6
Mother
M
4.4
7.2
5.6
6.3
2.5
SD
2.6
1.5
2.9
2.0
1.9


26
perception's of their children's mathematics aptitude despite the similarity of performance
of boys and girls. Parents of girls thought their daughters had to work harder to do well
in mathematics than did parents of sons. Parents of boys believed advanced mathematics
was more important for their sons when compared to their daughters.
In a study of Holloway and Hess (1984) investigating gender differences in
teacher and in parental beliefs, no significant sex differences on an achievement
assessment for mathematics performance were found for boys' and girls' mathematics
performance in 6th grade.
In a study by Yee and Eccles (1988), 7th grade students and parents were asked to
complete questionnaires at home about beliefs, expectations, and causal attributions for
their children's mathematics achievement. The results indicated that parents' perceptions
and expectations were commensurate with their childs level of mathematics ability.
Mothers attributions for mathematics successes differed for boys and girls.
Dunton, McDevitt, and Hess (1988) conducted a longitudinal study investigating
the origins of mothers' attributions about their daughters' and sons' academic performance
at 5 and 6 years of age and again at age 12. The findings indicated that there are both
similarities and differences in the sources of attributions given by mothers for their
children's performance. Mothers of boys attributed the source of their attributions to their
son's prior performance.
Mothers of son's attributed relative success to having ability and the relative
failure to lack of effort. The source of the mother's attributions for their daughters was
the affective relationship they have with them. Mothers of girls attributed their daughters'


REFERENCES
Alexander, K., & Entwisle, D. (1988). Achievement in the first two years of
school: Patterns and processes. Monographs of the Society for Research in Child
Development 53. (2, Serial No. 218).
Bar-Tal, D., & Guttmann, J. (1981). A comparison of teachers', pupils', and
parents' attributions regarding pupils' academic achievements. Journal of Educational
Psychology. 51. 301-311.
Benson, M. J. (1989). Attributional measurement techniques: Classification and
comparison of approaches for measuring causal dimensions. Journal of Social
Psychology. 129. 307-323.
Bird, J. E., & Berman, L. S. (1984). Differing perceptions of mothers, fathers, and
children concerning children's academic performance. Journal of Psychology, 119. 113-
124.
Cashmore, J. (1980, May). Models of intelligence and development: A child's eve
view. Paper presented at the Jubilee Congress of ANZAAS, Adelaide, Australia.
Cashmore, J. (1982). Parent-child agreement on attributional beliefs: Differences
associated with ethnic background and gender of child. Paper presented to the second
Child Development Conference, Melbourne, Australia.
Cashmore, J. A., & Goodnow, J. J. (1986). Parent-child agreement on attributional
beliefs. International Journal of Behavioral Development. 9. 191-204.
Dunton, K. J., McDevitt, T. M., & Hess, R. D. (1988). Origins of mothers'
attributions about their daughters' and sons' performance in mathematics in sixth grade.
Merrill-Palmer Quarterly. 34. 47-70.
117


96
Therefore, the primary purpose of this research was to compare the causal
attributions given by low SES African American mothers with their children's attributions
for the children's mathematics performance. Mothers' and children's attributions were
compared to investigate several issues: (a) attributional agreement between mothers and
children, (b) mothers and gender-linked attributions, (c) mothers' and children's
attributional predictions, and (d) attributional agreement within individual family units.
A secondary purpose was to compare two methodological instruments~the rating scale
instrument and the chip distribution instrument--to measure the attributions of low SES
African American mothers and children concerning these issues.
The mothers and children were selected from 6 government housing projects in
Alachua County, Florida. The children were in middle school grades 6, 7, or 8. The 200
participants were divided into two sample pools. Fifty mothers and fifty children were
selected for the rating scale measure and 50 mothers and 50 children were chosen for the
chip distribution measure. The administration of both measures were conducted in the
participants homes. Attributions of mothers and children were measured separately
without the other present.
Instructions to the participants were identical except for wording changes
necessary for the children and mothers. Initially mothers and children were asked to
evaluate on a 6-point likert-type scale the children's mathematics grade from the most
recent report card. From the evaluation, the researcher asked mothers, for the success
condition, why they felt their children were performing this well, and for the failure
condition, why they thought their children were not doing even better. Children were


38
do you think he/she isn't doing even better in mathematics?" The purpose was to have the
mother think of her child as a "relative failure."
If the mothers reported that the children were doing the very best in the class, the
interviewer suggested that the children might not be the best in the school district and
asked why the children were not doing even better.
Prediction. In the prediction portion of the study, a mother was asked to predict how her
child would rate his or her mathematics performance based on the most recent report
card. The mothers were to indicate their predictions about their children's rating on a 6-
point scale.
Mothers: relative success condition. For the relative success condition the
interviewer first noted the response made by the mothers to the prediction question. If the
mothers predicted their children would rate their performance as a 1 or 2, the interviewer
said, "You predicted your child would rate his/her mathematics performance as better
than some of the other children in his/her class. Why do you think your child thought
he/she did this well?"
If the mothers predicted their children would rate their mathematics performance
as a 3 or 4, "better than many" would be substituted into the above sentence. If the
mothers predicted their children would rate their mathematics performance as a 5, "better
than most" was read in the sentence. If the mothers predicted their children would rate
the children's performance as a 6, the interviewer stated, "You have predicted that your
child would rate his/her mathematics performance as the very best in the class. Why do
you think your child thinks he/she did this well?"


71
causes of mathematics success. On average, mothers weighted effort as most important,
followed by school and home training which were weighted equally, followed by ability,
then luck. Children on average, weighted effort as most important, followed by school
training, home training, luck and ability. Mothers and children also differed in the
emphasis they placed on the five types of attributions. Mothers placed more weight than
the children on the effort attribution, while children placed more emphasis on the luck
attribution for mathematics success. Also, children believed that school training was
more important for mathematics success than their mothers. Mothers and children were
similar in their weightings of ability and home training as causes of mathematics success.
Overall, mothers and children agreed that effort was most important for mathematics
success. However, mothers and children differed in the emphasis placed on effort. Also
mothers and children differed in their beliefs about the importance of school training and
luck as causes of mathematics success.
Pearson-product moment correlation coefficients presented in Table 4-31 indicate
that mothers and children within the family do not agree about the causes of mathematics
success. Mothers and children were also not in agreement about the childrens
performance in mathematics. Of the six correlations, only one coefficient was
significant: ability r = .50, indicating a moderate relationship. However, there was very
little agreement for the remaining attributions.


CHAPTER 2
REVIEW OF THE LITERATURE
The primary purpose of this study was to compare the causal attributions of low
SES African American mothers and their children for mathematics performance. A
second purpose was to examine whether the rating scale instrument and the chip
distribution instrument yield similar and consistent results when measuring mathematics
attributions of African American mothers and children.
While mothers' and children's academic attributional beliefs of numerous cultures
have been studied (e.g., Bar-Tal & Guttmann, 1981; Cashmore & Goodnow, 1986; Hess,
Chih-Mei, & McDevitt, 1987; Holloway, & Hess, 1982; Stevenson & Lee, 1990), African
American mothers and children have not been the focus of any studies in the attribution
literature. Related literature was therefore chosen for review. The review will be
discussed as follows: first, a general overview of Weiner's attribution theory; second,
attribution measurement issues; third, attribution patterns of mothers and their children
for school performance; fourth, mothers' attributions and gender differences; fifth,
children's accuracy in predicting parent's causal attributions for the children's
mathematics performance. The chapter concludes with a summary of the review.
9


3
Three basic problems have emerged from this research. First, there has been a
lack of consistency in the measurement of attributions across studies. Three methods
have been used: a ranking, a chip distribution, and a rating method. The ranking method
asks the subject to rank attributions (such as luck, effort, and ability) from most important
to least important. The chip method gives the subjects a limited number of chips (or
sometimes points -- e.g. Stevenson & Lee, 1990) that they use to designate the
importance of attributions. The rating scale method uses a scale that subjects use to
indicate the strength of each attribution. For example, Cashmore and Goodnow (1986)
employed a rank order measure when comparing Australian mothers' and children's
attributional beliefs. Holloway and Hess (1982) used a chip distribution measure to study
Caucasian American mothers and children while Parsons, Adler, and Kaczala (1982) used
a rating scale instrument with this population. A point distribution measure was used by
Stevenson and Lee (1990) to compare the attributional beliefs of Caucasian American,
Chinese, and Japanese mothers and children.
Studies of attributions have used one of these three methods, but not two or three
methods at the same time. Inconsistent findings in this area of attribution research may
have been caused by the way attributions were measured. Various measurement
techniques used in attribution studies have both advantages and disadvantages and
different results found in attribution research may vary according to the measurement
technique used. This raises questions of reliability and validity for attribution research
(Elig & Frieze 1979; Hau & Salili, 1993). The inconsistent use of methods and


70
for both the success and failure conditions. For example, if all ten chips are placed on
one attribution there will be no chips left to place on any of the other attributions.
Relative success in this study is actually mothers' and children's evaluation of the
children's current level of performance. For relative failure, mothers and children were
asked by the interviewer, "Why did your child not do even better?" (for mothers), and
"Why aren't you doing even better?" (for the children). This condition examines relative
failure as a judgment in contrast to the current level of performance.
It was hypothesized that low SES African American mothers will not agree with
their children about the causes of their children's success and failure in mathematics.
For the success condition means and standard deviations for each of the
attributional items are presented in Table 4-28. The results of an ANOVA of mothers'
and children's mathematics success attribution means are presented in Table 4-29. The
chip distribution method has no between subject effects because there were only 10 chips
available for distribution. Therefore, only within subject effects will be reported in this
section. The results in Table 4-29 include a significant main effect of attribution and a
significant Respondent x Type of Attribution interaction indicating some discrepancies
between mothers and children about the relative importance they place on the five types
of attributions for success in mathematics.
Mean attributions as a function of respondent and type of attribution are reported
in Table 4-30. These means show the nature of disagreement between mothers and
children about the relative importance of the five types of attributions. Mothers and
children differed slightly about the relative importance of the five types of attributions as


109
stressed effort. For failure, parents cited lack of effort, while children cited lack of talent
and of effort.
This study indicates that on average low SES African American mothers and
children give similar attributions for mathematics success. This population attributes
mathematics success primarily as a consequence of effort, with emphasis placed on
school and home training. For mathematics failure, mothers and children gave the same
order of importance for the attributions that they had given for success condition,
however mothers placed significant emphasis on lack of effort and little emphasis on the
remaining attributions. Their children, on the other hand, did not explain mathematics
failure with any one attribution but saw all five attributions as contributing equally. The
ratings and weightings, for failure attributions, tended to have similar means. This
indicates, that for failure, no one attribution received more emphasis than another.
Therefore, children did not see any of the five attributions contributing significantly to
their mathematics failure. This may be an area of concern because low SES African
American children tend not to achieve as well in mathematics as other groups and for the
children not to have an explanation for poor performance may be problematic.
This study is a beginning of exploratory research to examine motivational patterns
in the low SES African American family. Graham (1994) stated that there were no
studies examining the attributions of the African American family. Below are several
implications for this study.
Empirical Implications


13
future success more than failure attributed to bad luck, bad mood, and lack of effort
(Weiner, 1979). Weiner, Nirenburg, and Goldstein (1976) showed that American college
students who were successful at completing block designs because of ability and ease of
task anticipated a successful performance in the future, compared to students who were
successful because of effort and luck.
The dimension of control is linked to personal responsibility. The dimension of
control focuses on inferences about others and how beliefs about another's responsibility
for success and failure influence an individuals reactions toward that person. When a
person fails for controllable reasons, that person is blamed by others. Therefore, when a
person fails for uncontrollable reasons, that person does not receive blame.
Attributions. Expectations, and Consequences
Within Weiner's three-dimensional model, the stability dimension is posited to be
related to goal expectancy. According to Weiner (1985, p. 556), "the attributional
position is that the stability of a cause rather than its locus, determines expectancy shifts."
Therefore, when attributions are classified on the stability dimension they have a link to
expectations for future success. For example, if a subject attributed success on an
important examination to high ability, expectancy of future success will be high because
ability is a stable attribution which in most cases does not change from task to task. But,
if the reason for success was attributed to high effort one will not maintain such high
expectations for future success because effort may fluctuate from one task to the next.
Conversely, for the failure situation, if the cause for failure is one's low ability, the
expectancy of success might be decreased because one cannot change ability; but if


7
Significance of the Study
Examining the causal attributions of African American children and parents is
important for several reasons. First, achievement in school is important. However,
African American children have not done well in mathematics, even though their mothers
believe they are doing well and will continue to do well (Alexander & Enwistle, 1988;
Matthews, 1984; Reyes & Stanic, 1988). Next, achievement is related to motivation and
motivation is related to attributions (Weiner, 1986). This connection between attributions
and motivation can be seen in other cultures. For example, Japanese and Chinese
mothers' belief in hard work or high effort has shown to be highly motivating for their
children's mathematics achievement (Holloway, Kashiwagi, Hess, & Azuma, 1986;
Stevenson & Lee, 1990). Parents influence the attitudes of children. Interviews of
African American parents find they want their children to do well in school (Alexander &
Enwistle, 1988; Stevenson, Chin, & Uttal, 1990). Do the attributions held by African
American children about motivation correlate to those attributions held by their parents,
or are there differences in attributions between mothers and children?
Additionally, this research is important because it attempts to clarify some
measurement concerns found in the parents and children's attribution literature. The past
research has used numerous methods to measure causal attributions. This may be the
cause of some of the inconsistent findings. The present study was designed to determine
whether results would be consistent when different methods are used to collect the
attribution data. Therefore, this study will determine whether two widely used
measurement instruments in attribution research are comparable for assessing the causal


This dissertation was submitted to the Graduate Faculty of the College of Education and to
the Graduate School and was accepted as partial fulfillment of the requirements for the degree of
Doctor of Philosophy.
£)_
May, 1996 Chair, Foundations of Education
Dean, Graduate School


51
Table 4-9 Ratings
Summary of ANOVA for Failure Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
13.34
6.67
0.42
0.6624
Sex (S)
1
21.72
21.72
1.35
0.2508
GxS
2
15.90
7.95
0.50
0.6126
Error
44
705.90
16.04
Within
Respondent (R)
1
2.13
2.13
0.19
0.6636
RxG
2
9.57
4.79
0.43
0.6530
RxS
1
1.25
1.25
0.11
0.7392
R x G x S
2
8.78
4.39
0.39
0.6761
Error
44
489.27
11.12
Attribution (A)
4
389.59
97.40
14.38
0.0001
A x G
8
34.59
4.32
0.64
0.7445
A x S
4
29.90
7.48
1.10
0.3562
A x G x S
8
65.01
8.13
1.20
0.3014
Error
176
1191.71
6.77
Rx A
4
268.49
67.12
11.81
0.0001
Rx AxG
8
18.06
2.26
0.40
0.9211
R x A x S
4
46.27
11.57
2.03
0.0916
Rx A x G x S
8
45.57
5.70
1.00
0.4364
Error
176
1000.60
5.69


27
relative success or failure to ability or effort depending on the type of affective
relationship.
Several conclusions can be drawn from the attribution literature related to
mother's attributions and gender differences. Evidence supporting mothers' contributions
to gender related attributions is mixed. Some studies show that mothers do not hold
gender related differences for their children's school performance, particularly
mathematics. Additionally, researchers have used several methods and cultural groups
when examining mothers' attributions and gender differences. There were no studies that
examined African American mothers and their gender related attributions. Finally,
generalizing findings from this literature to other cultural groups is difficult because the
findings are inconsistent.
Children's Prediction of Their Mother's Causal Attributions
Parental accuracy in predicting their children's causal attributions for success and
failure at school has not received much research attention. Yamauchi (1989) asked
Japanese children to predict their mother's attributions for the child's mathematics
performance. Using a rating scale methodology, results showed a discrepancy between
children's predictions of mothers' attributions and mothers actual attributions which
varied by children's school performance. The researcher concluded that children's
predictions of their mothers' mathematics attributions were not related to their mother's
actual attributions. Further research was recommended. As mothers' predictions of their
children's attributions about mathematics performance were not investigated, they might
yield more insight into this area.


81
Table 4-37 Weightings
Children's Mathematics Success Attributions and Mother's Predictions of Their Children's
Mathematics Success Attributions by Grade and Sex
Grade
Sex
Subject
Statistic
Ability
Effort
School
Home
Luck
6
Female
Child
M
1.0
3.3
2.5
1.8
1.5
SD
0.8
1.0
0.6
1.3
1.3
Mother
M
0.8
6.3
1.8
1.3
0.0
SD
1.5
2.9
1.3
1.9
0.0
Male
Child
M
1.0
3.3
2.8
1.2
1.7
SD
1.7
1.5
0.8
1.2
1.5
Mother
M
1.7
4.3
2.3
0.8
.83
SD
4.1
3.3
2.1
1.6
1.3
7
Female
Child
M
0.8
4.0
3.0
1.0
1.2
SD
1.1
1.3
1.5
1.7
1.6
Mother
M
0.9
4.8
3.4
0.7
.20
SD
1.5
2.1
2.5
1.2
.63
Male
Child
M
1.2
4.2
1.8
2.0
.92
SD
2.0
2.4
1.7
1.9
1.1
Mother
M
1.4
4.5
2.1
1.3
.67
SD
2.1
3.0
1.6
2.3
1.2
8
Female
Child
M
0.5
4.8
2.3
1.5
1.0
SD
1.0
1.3
1.7
1.7
2.0
Mother
M
2.5
6.3
0.5
0.5
.25
SD
5.0
4.8
1.0
1.0
.50
Male
Child
M
1.2
3.9
2.9
1.3
.71
SD
1.3
2.3
1.5
1.3
1.1
Mother
M
1.6
3.7
2.1
1.8
.86
SD
1.7
1.9
1.6
1.6
1.6


Copyright 1996
by
Kenneth R. Cyrus


110
One empirical implication from this study involves the measurement of
attributions. Some researchers have argued that variations in measurement techniques
could result in different findings (Elig & Frieze, 1979; Hau & Salili, 1993). This study
showed that two separate measurement instruments investigating the same research
question yield comparable results. The findings from the chip distribution and rating
scale methods used in this study imply that the differences reported in the attribution
literature may not be due to the method employed to measure those attributions, but could
be due to other factors such as culture, personal bias of the subjects, or the wording of the
questions themselves.
Additionally, the results from this study imply that mothers do not pass on their
attributions directly to their children. This finding is consistent with Holloway,
Kashiwagi, Hess, & Azuma (1986) who reported that Japanese and American mothers are
not the primary source of attributions their children may hold regarding mathematics
performance. However, in this study, as a population, African American mothers and
children were in agreement about the attributions that are important for mathematics
success and failure. It is unclear why, in this study, that so much agreement was found in
the larger group, but little agreement was found within individual family units.
An additional empirical implication is the question of mothers' attributions in
relation to their sons' and daughters' mathematics performance. This study did not make
clear if African American mothers do make different attributions for their children
depending on their gender. The chip distribution instrument revealed that mothers do not
discriminate between their sons and daughters. However, the rating scale showed, for


57
Table 4-14 Ratings
Summary of ANOVA for Children's Mathematics Success Attributions and Mother's
Predictions of Their Children's Mathematics Success Attributions
Source
df
SS
MS
F
P
Between
Grade (G)
2
84.89
42.44
3.65
0.0340
Sex (S)
1
2.79
2.79
0.24
0.6262
GxS
2
51.74
25.87
2.23
0.1199
Error
44
511.15
11.61
Within
Respondent (R)
1
23.26
23.26
3.66
0.0623
RxG
2
9.36
4.68
0.74
0.4848
RxS
1
5.19
5.19
0.82
0.3715
Rx Gx S
2
7.79
3.89
0.61
0.5467
Error
44
279.81
6.36
Attribution (A)
4
1026.74
256.68
40.02
0.0001
AxG
8
33.18
4.15
0.65
0.7376
A x S
4
62.48
15.62
2.44
0.0490
AxGxS
8
48.12
6.01
0.94
0.4868
Error
176
1128.88
6.41
Rx A
4
66.52
16.63
3.40
0.0105
Rx A x G
8
30.22
3.78
0.77
0.6276
Rx A x S
4
39.78
9.95
2.03
0.0917
R x A x G x S
8
21.16
2.64
0.54
0.8246
Error
176
860.77
4.90


g
attributions of low SES African American mothers and children for the children's
mathematics performance.
This study also provides further testing of attribution theory. Understanding the
attributional beliefs of African Americans may assist in understanding the nature of the
beliefs that help create the academic conditions experienced by this population.
Attribution theory could provide a model to investigate attribution patterns that hinder
achievement motivation in mathematics (Graham, 1991; 1994).
Finally this study will assist in establishing future research directions for the study
of motivation in the African American family. Graham (1994) has argued for more
research involving African American parents and children. She states,
Future studies conducted within a motivational
framework should systematically examine the sources of African-
American parents' beliefs about their children's ability and
prospects, whether and how these beliefs get communicated to the
child, and how they get played out in parenting practices that have
the potential to either undermine or enhance achievement strivings,
(p. 107)