Citation
Birth order effects and fluid/crystallized intelligence

Material Information

Title:
Birth order effects and fluid/crystallized intelligence
Creator:
Witt, Sandra Johnson, 1946-
Copyright Date:
1978
Language:
English
Physical Description:
viii, 73 leaves : ; 28 cm.

Subjects

Subjects / Keywords:
Birth order ( jstor )
College students ( jstor )
Economic forecasting models ( jstor )
Economic models ( jstor )
Family size ( jstor )
Mathematical variables ( jstor )
Modeling ( jstor )
Multilevel models ( jstor )
Predictive modeling ( jstor )
Socioeconomic status ( jstor )
Birth order ( lcsh )
Dissertations, Academic -- Psychology -- UF ( lcsh )
Family size -- Psychological aspects ( lcsh )
Intellect ( lcsh )
Psychology thesis Ph. D ( lcsh )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Thesis:
Thesis--University of Florida.
Bibliography:
Bibliography: leaves 68-72.
Additional Physical Form:
Also available on World Wide Web
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Sandra Johnson Witt.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
022297106 ( ALEPH )
04394140 ( OCLC )
AAH1344 ( NOTIS )

Downloads

This item has the following downloads:


Full Text










LIRTA; OWifl EE.r LCTS
AID FLUIDr/CY SILLIZED INTELLiGENCE










By

Sa d .e < i..'son i Lt


A DYS:Ri A.r',i ,


E i, -L


S.,,i T: i T

DirTon 0


L. M L OF


i\L I)
~;II.


i'u' S F0 TI
. IY


L, R I 'v Y 0i
. 1 .


1078


;E I n 'i" ,i












ACKNOWLEDGMENTS


I would like to acknowledge my sincere appreciation to Dr. Walter

R. Cunrningham, the chairman of my doctoral committee and director of

this dissertation, whose valuable suggestions, constructive criticisms,

and encouragement made this study possible. Special thanks are extended

to the other members of my doctoral committee, Dr. W. Keith Berg, Dr.

Richard Griggs, Dr. James Whorton, and Dr. Nathan Perry. Their assistance

and practical s-. ]estions were greatly appreciated.

I would also like to thank Kathy Moser for her capable assistance

.ith the ration of tnis manuscript, Regis :l;,/ar for preparing thu

figures, and Patricia ,.ichardson and Tom Harbin for their assistance wi h

the t administration.

Sreover, this effort was made somewhat less difficult by the moral

sj; rt of .. husband, Bill, and the incredible motivating influence J.

my da ugters, ,.'y and Kate.














TABLE OF CONTENTS


Page


ACKNOWLEDGMENTS . . . . . . . . . .

LIST OF TABLES . . . . . . . . . .

LIST OF FIGURES . . . . . . . . . .

ABSTRACT . . . . . . . . . . . .

C'i'PTER I INTRODUCTION . . . . . . .

Birth Order/Family Size Theories. . .
Fluid and Crystallized Intelligence

C ^- TT- II METHOD ,' 3 DATA ,-,;ALYSIS . . . .

Overview and Design . . . . .
Study 1 . . . . . . . .
St'1-v2; 2 . . . . . . . .
S 3 . . . . . . . .

CHAF'-_. III DISCUSSION . . . . . . .

University Student Sample . . . .
High School Sample . . . . .
Ii,:plications . . .
directions for Future Research ..

SUPPLr, _,;,T ., REFERENCE . . . . . . . .

E FrI, -PI S..................

BIG3iAPHICAL SKETCH . . . . . . . . .












LIST OF TABLES


Table Page


1 Means and Standard Deviations for College
Student Sample . . . . . . ... .21

2 Promax Rotated Factor Loadings . . ... .23

3 Multiple Regression of Predictor Variables
on G for College Sample . . . . . 29

4 -,v, and Standard Deviations for High
School Sample . . . . . . . . 41

5 Multiple Regression of Predictor Variables
on G- for the High School Sample ...... .45












LIST OF FIGURES


Page

Figure i Linear Structural Equation Moaels .10

Figure 2 Intercorrelations among Variables in
each model for the College Student
Sample . . . . . . . 36

Figure 3 Intercorrelations among Variables in
each Model for the High School Sample. 43











.'r tract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy



BIRTH ORDER EFFECTS
AND FLUID/CRYSTALLIZED INTELLIGENCE

By

Sandra Johnson Witt

June 1978

Chairman: Walter R. Cunniii.h.min
" 'or rtnent: Ps ', .-lc .'

Th- .-. ,t investi--.tion was an att,_ t to separate out some of

the co. d relationships between the family confi ,i tion correlates

of abili. and achievement performance. In the first study, a battery

of 21 tests was administered to 111 university students. After factor

anal the results for optimal marker variables of the fluid intelligence

(Gf--active problem-solving abili; ) and c tallized intelli .nce (Gc--

ac ieve ent-relate F .,'-rmance) constructs c Cattell and Horn, four

liner structural tion models relati" Gc to various tnesized

Secictor variables were evaluated in the original university sample, and

an additional :- .,le consisting of 65 high school students. The

individual models emphasized the importance of either economic, intellectuall

nviro ent," biological, or socio-psychological influences in the

iedi ion of birth order and/or family size effects on intellectual

abili y -.for rnce. t was t.,. l.;,ized that the models were not mutually

exclusive, but mi .'it be differentially applicable in different -'..,lations.








Certain unexpected correlations obtained in the university student

sample, which might be specific to such a population, rendered

straightforward interpretation of the models difficult. The finding

of a significant positive correlation between family size and

socioeconomic status was in marked contrast to the often reported

negative intercorrelation of these two variables in the general

population. This result was discussed in terms of financial resources

and limitations, which may play a decisive role in college attendance.

The only model supported in this sample was a modified version of the

economic model.

The ability of the models to predict Gc performance in a more

hete-. -..i..ous high school sample was also evaluated. All of the models

were better supported in this sample. However, whereas the overall F

ratio for each of the models was significant, the theoretically

expected rc ;,-:ions for the individual predictor variables on Gc were

significant only for the economic and socio-psychological models. It

was concluded that, not only were these two models able to account for

substantial amounts of variance in the prediction of Gc performance,

but that the separate paths indicated by both models were strongly

supported by the obtained data. After comparing the individual paths

postulated by these two models, it was concluded that the socio-

psychological model was the optimal predictor of Gc performance. This

result was largely attributable to the highly significant regression

for the firstborn (versus later-born) variable on G

With regard to the very different results obtained in the college

student and high school student samples, it is apparent that one must

specify the kind of sample involved when addressing issues related to








family configuration and socioeconomic correlates of intellectual

ability '.

The very positive results obtained for the heterogeneous high

school sample indicate the significance of certain social and

psychological factors as mediators of family configuration

influences--particularly firstborn superiority--on intellectual

achievement. .-,'..over, economic influences seem to exert powerful

effects, both directly and indirectly, on intellectual ability

performance.

It was s': _-ted that the utilization of innovative statistical

techniques and conceptual approaches might serve to more effectively

isolate the extremely :,- ..lex family confi ur .-tion correlates of

intellectual ititude and achievement.


viii













CHAPTER I

INTRODUCTION


,iile the relationships of birth order and family size to

achievement-related variables have been studied extensively, the

conclusions which can be drawn from these research efforts are often

contradictory and, at best, tentative. Problems involved in the study

of such relationships are complicated by the fact that (a) family size

and birth order are highly correlated, (b) personality and

ability/achievement are not independent, and (c) socioeconomic status

(SES) is correlated with both family size and the personality-ability-

achievenent complex (Eysenck & Cookson, 1970).

A consistent finding of studies which have related birth order to

eminence has been a preponderance of firstborns among eminent individuals

(J. M'. attell, 1927; Ellis, 1904; Galton, 1874; Huntington, 1928). In

Ternan's (1' 5) study of gifted children, there were more firstborns than

would nave been expected by chance. Altus (1965a) reports that the

firstborn individual is greatly overrepresented at the college level.

Nichols (1",") presents data on National Merit Scholarship finalists.

Nearly 60% of the finalists who came from families of two, three, four,

and five children were firstborns. Altus (1965b) studies Scholastic

Aptitude Test scores and concluded that firstborns in college may be more

able verbally than later borns; no differences appeared in mathematical

aptitude. Several other studies have found firstborn superiority on







various achievement-related measures (Breland, 1974; Burton, 1968;

Glass, Neulinger, & Brim, 1974; Marjoribanks & Walberg, 1975).

In addition to the greater achievement of the firstborn, there

may exist hierarchies of aptitude among the intellectually able related

to birth order and family size. Zajonc (1976) reported that

intellectual level generally declines with family size and certain age

spacin-: relationships, which are important in terms of the family's

immediate intellectual environment. The dependence of the intellectual

development of each family member on that of all other family members

is emphasized in this theory. However, the relationships between

family size and intellectual performance appear to be attenuated or

may di r in higher SES families. In a study of 11-year-old

children, Cicirelli (1967) found that family size and achievement

were unrelated. It appears that, in middle and upper-middle SES

populations, children from larger families are able to obtain adequate

attention and learning opportunities. Kennett (1973) suggests that

the at. tance of an inverse relationship between family size and

intelli ice must be modified by the influence of SES on the

relationship, and that correlations may vary from social class to

social class.

ile firstborn superiority on achievement-related variables has

been widely reported, consistent patterns relating decreasing intellectual

ability scores to successive birth order position have been less

strai ihtforward. Although several studies ,'-a*:'t hierarchical

relationships (Altus, 1965b; Belmont & Marolla, 1973; Breland, 1974;

Burton, 1968; Nichols, 1964; Wark, Swanson, & Mack, 1974), findings are

often inconsistent. For example, eminence studies (Apperly, 1939;







J. M. Cattell, 1927; Clarke, 1916; Ellis, 1904; Terman, 1925;

Yoder, 1894) report a preponderance of firstborns and lastborns ar-on-

persons of genius. Similarly, studies of birth order among older

children and young adults often demonstrate superiority of firstborn

over later-born children on IQ and achievement, with the middle child

frequently making the poorest score (Cicirelli, 1967; Corliss, 1964;

Hall, 1963; Lees & Stewart, 1957; Maxwell & Pilliner, 1960; Rosenberg

& Sutton-Smith, 1964; Schachter, 1963). Record, McKeown, and Edwards

(1969) report that the strong association between verbal reasoning

scores and birth order in the general population is mainly due to

differences between rather than within families. In upper SES

families the di..'erence between consecutive siblings (0.7 IQ points)

is sial], but in poorer families the difference (2.0 IQ points) is

considerable.

The reasons for these discrepant data are unclear. Marjoribanks,

Wall and Ba .,, (1975) report that birth order effects are rarely

unitary, but often involve other familial aspects, such as the sex of

the sibli., and their differences in age. Cicirelli (1967) found

that such sibli constellation factors were related to measures of

creativity, arithmetic ability, and language achievement in a sixth-

grade sample.


Birth Order/Family Size Theories


There are at least four major points of view concernin- birth order

and family size influences on intelligence. The theories considered in

this investigation emphasize the importance of either economic,

"intellectual environment" (confluence), physiological, or socio-







psychological factors in the determination of birth order and

family size correlates of intellectual ability and achievement.

These models will be discussed individually.


Economic Model


Many investigators contend that birth order and family size

effects are largely reducible to SES and the economic factors involved.

For example, in the past the concept of primogeniture (the exclusive

right of inheritance bestowed on the eldest son) was directly related

to economics. Anastasi (1956) reports a negative correlation of IQ

with family size, which disappears or becomes positive in higher

SES levels. Fisbet and Entwistle (1967) note a negative correlation

of -. ;ly size with intellectual ability in school children, an effect

which is attenuated in upper SES groups. Kennett (1973) found a

si,..ificant inverse relationship between family size and IQ in junior

high school students, except in the upper two SES categories.

Marjoribanks et al. (1975) report that, while children with more

sibli: tend to score lower on verbal and number ability tests, the

relation is attenuated in high SES families. Schooler (1972, 1973)

conLends that birth order effects can be most parsimoniously explained

in terms of differences among SES trends in family size. These studies

sU t a changing relationship between family size and intelligence

in di' erent SES groups.


Zajonc- rkus Confluence Model


In the confluence model (Zajonc, 1976; Zajonc & Markus, 1975),

ordinal differences in intelligence are explained in terms of family




5



size and age spacing between siblings. Zajonc and Markus attempt to

account for the effects of the immediate "intellectual environment"

(represented as a function of the average of the absolute intellectual

levels of the family members) on intellectual development, and to

specify how individual differences emerge in the social context of the

family. For example, if the parents' intellectual levels are 30

arbitrary units each, the birth of the first child, whose intellectual

level is zero, would cause the family intellectual environment to have

an average value of (30 + 30 + 0)/3 = 20. If a second child is born

when the first child's intellectual level reaches 4, the second born

enters an environment of (30 + 30 + 4 + 0)/4 = 16. The importance

of age spacing between siblings could be seen, for example, if the

second child were not born until the firstborn child reaches an

intellectual level of 24. The newborn would enter an environment of

(30 + 30 + 24 + 0)/4 = 21, even more favorable than the environment

of entered into by the firstborn. Negative effects of birth order

can be nullified and even reversed with large enough age gaps between

sibli., s, accordir,- to this model. In addition, it is pro. osed that

only children and last-born children suffer a handicap due to their

lack of opportunity to serve as teachers to younger siblings. Thus,

it is suggested that the intellectual growth of each family member is

dependent on that of all the other members, and that the rate of

growth depends on family configuration.


P:', biological Model


Proponents of this point of view discuss the effects of increased

chance of c~netic error and "uterine fatigue" with later-born children







(Forer, 1976). Warren (1966) indicates that the intrauterine

environment may vary with the mother's age and number of previous preg-

nancies. Such influences would favor earlier-born children. However,

a smaller placenta, longer labor, increased perinatal complications,

and increased use of forceps are often associated with pregnancy and

birth of the firstborn, influences which would favor later-born

children (Weller, 1965). Thus, it is evident that the physiological

model may si'j:'est quite complex effects of family configuration.


Socio-Psychological Model


h.. recent research has supported the importance of socio-

ps.L' ,logical variables related to birth order and family size. Many

studies'indicate personality differences between different birth

orders and with differing family sizes (Eysenck & Cookson, 1970;

Forer, 1976). Bradley (1968) discusses personality factors which

favor firstborns in school-related behaviors:

Firstborns seem to more frequently (a) meet teachers'
expectations and (b) show more susceptibility to social
pressure than later borns. Exhibiting (c) greater
information-seeking behavior and (d) being more sensitive
to tension-producing situations, firstborns may be ju.jT- by
others as (e) serious and (f) low in ag session. These behav-
iors -, (g) strengthen firstborns' achievement motivation
and (h) help to enhance their academic performance. (p. 45)

Start and Start (1974) studied teacher ratings of first-grade children.

Firstborns received higher teacher estimates of conscientiousness and

effort. Glass et al. (1974) propose two related processes to account

for the hi- .r verbal ability and educational aspirations of firstborns.

They su,--st that better-educated parents encourage higher aspirations

and verbal skills in their children, and that this phenomenon is

especially true for earlier-born children:








The later child generates and moves into relationships
uniquely its own--"the principle of unoccupied space"--
specifically different from the role of the "achieving
child", which is already filled in the family. (p. 80)

Burton (1968) found a decreasing progression of IQ with birth

order. However, the.mean difference between firstborn and last born

was approximately 3 IQ points, a difference unlikely to have any

practical effects on the achievement of firstborn relative to that of

last born. He suggests that research on birth order effects should

focus on socio-psychological variables associated with ordinal

position.

In an investigation of family size and sibling spacing effects

(Nuttall, Nuttall, Polit, & Hunter, 1976), there c. -r' ._d a sex-specific

pattern I relationships between family size and academic achievement.

The authors discuss the significant family size effect for boys and

the significant firstborn effect for girls in terms of socio-

psycholo-ical factors. However, because none of the proponents of the

four models emphasized sex differences as a major contributing factor

to family configurational correlates of intelligence, sex was not

included as a predictor variable in these analyses.

a although the reasons for the relationships are not at

present clear, ordinal position at birth appears to be importantly

related to si iificant social factors.

It is therefore evident that birth order and family size influences

on intelligence can be discussed in terms of economic, physiological,

age-spacing and/or socio-psychological variables. It must be emphasized

that these theories are not mutually exclusive. Socioeconomic status

is inextricably related to other variables. For example, influences of

birth order and family size are largely attenuated in higher SES groups.







Prenatal care and diet, as well as family interaction patterns, are

related to social class. In higher SES populations later-born children

and children from larger families do not appear to manifest the handi-

caps generally found in lower SES populations. Thus, it is apparent

that, as one moves up the SES scale, economic, physiological, and age-

gap variables exert less influence and socio-psychological variables

exert relatively more influence on the birth order and family size

correlates of intelligence and achievement.


Fluid and Crystallized Intelligence


In a stuJ of the relationships of Primary Mental Ability Test

scores to birth order, "'rjoribanks et al. (1975) found that verbal and

nu ber 'abilities, but not reasoning and spatial abilities, were

sensitive to ordinal position effects. The authors discuss the results

in terms of the differential influences of the fluid and crystallized

intelli l.L-c, constructs of R. B. Cattell and Horn (Cattell, 1941, 1963,

1967; Horn, 1l;' 1l:C 1970, 1975; Horn & Cattell, 1966, 1967). Horn

(19 ) contends that, during development, a number of factors interact

to ke possible the appropriation of la' : sections of the "collective

intell i ,-... of a culture (represented by crystallized intelligence,

or G ). He c.i',hj;izes the importance of the quality of both school and

home environments in promoting and shaping an individual's crystallized

intelligence. On the other hand, fluid intelliei ice (Gf), or active

problem-solvi ability, is purported to reflect the functioning of the

neurol ical structures and is thus much less influenced by accultura-

tional factors:








A major implication of this theory is that at some order
of analysis in factoring among ability performances two
very broad factors, each having properties of what is
putatively intelligence, should be distinguishable. One
of these factors should be defined primarily by abilities
which can be seen to be quite closely related to intensive
acculturation, whereas the other should be defined primarily
by abilities.which are less closely linked to this. (p. 444)

The greater sensitivity of verbal ability, as compared to active

problem-solving ability, to environmental factors suggests that ordinal

position influences can be best explained in terms of social rather

than physiological factors (Murray, 1971). If Horn's contention is

correct, and if environmental factors within the family are the major

determinant of ordinal position effects on achievement-related

measures, there should be a significant birth order effect on Gc

measures, whereas there should be no significant birth order effect on

Gf measures.

Due to the important socio-psychological factors related to

ordinal position, it was predicted that firstborns would score higher

than later borns on achievement-related variables (Gc). Firstborn

superiority was not predicted for Gf measures, which purportedly reflect

neurophysiological functioning. A large battery of tests, hypotheti-

cally related to Gf, Gc, and other closely related cognitive abilities,

were administered to 111 college students and factor analyzed in order

to select optimal marker variables of fluid and crystallized intelli-

gence. Four linear structural equation models representing the economic,

Zajonc and Markus confluence, physiological, and socio-psychological

points of view are illustrated in Figure 1. In addition to comparing

these models in terms of the overall variance accounted for in the

observed results and the predictive ability of the individual






r- ... ... ] ,........ r .-


ECOji! (C /


1
(-I
| (7,Z J' oC ond Ui S4 R ";1


CO NFI.IU ENCE


On-y-c7ild
Hn! .cs
[ 1


PHiYS OL.OGICAL


Uirth orl //


[I-ii


SOCiO-, SY ECOLOGICAL


-7L~t


Figure 1
Linear Structural Equation Models







hypothesized components of each model, certain assumptions concerning

causal order were examined using path analysis procedures. Although path

analysis should not be considered a method for demonstrating causality, it

can be used to examine a set of causal assumptions imposed on a system of

relationships. Different deterministic relationships between the predictor

variables and achievement-related performance were posited by the four

models. Essential to the assumptions of the models was the order of inclu-

sion of the predictors, which implies the existence of certain relationships

among the variables, including direct and indirect influences on the

criterion variable. Moreover, potentially redundant predictors can only

be identified with later inclusion in the model. Multiple regression

procedures are used to estimate the relative strength of the individual

paths. ,These results indicate the magnitude of direct and indirect

(redundant) influences in terms of the hypothesized causal order among

the variables posited for each individual model.

Since Cattell and Horn (Cattell, 1941, 1963, 1V 7; Horn, 1966, C',,

1970, 1975; Horn & Cattell, 1966, 1967) postulate an important

deterministic relationship between Gf and Gc, the Gf variable was entered

first in the three models which included Gf as a predictor variable. The

order of inclusion of the predictor variables and the underlying assumptions

involved will be discussed separately for each model.

Predictors of G in the economic model were included in the following

order: Gf, family size, and SES. Family size was considered to be

i., C' .nt n G. since no neu-'c physiological correlates of family size

were discussed in this model. Due to the attenuation of family size

influences often reported for higher SES levels (Anastasi, 1956; Kennett,

1973; Marjoribanks et al., 1975; Nisbet & Entwistle, 1967; Schooler, 1972,







1973), SES was considered to influence Gc both directly (economic advantages

and restrictions in terms of educational and occupational opportunities)

and indirectly (through differential influences on the family size

correlates of achievement). SES was therefore entered as the third

variable in the multiple regression analysis for this model.

Very different relationships were suggested by Zajonc and Markus in

the confluence model. The proposed handicap of last-born and only children

was the first variable to enter due to its postulated direct influence on

Gc, independent of the other predictors. For example, Zajonc and Markus

(1975) treated "handicap" as a dummy variable--equal to 0 for last-born

and only children or 1 otherwise--in the regression equation used to describe

the Belmont and Marolla (1973) birth order-IQ results. Since neuro-

physiological influences were not discussed by Zajonc and Markus, Gf was

not included as a predictor variable. The second and third variables to

enter into the equation were sibling age spacing and family size, resrvc-

tively. Alt'h Ji, highly confounded, both variables were considered

important mediators of intellectual ability in this model. Since age

spaci was considered a potentially more important correlate of intellectual

abili' it was included before family size. The SES variable was entered

last due to its complex interrelationships with the other variables

postulated by the confluence model.

The first variable entered into the analysis of the physiological

model was Gf, purported to reflect neurophysiological functioning and

integriL '. Since economic factors were discussed in terms of their role

in the mediation of certain physiological influences (including diet,

i-ir.,tal care, and health), SES was entered next. Birth order--discussed

in terms of increased genetic error, "uterine fatigue," and perinatal








complications (Forer, 1976; Warren, 1966; Weller, 1967)--was postulated

to be only a potentially important influence on neurophysiological

functioning (Gf), and was thus the third variable to be considered in

this multiple regression analysis.

The first variable entered into the socio-psychological model was

Gf, as discussed above. Glass et al. (1974) contend that the higher

verbal abilities and educational aspirations of firstborn children may

be particularly prevalent at higher SES levels. The second and third

predictor variables to enter this equation were firstborn (versus later-

born) and SES, respectively. The SES variable was entered last due to its

potentially i:--ortant mediation of birth order influences and possible

direct influence on achievement-related performance through economic

resources and limitations (Forer, 1976).

Thus, the results of the present study should not only indicate the

predictive ability of these four models in terms of Gc performance, but

the evaluation of the individual components of the models may uncover

subtle interrelationships, both direct and indirect, among the predictor

variables themselves and with regard to their prediction of achievement-

related performance. Moreover, the present results should hopefully

clarify certain issues pertaining to the relationships between ordinal

position and aptitude (theoretically represented by Gf measures) versus

achievement-related measures (theoretically represented by Gc ability).













CHAPTER II


METHOD AND DATA ANALYSIS


Overview and Design

In an effort to evaluate the influence of various family size

variables, birth order variables, and fluid intelligence (Gf) on

crystallized intelligence (Gc), three separate procedures were carried

out. A preliminary factor analysis was performed on a large battery of

tests administered to 111 university students in order to identify optimal

marker variables for Gf and Gc. Following the factor analysis, the most

highly loading tests for each of these two constructs were combined and

utilized as variables in four linear structural equation models relating

G to various hy, othesized determiners, including Gf and several

sibli; I/-i ; ly relationship variables. The models were evaluated separately

for two i : (a) the original college student sample; and (b) a

s..ple consisting of 65 high school students.


Study 1


Subjects. A sample, consisting of 111 university students, including

seven graduate students, was recruited for test administration by classified

advertisement in the campus newspaper. Since this sample was to be

included as part of a larger grant-supported investigation of cognitive








abilities and aging, each subject was paid $10 for participation. The

sample consisted of 57 male and 54 female white, middle and upper-middle

SES students. The age range of the subjects was 17 to 30 years, with a

mean age of 20 years.

Instruments. A battery of 21 tests which were hypothesized to

represent Gf, Gc, and other closely related cognitive factors were

administered to the subjects by the author. The group test

administration was approximately four hours in length, with two 10-

minute rest intervals. The tests, in their order of presentation,

included the Gf-Gc "Sampler," an unpublished battery of tests constructed

by Horn which consists of three tests designed to represent Gf and two

tests designed to represent Gc the Army Alpha Examination (First

Nebraska Edition, Guilford, 1938), four subtests of the Primary

Mental Abilities (PMA) battery (Thurstone & Thurstone, 1949), three

Educational Testing Service (ETS) measures of French, Ekstrom, and

Price (1963), and the Omelet Test from the Structure-of-Intellect

measures ('"cpfner & Guilford, 1969). A description of each measure

follows:

1. Vocabulary (Horn "Sampler"). Subjects are asked to select the word

which is closest in meaning to a given word from five test words. There

are 20 items in this subtest. This test, like all of Horn's test in

the present investigation, was untimed.

2. Analogies (Horn "Sampler"). This test consists of fairly simple

verbal analogies in the form: "A is to B as C is to ." In each of

15 items .the subject is given five test words from which to choose the

appropriate answer. Since the words are relatively simple, perceiving







the relationship between them is theoretically what is evaluated, which

should reflect Gf ability.

3. High-Level Vocabulary Analogies (Horn "Sampler"). This test involves

exactly the same format as Horn's Analogies Test. However, this test

consists of more difficult verbal analogies in the sense that it

contains higher-level vocabulary words designed to involve more advanced

verbal ability, thus reflecting Gc ability.

4. Letter Series (Horn "Sampler"). After examining a sequence of from

5 to 15 letters, the subject is required to write the next letter in the

series in a blank space which follows the sequence (15 items).

5. Fi .:..-l Relations (Horn "Sampler"). Subjects are presented with a

square form with geometric, simple figural, and letter patterns in 3 out

of 4 grants, and, on the basis of the interrelationships of these

fi 'u, must select from several choices the appropriate figure for the

fourth quadrant (20 items).

6. Following Directions (Army Alpha). Subjects must carry out verbally

presented instructions on items consisting of geoi.etric and letter

patterns. Short-term memory ability is required for the rapid execution

of these 12 items.

7. Arithmetic Problems (Army Alpha). This test consists of verbal prob-

lems which require arithmetic manipulations.

8. Common Sense (Army Alpha). This test involves questions designed to

evaluate practical judgment and reading comprehension.

9. .. -Ant.. ., (Army Alpha). Subjects must designate whether 40

sets of word pairs are the same or opposite in meaning.

10. Disarranged Sentences (Army Alpha). After mentally re-arranging

a set of words in order to make a sentence, subjects are instructed to







indicate whether each of the resulting 24 sentences is true or false.

11. Number Series Completion (Army Alpha). Subjects must examine the

relationship which exists within a series of numbers and indicate what

two numbers should logically follow (for each of 20 number series).

12. Analogies (Army Alpha). After determining the relationship between

a pair of words, subjects must select one of a group of four words which

is related to a second word in an analogous manner.

13. Information (Army Alpha). Subjects must answer 40 multiple-choice

items which involve general information.

14. Word Grouping Test (PMA). In this test the ability to understand

and group words of the same type is involved. Subjects must select

which of five presented words does not belong with the other words.

Each of.the PMA subtests is timed.

15. Letter Series Test (PMA). This test consists of 20 letter series,

each of which has a specific pattern. The task is to select which of

five letters should logically follow for each individual letter sequence.

16. Spatial Relations Test (PMA). Subjects are presented with 30 geo-

metric forms, to the right of which are five similar or identical forms

which have been rotated to different positions. The task is to identify

which of these forms are identical to the original form.

17. Number Series Test (PMA). This test consists of 20 multiple-choice

items in which the task is to select the number which should come next

in a distinctive pattern of numbers.

18. Addition Test, N-l (ETS). This two-part test assesses how quickly

and accurately subjects can add series of numbers. The time limit is

two minutes for each 60-problem part.







19. ;:.,n;er Comparison Test, P-2 (ETS). This is a highly speeded test

consisting of two parts. The task is to compare two series of numbers

(the pairs of number sequences are of different lengths) and indicate

whether or not they are the same by inserting an X between the number

series that are not identical.

20. Identical Pictures Test, P-3 (ETS). This is a two-part test in

which subjects must select from five alternative pictures the one that

matches a target picture.

21. Omelet Test (Hoepfner & Guilford, 1969). In this test the task

is to rearranr,_ sets of four letters each to make familiar words. This

test was constructed to measure Guilford's Structure-of-Intellect factor

of convergent production, defined as the "generation of logical conclusions

from given information where emphasis is upon achieving unique or

conventionally best outcomes" (Guilford & Hoepfner, 1971, p. 20).

Variables. The variables included for analysis were determined by

the assumptions of the four models, which are illustrated in Figure 1.

The G construct was the criterion variable for each model. The predictor

variables included Gf, SES, birth order, family size, firstborn (as opposed

to later-born siblings), age spacing between siblings, and the "handicap"

of the only child and last-born child (Zajonc & Markus, 1975). The SES

scale utilized in this study was a composite of three separate standardized

variables: (a) prestirn value of father's occupation (Treiman, 1977);

(b) father's educational level; and (c) mother's educational level.

Educational level was rated along a 6-point scale, from "not a high

school graduate" to "doctorate." The number of children in the

families in this sample ranged front 1 to 13, with a mean family size of

3.432. There were 40 firstborns (including four only children).







Birtn order ranged from 1 to 11 (mean = 2.234). In order to normalize

the extremely skewed birth order distribution, birth order was divided

into three categories before any further analysis took place. These

categories were: (a) firstborn (n = 40); (b) second- or third-born

(n = 56); and (c) fourth- or later-born (n = 15). The adjusted percent-

ages of the total sample for these three categories were 36%, 50"', and

14%, respectively. The sample included 36 middle children and 35

lastborn children. Due to certain assumptions specific to the different

models, two dichotomizations of birth order frequencies were calculated.

The firstborn (versus later-born) construct was a predictor variable

in the socio-psychological model. The "last-born/only-child handicap"

was included as a variable in the Zajonc and Markus confluence model.

This category included 39 subjects. In addition, this model proposed

that spacii.' between siblings would be an important correlate of

intellectual development. Age spacing was calculated as the sum of two

separate indices, the number of years to the next oldest sibling and the

number of years to the next youngest sibling. Since this model proposed

that an arbitrary maximum intellectual level of 100 would be attained at

approximately 19 *errs of age, 19 years was used as the maximum age

separation index. For example, the age spacing index for an only child

would be (19 + 19) = 38. This somewhat crude index of age spacing

between siblings was utilized die to the extreme difficulty encountered

when attempting to calculate each individual subject's intellectual

level according to the sigmoid function presented by Zajonc and Markus

(1975). This equation was analyzed in terms of average age gaps, not

individual age separation data. Other approaches to this age gap index







have included mean age separation between siblings (McCutcheon, 1977)

and median number of months separating the siblings in the entire

family (Nuttall et al., 1976).

The means and standard deviations of the variables for the college

student sample are listed in Table 1.

Factor Analysis. A 21-by-21 variable correlation matrix was

computed from the scores of the 111 subjects on the 21 measures. The

factor analysis was carried out with the Exploratory Factor Analysis

Program (EFAP), devised by Joreskog and Sorbom (1976a). Rotation was by

the Promax method, utilizing the Varimax orthogonal rotation procedure

as the basis for the subsequent oblique solution.

Kimum likelihood procedures (Joreskog & Sorbom, 1976b) of factor

extraction were utilized to obtain 4- through 10-factor solutions. The

most easily interpretable solution appeared to be the 4-factor solution.

The factor loadings and factor correlation matrix are found in Table 2.

The first factor seemed to be a numerical facility factor which had

substantial loadings on the two number series tests, the Addition Test,

Arithmetic Problems, and also the Omelet Test. Inclusion of the Omelet

Test su--e.,ts the possibility that convergent production may be

involved in the first factor. Factor II appeared to be a verbal ability

factor, hypothetically representing Gc. Tests which had interpretable

loadi : on this factor included Vocabulary, High-Level Vocabulary

.r.,,l ies, Syn.' .- -Antonym, Information, Common Sense, Disarranged

Sentences, Com on ,.~,logies, and the 'Word Grouping Test. The loading

of Horn's Common Analogies subtest on this factor was somewhat

surprising since this measure was constructed as a marker of Gf ability.

However, the low correlations of this test with other nonverbal measures








Table 1

Neans and Standard Deviations for College Student Sample


Mean Standard Deviation


Age 20.2160 1.9970

Family Size 3.4324 1.6713

Birth Order 2.2340 1.4270

Mother's Education 2.8200 1.1380

Father's Education 3.3578 1.3846

Occupational Prestige 50.4364 13.1961

Age-Spacing Index 17.9550 8.8360

Vocabularya 10.8830 2.0960

Vocabulary .ilogiesa 10.6220 2.2360

Common Analogiesa 11.6577 1.8360

Letter Seriesa 12.4230 1.8660

Figural -lationsa 14.6130 2.9300

Follow, Directionsb 9.4050 1.7960

Arithmetic Problemsb 11.9910 2.8170

Common Senseb 11.5500 2.4600

Synonym-Antony.,b. 30.8010 5.1610

Disarr.in ed Sentencesb 20.2700 2.6250

Number Seriesb 14.6670 3.3070

Analogiesb 34.5860 4.6720

Information 29.3330 3.2680















Table 1 continued


Mean Standard Deviation



Word Groupingc 23.3330 3.1400

Letter Seriesc 15.1530 2.7640

Spatial Relationsc 120.4230 13.1670

Number Seriesc 11.5860 2.8870

Addition 43.1980 13.2110

Number Comparisond 26.1350 5.9980

Identical Picturesd 79.4180 11.4440

Omelet Teste 21.5590 4.8570





,irn "Sampler"
uArmy Alpha Examination
cPrimary Mental Abilities Test
dEducational Testing Services
eSheridan Psychological Services











Table 2

Promax Rotated Factor Loadings


Numerical G Speed G
Facility c f

1 2 3 4


Common Analogiesa 0.006 0.465 -0.132 0.178

Analogiesb -0.085 0.693 0.133 0.119

Word Groupingc 0.021 0.701 -0.179 0.175

Letter Seriesa 0.160 0.115 -0.046 0.565

Letter Seriesc 0.157 0.043 0.118 0.630

Figural Relationsa 0.063 0.008 -0.019 0.530

Spatial Relationsc -0.031 -0.135 0.058 0.716

Number Seriesb 0.730 -0.109 -0.002 0.357

Number Seriesc 0.741 -0.013 0.001 0.138

Following Directionsb 0.074 0.230 0.067 0.312

Vocabularya -0.263 0.792 0.026 0.076

Vocabulary Analogiesa 0.071 0.663 -0.091 0.022

Synonym-Antonymb -0.053 0.734 0.268 -0.239

b
Informationb 0.150 0.779 -0.213 -0.068

Arithmetic Problemsb 0.387 0.209 -0.018 0.300

Additiond 0.498 -0.092 0.504 0.055

Number Comparisond 0.065 -0.057 0.756 0.088












continued


Numerical
Facility c Speed Gf

1 2 3 4


Identical Pictures -0.122 0.094 0.287 0.290

Common Senseb 0.020 0.648 0.225 -0.076

Disarranged Sentences 0.137 0.470 0.074 -0.022

Omelet Teste 0.448 0.132 0.227 -0.055


Factor Correlations


1.000

0.413

0.325

0.453


1.000

0.273

0.448


1.000

0.263


1 .000


..,rn "Sampler"
A,-i. Alpha Examination
CPrimary Mental Abilities Test
educational Testing Services
hheridan ;ychological Services


~------- ---~-'~


Table 2 -








may have been a result of the restriction of range in this particular

sample. It would be informative to analyze the intercorrelations of these

tests in a more heterogeneous sample. Factor III was a highly speeded

factor, which had its highest loading on the Number Comparison Test.

Factor IV, hypothesized to represent Gf, had interpretable loadings on

the two letter series tests, Figural Relations, the Spatial Relations Test,

,Number Series Completion, and the Following Directions Test. Factor

interrelationships were moderately oblique ranging from .263 to .453.

After interpreting the factor loading matrix, it was decided that

the five best marker variables of the Gf construct were: (a) the PMA

Letter Series Test; (b) the PMA Spatial Relations Test; (c) the Army Alpha

Number Series Completion Test; (d) Horn's Letter Series subtest; and (e)

Horn's Fi'gural Relations subtest. Since the two letter series tests were

measures of the same ability, it was decided to combine them as a single

measure in the Gf composite. The measures selected to represent Gc were:

(a) the PMA Word Grouping Test; (b) the Army Alpha Synonym-Antonym subtest;

(c) the .nrmn Alpha Information subtest; (d) Horn's Vocabulary subtest; and

(e) Horn's High-Level Vocabulary Analogies subtest. The marker variables

for both composites were individually standardized before being added

together. As has been found in the past (Horn, 1970, 1975) and is expected

theoretically, the Gf and Gc composite scores were substantially positively

intercorrelated, r = + .3674, p <.01.


Study 2


Subjects. The sample consisted of the same 111 university students

examined in the previous study.






Method. A multiple regression procedure was employed to examine

the results in terms of both the overall variance accounted for in the

observed data and the hypothesized causal orderings for the individual

components for each of the models described in Chapter 1. Multiple

regression was the method of choice since it is a statistical technique

which permits analysis of the relationship between a criterion variable

and a set of predictor variables. A hierarchical specified inclusion

method was used. The criterion variable for each model was Gc. This

procedure permits the addition of variables to the regression equation

in a predetermined order. The estimation of the relative strengths of

the individual paths indicates the magnitude of direct and indirect

influences in terms of the hypothesized causal order posited for each

model. The order of inclusion of the predictor variables was determined

a priori by the author with regard to the hypothesized influences of

family configuration variables and SES on intellectual aptitude and

achievement as specified by the individual models. In the economic

model, such influences are discussed in terms of SES and the related

economic advantages and restrictions. Physiological mediators of birth

order differences in cognitive ability are considered in the physio-

logical model. In the confluence model, ordinal differences in intel-

lectual ability are discussed in terms of family size and age spacing

between siblings. The relationship of social and psychological influences

and birth order differences are emphasized in the socio-psychological

model. These models, which are illustrated in Figure 1, are presented

in more detail in Chapter 1.

In the hierarchical decomposition procedure, each predictor variable

is added to the equation in a single step, and the increment in R2, or the








explained variance, at each step is considered to be the component of

variation attributable to that particular variable, given the contri-

bution of the other variables to the equation. The first predictor is

evaluated by the F ratio:

r 1/1
F= --
(1 R2-12,....,k)/(N k 1)
-y

.where r 1 is the incremental sum of squares due to the independent
-y
variable X., 1 R2-12,....,k is the sum of squares of the residuals,
S -y
N is the sample size, and k is the number of independent or predictor

variables. Each successive predictor variable is tested by an F ratio

with 1 and (N k 1) degrees of freedom. This procedure indicates

the total influence of each successive variable since it refers to

adjust 6nts only for those variables that precede each given variable

in a predetermined order of inclusion.

The F ratios were calculated for each model and for the inclusion of

each predictor variable within each model. In addition, the models were

compared to each other for overall variance accounted for in terms of

tne obtained data. The computer methodology used to evaluate these

results was outlined in the Statistical Package for the Social Sciences

(SPSS), edited by Nie, Hull, Jenkins, Steinbrenner, and Bent (1975).

Results. Since Gc ability may be much more sensitive to accultura-

tional and environmental influences than Gf ability, which purportedly

reflects neu:-rp', biological functioning, it was hypothesized that there

would be a si nificant birth order influence on the Gc construct, but not

on the Gf construct. As predicted, while the correlation between birth

order and Gc was significant (r = -.1633, p_<.05), the correlation







between birth order and Gf was not significant (r = .0710). Since no

significant birth order patterns were found for the Gf construct, it

appears that birth order differences are not mediated by neurophysio-

logical factors in the present sample.

However, the prediction that, due to certain socio-psychological

influences related to ordinal position, particularly in the restricted

SES range of the present sample, that firstborns would obtain higher

scores than later borns on Gc but not on Gf measures was not upheld.

While the correlations of the firstborn (versus later-born) variable

on the Gc and Gf constructs were in the predicted direction

(r = .1398 and r = .0393, respectively), neither correlation was

statistically significant.

.iults of the multiple regression analyses are listed in Table 3.

Althiu .j three of the models--economic, physiological, and socio-

psychological--obtained significant overall F ratios (p<.01), it is

evident that these results were attributable to the highly significant

F ratio obtained in the first step in the equations, i.e., the regression

of Gf on Gc. lone of the other predicted paths between the predictor

variables and Gc were significant for any of the models. Regarding the

Zajonc and M.arkus confluence model, neither the overall F ratio nor any

of the predicted paths were significant. The overall variance explained

by the economic, confluence, physiological, and socio-psychological

Sodels was i roximately 1 C, C 17', and 17 respectively. Thus,

alti the economic model appeared to be the most predictive model in

terms of variance accounted for, the explained variance was not large

in absolute terms.




































Q)

CL
E
n3
CD
V)





C)



4-
o
U)



(a




-o




S0

0


r.-


O







C--
re


LL.I








co







sCI




E

o)




c-










Q;


co
Cn


[^


Cr-


O
c-)

0

L)
w


CD

O



C---
C')


Ln
0^





U3

(^


V)
N
U-




E o n
4- ro L W
C Ll. V)


- CMj C)


m

O
CO








-')


C\J

c"j


Ln





I





1-

C)


()
N



S co >,


c" c) E


LC

-0
LO
















CO



Ln







C)































V)

U)
00





































COD
07










LO

CO
r--.
Lo






























r-
m--
m-




p--


s-
CL
-o



Q^ -
c-
So




4- LU *
CL0 VC Co


C-- J M0


uI-

















cy








0)
0)




0-

(0
cl


00





































0
-r~







































































0




-n




Q-r
0



L/1


5-

O4




_0 U0 0
4- C-J C'
n n
L- - n


%0
O
E



C r-
4- UZ
ou-
OU
-LL
S-

-- r-



> .C





r-3 C







C
0) c
4-3 0
(0 r-
U U)
*r- 3




0 )-
cO u



E r-r



c l


r1 0
S-

E


























c O
S-
*r- 0


4-




-- 4,-





























oum
U -0







3CO
ID -


3- S-


nO



QUU

4-j



CL) )

s-a
S .-


0 CD
SCM
5- *c-







co _0
> 0 *

Ll ) *r-

L Cn


SC LO -








Q) -


CO













LO
m-




Lo
Cd
ta


-U-
Co


r-

O




r-,

CL


r-
0)







Therefore, upon initial investigation, it appeared that none of

the four models could effectively account for Gc in the present sample.

The possibility was considered that these negative results were largely

attributable to the restriction in range of the present university

student population. Upon closer analysis, however, certain unexpected

relationships within this particular sample were uncovered. It became

apparent that, besides restriction in range, certain relationships

related to SES and family size which might be specific to a college

sample were statistically suppressing certain predicted family configur-

ational influences. Specifically, these relationships appear to have

suppressed the correlations between several of the predictor variables

and the criterion variable G It was decided to use partial correla-

tion procedures to separate out some of these relationships. Since

partial correlation statistically controls for the effects of one or

more specified variables on the relationship between other variables,

this technique can be utilized to uncover suppressor relationships. The

general form of suppressor influences can be described as: A shows no

relationship to B because A is negatively related to C which is positively

related to B. Thus, when the effects of C are controlled, A may be

positively related to B. Further assumptions and issues concerning such

suppressor relationships will be discussed in further detail after dis-

cussion of the unexpected SES-family size relationships found in the

college student sample.

SES-family size relationships which may be specific to university

student samples. Several unexpected relationships among the predictor

variables in the present sample may have suppressed their relationship

to G These results rendered straightforward interpretation of







the models difficult. It is conceivable that certain possibly artifact-

ual relationships may be specific to college student samples. Such

relationships may complicate attempts to generalize from more hetero-

geneous populations regarding family configurational influences. Schooler's

(1973) contention that birth order effects do not appear to persist into

adulthood may be particularly apparent in a college sample. Firstborn

overrepresentation appears to be the stablest finding reported in

studies of university students (Altus, 1966; Capra & Dittes, 1962;

Schachter, 1963). The finding that 36% of the current sample were

firstborns was slightly, although not significantly, higher

(Z = 1.''".3, p(<.10) than the expected 30 firstborn representation

for this particular age group (Dunn, 1956).

r -- ..... i- effect Kennett (1973) discusses the changing rela-

tionship between family size influences on intellectual ability. He

suggests that the acceptance of a negative correlation between family

size and intelligence must be qualified by the possibility that SES may

influence the relationship. Thus, the correlations may vary from social

class to social class. In this r-c 'rd, perhaps the most surprising find-

ing in the -e- nt college sample was the significant positive corre-

lation between family size and SES (r = +.1972, p <.02). This finding

was in marked contrast to the widely reported negative correlation

between these two variables in the general population (Belmont & Marolla,

1973; Eysenck & Cookson, 1970; Murray, 1971; Nisbet & Entwistle, 1967;

Record et al., 1`69). The observation that students in this sample who

belc,., to larger families also tend to be of a higher SES level suggests

that financial resources and related issues may play an important and

perhaps decisive role in the opportunity to attend college. This posi-







tive relationship between family size and SES might be expected to be

even more substantial in a private, more expensive institution.

Birth order effects. Family size and birth order are highly con-

founded variables, as evidenced by their substantial intercorrelation

of +.4775 (p_, .001). While the economic model and the confluence model

discuss family configurational influences primarily in terms of family

size effects, the physiological model posits such effects in terms of

sibling birth order while the socio-psychological model emphasizes the

influence of firstborn versus later-born on G c

Family size and, therefore, birth order are negatively correlated

with SES in the general population. However, in the college student

sample who participated in this study, there existed a positive although

nonsignificant correlation between these two variables (r = +.1200, p< .10).

Despite the possibility that this positive intercorrelation may have

suppressed to some extent the negative correlation between birth order

and Gc, as was predicted by the physiological model, the obtained corre-

lation between birth order and G was nevertheless significant

(r = -.1633, p <.05).


Statistical Su ,pression


Cohen and Cohen (1975) discuss the effects of three types of

suppressor relationship--classical, net, and cooperative suppression--

on the patterns of association between the predictor variables. In the

present sample, the positive correlation between family size and SES

along with the resulting effects on the criterion variable, Gc is an

example of cooperative suppression. Cohen and Cohen describe cooperative

suppression as the case in which the intercorrelation of two independent







variables involves a portion of their variance which is irrelevant to

the criterion variable. The partialling of these predictor variables

from the others enhances all indices of relationship with the criterion

variable. It is a completely symmetrical phenonemon; whenever it is

determined that X2 suppresses X it is also evident that X suppresses

X2. One indication of cooperative suppression is a situation in which
a beta coefficient exceeds its simple r with the dependent variable and

is of the same sign. With reference to Table 3, it can be seen that

beta coefficients for both SES and family size in the economic model

meet this criterion.

Analysis of suppression effects was performed to ascertain what,

if any, relationships might exist in the present sample which could have

resulted in the suppression of predicted family confirJration effects on

Gc. In order to identify the nature of the specific suppressor relation-

ships between the predictor and criterion variables, the partial corre-

lation coefficients were calculated for several predictor variables and

Gc, with the potential suppressor effects of the predictor variables

statistically controlled. The obtained multiple regression and partial

correlation coefficients are found in Figure 2. Results for the partial

correlation procedure will be discussed separately for each individual

model.

Economic model. With reference to the economic model, it was evi-

dent that the unexpected positive correlation between family size and

SES i -ressed the predicted significant correlations of both family

size and SES with the Gc construct. When the effect of family size was

partialled from the SES-Gc relationship, the correlation between these

tw.o variables was significant (+.1683, p <.05). Similarly, when the
























Figure 2

Intercorrelations among Variables in each Model
for the College Student Sample.
Numbers in parentheses indicate partial correlations between
the independent and criterion variables.
Significance levels: <.05, **p<.01.









Sj S.!3)0 >- [.,
E C
[-. C ", I C; 91 -. 7-;8 .3674*;
(-.1794)





/ L I. !

.3972 .13. 0
CO( FLULrC : I C"



-.5557"* ..,47

IL 2





PIiSOlGQ YCHOLAL -. 071OGCL .1 ) .7



|3 .1340




G-3-- -






--- 0.- --- 1 i ( rs o
.1340 ..] L







suppressor effects of the significant positive correlation between

family size and SES were controlled, a modified version of the economic

model was supported by the present results. The hypothesized relation-

ships between the predictor variables and the criterion variable were

significant when the suppressor effects were controlled. This model

was termed "modified" because of the significant positive, not nega-

tive, correlation between family size and SES. In a more heterogen-

eous sample, it would appear likely that the unmodified version of the

economic model would be supported due to the generally reported nega-

tive correlation between family size and SES (Belmont & Marolla, 1973;

Eysenck & Cookson, 1970; Murray, 1971; Nisbet & Entwistle, 1967;

Record et al., 1969).

Zajonc-Markus confluence model. Since the overall F ratio was

non-significant, it was considered inappropriate to further analyze

specific predicted relationships of this model. It is quite possible

that the unimpressive predictive power of this model may have been

attributable to the restricted range of the present sample and/or the

unexpected relationships which may be specific to a college student

population. Such unexpected trends might suppress the relationships

between SES and age spacing between siblings and, in turn, between age

spacing and G Similarly, in a study of ability of 291 community

college students, as measured by Factor B of the 16 PF, McCutcheon

(1977) found little support for the confluence model. She concluded

that this model may have limited application. The overall validity of

this model might be expected to be substantially higher in a more heter-

ogeneous sample.







Physiological model. The generally observed negative correlation

between birth order and SES, an important component of this model, was

not obtained in the college student sample. However, due to this

model's predictions that birth order and SES influences would be mediated

through Gf, which reflects neurophysiological functioning, further

analyses for suppressor relationships were precluded. Since Gf ability

was for all practical purposes unrelated to both birth order

(r = .0710) and SES (r = .0404), any potential suppression effects

among these variables would have been statistically trivial in this par-

ticular case.

Socio-psychological model. The possibility of cooperative suppres-

sion between the firstborn construct and SES was considered. However,

when t1e relationship between the firstborn predictor variable and Gc was

par-ialled for SES effects, the resulting firstborn-Gc intercorrelation

(r = +.1423, L1.05) failed to reach significance. Similarly, although

the partial lig of the firstborn construct from the SES-Gc correlation

sli i.tly i --roved the predictability of G from SES, the correlation

(r = +.1366, p>.05) was still nonsignificant.

Although the overall F ratios for three of the four models were

significant, when the individual models were analyzed in terms of their

separate paths, they were not supported. However, when the existence

of certain statistical suppressor effects was recognized and these effects

were controlled by partial correlational techniques, a modified version

of the economic model was supported.








Study 3


Subjects. Due to certain relationships found in the previous

analyses which appeared to be specific to a university student popula-

tion, a more heterogeneous sample was utilized in the attempt to evaluate

family configurational and SES correlates of the Gc construct. This

sample consisted of 65 twelfth grade students from the P. K. Yonge

Laboratory School of the University of Florida. The student population

of this school is stratified so as to be demographically representative

of the general population of the state of Florida in terms of economic

level and race. This tested sample consisted of 55 white and 10 black

students, whose age ranged from 17 to 18 years (mean age = 17.37 years).

There were 35 females, 30 males, including 14 firstborns, 2 only child-

ren, and 25 last-born children.

Instruments. Six tests were administered to these subjects. The

group administration was approximately one hour in length. The tests

included three markers of Gf (PMA Spatial Relations, PMA Letter Series,

and the Army Alpha Number Series subtests) and three markers of Gc

(Horn's Vocabulary subtest, Horn's High-Level Vocabulary Analogies sub-

test, and the ETS Wide-Range Vocabulary Test). Although a somewhat

abbreviated battery was utilized in the third study, the tests were

optimal markers of Gf and Gc ability in the initial factor analysis.

They were thus considered equivalent theoretically and practically to

the longer battery administered to the college student sample. The ETS

Wide-Range Vocabulary Test was included because of its stabilized relia-

bility and its particular appropriateness for a high school level

sample.







Variables. As in Procedure 2, the variables analyzed were

determined a priori by the assumptions of the individual models found

in Figure 1 and described in Chapter 1. The variables were fairly

normally distributed, with the exception of father's educational level

(fathers of 25 subjects had obtained doctorates) and the occupational

prestige index (Treiman, 1977). Both variables were somewhat skewed,

with relatively more subjects at the higher levels. These two variables,

along with mother's educational level, were standardized before they

added together for the SES index. Similarly, the individual tests were

standardized before they were summed for the Gf and Gc composites.

Family size in this sample ranged from 1 to 10, with a mean family

size of 3.6 children. Birth order also ranged from 1 to 10 (mean =

2.4?1): The age spacing index for the confluence model was calculated

in the same manner as in Study 2, that is, the sum of the number of years

to the next oldest sibling and the number of years to the next youngest

sibling. Also, the two birth order dichotomizations, firstborn (versus

later-born) and the "last-born/only child handicap," were included as

predictor variables exactly as they were for the college student sample.

The means and standard deviations of the variables studied in the

high school sample are listed in Table 4. The intercorrelations between

the variables for the individual models are found in Figure 3.

method. As in Procedure 2, multiple regression procedures were used

to examine the amount of variance accounted for in terms of the observed

data and the direct and indirect influences of the hypothesized paths

in terms of Gc with regard to the economic, physiological, Zajonc and

Markus confluence, and socio-psychological models. Since the












Table 4

Means and Standard Deviations for High School Sample


Mean Standard Deviation



Age 17.3692 0.4862

Family Size 3.6000 1.6845

Birth Order 2.4963 1.5220

Mother's Education 3.6615 1.3610

Father's Education 4.2308 1.7657

Occupational Prestige 58.0000 17.5944

Age-Spacing Index 15.9219 8.5027

Vocabularya 10.3077 2.1645

Vocabulary Analogiesa 10.3231 2.3326

Wide-F.Pie, Vocabularyb 14.1231 3.3237

Letter Seriesc 13.6462 3.4705

Spatial Relationsc 121.0615 13.6516

Number Seriesd 12.3538 2.9709


aHorn "Sampler"
Educational Testing Services
cPrimary Mental Abilities Test
dArmy Alpha Examination






















Figure 3

Intercorrelations among Variables in each Model
for the High School Student Sample.
Significance levels: *p <.05, ** <.01.








I ..'': ... . rV !:,C ..... . C .
S1/.

. ,0C -.413 -.210 .5 9

vI..


( Z"JO .C ,c: ..,A,.i,KUS) /



SI1
*,r i Cap <*


P- 1-!Y 10 -0 GICAL525

-A I
0 1 S.27CIC, ) 1C ., 13 C|3
-v. *y \ 1 ---"- -
-5C92 .1 75







PH YSI LOGICAL .O~,6 ,)

[nirth orc.cL .3380- G:, ]
.6136
--- /







S 0 C I O- S Y C" L O --, 1 ( .L ,136 30 o
L/ii


..S .2cl --- I -orn







assumptions of the individual models remained the same, the hierarchical

specified inclusion method with G as the criterion variable which was

described in Study 2 was used. F ratios were calculated for each

model and for the successive inclusion of each predictor variable within

each model. The individual models were also compared to each other for

the overall variance accounted for in terms of the obtained data.

Results. The results of the multiple regression analyses are

listed in Table 5. It is apparent that all of these models yielded

better predictions of Gc performance in the more heterogeneous high

school sample examined in this procedure. Not only were the overall

F ratios for all of the models significant (p <.01), but the overall

variance explained by the economic, physiological, Zajonc-Markus, and

soci( ychol- ical models was Epr: -,.imately 51", 52';, 40%, and 54",

res-_. ively. However, while the variance accounted for by each model

was substantial, the regressions of all of the individual predictor

variables on Gc were significant only for the economic and socio-

psychological models. Results for the multiple regression analyses

will be discussed separately for each individual model.

Economic model. In contrast to the unexpected positive correlation

between family size and SES (r = +.1972, p_<.02) found in the more homo-

geneous colle-- sample, the predicted negative correlation between these

two variables was found for the high school population (r = -.2104, p_<.05).

This .. ,tive correlation has been widely reported in other studies

(Beli-;ont & Marolla, 1973; Eysenck & Cookson, 1970; Murray, 1971; Nisbet

& Entwistle, 1-.7; Record et al., 1969) and is an essential component of

the economic model, in which it is proposed that birth order and family

size influences are largely reducible to SES and the economic factors

























-Y -Y
S LMn mL .D 0 m c\j .D h- LO

Q 0 CO I- Lr ) 't oz o
S[ C m)D L) Cj m C D G




o

U -.- C0 O CD (\ c 'O

C/ C) O d) 1 1 O O,




C
*r--


c- C 0 0m L- L C 0n D








O O
C LO LLO C C -


D O ,-
/ I
.r-




D C CJ Or r- CD CC OM



S. o 04 00 r- r O0 o co ( -. -
c-- i- M 0 LO Oh LO LO
CD LO C CD LO r-\- O'-



C 4-- m 0) LO LfCD O ) C m
C- CD CD- CD CD0









i---
S- U











C) 0 (A C/ .


4- o o C oo -) O o "ow"
S. -- C0 i 0 0 0 .
O I
I( I -m l l l ^1 r- r



(D


i! I













0 0 0
O U 0 >,










0 I































0h









(1
r-










O1

C\r














C)
0,
cli



Cl

^j.















oa


SL
C)


-0

4-)

-- I .-



- c\j n


rne













*r
CD)
e-I-



0

U


0.




o0
(3


0,


If




C\J


o


0


4-)
V1

CD- V-)
4- r LU


LL-








4-'
C)




0



C-I





of
-,






CNJ2
cn
E:
n3
rc
<_>

C>
0;


4-)
c-
0




In

-)



H-


O
0






0 Q.

,--




C) C)

> -f
0 U









04-)


(D 0
4-L 0








C )




r --)
O)
















40--
S0)












C-

5-
o- C)
.r-












C) -
c0 S-


0)



S.-c : )
1/)








Ca -



( 0



UL


r- .3
.- C"











0)
-"4-



C .
05-






r- 0)











O 0
c









a tI

L C
0, v


- e C)







0 -o
0) r

.-
L)C


C.j
= I
rz~l


4--
Sm


0
4-'



r-



0








involved (Anastasi, 1956; Kennett, 1973; Schooler, 1972, 1973).

Moreover, the predicted significant increments to the explained sum

squares attributed to each successive predictor variable (Gf, family

size, and SES) in the hierarchical decomposition procedure were statis-

tically supported. It was concluded that, in addition to the ability

to account for a very substantial amount of variance in the obtained

data, an unmodified version of the economic model was fully supported

in the more heterogeneous high school sample.

Zajonc-Markus confluence model. In contrast to the findings with

regard to this model (Zajonc, 1976; Zajonc & Markus, 1975) in the college

student sample, the overall significant F ratio for the high school sample

permitted the further analysis of the individual predicted relationships

of the confluence model. After examining these results, however, it

became evident that, while the age-spacing index, family size, and SES

were adequate predictors of the criterion variable, the "handicap" of

lastborn and only children (ostensibly due to their lack of opportunity to

serve as teachers to younger siblings) was not an adequate predictor of

Gc performance. Therefore, the confluence model was not fully supported

by the obtained data.

Physiological model. In the physiological model (Warren, 1966; Weller,

1965), emphasis is placed on physiological mediators of birth order

differences in cognitive ability, such as mother's age, length of labor,

and perinatal complications. Again, an overall F ratio and a substantial

amount of the variance in Gc performance accounted for permitted

further analysis of the individual components of this model. The first

two predictor variables in the multiple regression equation, Gf and SES,

were significantly predictive of G scores. However, birth order, whose
c^







correlation of -.1752 (p >.05) with Gc failed to attain significance in

this sample, also failed to add significantly to the explained sum of

squares for the physiological model. Thus, it was concluded that this

model was not supported by the data.

Socio-psychological model. Proponents of this model (Bradley,

1968; Burton, 1968; Glass et al., 1974; Murray, 1971; Nuttall et al.,

1976; Start & Start, 1974) discuss family configuration influences in

terms of significant social and psychological factors. The variance

accounted for in terms of the data from the high school sample was 54

for this model, the most substantial of all the models. Also, after

analyzing the individual paths, it was evident that the predictor

variables, Gf, firstborn (versus later-born), and SES, were each sig-

nificant predictors of the criterion variable, in terms of both simple

correlations and individual paths.

Summary. After analyzing the economic, confluence, physiological,

and socio-psy,'. .logical models in terms of both total variance accounted

for and in terms of the individual paths, the models which were fully

supported were the economic and socio-psychological models, which

explained 51. and 54 of the variance, respectively. Although both

models were supported, the question as to which model was the optimal

predictor of Gc performance in the heterogeneous high school sample had

to be considered. The overall F ratios for both models were highly

significant (- <.01). Also, highly significant F ratios (p <.01) were

obtained for both Gf and SES, predictor variables in both models. How-

ever, when family size (the second variable to enter the regression

equation of the economic model) was compared to firstborn (the second








variable of the socio-psychological model), it was found that the F

ratio for the firstborn variable was 12.2787 (df = 1/62, p<.01), more

significant than the F ratio for family size (F = 6.4335, df = 1/62,

p_<.05). Thus, when the F ratios for the individual hypothesized paths

were evaluated, it was evident that the predictor variables of the socio-

psychological model were more strongly related to Gc than were the

individual components of the economic model. It was concluded that--

although both the economic and socio-psychological models were fully

supported by the data--the socio-psychological model was the optimal

predictor of achievement-related performance in this sample, in terms of

both overall variance accounted for and with regard to the significance

levels of the individual hypothesized paths.













CHAPTER III


DISCUSSION


Although sibling constellation correlates of intellectual

abilities have been studied extensively, the results have often

been difficult to interpret. The contradictory findings and tentative

conclusions are partially attributable to the highly confounded

nature of birth order and family size variables. Interpretations

are further complicated by the relationships between ability,

achievement, and personality and the intercorrelations of SES with

both family size and the personality-ability-achievement complex

(Eysenck & Cookson, 1970).

In the present study, an attempt was made to separate out some

of the confounded relationships between family configuration, SES,

and the performance on ability and achievement measures. In the

initial procedure, a battery of 21 tests was administered to 111

university students. After factor analyzing the resulting correlation

matrix for optimal marker variables of the fluid intelligence (Gf or

active problem-solving ability) and crystallized intelligence (Gc or

achievement-related performance) constructs of Cattell and Horn

(Cattell, 1941, 1963, 1967; Horn, 1966, 1968, 1970, 1975; Horn &

Cattell, 1966, 1967), four linear structural equation models








relating Gc to various hypothesized predictor variables were

evaluated for two separate samples, the original university student

sample and a more heterogeneous sample of high school students.

The four models emphasized the importance of either economic,

"intellectual environment" (confluence), physiological, or socio-

psychological influences in the mediation of birth order and/or

family size effects on intellectual ability. Proponents of the

economic model (Anastasi, 1956; Kennett, 1973; Nisbet & Entwistle,

1967; Schooler, 1972, 1973) contend that family size effects are

largely reducible to SES and related economic advantages and

restrictions. The confluence model (Zajonc, 1976; Zajonc & Markus,

1975) considers ordinal differences in intelligence in terms of

family size and age spacing between siblings and the resultant

influences of the family's immediate "intellectual environment" on

intellectual development. In the physiological model (Warren, 1966;

Weller, 1965), it is suggested that physiological factors are

important mediators of birth order differences in cognitive ability.

Advocates of the socio-psychological model (Bradley, 1968; Burton,

1968; Glass et al., 1974; Nuttall et al., 1976; Start & Start, 1974)

contend that birth order, particularly firstborn academic superiority,

appears to be importantly related to significant social and psychological

factors. Such influences are often discussed in terms of personality

and motivational differences which may be manifested in individuals

of differing family sizes and ordinal positions. Issues and

assumptions relevant to these models were discussed in the first

chapter. It was emphasized that these theories are not mutually








exclusive. It was suggested that, as SES level increases, physiological,

"intellectual environment," and economic variables might conceivably

exert less influence, and that socio-psychological variables might

exert relatively more influence on family configurational effects.


University Student Sample


It was predicted that the socio-psychological model would be the

best predictor of achievement-related performance, or Gc, in this

sample. In the second procedure, multiple regression linear

structural equation techniques were utilized to evaluate the amount

of variance accounted for in terms of the observed data and for the

individual hypothesized paths for each of the models in a college

student'sample. The overall significant F ratios for the economic,

physiological, and socio-psychological models were found to be

attributable to the significant positive regression of Gf on Gc

(r = +.3674, p <.01), the variable entered at the first step in

each of these models. The overall F ratio of 2.2989 for the confluence

model was not significant. The results of this multiple regression

analysis are listed in Table 3. Therefore, while the prediction

that ordinal position would significantly influence Gc performance

(r = .1633, p-<.05) but not Gf performance (r = .0710) was supported,

none of the models appeared to be adequate predictors of the G

construct in this sample. However, upon closer examination of the

results, certain unexpected relationships among the predictor variables

were observed. It seemed reasonable that such relationships might

be specific to a college sample, as they have not been generally







reported in studies of more heterogeneous samples. These relationships

acted to statistically suppress the validity of several of the

predictor variables to explain performance on the Gc composite.

It was concluded that the contention that ordinal position

effects might not persist into adulthood (Schooler, 1972, 1973) may

be particularly evident in a university student sample. While the

attenuation of birth order and family size effects in higher SES

samples has been widely reported, the results of the present procedure

suggested that such effects might not only be attenuated, but might

be reversed, in a college population. These reversals appear to be

largely attributable to certain relationships which may be specific

to such a sample. Specifically, the unexpected significant positive

correlation between family size and SES (r = +.1972, p <.02) was in

marked contrast to the often reported negative intercorrelation of

these two variables in more heterogeneous samples (Belmont & Marolla,

1973; Eysenck & Cookson, 1970; Murray, 1971; Nisbet & Entwistle, 1967;

Record et al., 1969). This unexpected finding was interpreted in terms

of financial resources and limitations, which play an important and

perhaps decisive role in college attendance. Given this positive

relationship between family size and SES in addition to the highly

confounded nature of the relationship between family size and birth

order, it was not surprising to find a positive, although nonsignificant,

correlation between birth order and SES (r = +.1200, p <.10). The

effect of these relationships was to alter the hypothesized inter-

relationships among certain predictor variables, which in turn rendered

straightforward interpretation of the individual models difficult.







After further analysis of the data, it was determined that

cooperative suppression, as described by Cohen and Cohen (1975),

existed among several of the predictor variables. When partial

correlation procedures were utilized to statistically control for

these suppressor effects, a modified version of the economic model

was supported, in terms of both overall predictive significance and

with regard to the hypothesized relationships of the individual paths

and the achievement-related construct. The economic model was termed

"modified" due to the significant positive, not the predicted negative,

correlation between family size and SES.

Even with suppressor effects controlled, the prediction that the

socio-psychological model would be the optimal predictor of G

performance in this sample was not supported. Reasons for this

result were not obvious, although the unexpected positive relationships

between SES level and the family size/birth order variables appear

to have been an important contributing factor to these negative

results. Economic restrictions and other issues related to SES

might conceivably exert less influence on the opportunity of firstborn--

as opposed to later-born--siblings to receive a higher education.

The widely reported finding of firstborn overrepresentation at the

college level (Altus, 1966; Capra & Dittes, 1962; Schachter, 1963)

has led some investigators to contend that firstborns may be more

able (Nichols, 1964; Terman, 1925) or more achievement-oriented

(Breland, 1974; Burton, 1968; Forer, 1976; Glass et al., 1974; Nuttall

et al., 1976). However, in light of the present findings, the








possibility that economic factors importantly mediate firstborn

college attendance should also be considered.


High School Sample


Due to certain unexpected relationships in the previous procedure

which might conceivably be specific to a college student sample, it

was decided to evaluate the ability of the four models to predict Gc

performance in a more heterogeneous sample. Six tests which were

assumed to represent Gf or Gc ability were administered to 65 high

school seniors. The results for the hierarchical specified

inclusion methods for this sample are listed in Table 5. It was

found that all of the models were better predictors of G

performance in terms of overall variance and with regard to the

individual paths. In contrast to the college student sample results,

the predicted negative correlation between family size and SES

(r = -.2104, p <.05) was obtained for this more heterogeneous sample.

However, while the overall F ratios for all of the models were

significant, the regressions of the individual predictor variables

on Gc were significant only for the economic and socio-psychological

models, which explained approximately 51% and 54% of the variance,

respectively. It was concluded that, not only were the economic

and socio-psychological models fully supported in this sample, but

that t;,,y were both able to account for very substantial amounts of

the variance in the prediction of Gc performance. In an effort to

determine which of these two models was the most predictive of Gc,

the individual paths postulated by these models were examined. Due








to the observation that the firstborn variable of the socio-

psychological model was a more highly significant predictor of G
c
performance than was the family size variable of the economic model,

it was concluded that the hypothesis that the socio-psychological

model would most adequately explain achievement-related performance

was supported.

It was evident that the current versions of the physiological

and Zajonc and Markus confluence models were less adequate predictors

of Gc performance, particularly for the more heterogeneous high school

sample. This observation might have important implications with regard

to future consideration of these models. It is conceivable that

modified versions may have to be considered. For example, the present

results indicate that the Zajonc and Markus "last born/only child

handicap" variable may have to be more carefully formulated. This

predictor variable was not significantly related to G for either

3nI11le. Moreover, contrary to the predictions, there was a trend for

only children to perform somewhat better than the other subjects on

Gc measures. For the college sample the correlation between only

child and Gc was +.1438 (p .10), in contrast to the significant

negative correlation between lastborn and Gc (r = -.1831, pR<.05).

Similarly, the correlations of Gc with only child and lastborn for the

high school sample were +.1299 (p) .10) and -.1236 (p >.10),

respectively. Thus, it is conceivable that only children should not

be included in this "handicap" category. However, due to the small

number of only children in this investigation and the very different

relative representations in both samples (4 only children as opposed








to 35 lastborn children in the college sample; 2 only children as

opposed to 25 lastborn children in the high school sample), any

statistical comparison with regard to these constructs would have been

extremely unstable.

With regard to the physiological model, the contention that birth

order correlates of intellectual performance might be largely physiological

in origin was seriously challenged by the statistically trivial inter-

correlations between birth order and Gf of .0710 for the college sample

and .0526 for the high school sample. This very negative result

concerning the basic component of this model--the influence of birth

order on the physiological mediation of intellectual ability performance--

indicates the need for considerable reformulation in terms of the

theoretical predictions of the physiological point of view, particularly

in terms of birth order effects. A serious shortcoming appears to be the

contradictory predictions associated with ordinal position. More

specifically,whereas the discussion of mother's age, increased chance of

genetic error, and "uterine fatigue" (Warren, 1966) indicates deleterious

birth order influences for later-born children, the problems often

associated with the pregnancy and birth of the firstborn child, including

a smaller placenta, longer labor, and increased perinatal complications

(Weller, 1965) suggest relative physiological advantages for later-born

children. Before further consideration of even a modified version of

this model is indicated, it is apparent that such complex predictions

concerning the physiological influences of birth order will have to be

more carefully isolated.







It is apparent from the present results that certain social and

psychological factors may be significant mediators of family configuration

influences--particularly firstborn superiority--on intellectual

achievement. Moreover, economic influences seem to exert powerful

effects, both directly and indirectly, on intellectual ability

performance.

Implications


With regard to the very different results obtained for the college

student and high school student samples, it is apparent that one must

specify the kind of sample involved when addressing issues related to

family configuration and SES correlates of intellectual ability.

Kennett',s (1973) discussion of the importance of SES influences on the

relationships between family size and intelligence seems particularly

appropriate to the results of the present investigation. The failure

of many studies to separate out the confounded relationships between

family configuration, SES, and other related variables, which may differ

widely depending on sampling techniques, may be a major contributor to

the contradictory findings and tentative conclusions so prevalent in

this literature.

An issue which should be considered in the study of relatively

homogeneous samples, such as the university students in the present

investigation, is the possible restriction in range in terms of certain

family configuration variables, SES, and ability. Differences in

restriction in range across samples may result in difficulties in

comparison and interpretation. Moreover, such restrictions may be







differentially manifested for different variables. For example, in

the college sample, SES appeared to be the predictor variable which

manifested the least restriction in range. In contrast, while the

SES construct had relatively more variance in the heterogeneous high

school sample, the age range of from 17 to 18 years was extremely

narrow. This age constant aspect of the high school sample may have

been an important contributing factor to the increased correlations

obtained among the predictor variables and Gc.

Thus, while age was not included as a predictor variable in any

of the four models evaluated in this study, it is conceivable that a

potentially important predictor of G performance of the university

students in this study was the age of the subjects. While it might

generally be assumed that a college student population is homogeneous

in many ways, this assumption may be decreasingly valid with respect

to age. The age range of 17 to 30 years in this sample reflects the

increasing tendency for older students to attend universities, at both

undergraduate and graduate levels. Future studies of family

configuration correlates of intelligence and achievement among college

students may have to take this trend into account. Considerable

differences in age may present problems in interpretation in such

studies. For example, there existed in the present college sample a

highly significant positive correlation between age and Gc performance

(r = +.2753, p <.01) but a nonsignificant negative relationship between

age and Gf ability (r = -.0999). It is interesting to note that this

age trend for these constructs is in agreement with Horn's (1970, 1975)

postulated age-range functions of Gf and Gc abilities. As described
c L







by Cunningham, Clayton, and Overton (1975), this theory predicts that:

Fluid intelligence, which is purported to reflect the
functioning of neurological structures, increases until
the cessation of neural maturation, generally during
adolescence, and then declines thereafter. In contrast,
crystallized intelligence is believed to reflect cultural
assimilation. In particular, it seems to be highly
influenced by formal and informal educational factors
throughout the life span. Assuming adequate health,
crystallized intelligence is presumed to increase steadily
across the adult age span. (p. 53)

However, although of theoretical interest, this cross-sectional age-

related increase on Gc or achievement-related measures may be

attributable to factors unrelated to Horn's contentions. For example,

age and college level (freshman, junior, graduate student, etc.) are

highly confounded. It is very difficult to separate the effects of

age and ability in a cross-sectional design such as that employed in

the present study. However, the influence of age in such samples may

be an important factor which should be taken into consideration in

future studies.

Another potentially important issue is the possible existence

of different family configuration mediators of intellectual ability

performance with regard to racial background. Such race-related

differences might be expected to be particularly evident in terms of

SES and family size influences on intellectual ability measures.

Zajonc and Markus (1975) discuss the possible relationships between

family configuration variations among different national, regional,

ethnic, and racial groups and differences in intellectual test

performance. They suggest that differences in family configuration

patterns, including larger families and closer age spacing between

siblings, in the black population may be a contributing factor to








racial differences in intellectual performance scores. The high

school sample in the present investigation included ten black subjects.

The student body of this particular school is stratified so as to be

demographically representative of the state of Florida in terms of

economic level and race. Therefore, this sample was considered to

be a representative one, in contrast to the relatively homogeneous

and somewhat artificial university student sample. Although it would

have been interesting to examine specific patterns among the predictor

variables and Gc in addition to the predictive ability of the

individual models, across the two racial groups of the high school

sample, the small absolute number of black students and the very

different relative sample sizes (10 black students and 55 white

students) precluded further statistical analysis.

The present investigation introduced several new approaches to

the study of family configuration and intellectual achievement

which may possibly have important application for this research area.

Specifically, the use of the Gf and Gc constructs of Cattell and Horn

(Cattell, 1941, 1963, 1967; Horn, 1966, 1968, 1970, 1975; Horn &

Cattell, 1966, 1967) as measures of aptitude- as opposed to achievement-

related ability, the use of multiple regression techniques to

evaluate the predictive ability of the different path models, and

the analysis of suppressor effects may be potentially effective tools

for future studies of birth order and family size relationships to

intelligence.


Fluid and Crystallized Intelligence


Significant ordinal position influences on achievement-related







variables but not on certain aptitude or ability measures have been

reported (Burton, 1968; Forer, 1976; Glass et al., 1974; Nuttall et al.,

1976). These findings suggested to the author the possible

application of the Gf and Gc constructs of Cattell and Horn as

measures of aptitude and achievement-related performance. Horn (1966,

1968, 1970, 1975) hypothesizes that Gf reflects neurophysiological

functioning and active problem-solving ability. Gc is defined by

abilities appropriated through education and acculturation. Thus,

it was decided to utilize Gf measures to represent active problem-

solving ability or aptitude and G measures to represent achievement-

related performance.


Multiple Regression


Marjoribanks (1976a, 1976b) contends that the equivocal results

reported in the birth order literature are partially due to the use of

restricted statistical techniques which have precluded examination of

the full character of the sibling constellation correlates of

intellectual abilities. The majority of studies have used product-

moment correlations, which reveal only bivariate relationships, and

analysis of variance, which requires the grouping of variables into

levels. Marjoribanks et al. (1975) suggest that the use of multiple

regression techniques--which provide a goodness-of-fit for any

functional form of the variables and do not require grouping--should

overcome some of the statistical shortcomings of many previous

investigations. In the present study, hierarchical specified inclusion

techniques were used to test the assumptions of four individual models.







This statistical technique was chosen because it permits the

analysis of the relationship between a criterion variable and a

set of predictor variables without the artificial grouping of

variables into levels. Moreover, the estimation of the relative

strengths of the individual paths in the models indicates the

magnitude of both direct and indirect influences in terms of the

hypothesized causal ordering for each model. In the hierarchical

inclusion procedure, each predictor variable is added in a single
2
step, and the increment in R2 at each successive step is considered

to be the component of variation attributable to that particular

variable.


Statistical Suppression


Although statistical suppressor effects are rather uncommon in

behavioral sciences research, their existence can be devastating to

data interpretation. It was determined that certain unexpected

relationships which were observed in the college sample, particularly

the significant positive correlation between family size and SES, were

acting as cooperative suppressors (Cohen & Cohen, 1975) in at least

two of the proposed models. These suppressor effects resulted in a

decreased relationship between the family configuration and SES

variables and the achievement-related criterion variable. When

partial correlation techniques were utilized to control for these

statistical suppressor effects, a modified version of the economic

model was supported in terms of both overall variance accounted for

and the predictive ability of the individual paths.







The discovery of the existence of suppressor effects in the

college student sample may have potentially important implications

for the application of multiple regression techniques in general, and

for the use of these procedures in family configuration research in

particular. It is evident that birth order and family size variables

may exert very different effects on intellectual ability and achievement,

depending on different sampling procedures and the specific sample

investigated. The use of partial correlation techniques to control

for statistical suppression may be an important tool in the isolation

of the many complex relationships and issues in this literature.


Directions for Future Research


In the present study, an attempt was made to separate out some of

the confounded relationships of certain family configuration and SES

level correlates of intellectual ability and achievement. The present

results--along with the contradictory and tentative conclusions

prevalent in this research area--indicate the need for the generation

of new theories and hypotheses, which may be specific to particular

samples. The four models evaluated in this investigation should not

be considered to be mutually exclusive. Moreover, it is conceivable

that alternative models may be equally or even more predictive of

certain family configuration influences. For example, in a sample

consisting of 185 11-year-old boys, Marjoribanks et al. (1975)

found that a multiple regression equation containing father's

occupational status, inverse of the number of children in the family,

and the product of the two variables accounted for as much significant








variance as complex equations with many terms.

With regard to the present results, it is evident that, besides

the variables examined in this investigation, certain additional

variables should possibly be considered in future family configuration

studies. Such general influences include subject sex differences,

sex of siblings, age range of subjects, and the possible mediation of

racial and ethnic influences on intellectual ability performance.

Moreover, the supported predictions concerning firstborn

superiority on achievement-related variables might be more analytically

evaluated in a variety of ways. Such analytical paths might include

separate analyses for specific mechanisms related to the performance

of firstborn children on achievement-related measures. For example,

SES mediators of such firstborn superiority might include parental

attitudes toward achievement and educational aspirations which may

differ across SES levels, especially regarding earlier-born children

(Glass et al., 1974), certain concrete measures such as the number of

books, periodicals, and other literature in the home, and parental role

models--which might be particularly interesting for the daughters of

achieving women. Another area of investigation might involve certain

personality attributes which might be related to firstborn performance

on achievement-related variables. Such attributes might involve

increased susceptibility to social pressure and sensitivity to tension-

producing situations (Bradley, 196S), conscientiousness and effort

(Start & Start, 1974), social conformity and achievement motivation

(Forer, 1976). The examination of these hypothesized contributors to

firstborn superiority on achievement-related performance might further







clarify certain family configuration correlates of intellectual

ability performance. Of particular relevance to these considerations

is the importance of differential learning environments created by

families for their children (Marjoribanks, 1976a). These learning

environments were described in terms of five process variables:

(a) press for achievement; (b) press for activeness; (c) press for

intellectuality; (d) press for independence; and (e) press for English

(related to the reinforcement of the correct use of English).

Marjoribanks indicates that the relationships between birth order and

cognitive performance are complex:

The findings support the proposition that, if parents
provide different environmental experiences for their
children, then the environmental differences will be
related to birth-order variation in the cognitive
performance of children. (p. 764)

It is thus apparent that the often contradictory findings and

tentative conclusions in the birth order and family size literature

may be largely attributable to the failure to adequately control for

the many confounded variables and possible artifactual relationships

which may exist in certain samples. The results of the present

investigation indicate the need for the generation of new, more

analytical hypotheses with regard to family constellation influences

on aptitude and achievement. Moreover, the utilization of innovative

statistical techniques and conceptual approaches may serve to

effectively isolate the extremely complex family configuration correlates

of intellectual ability performance.















St' i Li'/F[ ARY F'. ::' F [


" 1-t z I


, "S -.:r." Unpuh!is icd
Li I. i., fc Delnver,


S Li
tr L .












REFERENCES


Altus, W. D. Birth order and academic primogeniture. Journal of
Personality and Social Psychology, 1965, 2, 872-876. (a)

Altus, W. D. Birth order and scholastic aptitude. Journal of Consulting
Psychology, 1965, 29, 202-205. (b)

Altus, W. D. Birth order and its sequelae. Science, 1966, 151, 44-49.

Anastasi, A. Intelligence and family size. Psychological Bulletin,
1956, 53, 187-209.

Apperly, F. L. Characteristics of birth order position. Journal of
Heredity, 1939, 30, 493.

Belmont, L., & Marolla, F. A. Birth order, family size, and intelligence.
Science, 1973, 182, 1096-1101.

Bradln-, R. '. Birth order and school-related behavior: A heuristic
review. Psychological Bulletin, 1968, 70, 45-51.

Breland, H. M. Birth order, family configuration, and verbal achievement.
Child Development, 1974, 45, 1011-1019.

Burton, D. Birth order and intelligence. Journal of Social Psychology,
l.J, 76, 199-206.

Capra, P. C., & Dittes, J. E. Birth order as a selective factor among
volunteer subjects. Journal of Abnormal and Social Psychology,
1 52, 64, 602-608.

Cattell, J. M. Men of science. (4th ed.) Garrison, New York: Science
Press, 1927.

Cattell, R. B. Some theoretical issues in adult intelligence testing.
Psychological Bulletin, 1941, 38, 592.

Cattell, R. B. Theory of fluid and crystallized intelligence: A critical
( ..'.iment. Journal of Educational Psychology, 1963, 54, 122.

Cattell, R. B. The theory of fluid and crystallized intelligence checked
at 5-6 year-old level. British Journal of Educational Psychology,
137, 37, 209-224.








Cicirelli, V. G. Sibling constellation, creativity, IQ, and academic
achievement. Child Development, 1967, 38, 481-490.

Clarke, E. L. American men of letters, their nature and nurture. New
York: Columbia University Press, 1916.

Cohen, J., & Cohen, P. Applied multiple regression/correlation analysis
for the behavioral sciences. New York: Wiley & Sons, 1975.

Corliss, W. S. Relationships in achievement by sex and number of
children in the family. Michigan Education Review, 1964, 42,
28-29.

Cunningham, W. R., Clayton, V., & Overton, W. Fluid and crystallized
intelligence in young adulthood and old age. Journal of Gerontology,
1975, 30, 53-55.

Dunn, H. L. (Ed.), Vital statistics of the United States, 1952 (Vol. 1).
Washington, D.C.: U.S. Government Printing Office, 1956.

Ellis, H. A study of British genius. London: Hurst & Blackett, 1904.

Eysenck, H.J., & Cookson, D. Personality in primary school children:
Family background. British Journal of Educational Psychology, 1970,
117-131.

Forer, L. K. The birth order factor: How your personality is influenced
by your place in the family. New York: McKay, 1976.

French, J. W., Ekstrom, R. B., & Price, L. A. Manual for kit of reference
tests for cognitive factors. Princeton: Educational Testing Service,
1 i...3.

Galton, F. English men of science. London: MacMillan, 1874.

Glass, D. C., Neulinger, J., & Brim, C. G. Birth order, verbal
intelli ence, and educational aspirations. Child Development,
1974, 45, 807-811.

Guilford, J. P. A new revision of the Army Alpha Examination and a
weighted scoring for three primary factors. Journal of Applied
Psychology, 1938, 5, 239-246.

Guilford, J. P., & Hoepfner, R. The analysis of intelligence. New
York: McGraw-Hill, 1971.

Hall, E. E. Psychological correlates of ordinal position in the two-child
family. Unpublished doctoral dissertation, University of Florida,
1963.







Hoepfner, R., & Guilford, J. P. Structure of Intellect Tests. Orange,
California: Sheridan Psychological Services, 1969.

Horn, J. L. Integration of structural and developmental concepts in the
theory of fluid and crystallized intelligence. In R. B. Cattell
(Ed.), Handbook of multivariate experimental psychology. Chicago:
Rand McNally, 1966.

Horn, J. L. Organization of abilities and the development of
intelligence. Psychological Review, 1968, 75, 242-259.

Horn, J. L. Organization of data on lifespan development of human abil-
ities. In L. R. Goulet & P. B. Baltes (Eds.), Lifespan develop-
mental psychology. New York: Academic Press, 1970.

Horn, J. L. Psychometric studies of aging and intelligence. In S.
Gershon & A. Ruskin (Eds.), Aging: Genesis and treatment of psycho-
logic disorders in the elderly. New York: Raven Press, 1975.

Horn, J. L., & Cattell, R. B. Refinement and test of the theory of
fluid and crystallized intelligence. Journal of Educational
Psychology, 1966, 57, 252-270.

Horn, J. L., & Cattell, R. B. Age differences in fluid and crystallized
intelligence. Acta Psychologica, 1967, 26, 107-129.

Huntington, E. Season of birth. New York: Wiley & Sons, 1928.

Jureskog, K. G., & Sorbom, D. EFAP: Exploratory factor analysis
program. Chicago: National Educational Resources, 1976. (a)

Joresk--, K. G., & Sorbom, D. LISRELL III: Estimation of linear
structural equation systems by maximum likelihood methods.
Chicago: National Educational Resources, 1976. (b)

Kennett, K. F. Measured intelligence, family size, and socio-economic
status. Alberta Journal of Educational Research, 1973, 19, 314-320.

Lees, J. P., & Stewart, A. H. Family or sibship position and scholastic
ability. Sociological Review (British), 1957 (July), 85-106.

Marjoribanks, K. Birth order, family environment, and mental abilities:
A rVct tssion surface analysis. Psychological Reports, 1976, 39,
759-7 3. (a)

Marj -ibanks, K. Sibsize, family environment, cognitive performance,
and a,. active characteristics. Journal of Psychology, 1976, 94,
195-204. (b)

Marjoribanks, K., & Walberg, H. J. Ordinal position, family environment,
and mental abilities, Journal of Social Psychology, 1975, 95, 77-84.







Marjoribanks, K., Walberg, H. J., & Bargen, M. Mental abilities:
Sibling constellations and social class correlations. British
Journal of Social and Clinical Psychology, 1975, 14, 109-116.

Maxwell, J., & Pilliner, A. E. The intellectual resemblance between
sibs. Annals of Human Genetics, 1960, 24, 23-32.

McCutcheon, L. E. Dumber by the dozen? Not necessarily Psychological
Reports, 1977, 40, 109-110.

Murray, C. The effects of ordinal position on measured intelligence
and peer acceptance in adolescence. British Journal of Social
Psychology, 1971, 10, 221-227.

Nichols, R. G. Birth order and intelligence. Unpublished manuscript,
1964. (Available from the National Merit Scholarship Corporation.)

Nie, N. H., Hull, C. H., Jenkins, J. G., Steinbrenner, K., & Bent, D. H.
(Eds.) SPSS: Statistical package for the social sciences (2nd ed.).
New York: McGraw-Hill, 1975.

Nisbet, J. D., & Entwistle, N. J. Intelligence and family size, 1949-1965.
British Journal of Educational Psychology, 1967, 37, 188-193.

Nuttall E. V., Nuttall, R. L., Polit, D., & Hunter, J. B. The effects
of family size, birth order, sibling separation and crowding on
the academic achievement of boys and girls. American Educational
Research Journal, 1976, 13, 217-223.

Record, R. G., McKeown, T., & Edwards, J. H. The relation of measured
intelligence to birth order and maternal age. Annals of Human
Genetics, 1969, 33, 61-69.

Rosenberg, B. G., & Sutton-Smith, B. The relationship of ordinal position
and sibling sex status to cognitive abilities. Psychonomic Science,
1964, 1, 81-82.

Schachter, S. Birth order, eminence, and higher education. American
Sociological Review, 1963, 28, 757-768.

Schooler, C. Birth order effects: Not here, not now: Psychological
Bulletin, 1972, 78, 161-175.

Schooler, C. Birth order effects: A reply to Breland. Psychological
Bulletin, 1973, 80, 213-214.

Start, A., & Start, K. B. The relation between birth order and effort
or conscientiousness among primary school children. Research in
Education, 1974, 9, 1-8.

Terman, L. M. Genetic studies of genius. The mental and physical traits
of a thousand gifted children. Stanford, California: Stanford
University Press, 1925.








Thurstone, L. L., & Thurstone, T. G. SRA primary mental abilities.
Chicago: Science Research Associates, 1949.

Treiman, M. Occupational prestige in comparative perspective. New
York: Academic Press, 1977.

Wark, D. M., Swanson, E. D., & Mack, J. More on birth order:
Intelligence and college plans. Journal of Individual Psychology,
1974, 30, 221-226.

Warren, J. R. Birth order and social behavior. Psychological Bulletin,
1_:3, 65, 38-49.

Weller, G. M. Arousal effects of the newborn infant of being first or
later born. American Journal of Orthopsychiatry, 1965, 35, 341-342.

Yoder, A. H. Birth order. Pedagogical Seminary, 1894, 3, 146.

Zajonc, R. B. Family configuration and intelligence. Science, 1976,
192, 227-136.

Zajonc, R. B., & Markus, G. B. Birth order and intellectual development.
Psychological Review, 1975, 82, 74-88.












BIOGRAPHICAL SKETCH


Sandra Johnson Witt was born in Sanford, Florida, on July 17, 1946,

the daughter of Betty Beeler Johnson and Elmer Hunter Johnson. She

graduated from Hialeah Senior High School in Hialeah, Florida. A German

major at the University of Florida, she graduated in 1968 with high honors

and was elected to Phi Beta Kappa. She received her master's degree in

German and French from the University of Florida in 1969. She worked for

Trans World Airlines in New York until 1971 as an international airline

hostess and in-flight instructor. She married William Morris Witt in 1971.

In 1973 she received a master's degree in psychology from Emory

University under the direction of Dr. Philip Dreyer and Dr. Boyd

McCandless. She worked as staff psychologist and director of adult

remedial education at the Key Training Center for Mentally Retarded and

Physically Handicapped Adults in Lecanto, Florida, from 1973 until 1974.

In 1975 she entered the Ph.D. program in developmental psychology at the

University of Florida. While in this program, she received a pre-doctoral

fellowship from the Center for Gerontological Studies and worked as a

research assistant for Dr. Walter R. Cunningham, who is also the chairman

of her doctoral committee. She has two daughters, Amanda and Katherine.











I certify that I have read this study and that in my opinion
it conforms to acceptable standards of scholarly presentation and
is fully .dequ te, in scope and quality, as a dissertation for the
degree of Doctor of Philosophy.





Walter R. Cunningham, Chaiir *maI
Assistant Professor of Psych.ology


I certify that 1 hdve read this study and that in my cpinion
it conforms to acceptable standards of scholarly presentation and
is fully adequate, in scope and quality, as a dissertation for the
decree of Doctor of Philosophy.




William Keith ero /
Associate Prof.ssor of Psychology


I certify that I have read this study znd that in my opinion
it con*aor;,m to acceptable standards of scholarly presentation and
is f;lly adecuate, in scope and quality, as a dissertations for the
dea--e of Dcctc" of Philosophy.





Richard A. CGriggs '
Assistant Professor of Psychology




I certify that I have read this study and that in my opinion
it conforms to acceptable standards of scholarly oresentatior and
is fu iy dn-quate, i1 scope and -' ;-lity, as a dissertation for the
de;i ee 0on Duo"i- of Philosophy.




NaPt'ian ; Perry
Prfc essor of C inici1 Psvchrclogy











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.





JAes E. Wh'rton
Us ociate Professor of Special Education


This dissertation was submitted to the Graduate Faculty of the
Department of Psychology in the College of Arts and Sciences and
the Graduate Council, and was accepted as partial fulfillment of
the requirements for the degree of Doctor of Philosophy.


June 1978


Dean, Graduate School




Full Text

PAGE 1

Bli Di R El EC'I A D FLUID/CM LLJ ED INTELI [I By Sand! i Wi LI A DIS! ERTA1 i! ' " " C( It 01 ' VERSIT V [| P/ riAl ULFILI i F Tl REOU] NTS FOR Tl ii i R ( PHII I i/ERS] FY KLOI iOA

PAGE 2

ACKNOWLEDGMENTS I would like to acknowledge my sincere appreciation to Dr. Walter R. Cunningham, the chairman of my doctoral committee and director of this dissertation, whose valuable suggestions, constructive criticisms, and encouragement made this study possible. Special thanks are extended to the other members of my doctoral committee, Dr. W. Keith Berg, Dr. Richard Griggs, Dr. James Whortor. , and Dr. Nathan Perry. Their assistance and practical suggestions were greatly appreciated. I would also like to thank Kathy Moser for her capable assistance with the preparation of this manuscript, Regis Magyar for preparing tl figures, and Patricia Richardson and Tom Harbin for their assistance with the test administration. Moreover, this effort was made somewhat less difficult by the moral support of my husband, Bill, and the incredible motivating influence of my daughters, Mandy and Kate. n

PAGE 3

TABLE OF CONTENTS Pa^e ACKNOWLEDGMENTS ii LIST OF TABLES iv LIST OF FIGURES v ABSTRACT vi CHAPTER I INTRODUCTION 1 Birth Order/Family Size Theories. ... 3 Fluid and Crystallized Intelligence . . 8 t CHAPTER II METHOD AND DATA ANALYSIS 14 Overview and Design 14 Study 1 14 Study 2 25 Study 3 39 CHAPTER III DISCUSSION 50 University Student Sample 52 High School Sample 55 Implications 58 Directions for Future Research .... 64 SUPPLEMENTARY REFERENCE 67 REFERENCES 68 EIOGRAPHICAL SKETCH 73 m

PAGE 4

LIST OF TABLES Table Page 1 Means and Standard Deviations for College Student Sample 21 2 Promax Rotated Factor Loadings 23 3 Multiple Regression of Predictor Variables on G c for College Sample 29 4 Means and Standard Deviations for High School Sample 41 5 Multiple Regression of Predictor Variables , on G c for the High School Sample 45 IV

PAGE 5

LIST OF FIGURES Page Figure 1 Linear Structural Equation Moaels . . 10 Figure 2 Intercorrelations among Variables in each model for the College Student Sample 36 Figure 3 Intercorrelations among Variables in each Model for the High School Sample. 43

PAGE 6

Abstract of Dissertation Presented to the Graduate Council of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy BIRTH ORDER EFFECTS AND FLUID/CRYSTALLIZED INTELLIGENCE By Sandra Johnson Witt June 1978 Chairman: Walter R. Cunningham Major Department: Psychology The present investigation was an attempt to separate out some of the confounded relationships between the family configuration correlates of ability and achievement performance. In the first study, a battery of 21 tests was administered to 111 university students. After factor analyzing the results for optimal marker variables of the fluid intelligen (G f --active problem-solving ability) and crystallized intelligence (G — achievement-related performance) constructs of Cattell and Horn, four linear structural equation models relating G to various hypothesized predictor variables were evaluated in the original university sample, and an additional sample consisting of 65 high school students. The individual models emphasized the importance of either economic, "intellectual environment," physiological, or socio-psychological influences in the mediation of birth order and/or family size effects on intellectual ability performance. It was emphasized that the models were not mutually exclusive, but might be differentially applicable in different populations. VI

PAGE 7

Certain unexpected correlations obtained in the university student sample, which might be specific to such a population, rendered straightforward interpretation of the models difficult. The finding of a significant positive correlation between family size and socioeconomic status was in marked contrast to the often reported negative intercorrelation of these two variables in the general population. This result was discussed in terms of financial resources and limitations, which may play a decisive role in college attendance. The only model supported in this sample was a modified version of the economic model . The ability of the models to predict G c performance in a more heterogeneous high school sample was also evaluated. All of the models were better supported in this sample. However, whereas the overall F ratio for each of the models 'was significant, the theoretically expected regressions for the individual predictor variables on G c were significant only for the economic and socio-psychological models. It was concluded that, not only were these two models able to account for substantial amounts of variance in the prediction of G c performance, but that the separate paths indicated by both models were strongly supported by the obtained data. After comparing the individual paths postulated by these two models, it was concluded that the sociopsychological model was the optimal predictor of G c performance. This result was largely attributable to the highly significant regression for the firstborn (versus later-born) variable on G c With regard to the very different results obtained in the college student and high school student samples, it is apparent that one must specify the kind of sample involved when addressing issues related to vn

PAGE 8

family configuration and socioeconomic correlates of intellectual ability. The very positive results obtained for the heterogeneous high school sample indicate the significance of certain social and psychological factors as mediators of family configuration influences—particularly firstborn superiority—on intellectual achievement. Moreover, economic influences seem to exert powerful effects, both directly and indirectly, on intellectual ability performance. It was suggested that the utilization of innovative statistical techniques and conceptual approaches might serve to more effectively isolate the extremely complex family configuration correlates of intellectual aptitude and achievement. vm

PAGE 9

CHAPTER I INTRODUCTION While the relationships of birth order and family size to achievement-related variables have been studied extensively, the conclusions which can be drawn from these research efforts are often contradictory and, at best, tentative. Problems involved in the study of such relationships are complicated by the fact that (a) family size and birth order are highly correlated, (b) personality and ability/achievement are not independent, and (c) socioeconomic status (SES) is correlated with both family size and the personality-abilityachievement complex (Eysenck & Cookson, 1970). A consistent finding of studies which have related birth order to eminence has been a preponderance of firstborns among eminent individuals (J. M. Cattell, 1927; Ellis, 1904; Galton, 1874; Huntington, 1928). In Terman's (1925) study of gifted children, there were more firstborns than would have been expected by chance. Altus (1965a) reports that the firstoorn individual is greatly overrepresented at the college level. Nichols (1964) presents data on National Merit Scholarship finalists. Nearly 60% of the finalists who came from families of two, three, four, and five children were firstborns. Altus (1965b) studies Scholastic Aptitude Test scores and concluded that firstborns in college may be more able verbally than later borns; no differences appeared in mathematical aptitude. Several other studies have found firstborn superiority on 1

PAGE 10

various achievement-related measures (Breland, 1974; Burton, 1968; Glass, Neulinger, & Brim, 1974; Marjoribanks & Walberg, 1975). In addition to the greater achievement of the firstborn, there may exist hierarchies of aptitude among the intellectually able related to birth order and family size. Zajonc (1976) reported that intellectual level generally declines with family size and certain age spacing relationships, which are important in terms of the family's immediate intellectual environment. The dependence of the intellectual development of each family member on that of all other family members is emphasized in this theory. However, the relationships between family size and intellectual performance appear to be attenuated or may disappear in higher SES families. In a study of 11-year-old children, Cicirelli (1967) found that family size and achievement were unrelated. It appears that, in middle and upper-middle SES populations, children from larger families are able to obtain adequate attention and learning opportunities. Kennett (1973) suggests that the acceptance of an inverse relationship between family size and intelligence must be modified by the influence of SES on the relationship, and that correlations may vary from social class to social class. While firstborn superiority on achievement-related variables has been widely reported, consistent patterns relating decreasing intellectual ability scores to successive birth order position have been less straightforward. Although several studies report hierarchical relationships (Altus, 1965b; Belmont & Marolla, 1973; Breland, 1974; Burton, 1968; Nichols, 1964; Wark, Swanson, & Mack, 1974), findings are often inconsistent. For example, eminence studies (Apperly, 1939;

PAGE 11

J. M. Cattell, 1927; Clarke, 1916; Ellis, 1904; Terman, 1925; Yoder, 1894) report a preponderance of firstborns and lastborns among persons of genius. Similarly, studies of birth order among older children and young adults often demonstrate superiority of firstborn over later-born children on IQ and achievement, with the middle child frequently making the poorest score (Cicirelli, 1957; Corliss, 1964; Hall, 1963; Lees & Stewart, 1957; Maxwell & Pilliner, 1960; Rosenberg & Sutton-Smith, 1964; Schachter, 1963). Record, McKeown, and Edwards (1969) report that the strong association between verbal reasoning scores and birth order in the general population is mainly due to differences between rather than within families. In upper SES families the difference between consecutive siblings (0.7 IQ points) is small, but in poorer families the difference (2.0 IQ points) is considerable. The reasons for these discrepant data are unclear. Marjoribanks, Walberg, and Bargen (1975) report that birth order effects are rarely unitary, but often involve other familial aspects, such as the sex of the siblings and their differences in age. Cicirelli (1967) found that such sibling constellation factors were related to measures of creativity, arithmetic ability, and language achievement in a sixthgrade sample. Birth Order/Family Size Theories There are at least four major points of view concerning birth order and family size influences on intelligence. The theories considered in this investigation emphasize the importance of either economic, "intellectual environment" (confluence), physiological, or socio-

PAGE 12

psychological factors in the determination of birth order and family size correlates of intellectual ability and achievement. These models will be discussed individually. Economic Model Many investigators contend that birth order and family size effects are largely reducible to SES and the economic factors involved. For example, in the past the concept of primogeniture (the exclusive right of inheritance bestowed on the eldest son) was directly related to economics. Anastasi (1956) reports a negative correlation of IQ with family size, which disappears or becomes positive in higher SES levels. Nisbet and Entwistle (1957) note a negative correlation of family size with intellectual ability in school children, an effect which is attenuated in upper SES groups. Kennett (1973) found a significant inverse relationship between family size and IQ in junior high school students, except in the upper two SES categories. Marjoribanks et al . (1975) report that, while children with more siblings tend to score lower on verbal and number ability tests, the relation is attenuated in high SES families. Schooler (1972, 1973) contends that birth order effects can be most parsimoniously explained in terms of differences among SES trends in family size. These studies suggest a changing relationship between family size and intelligence in different SES groups. Zajonc-Markus Confluence Model In the confluence model (Zajonc, 1976; Zajonc & Markus, 1975), ordinal differences in intelligence are explained in terms of family

PAGE 13

size and age spacing between siblings. Zajonc and Markus attempt to account for the effects of the immediate "intellectual environment" (represented as a function of the average of the absolute intellectual levels of the family members) on intellectual development, and to specify how individual differences emerge in the social context of the family. For example, if the parents' intellectual levels are 30 arbitrary units each, the birth of the first child, whose intellectual level is zero, would cause the family intellectual environment to have an average value of (30 + 30 + 0)/3 = 20. If a second child is born when the first child's intellectual level reaches 4, the second born enters an environment of (30 + 30 + 4 + 0)/4 = 16. The importance of age spacing between siblings could be seen, for example, if the second' child were not born until the firstborn child reaches an intellectual level of 24. The newborn would enter an environment of (30 + 30 + 24 + 0)/4 = 21, even more favorable than the environment of 20 entered into by the firstborn. Negative effects of birth order can be nullified and even reversed with large enough age gaps between siblings, according to this model. In addition, it is proposed that only children and last-born children suffer a handicap due to their lack of opportunity to serve as teachers to younger siblings. Thus, it is suggested that the intellectual growth of each family member is dependent on that of all the other members, and that the rate of growth depends on family configuration. Physiological Model Proponents of this point of view discuss the effects of increased chance of genetic error and "uterine fatigue" with later-born children

PAGE 14

(Forer, 1976). Warren (1966) indicates that the intrauterine environment may vary with the mother's age and number of previous pregnancies. Such influences would favor earlier-born children. However, a smaller placenta, longer labor, increased perinatal complications, and increased use of forceps are often associated with pregnancy and birth of the firstborn, influences which would favor later-born children (Weller, 1965). Thus, it is evident that the physiological model may suggest quite complex effects of family configuration. Socio-Psychological Model Much recent research has supported the importance of sociopsychological variables related to birth order and family size. Many studies 'indicate personality differences between different birth orders and with differing family sizes (Eysenck & Cookson, 1970; Forer, 1976). Bradley (1968) discusses personality factors which favor firstborns in school -related behaviors: Firstborns seem to more frequently (a) meet teachers' expectations and (b) show more susceptibility to social pressure than later borns. Exhibiting (c) greater information-seeking behavior and (d) being more sensitive to tension-producing situations, firstborns may be judged by others as (e) serious and (f) low in aggression. These behaviors may (g) strengthen firstborns' achievement motivation and (h) help to enhance their academic performance. (jd_. 45) Start and Start (1974) studied teacher ratings of first-grade children. Firstborns received higher teacher estimates of conscientiousness and effort. Glass et al . (1974) propose two related processes to account for the higher verbal ability and educational aspirations of firstborns, They suggest that better-educated parents encourage higher aspirations and verbal skills in their children, and that this phenomenon is especially true for earlier-born children:

PAGE 15

The later child generates and moves into relationships uniquely its own--"the principle of unoccupied space"-specifically different from the role of the "achieving child", which is already filled in the family. (p_. 80) Burton (1968) found a decreasing progression of IQ with birth order. However, the mean difference between firstborn and last born was approximately 3 IQ points, a difference unlikely to have any practical effects on the achievement of firstborn relative to that of last born. He suggests that research on birth order effects should focus on socio-psychological variables associated with ordinal position. In an investigation of family size and sibling spacing effects (Nuttall, Nuttall , Polit, & Hunter, 1976), there emerged a sex-specific pattern of relationships between family size and academic achievement. r The authors discuss the significant family size effect for boys and the significant firstborn effect for girls in terms of sociopsychological factors. However, because none of the proponents of the four models emphasized sex differences as a major contributing factor to family configurational correlates of intelligence, sex was not included as a predictor variable in these analyses. Thus, although the reasons for the relationships are not at present clear, ordinal position at birth appears to be importantly related to significant social factors. It is therefore evident that birth order and family size influences on intelligence can be discussed in terms of economic, physiological, age-spacing and/or socio-psychological variables. It must be emphasized that these theories are not mutually exclusive. Socioeconomic status is inextricably related to other variables. For example, influences of birth order and family size are largely attenuated in higher SES groups.

PAGE 16

Prenatal care and diet, as well as family interaction patterns, are related to social class. In higher SES populations later-born children and children from larger families do not appear to manifest the handicaps generally found in lower SES populations. Thus, it is apparent that, as one moves up the SES scale, economic, physiological, and agegap variables exert less influence and socio-psychological variables exert relatively more influence on the birth order and family size correlates of intelligence and achievement. Fluid and Crystallized Intelligence In a study of the relationships of Primary Mental Ability Test scores to birth order, Marjoribanks et al . (1975) found that verbal and number abilities, but not reasoning and spatial abilities, were sensitive to ordinal position effects. The authors discuss the results in terms of the differential influences of the fluid and crystallized intelligence constructs of R. B. Cattell and Horn (Cattell, 1941, 1963, 1967; Horn, 1966, 1968, 1970, 1975; Horn & Cattell, 1966, 1967). Horn (1970) contends that, during development, a number of factors interact to make possible the appropriation of large sections of the "collective intelligence" of a culture (represented by crystallized intelligence, or G c ). He emphasizes the importance of the quality of both school and home environments in promoting and shaping an individual's crystallized intelligence. On the other hand, fluid intelligence (Gf), or active problem-solving ability, is purported to reflect the functioning of the neurological structures and is thus much less influenced by acculturational factors:

PAGE 17

A major implication of this theory is that at some order of analysis in factoring among ability performances two very broad factors, each having properties of what is putatively intelligence, should be distinguishable. One of these factors should be defined primarily by abilities which can be seen to be quite closely related to intensive acculturation, whereas the other should be defined primarily by abilities. which are less closely linked to this. (p_. 444) The greater sensitivity of verbal ability, as compared to active problem-solving ability, to environmental factors suggests that ordinal position influences can be best explained in terms of social rather than physiological factors (Murray, 1971). If Horn's contention is correct, and if environmental factors within the family are the major determinant of ordinal position effects on achievement-related measures, there should be a significant birth order effect on G c measures, whereas there should be no significant birth order effect on Gf measures. Due to the important socio-psychological factors related to ordinal position, it was predicted that firstborns would score higher than later borns on achievement-related variables (G c ). Firstborn superiority was not predicted for G f measures, which purportedly reflect neurophysiological functioning. A large battery of tests, hypothetical^ related to G f , G c , and other closely related cognitive abilities, were administered to 111 college students and factor analyzed in order to select optimal marker variables of fluid and crystallized intelligence. Four linear structural equation models representing the economic, Zajonc and Markus confluence, physiological, and socio-psychological points of view are illustrated in Figure 1. In addition to comparing these models in terms of the overall variance accounted for in the observed results and the predictive ability of the individual

PAGE 18

I
PAGE 19

11 hypothesized components of each model, certain assumptions concerning causal order were examined using path analysis procedures. Although path analysis should not be considered a method for demonstrating causality, it can be used to examine a set of causal assumptions imposed on a system of relationships. Different deterministic relationships between the predictor variables and achievement-related performance were posited by the four models. Essential to the assumptions of the models was the order of inclusion of the predictors, which implies the existence of certain relationships among the variables, including direct and indirect influences on the criterion variable. Moreover, potentially redundant predictors can only be identified with later inclusion in the model. Multiple regression procedures are used to estimate the relative strength of the individual paths. These results indicate the magnitude of direct and indirect (redundant) influences in terms of the hypothesized causal order among the variables posited for each individual model. Since Cattell and Horn (Cattell, 1941, 1963, 1967; Horn, 1966, 1968, 1970, 1975; Horn & Cattell, 1966, 1967) postulate an important deterministic relationship between G f and G c> the G f variable was entered first in the three models which included G f as a predictor variable. The order of inclusion of the predictor variables and the underlying assumptions involved will be discussed separately for each model. Predictors of G in the economic model were included in the following order: G f , family size, and SES. Family size was considered to be independent from G f since no neurophysiology! correlates of family size were discussed in this model. Due to the attenuation of family size influences often reported for higher SES levels (Anastasi, 1956; Kennett, 1973; Marjori banks et al . , 1975; Misbet & Entwistle, 1967; Schooler, 1972,

PAGE 20

12 1973), SES was considered to influence G both directly (economic advantages and restrictions in terms of educational and occupational opportunities) and indirectly (through differential influences on the family size correlates of achievement). SES was therefore entered as the third variable in the multiple regression analysis for this model. Very different relationships were suggested by Zajonc and Markus in the confluence model. The proposed handicap of last-born and only children was the first variable to enter due to its postulated direct influence on G , independent of the other predictors. For example, Zajonc and Markus (1975) treated "handicap" as a dummy variable—equal to for last-born and only children or 1 otherwise--in the regression equation used to describe the Belmont and Marolla (1973) birth order-IQ results. Since neurophysiological influences were not discussed by Zajonc and Markus, G f was not included as a predictor variable. The second and third variables to enter into the equation were sibling age spacing and family size, respectively. Although highly confounded, both variables were considered important mediators of intellectual ability in this model. Since age spacing was considered a potentially more important correlate of intellectual ability, it was included before family size. The SES variable was entered last due to its complex interrelationships with the other variables postulated by the confluence model. The first variable entered into the analysis of the physiological model was G f , purported to reflect neurophysiological functioning and integrity. Since economic factors were discussed in terms of their role in the mediation of certain physiological influences (including diet, prenatal care, and health), SES was entered next. Birth order--discussed in terms of increased genetic error, "uterine fatigue," and perinatal

PAGE 21

13 complications (Forer, 1976; Warren, 1966; Weller, 1967)--was postulated to be only a potentially important influence on neurophysiological functioning (G f ), and was thus the third variable to be considered in this multiple regression analysis. The first variable entered into the socio-psychological model was G f , as discussed above. Glass et al . (1974) contend that the higher verbal abilities and educational aspirations of firstborn children may be particularly prevalent at higher SES levels. The second and third predictor variables to enter this equation were firstborn (versus laterborn) and SES, respectively. The SES variable was entered last due to its potentially important mediation of birth order influences and possible direct influence on achievement-related performance through economic resources and limitations (Forer, 1976). Thus, the results of the present study should not only indicate the predictive ability of these four models in terms of G performance, but the evaluation of the individual components of the models may uncover subtle interrelationships, both direct and indirect, among the predictor variables themselves and with regard to their prediction of achievementrelated performance. Moreover, the present results should hopefully clarify certain issues pertaining to the relationships between ordinal position and aptitude (theoretically represented by G f measures) versus achievement-related measures (theoretically represented by G ability).

PAGE 22

CHAPTER II METHOD AND DATA ANALYSIS Overview and Design In an effort to evaluate the influence of various family size variables, birth order variables, and fluid intelligence (G f ) on crystallized intelligence (G ), three separate procedures were carried out. A preliminary factor analysis was performed on a large battery of tests administered to 111 university students in order to identify optimal marker variables for G f and G . Following the factor analysis, the most highly loading tests for each of these two constructs were combined and utilized as variables in four linear structural equation models relating G c to various hypothesized determiners, including G f and several sibling/family relationship variables. The models were evaluated separately for two groups: (a) the original college student sample; and (b) a sample consisting of 65 high school students. Study 1 Subjects . A sample, consisting of 1 11 university students, including seven graduate students, was recruited for test administration by classified advertisement in the campus newspaper. Since this sample was to be included as part of a larger grant-supported investigation of cognitive 14

PAGE 23

15 abilities and aging, each subject was paid $10 for participation. The sample consisted of 57 male and 54 female white, middle and upper-middle SES students. The age range of the subjects was 17 to 30 years, with a mean age of 20 years. Instruments . A battery of 21 tests which were hypothesized to represent G f , G , and other closely related cognitive factors were administered to the subjects by the author. The group test administration was approximately four hours in length, with two 10minute rest intervals. The tests, in their order of presentation, included the G f -G "Sampler," an unpublished battery of tests constructed by Horn which consists of three tests designed to represent G f and two tests designed to represent G> Q , the Army Alpha Examination (First Nebraska Edition, Guilford, 1938), four subtests of the Primary Mental Abilities (PMA) battery (Thurstone & Thurstone, 1949), three Educational Testing Service (ETS) measures of French, Ekstrom, and Price (1963), and the Omelet Test from the Structure-of-Intellect measures (Koepfner & Guilford, 1969). A description of each measure follows: 1. Vocabulary (Horn "Sampler"). Subjects are asked to select the word which is closest in meaning to a given word from five test words. There are 20 items in this subtest. This test, like all of Horn's test in the present investigation, was untimed. 2. Analogies (Horn "Sampler"). This test consists of fairly simple verbal analogies in the form: "A is to B as C is to ." In each of 15 items .the subject is given five test words from which to choose the appropriate answer. Since the words are relatively simple, perceiving

PAGE 24

16 the relationship between them is theoretically what is evaluated, which should reflect Gf ability. 3. High-Level Vocabulary Analogies (Horn "Sampler"). This test involves exactly the same format as Horn's Analogies Test. However, this test consists of more difficult verbal analogies in the sense that it contains higher-level vocabulary words designed to involve more advanced verbal ability, thus reflecting G c ability. 4. Letter Series (Horn "Sampler"). After examining a sequence of from 5 to 15 letters, the subject is required to write the next letter in the series in a blank space which follows the sequence (15 items). 5. Figural Relations (Horn "Sampler"). Subjects are presented with a square form with geometric, simple figural, and letter patterns in 3 out of 4 quadrants, and, on the basis of the interrelationships of these figures, must select from several choices the appropriate figure for the fourth quadrant (20 items). 6. Following Directions (Army Alpha). Subjects must carry out verbally presented instructions on items consisting of geometric and letter patterns. Short-term memory ability is required for the rapid execution of these 12 items. 7. Arithmetic Problems (Army Alpha). This test consists of verbal problems which require arithmetic manipulations. 8. Common Sense (Army Alpha). This test involves questions designed to evaluate practical judgment and reading comprehension. 9. Synonym-Antonym (Army Alpha). Subjects must designate whether 40 sets of word pairs are the same or opposite in meaning. 10. Disarranged Sentences (Army Alpha). After mentally re-arranging a set of words in order to make a sentence, subjects are instructed to

PAGE 25

17 indicate whether each of the resulting 24 sentences is true or false. 11. Number Series Completion (Army Alpha). Subjects must examine the relationship which exists within a series of numbers and indicate what two numbers should logically follow (for each of 20 number series). 12. Analogies (Army Alpha). After determining the relationship between a pair of words, subjects must select one of a group of four words which is related to a second word in an analogous manner. 13. Information (Army Alpha). Subjects must answer 40 multiple-choice items which involve general information. 14. Word Grouping Test (PMA). In this test the ability to understand and group words of the same type is involved. Subjects must select which of five presented words does not belong with the other words. Each of • the PMA subtests is timed. 15. Letter Series Test (PMA). This test consists of 20 letter series, each of which has a specific pattern. The task is to select which of five letters should logically follow for each individual letter sequence. 16. Spatial Relations Test (PMA). Subjects are presented with 30 geometric forms, to the right of which are five similar or identical forms which have been rotated to different positions. The task is to identify which of these forms are identical to the original form. 17. Number Series Test (PMA). This test consists of 20 multiple-choice items in which the task is to select the number which should come next in a distinctive pattern of numbers. 18. Addition Test, N-l (ETS). This two-part test assesses how quickly and accurately subjects can add series of numbers. The time limit is two minutes for each 60-problem part.

PAGE 26

18 19. Number Comparison Test, P-2 (ETS). This is a highly speeded test consisting of two parts. The task is to compare two series of numbers (the pairs of number sequences are of different lengths) and indicate whether or not they are the same by inserting an X between the number series that are not identical. 20. Identical Pictures Test, P-3 (ETS). This is a two-part test in which subjects must select from five alternative pictures the one that matches a target picture. 21. Omelet Test (Hoepfner & Guilford, 1969). In this test the task is to rearrange sets of four letters each to make familiar words. This test was constructed to measure Guilford's Structure-of-Intellect factor of convergent production, defined as the "generation of logical conclusions from given information where emphasis is upon achieving unique or conventionally best outcomes" (Guilford & Hoepfner, 1971, p_. 20). Variables . The variables included for analysis were determined by the assumptions of the four models, which are illustrated in Figure 1. The G construct was the criterion variable for each model. The predictor variables included G f , SES, birth order, family size, firstborn (as opposed to later-born siblings), age spacing between siblings, and the "handicap" of the only child and last-born child (Zajonc & Markus, 1975). The SES scale utilized in this study was a composite of three separate standardized variables: (a) prestige value of father's occupation (Treiman, 1977); (b) father's educational level; and (c) mother's educational level. Educational level was rated along a 6-point scale, from "not a high school graduate" to "doctorate." The number of children in the families in this sample ranged from 1 to 13, with a mean family size of 3.432. There were 40 firstborns (including four only children).

PAGE 27

Birth order ranged from 1 to 11 (mean 2.234). In order to normalize the extremely skewed birth order distribution, birth order was divided into three categories before any further analysis took place. These categories were: (a) firstborn (n = 40); (b) secondor third-born (n = 56); and (c) fourthor later-born (n = 15). The adjusted percentages of the total sample for these three categories were 36%, 50%, and 14%, respectively. The sample included 36 middle children and 35 lastborn children. Due to certain assumptions specific to the different models, two dichotomizations of birth order frequencies were calculated. The firstborn (versus later-born) construct was a predictor variable in the socio-psychological model. The "last-born/only-child handicap" was included as a variable in the Zajonc and Markus confluence model. This category included 39 subjects. In addition, this model proposed that age spacing between siblings would be an important correlate of intellectual development. Age spacing was calculated as the sum of two separate indices, the number of years to the next oldest sibling and the number of years to the next youngest sibling. Since this model proposed that an arbitrary maximum intellectual level of 100 would be attained at approximately 19 years of age, 19 years was used as the maximum age separation index. For example, the age spacing index for an only child would be (19 + 19) = 38. This somewhat crude index of age spacing between siblings was utilized die to the extreme difficulty encountered when attempting to calculate each individual subject's intellectual level according to the sigmoid function presented by Zajonc and Markus (1975). This equation was analyzed in terms of average age gaps, not individual age separation data. Other approaches to this age gap index

PAGE 28

zu have included mean age separation between siblings (McCutcheon, 1977) and median number of months separating the siblings in the entire family (Nuttall et al., 1976). The means and standard deviations of the variables for the college student sample are listed in Table 1. Factor Analysis . A 21-by-21 variable correlation matrix was computed from the scores of the 111 subjects on the 21 measures. The factor analysis was carried out with the Exploratory Factor Analysis Program (EFAP), devised by Joreskog and Sorbom (1976a). Rotation was by the Promax method, utilizing the Varimax orthogonal rotation procedure as the basis for the subsequent oblique solution. Maximum likelihood procedures (Joreskog & Sorbom, 1976b) of factor extraction were utilized to obtain 4through 10-factor solutions. The most easily interpretable solution appeared to be the 4-factor solution. The factor loadings and factor correlation matrix are found in Table 2. The first factor seemed to be a numerical facility factor which had substantial loadings on the two number series tests, the Addition Test, Arithmetic Problems, and also the Omelet Test. Inclusion of the Omelet Test suggests the possibility that convergent production may be involved in the first factor. Factor II appeared to be a verbal ability factor, hypothetically representing 6 . Tests which had interpretable loadings on this factor included Vocabulary, High-Level Vocabulary Analogies, Synonym-Antonym, Information, Common Sense, Disarranged Sentences, Common Analogies, and the Word Grouping Test. The loading of Horn's Common Analogies subtest on this factor was somewhat surprising since this measure was constructed as a marker of G f ability. However, the low correlations of this test with other nonverbal measures

PAGE 29

21 Table 1 Means and Standard Deviations for College Student Sample Mean Standard Deviation Age 20.2160 1.9970 Family Size 3.4324 1.6713 Birth Order 2.2340 1.4270 Mother's Education 2.8200 1.1380 Father's Education 3.3578 1.3846 Occupational Prestige 50.4364 13.1961 Age-Spacing Index 17.9550 8.8360 Vocabulary 3 10.8830 2.0960 Vocabulary Analogies 3 10.6220 2.2360 Common Analogies 3 11.6577 1.8360 Letter Series 3 12.4230 1.8660 Figural Relations 3 14.6130 2.9300 Following Directions 5 9.4050 1.7960 Arithmetic Problems 5 11.9910 2.8170 Common Sense b 11.5500 2.4600 Synonym-Antonym 5 30.8010 5.1610 Disarranged Sentences 5 20.2700 2.6250 Number Series 5 14.6670 3.3070 Analogies 5 34.5860 4.6720 Information 5 29.3330 3.2680

PAGE 30

22 Table 1 continued Mean Standard Deviation Word Grouping 23.3330 3.1400 Letter Series 15.1530 2.7640 Spatial Relations 120.4230 13.1670 Number Series 11.5860 2.8870 Addition 01 43.1980 13.2110 Number Comparison 01 26.1350 5.9980 Identical Pictures 01 79.4180 11.4440 Omelet Test e 21.5590 4.8570 jjHorn "Sampler" Army Alpha Examination °Primary Mental Abilities Test ^Educational Testing Services e Sheridan Psychological Services

PAGE 31

23

PAGE 32

24 Table 2 continued

PAGE 33

25 may have been a result of the restriction of range in this particular sample. It would be informative to analyze the intercorrelations of these tests in a more heterogeneous sample. Factor III was a highly speeded factor, which had its highest loading on the Number Comparison Test. Factor IV, hypothesized to represent Gf, had interpretable loadings on the two letter series tests, Figural Relations, the Spatial Relations Test, , Number Series Completion, and the Following Directions Test. Factor interrelationships were moderately oblique ranging from .263 to .453. After interpreting the factor loading matrix, it was decided that the five best marker variables of the G^ construct were: (a) the PMA Letter Series Test; (b) the PMA Spatial Relations Test; (c) the Army Alpha Number Series Completion Test; (d) Horn's Letter Series subtest; and (e) Horn's Fi'gural Relations subtest. Since the two letter series tests were measures of the same ability, it was decided to combine them as a single measure in the Gf composite. The measures selected to represent G c were: (a) the PMA Word Grouping Test; (b) the Army Alpha Synonym-Antonym subtest; (c) the Army Alpha Information subtest; (d) Horn's Vocabulary subtest; and (e) Horn's High-Level Vocabulary Analogies subtest. The marker variables for both composites were individually standardized before being added together. As has been found in the past (Horn, 1970, 1975) and is expected theoretically, the G f and G c composite scores were substantially positively intercorrelated, r = + .3674, £<.01. Study 2 Subjects . The sample consisted of the same 111 university students examined in the previous study.

PAGE 34

26 Method . A multiple regression procedure was employed to examine the results in terms of both the overall variance accounted for in the observed data and the hypothesized causal orderings for the individual components for each of the models described in Chapter 1. Multiple regression was the method of choice since it is a statistical technique which permits analysis of the relationship between a criterion variable and a set of predictor variables. A hierarchical specified inclusion method was used. The criterion variable for each model was G c . This procedure permits the addition of variables to the regression equation in a predetermined order. The estimation of the relative strengths of the individual paths indicates the magnitude of direct and indirect influences in terms of the hypothesized causal order posited for each model. f The order of inclusion of the predictor variables was determined a priori by the author with regard to the hypothesized influences of family configuration variables and SES on intellectual aptitude and achievement as specified by the individual models. In the economic model, such influences are discussed in terms of SES and the related economic advantages and restrictions. Physiological mediators of birth order differences in cognitive ability are considered in the physiological model. In the confluence model, ordinal differences in intellectual ability are discussed in terms of family size and age spacing between siblings. The relationship of social and psychological influences and birth order differences are emphasized in the socio-psychological model. These models, which are illustrated in Figure 1, are presented in more detail in Chapter 1. In the hierarchical decomposition procedure, each predictor variable is added to the equation in a single step, and the increment in R^, or the

PAGE 35

27 explained variance, at each step is considered to be the component of variation attributable to that particular variable, given the contribution of the other variables to the equation. The first predictor is evaluated by the £_ ratio: r 2 l/l F = =a (1 R^12,....,k)/(N k 1) where r 1 is the incremental sum of squares due to the independent 2 variable X-, 1 R_ -12 ,k_ is the sum of squares of the residuals, N_ is the sample size, and k_ is the number of independent or predictor variables. Each successive predictor variable is tested by an £_ ratio with 1 and (N_ k_ 1) degrees of freedom. This procedure indicates the total influence of each successive variable since it refers to adjustments only for tiiose variables that precede each given variable in a predetermined order of inclusion. The £ ratios were calculated for each model and for the inclusion of each predictor variable within each model. In addition, the models were compared to each other for overall variance accounted for in terms of tiie obtained data. The computer methodology used to evaluate these results was outlined in the Statistical Package for the Social Sciences ( SPSS ), edited by Nie, Hull, Jenkins, Steinbrenner, and Bent (1975). Results . Since G ability may be much more sensitive to acculturational and environmental influences than G f ability, which purportedly reflects neurophysiological functioning, it was hypothesized that there would be a significant birth order influence on the G construct, but not on the G, construct. As predicted, while the correlation between birth order and G was significant (_r = -.1633, p_<.05), the correlation

PAGE 36

28 between birth order and G f was not significant (r. = .0710). Since no significant birth order patterns were found for the G f construct, it appears that birth order differences are not mediated by neurophysiological factors in the present sample. However, the prediction that, due to certain socio-psychological influences related to ordinal position, particularly in the restricted SES range of the present sample, that firstborns would obtain higher scores than later borns on G but not on G f measures was not upheld. While the correlations of the firstborn (versus later-born) variable on the G and G f constructs were in the predicted direction (r_ = .1398 and r_ = .0393, respectively), neither correlation was statistically significant. Results of the multiple regression analyses are listed in Table 3. Although three of the models — economic, physiological, and sociopsychological--obtained significant overall £ ratios (p_<.01), it is evident that these results were attributable to the highly significant £ ratio obtained in the first step in the equations, i.e., the regressi of G f on G . None of the other predicted paths between the predictor variables and G were significant for any of the models. Regarding the Zajonc and Markus confluence model, neither the overall £ ratio nor any of the predicted paths were significant. The overall variance explained by the economic, confluence, physiological, and socio-psychological models was approximately 18%, 8%, 17%, and 17%, respectively. Thus, although the economic model appeared to be the most predictive model in terms of variance accounted for, the explained variance was not large in absolute terms. on

PAGE 37

29 U-| co CD J3 Q. E oo CD en o c_> o CD -Q o io •1 — to 00 CD s_ CO CD or. 0) Q. +-> 3 X * CO co LO to -CD. CD Q. E: OO O) CD c: ro _c CD ^1 CM IX. *

PAGE 38

30 CU c o o CO 0) (13 cu s-l cu Q. E oo 0J CD ro O °° l a: 00 , a: ro cm *

PAGE 39

31 Therefore, upon initial investigation, it appeared that none of the four models could effectively account for G c in the present sample. The possibility was considered that these negative results were largely attributable to the restriction in range of the present university student population. Upon closer analysis, however, certain unexpected relationships within this particular sample were uncovered. It became apparent that, besides restriction in range, certain relationships related to SES and family size which might be specific to a college sample were statistically suppressing certain predicted family configurational influences. Specifically, these relationships appear to have suppressed the correlations between several of the predictor variables and the criterion variable G . It was decided to use partial correlation procedures to separate out some of these relationships. Since partial correlation statistically controls for the effects of one or more specified variables on the relationship between other variables, this technique can be utilized to uncover suppressor relationships. The general form of suppressor influences can be described as: A shows no relationship to B because A is negatively related to C which is positively related to B. Thus, when the effects of C are controlled, A may be positively related to B. Further assumptions and issues concerning such suppressor relationships will be discussed in further detail after discussion of the unexpected SES-family size relationships found in the college student sample. SES-family size relationships which may be specific to university student samples . Several unexpected relationships among the predictor variables in the present sample may have suppressed their relationship to G . These results rendered straightforward interpretation of

PAGE 40

32 the models difficult. It is conceivable that certain possibly arti factual relationships may be specific to college student samples. Such relationships may complicate attempts to generalize from more heterogeneous populations regarding family configurational influences. Schooler's (1973) contention that birth order effects do not appear to persist into adulthood may be particularly apparent in a college sample. Firstborn overrepresentation appears to be the stablest finding reported in studies of university students (Altus, 1966; Capra & Dittes, 1962; Schachter, 1963). The finding that 36% of the current sample were firstborns was slightly, although not significantly, higher [1_ = 1.3803, jo^.IO) than the expected 30% firstborn representation for this particular age group (Dunn, 1956). Family size effects . Kennett (1973) discusses the changing relationship between family size influences on intellectual ability. He suggests that the acceptance of a negative correlation between family size and intelligence must be qualified by the possibility that SES may influence the relationship. Thus, the correlations may vary from social class to social class. In this regard, perhaps the most surprising finding in the present college sample was the significant positive correlation between family size and SES (r = +.1972, p_ <.02). This finding was in marked contrast to the widely reported negative correlation between these two variables in the general population (Belmont & Marolla, 1973; Eysenck & Cookson, 1970; Murray, 1971; Nisbet & Entwistle, 1967; Record et al . , 1969). The observation that students in this sample who belong to larger families also tend to be of a higher SES level suggests that financial resources and related issues may play an important and perhaps decisive role in the opportunity to attend college. This posi-

PAGE 41

33 tive relationship between family size and SES might be expected to be even more substantial in a private, more expensive institution. Birth order effects . Family size and birth order are highly confounded variables, as evidenced by their substantial intercorrelation of +.4775 (p_<.001). While the economic model and the confluence model discuss family configurational influences primarily in terms of family size effects, the physiological model posits such effects in terms of sibling birth order while the socio-psychological model emphasizes the influence of firstborn versus later-born on G . Family size and, therefore, birth order are negatively correlated with SES in the general population. However, in the college student sample who participated in this study, there existed a positive although nonsignificant correlation between these two variables [r_ = +.1200, p_< .10) Despite the possibility that this positive intercorrelation may have suppressed to some extent the negative correlation between birth order and G , as was predicted by the physiological model, the obtained correlation between birth order and G was nevertheless significant c J (r = -.1633, p_<.05). Statistical Suppression Cohen and Cohen (1975) discuss the effects of three types of suppressor relationship—classical , net, and cooperative suppression-on the patterns of association between the predictor variables. In the present sample, the positive correlation between family size and SES along with the resulting effects on the criterion variable, G , is an example of cooperative suppression. Cohen and Cohen describe cooperative suppression as the case in which the intercorrelation of two independent

PAGE 42

34 variables involves a portion of their variance which is irrelevant to the criterion variable. The partialling of these predictor variables from the others enhances all indices of relationship with the criterion variable. It is a completely symmetrical phenonemon; whenever it is determined that X 2 suppresses X^ , it is also evident that X, suppresses X^. One indication of cooperative suppression is a situation in which a beta coefficient exceeds its simple r with the dependent variable and is of the same sign. With reference to Table 3, it can be seen that beta coefficients for both SES and family size in the economic model meet this criterion. An analysis of suppression effects was performed to ascertain what, if any, relationships might exist in the present sample which could have resulted in the suppression of predicted family configuration effects on G c In order to identify the nature of the specific suppressor relationships between the predictor and criterion variables, the partial correlation coefficients were calculated for several predictor variables and G c , with the potential suppressor effects of the predictor variables statistically controlled. The obtained multiple regression and partial correlation coefficients are found in Figure 2. Results for the partial correlation procedure will be discussed separately for each individual model . Economic model . With reference to the economic model, it was evident that the unexpected positive correlation between family size and SES suppressed the predicted significant correlations of both family size and SES with the G„ construct. When the effect of family size was c J partialled from the SES-G c relationship, the correlation between these two variables was significant (+.1683, p <.05). Similarly, when the

PAGE 43

Figure 2 Intercorrelations among Variables in each Model for the College Student Sample. Numbers in parentheses indicate partial correlations between the independent and criterion variables. Significance levels: *p_<.05, **p_<.01.

PAGE 44

Jfa r rpri (.1683) * — r[ .1340 '' , E C N C V, i C .3 97?' 1478 .3674^ (-.1794) Family Siz< ' nn (ZAJONC one! MARKUS) [Tes / l I .1972 / .1340 CONFLUENCE / E ami ].y Siz .1067 r ir -7 * * h .0347 . 12 2 :, V i_ Ago :ing Las -1 " :' \ On.l y-chj : h . idj cap PHYSIOLOGICAL *Gd 0710 B i r th o i .3674 0404 ' G r I a\ — £.] LED .1340 SOCIO -PSYCHOLOGICAL .367 4' (.1366) 1340 .1398 SES J -.0091 : "* J Firstborn!

PAGE 45

37 suppressor effects of the significant positive correlation between family size and SES were controlled, a modified version of the economic model was supported by the present results. The hypothesized relationships between the predictor variables and the criterion variable were significant when the suppressor effects were controlled. This model was termed "modified" because of the significant positive, not negative, correlation between family size and SES. In a more heterogeneous sample, it would appear likely that the unmodified version of the economic model would be supported due to the generally reported negative correlation between family size and SES (Belmont & Marolla, 1973; Eysenck & Cookson, 1970; Murray, 1971; Nisbet & Entwistle, 1967; Record et al . , 1969). Zanonc-Markus confluence model . Since the overall £_ ratio was non-significant, it was considered inappropriate to further analyze specific predicted relationships of this model. It is quite possible that the unimpressive predictive power of this model may have been attributable to the restricted range of the present sample and/or the unexpected relationships which may be specific to a college student population. Such unexpected trends might suppress the relationships between SES and age spacing between siblings and, in turn, between age spacing and G c Similarly, in a study of ability of 291 community college students, as measured by Factor B of the 16 PF, McCutcheon (1977) found little support for the confluence model. She concluded that this model may have limited application. The overall validity of this model might be expected to be substantially higher in a more heterogeneous sample.

PAGE 46

38 Physiological model . The generally observed negative correlation between birth order and SES, an important component of this model, was not obtained in the college student sample. However, due to this model's predictions that birth order and SES influences would be mediated through G f , which reflects neurophysiological functioning, further analyses for suppressor relationships were precluded. Since G f ability was for all practical purposes unrelated to both birth order (r_ = .0710) and SES (_r = .0404), any potential suppression effects among these variables would have been statistically trivial in this particular case. Socio-psychological model . The possibility of cooperative suppression between the firstborn construct and SES was considered. However, when th-e relationship between the firstborn predictor variable and G was partialled for SES effects, the resulting firstborn-G intercorrelation [r_ = +.1423, £>.05) failed to reach significance. Similarly, although the partial! ing of the firstborn construct from the SES-G correlation slightly improved the predictability of G from SES, the correlation (r. = +.1366, p_>.05) was still nonsignificant. Although the overall £ ratios for three of the four models were significant, when the individual models were analyzed in terms of their separate paths, they were not supported. However, when the existence of certain statistical suppressor effects was recognized and these effects were controlled by partial correlational techniques, a modified version of the economic model was supported.

PAGE 47

39 Study 3 Subjects . Due to certain relationships found in the previous analyses which appeared to be specific to a university student population, a more heterogeneous sample was utilized in the attempt to evaluate family configuration^ and SES correlates of the G c construct. This sample consisted of 65 twelfth grade students from the P. K. Yonge Laboratory School of the University of Florida. The student population of this school is stratified so as to be demographically representative of the general population of the state of Florida in terms of economic level and race. This tested sample consisted of 55 white and 10 black students, whose age ranged from 17 to 18 years (mean age = 17.37 years). There were 35 females, 30 males, including 14 firstborns, 2 only children, and 25 last-born children. Instruments . Six tests were administered to these subjects. The group administration was approximately one hour in length. The tests included three markers of G f (PMA Spatial Relations, PMA Letter Series, and the Army Alpha Number Series subtests) and three markers of G c (Horn's Vocabulary subtest, Horn's High-Level Vocabulary Analogies subtest, and the ETS Wide-Range Vocabulary Test). Although a somewhat abbreviated battery was utilized in the third study, the tests were optimal markers of G f and G ability in the initial factor analysis. They were thus considered equivalent theoretically and practically to the longer battery administered to the college student sample. The ETS Wide-Range Vocabulary Test was included because of its stabilized reliability and its particular appropriateness for a high school level sample.

PAGE 48

40 Variables . As in Procedure 2, the variables analyzed were determined a priori by the assumptions of the individual models found in Figure 1 and described in Chapter 1. The variables were fairly normally distributed, with the exception of father's educational level (fathers of 25 subjects had obtained doctorates) and the occupational prestige index (Treiman, 1977). Both variables were somewhat skewed, with relatively more subjects at the higher levels. These two variables, along with mother's educational level, were standardized before they added together for the SES index. Similarly, the individual tests were standardized before they were summed for the G f and G composites. Family size in this sample ranged from 1 to 10, with a mean family size of 3.6 children. Birth order also ranged from 1 to 10 (mean = 2.4963): The age spacing index for the confluence model was calculated in the same manner as in Study 2, that is, the sum of the number of years to the next oldest sibling and the number of years to the next youngest sibling. Also, the two birth order dichotomizations , firstborn (versus later-born) and the "last-born/only child handicap," were included as predictor variables exactly as they were for the college student sample. The means and standard deviations of the variables studied in the high school sample are listed in Table 4. The intercorrelations between the variables for the individual models are found in Figure 3. Method . As in Procedure 2, multiple regression procedures were used to examine the amount of variance accounted for in terms of the observed data and the direct and indirect influences of the hypothesized paths in terms of G with regard to the economic, physiological, Zajonc and Markus confluence, and socio-psychological models. Since the

PAGE 49

41 Table 4 Means and Standard Deviations for High School Sample Age

PAGE 50

Figure 3 Intercorrelations among Variables in each Model for the High School Student Sample. Significance levels: *£<.05, **p_ <.01 .

PAGE 51

43 ; . ,, | 6136**_*| 1 . v U N w ..'! i L, -.413c -.2104 .5-190 Family Si z J I ( ZAJOMC ond MARKUS) /L_£!L S J 4, / 4138 CO;:: ! i':mce 6J 36 I V . 2 714 -.5C92 G . J V L575 .0759 _ i .... borti/ hi Id dicap /. n PHYSIOLOGICAL LTJ0526 v Birth ^£3' .549 i "s . r 3330 /' L ~ J D:£] .6136 a 5490*. ? *» [3] SOCIO-PSYCHOLOGICAL st;s 6136 / 3 300 I i | -.2911*d» Fir: tborn

PAGE 52

44 assumptions of the individual models remained the same, the hierarchical specified inclusion method with G as the criterion variable which was described in Study 2 was used. £ ratios were calculated for each model and for the successive inclusion of each predictor variable within each model. The individual models were also compared to each other for the overall variance accounted for in terms of the obtained data. Results . The results of the multiple regression analyses are listed in Table 5. It is apparent that all of these models yielded better predictions of G performance in the more heterogeneous high school sample examined in this procedure. Not only were the overall £ ratios for all of the models significant (p_<.01), but the overall variance explained by the economic, physiological, Zajonc-Markus, and socio-psychological models was approximately 51%, 522, 40%, and 54 , respectively. However, while the variance accounted for by each model was substantial, the regressions of all of the individual predictor variables on G were significant only for the economic and sociopsychological models. Results for the multiple regression analyses will be discussed separately for each individual model. Economic model . In contrast to the unexpected positive correlation between family size and SES (jr = +.1972, p_<.02) found in the more homogeneous college sample, the predicted negative correlation between these two variables was found for the high school population (r_ = -.2104, £<.05) This negative correlation has been widely reported in other studies (Belmont & Marolla, 1973; Eysenck & Cookson, 1970; Murray, 1971; Nisbet & Entwistle, 1957; Record et al., 1969) and is an essential component of the economic model, in which it is proposed that birth order and family size influences are largely reducible to SES and the economic factors

PAGE 53

45 CD ro CD CL E 03 CO O o o CO CD o 4(/) CD ro si ro So 4-> u -o cu s_ c/> cu Scn CD OJ ro +J O) -Q 5-1 CD CDCD CD ro <*! CM , LO CO o *

PAGE 54

46 "O CD =3 c o u LO CD -Q ro rO +-> O) JO S-| CD CL cn ro CO cn CM cc\ CNJ -X

PAGE 55

47 involved (Anastasi , 1956; Kennett, 1973; Schooler, 1972, 1973). Moreover, the predicted significant increments to the explained sum squares attributed to each successive predictor variable (G f , family size, and SES) in the hierarchical decomposition procedure were statistically supported. It was concluded that, in addition to the ability to account for a very substantial amount of variance in the obtained data, an unmodified version of the economic model was fully supported in the more heterogeneous high school sample. Zajonc-Markus confluence model . In contrast to the findings with regard to this model (Zajonc, 1976; Zajonc & Markus, 1975) in the college student sample, the overall significant £ ratio for the high school sample permitted the further analysis of the individual predicted relationships of the confluence model. After examining these results, however, it became evident that, while the age-spacing index, family size, and SES were adequate predictors of the criterion variable, the "handicap" of lastborn and only children (ostensibly due to their lack of opportunity to serve as teachers to younger siblings) was not an adequate predictor of G performance. Therefore, the confluence model was not fully supported by the obtained data. Physiological model . In the physiological model (Warren, 1966; Weller, 1965), emphasis is placed on physiological mediators of birth order differences in cognitive ability, such as mother's age, length of labor, and perinatal complications. Again, an overall f_ ratio and a substantial amount of the variance in G performance accounted for permitted further analysis of the individual components of this model. The first two predictor variables in the multiple regression equation, G f and SES, were significantly predictive of G scores. However, birth order, whose

PAGE 56

43 correlation of -.1752 (p. >.05) with G c failed to attain significance in this sample, also failed to add significantly to the explained sum of squares for the physiological model. Thus, it was concluded that this model was not supported by the data. Socio-psychological model . Proponents of this model (Bradley, 1968; Burton, 1968; Glass et al . , 1974; Murray, 1971; Nuttall et al . , 1976; Start & Start, 1974) discuss family configuration influences in terms of significant social and psychological factors. The variance accounted for in terms of the data from the high school sample was 54% for this model, the most substantial of all the models. Also, after analyzing the individual paths, it was evident that the predictor variables, G f , firstborn (versus later-born), and SES, were each significant predictors of the criterion variable, in terms of both simple correlations and individual paths. Summary . After analyzing the economic, confluence, physiological, and socio-psychological models in terms of both total variance accounted for and in terms of the individual paths, the models which were fully supported were the economic and socio-psychological models, which explained 51% and 54% of the variance, respectively. Although both models were supported, the question as to which model was the optimal predictor of G performance in the heterogeneous high school sample had to be considered. The overall £ ratios for both models were highly significant (£<.01). Also, highly significant IF ratios (£<.01) were obtained for both G f and SES, predictor variables in both models. However, when family size (the second variable to enter the regression equation of the economic model) was compared to firstborn (the second

PAGE 57

49 variable of the socio-psychological model), it was found that the £ ratio for the firstborn variable was 12.2787 (df = 1/62, jd <.01), more significant than the £ ratio for family size (£ = 6.4335, df_ = 1/62, jd<.05). Thus, when the £ ratios for the individual hypothesized paths were evaluated, it was evident that the predictor variables of the sociopsychological model were more strongly related to G than were the individual components of the economic model. It was concluded that-although both the economic and socio-psychological models were fully supported by the data--the socio-psychological model was the optimal predictor of achievement-related performance in this sample, in terms of both overall variance accounted for and with regard to the significance levels of the individual hypothesized paths.

PAGE 58

CHAPTER III DISCUSSION Although sibling constellation correlates of intellectual abilities have been studied extensively, the results have often been difficult to interpret. The contradictory findings and tentative conclusions are partially attributable to the highly confounded nature of birth order and family size variables. Interpretations are further complicated by the relationships between ability, achievement, and personality and the intercorrelations of SES with both family size and the personality-ability-achievement complex (Eysenck & Cookson, 1970). In the present study, an attempt was made to separate out some of the confounded relationships between family configuration, SES, and the performance on ability and achievement measures. In the initial procedure, a battery of 21 tests was administered to 111 university students. After factor analyzing the resulting correlation matrix for optimal marker variables of the fluid intelligence (G. or active problem-solving ability) and crystallized intelligence (6 or achievement-related performance) constructs of Cattell and Horn (Cattell, 1941, 1963, 1967; Horn, 1966, 1963, 1970, 1975; Horn & Cattell, 1966, 1967), four linear structural equation models 50

PAGE 59

51 relating G to various hypothesized predictor variables were evaluated for two separate samples, the original university student sample and a more heterogeneous sample of high school students. The four models emphasized the importance of either economic, "intellectual environment" (confluence), physiological, or sociopsychological influences in the mediation of birth order and/or family size effects on intellectual ability. Proponents of the economic model (Anastasi, 1956; Kennett, 1973; Nisbet & Entwistle, 1967; Schooler, 1972, 1973) contend that family size effects are largely reducible to SES and related economic advantages and restrictions. The confluence model (Zajonc, 1976; Zajonc & Markus, 1975) considers ordinal differences in intelligence in terms of family' size and age spacing between siblings and the resultant influences of the family's immediate "intellectual environment" on intellectual development. In the physiological model (Warren, 1966; Weller, 1965), it is suggested that physiological factors are important mediators of birth order differences in cognitive ability. Advocates of the socio-psychological model (Bradley, 1968; Burton, 1963; Glass et a!., 1974; Nuttall et al . , 1976; Start & Start, 1974) contend that birth order, particularly firstborn academic superiority, appears to be importantly related to significant social and psychological factors. Such influences are often discussed in terms of personality and motivational differences which may be manifested in individuals of differing family sizes and ordinal positions. Issues and assumptions relevant to these models were discussed in the first chapter. It was emphasized that these theories are not mutually

PAGE 60

52 exclusive. It was suggested that, as SES level increases, physiological, "intellectual environment," and economic variables might conceivably exert less influence, and that socio-psychological variables might exert relatively more influence on family configurational effects. University Student Sample It was predicted that the socio-psychological model would be the best predictor of achievement-related performance, or G , in this sample. In the second procedure, multiple regression linear structural equation techniques were utilized to evaluate the amount of variance accounted for in terms of the observed data and for the individual hypothesized paths for each of the models in a college student 'sample. The overall significant £ ratios for the economic, physiological, and socio-psychological models were found to be attributable to the significant positive regression of G f on G (_r = +.3674, £<.01), the variable entered at the first step in each of these models. The overall £ ratio of 2.2989 for the confluence model was not significant. The results of this multiple regression analysis are listed in Table 3. Therefore, while the prediction that ordinal position would significantly influence G performance (_r = .1633, £_<..05) but not G f performance (r = .0710) was supported, none of the models appeared to be adequate predictors of the G construct in this sample. However, upon closer examination of the results, certain unexpected relationships among the predictor variables were observed. It seemed reasonable that such relationships might be specific to a college sample, as they have not been generally

PAGE 61

53 reported in studies of more heterogeneous samples. These relationships acted to statistically suppress the validity of several of the predictor variables to explain performance on the G composite. It was concluded that the contention that ordinal position effects might not persist into adulthood (Schooler, 1972, 1973) may be particularly evident in a university student sample. While the attenuation of birth order and family size effects in higher SES samples has been widely reported, the results of the present procedure suggested that such effects might not only be attenuated, but might be reversed, in a college population. These reversals appear to be largely attributable to certain relationships which may be specific to such a sample. Specifically, the unexpected significant positive correlation between family size and SES (jr = +.1972, p_ < .02) was in marked contrast to the often reported negative intercorrelation of these two variables in more heterogeneous samples (Belmont & Marolla, 1973; Eysenck & Cookson, 1970; Murray, 1971; Nisbet & Entwistle, 1967; Record et a!., 1959). This unexpected finding was interpreted in terms of financial resources and limitations, which play an important and perhaps decisive role in college attendance. Given this positive relationship between family size and SES in addition to the highly confounded nature of the relationship between family size and birth order, it was not surprising to find a positive, although nonsignificant, correlation between birth order and SES (r = +.1200, p_<.10). The effect of these relationships was to alter the hypothesized interrelationships among certain predictor variables, which in turn rendered straightforward interpretation of the individual models difficult.

PAGE 62

54 After further analysis of the data, it was determined that cooperative suppression, as described by Cohen and Cohen (1975), existed among several of the predictor variables. When partial correlation procedures were utilized to statistically control for these suppressor effects, a modified version of the economic model was supported, in terms of both overall predictive significance and with regard to the hypothesized relationships of the individual paths and the achievement-related construct. The economic model was termed "modified" due to the significant positive, not the predicted negative, correlation between family size and SES. Even with suppressor effects controlled, the prediction that the socio-psychological model would be the optimal predictor of G performance in this sample was not supported. Reasons for this result were not obvious, although the unexpected positive relationships between SES level and the family size/birth order variables appear to have been an important contributing factor to these negative results. Economic restrictions and other issues related to SES might conceivably exert less influence on the opportunity of firstbornas opposed to later-born--siblings to receive a higher education. The widely reported finding of firstborn overrepresentation at the college level (Altus, 1966; Capra & Dittes, 1962; Schachter, 1963) has led some investigators to contend that firstborns may be more able (Nichols, 1964; Terman, 1925) or more achievement-oriented (Breland, 1974; Burton, 1968; Forer, 1976; Glass et a!., 1974; Nuttall et al., 1976). However, in light of the present findings, the

PAGE 63

55 possibility that economic factors importantly mediate firstborn college attendance should also be considered. High School Sample Due to certain unexpected relationships in the previous procedure which might conceivably be specific to a college student sample, it was decided to evaluate the ability of the four models to predict G c performance in a more heterogeneous sample. Six tests which were assumed to represent G f or G ability were administered to 65 high school seniors. The results for the hierarchical specified inclusion methods for this sample are listed in Table 5. It was found that all of the models were better predictors of G performance in terms of overall variance and with regard to the individual paths. In contrast to the college student sample results, the predicted negative correlation between family size and SES (r_ = -.2104, p_<.05) was obtained for this more heterogeneous sample. However, while the overall F ratios for all of the models were significant, the regressions of the individual predictor variables on G were significant only for the economic and socio-psychological models, which explained approximately 51% and 54% of the variance, respectively. It was concluded that, not only were the economic and socio-psychological models fully supported in this sample, but that they were both able to account for very substantial amounts of the variance in the prediction of G performance. In an effort to determine which of these two models was the most predictive of G , the individual paths postulated by these models were examined. Due

PAGE 64

56 to the observation that the firstborn variable of the sociopsychological model was a more highly significant predictor of G performance than was the family size variable of the economic model, it was concluded that the hypothesis that the socio-psychological model would most adequately explain achievement-related performance was supported. It was evident that the current versions of the physiological and Zajonc and Markus confluence models were less adequate predictors of G c performance, particularly for the more heterogeneous high school sample. This observation might have important implications with regard to future consideration of these models. It is conceivable that modified versions may have to be considered. For example, the present results indicate that the Zajonc and Markus "last born/only child handicap" variable may have to be more carefully formulated. This predictor variable was not significantly related to G for either sample. Moreover, contrary to the predictions, there was a trend for only children to perform somewhat better than the other subjects on G c measures. For the college sample the correlation between only child and G c was +.1438 (p_<.10), in contrast to the significant negative correlation between lastborn and G (r_ = -.1831, p_<.05). Similarly, the correlations of G with only child and lastborn for the high school sample were +.1299 (p_>.10) and -.1236 (p_>.10), respectively. Thus, it is conceivable that only children should not be included in this "handicap" category. However, due to the small number of only children in this investigation and the very different relative representations in both samples (4 only children as opposed

PAGE 65

57 to 35 lastborn children in the college sample; 2 only children as opposed to 25 lastborn children in the high school sample), any statistical comparison with regard to these constructs would have been extremely unstable. With regard to the physiological model, the contention that birth order correlates of intellectual performance might be largely physiological in origin was seriously challenged by the statistically trivial intercorrelations between birth order and G f of .0710 for the college sample and .0526 for the high school sample. This very negative result concerning the basic component of this model --the influence of birth order on the physiological mediation of intellectual ability performance-indicates the need for considerable reformulation in terms of the theoretical predictions of the physiological point of view, particularly in terms of birth order effects. A serious shortcoming appears to be the contradictory predictions associated with ordinal position. More specifically , whereas the discussion of mother's age, increased chance of genetic error, and "uterine fatigue" (Warren, 1956) indicates deleterious birth order influences for later-born children, the problems often associated with the pregnancy and birth of the firstborn child, including a smaller placenta, longer labor, and increased perinatal complications (Weller, 1965) suggest relative physiological advantages for later-born children. Before further consideration of even a modified version of this model is indicated, it is apparent that such complex predictions concerning the physiological influences of birth order will have to be more carefully isolated.

PAGE 66

58 It is apparent from the present results that certain social and psychological factors may be significant mediators of family configuration influences—particularly firstborn superiority—on intellectual achievement. Moreover, economic influences seem to exert powerful effects, both directly and indirectly, on intellectual ability performance. Implications With regard to the very different results obtained for the college student and high school student samples, it is apparent that one must specify the kind of sample involved when addressing issues related to family configuration and SES correlates of intellectual ability. Kennett'.s (1973) discussion of the importance of SES influences on the relationships between family size and intelligence seems particularly appropriate to the results of the present investigation. The failure of many studies to separate out the confounded relationships between family configuration, SES, and other related variables, which may differ widely depending on sampling techniques, may be a major contributor to the contradictory findings and tentative conclusions so prevalent in this literature. An issue which should be considered in the study of relatively homogeneous samples, such as the university students in the present investigation, is the possible restriction in range in terms of certain family configuration variables, SES, and ability. Differences in restriction in range across samples may result in difficulties in comparison and interpretation. Moreover, such restrictions may be

PAGE 67

59 differentially manifested for different variables. For example, in the college sample, SES appeared to be the predictor variable which manifested the least restriction in range. In contrast, while the SES construct had relatively more variance in the heterogeneous high school sample, the age range of from 17 to 18 years was extremely narrow. This age constant aspect of the high school sample may have been an important contributing factor to the increased correlations obtained among the predictor variables and G c . Thus, while age was not included as a predictor variable in any of the four models evaluated in this study, it is conceivable that a potentially important predictor of G c performance of the university students in this study was the age of the subjects. While it might generally be assumed that a college student population is homogeneous in many ways, this assumption may be decreasingly valid with respect to age. The age range of 17 to 30 years in this sample reflects the increasing tendency for older students to attend universities, at both undergraduate and graduate levels. Future studies of family configuration correlates of intelligence and achievement among college students may have to take this trend into account. Considerable differences in age may present problems in interpretation in such studies. For example, there existed in the present college sample a highly significant positive correlation between age and G c performance (r = +.2753, p_<.01) but a nonsignificant negative relationship between age and G f ability (r = -.0999). It is interesting to note that this age trend for these constructs is in agreement with Horn's (1970, 1975) postulated age-range functions of G f and G £ abilities. As described

PAGE 68

60 by Cunningham, Clayton, and Overton (1975), this theory predicts that: Fluid intelligence, which is purported to reflect the functioning of neurological structures, increases until the cessation of neural maturation, generally during adolescence, and then declines thereafter., In contrast, crystallized intelligence is believed to reflect cultural assimilation. In particular, it seems to be highly influenced by formal and informal educational factors throughout the life span. Assuming adequate health, crystallized intelligence is presumed to increase steadily across the adult age span. (p_. 53) However, although of theoretical interest, this cross-sectional agerelated increase on G c or achievement-related measures may be attributable to factors unrelated to Horn's contentions. For example, age and college level (freshman, junior, graduate student, etc.) are highly confounded. It is very difficult to separate the effects of age and ability in a cross-sectional design such as that employed in the present study. However, the influence of age in such samples may be an important factor which should be taken into consideration in future studies. Another potentially important issue is the possible existence of different family configuration mediators of intellectual ability performance with regard to racial background. Such race-related differences might be expected to be particularly evident in terms of SES and family size influences on intellectual ability measures. Zajonc and Markus (1975) discuss the possible relationships between family configuration variations among different national, regional, ethnic, and racial groups and differences in intellectual test performance. They suggest that differences in family configuration patterns, including larger families and closer age spacing between siblings, in the black population may be a contributing factor to

PAGE 69

61 racial differences in intellectual performance scores. The high school sample in the present investigation included ten black subjects. The student body of this particular school is stratified so as to be demographically representative of the state of Florida in terms of economic level and race. Therefore, this sample was considered to be a representative one, in contrast to the relatively homogeneous and somewhat artificial university student sample. Although it would have been interesting to examine specific patterns among the predictor variables and G , in addition to the predictive ability of the individual models, across the two racial groups of the high school sample, the small absolute number of black students and the very different relative sample sizes (10 black students and 55 white students) precluded further statistical analysis. The present investigation introduced several new approaches to the study of family configuration and intellectual achievement which may possibly have important application for this research area. Specifically, the use of the G f and G c constructs of Cattell and Horn (Cattell, 1941, 1963, 1967; Horn, 1966, 1968, 1970, 1975; Horn & Cattell, 1966, 1967) as measures of aptitudeas opposed to achievementrelated ability, the use of multiple regression techniques to evaluate the predictive ability of the different path models, and the analysis of suppressor effects may be potentially effective tools for future studies of birth order and family size relationships to intelligence. Fluid and Crystallized Intelligence Significant ordinal position influences on achievement-related

PAGE 70

62 variables but not on certain aptitude or ability measures have been reported (Burton, 1968; Forer, 1976; Glass et al., 1974; Nuttall et al., 1976). These findings suggested to the author the possible application of the G^ and G constructs of Cattell and Horn as measures of aptitude and achievement-related performance. Horn (1966, 1968, 1970, 1975) hypothesizes that G f reflects neurophysiological functioning and active problem-solving ability. G is defined by abilities appropriated through education and acculturation. Thus, it was decided to utilize G f measures to represent active problemsolving ability or aptitude and G measures to represent achievementrelated performance. Multiple Regression Marjoribanks (1976a, 1976b) contends that the equivocal results reported in the birth order literature are partially due to the use of restricted statistical techniques which have precluded examination of the full character of the sibling constellation correlates of intellectual abilities. The majority of studies have used productmoment correlations, which reveal only bivariate relationships, and analysis of variance, which requires the grouping of variables into levels. Marjoribanks et al . (1975) suggest that the use of multiple regression techniques—which provide a goodness-of-fit for any functional form of the variables and do not require grouping—should overcome some of the statistical shortcomings of many previous investigations. In the present study, hierarchical specified inclusion techniques were used to test the assumptions of four individual models.

PAGE 71

63 This statistical technique was chosen because it permits the analysis of the relationship between a criterion variable and a set of predictor variables without the artificial grouping of variables into levels. Moreover, the estimation of the relative strengths of the individual paths in the models indicates the magnitude of both direct and indirect influences in terms of the hypothesized causal ordering for each model. In the hierarchical inclusion procedure, each predictor variable is added in a single 2 step, and the increment in R_ at each successive step is considered to be the component of variation attributable to that particular variable. Statistical Suppression Although statistical suppressor effects are rather uncommon in behavioral sciences research, their existence can be devastating to data interpretation. It was determined that certain unexpected relationships which were observed in the college sample, particularly the significant positive correlation between family size and SES, were acting as cooperative suppressors (Cohen & Cohen, 1975) in at least two of the proposed models. These suppressor effects resulted in a decreased relationship between the family configuration and SES variables and the achievement-related criterion variable. When partial correlation techniques were utilized to control for these statistical suppressor effects, a modified version of the economic model was supported in terms of both overall variance accounted for and the predictive ability of the individual paths.

PAGE 72

64 The discovery of the existence of suppressor effects in the college student sample may have potentially important implications for the application of multiple regression techniques in general, and for the use of these procedures in family configuration research in particular. It is evident that birth order and family size variables may exert very different effects on intellectual ability and achievement, depending on different sampling procedures and the specific sample investigated. The use of partial correlation techniques to control for statistical suppression may be an important tool in the isolation of the many complex relationships and issues in this literature. Directions for Future Research In the present study, an attempt was made to separate out some of the confounded relationships of certain family configuration and SES level correlates of intellectual ability and achievement. The present results—along with the contradictory and tentative conclusions prevalent in this research area — indicate the need for the generation of new theories and hypotheses, which may be specific to particular samples. The four models evaluated in this investigation should not be considered to be mutually exclusive. Moreover, it is conceivable that alternative models may be equally or even more predictive of certain family configuration influences. For example, in a sample consisting of 185 11-year-old boys, Marjoribanks et al . (1975) found that a multiple regression equation containing father's occupational status, inverse of the number of children in the family, and the product of the two variables accounted for as much significant

PAGE 73

65 variance as complex equations with many terms. With regard to the present results, it is evident that, besides the variables examined in this investigation, certain additional variables should possibly be considered in future family configuration studies. Such general influences include subject sex differences, sex of siblings, age range of subjects, and the possible mediation of racial and ethnic influences on intellectual ability performance. Moreover, the supported predictions concerning firstborn superiority on achievement-related variables might be more analytically evaluated in a variety of ways. Such analytical paths might include separate analyses for specific mechanisms related to the performance of firstborn children on achievement-related measures. For example, SES mediators of such firstborn superiority might include parental attitudes toward achievement and educational aspirations which may differ across SES levels, especially regarding earlier-born children (Glass et al., 1974), certain concrete measures such as the number of books, periodicals, and other literature in the home, and parental role models— which might be particularly interesting for the daughters of achieving women. Another area of investigation might involve certain personality attributes which might be related to firstborn performance on achievement-related variables. Such attributes might involve increased susceptibility to social pressure and sensitivity to tensionproducing situations (Bradley, 1968), conscientiousness and effort (Start & Start, 1974), social conformity and achievement motivation (Forer, 1976). The examination of these hypothesized contributors to firstborn superiority on achievement-related performance might further

PAGE 74

66 clarify certain family configuration correlates of intellectual ability performance. Of particular relevance to these considerations is the importance of differential learning environments created by families for their children (Marjoribanks, 1976a). These learning environments were described in terms of five process variables: (a) press for achievement; (b) press for activeness; (c) press for intellectuality; (d) press for independence; and (e) press for English (related to the reinforcement of the correct use of English). Marjoribanks indicates that the relationships between birth order and cognitive performance are complex: The findings support the proposition that, if parents provide different environmental experiences for their children, then the environmental differences will be related to birth-order variation in the cognitive > performance of children. (£_. 764) It is thus apparent that the often contradictory findings and tentative conclusions in the birth order and family size literature may be largely attributable to the failure to adequately control for the many confounded variables and possible artifactual relationships which may exist in certain samples. The results of the present investigation indicate the need for the generation of new, more analytical hypotheses with regard to family constellation influences on aptitude and achievement. Moreover, the utilization of innovative statistical techniques and conceptual approaches may serve to effectively isolate the extremely complex family configuration correlates of intellectual ability performance.

PAGE 75

supi i n ' ' i,J. L. FTui I • ; " L d." Unpublisl I Psychol U i\ rsity of Denver, Denvei 67

PAGE 76

REFERENCES Altus, W. D. Birth order and academic primogeniture. Journal of Personality and Social Psychology , 1965, 2, 872-87(T [a) Altus, W.D. Birth order and scholastic aptitude. Journal of Consulting Psychology , 1965, 29, 202-205. (b) Altus, W. D. Birth order and its sequelae. Science , 1966, 251, 44-49. Anastasi, A. Intelligence and family size. Psychologica l Bulletin, 1956, 53, 187-209. ' Apperly, F. L. Characteristics of birth order position. Journal of Heredity , 1939, 30, 493. Belmont, L., & Marolla, F. A. Birth order, family size, and intelligence. Scienc e, 1973, 182, 1096-1101. Bradley, R. W. Birth order and school-related behavior: A heuristic review. Psychological Bulletin , 1968, 70, 45-51. Breland, H. M. Birth order, family configuration, and verbal achievement. Child Development , 1974, 45, 1011-1019. Burton, D. Birth order and intelligence. Journal of Social Psychology , 1968, 76, 199-206. Capra, P. C, & Dittes, J. E. Birth order as a selective factor among volunteer subjects. Journal of Abnormal and Social Psychology , 1962, 64, 602-608. Cattell, J. M. Men of science . (4th ed.) Garrison, New York: Science Press, 1927. Cattell, R. B. Some theoretical issues in adult intelligence testing. Psychological Bulletin , 1941, 38, 592. Cattell, R. B. Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology , 1963, 54, 122. Cattell, R. B. The theory of fluid and crystallized intelligence checked at 5-6 year-old level. British Journal of Educational Psychology , 1967, 37, 209-224. 68

PAGE 77

69 Cicirelli, V. G. Sibling constellation, creativity, IQ, and academic achievement. Child Development , 1967, 38, 481-490. Clarke, E. L. American men of letters, their nature and nurture . New York: Columbia University Press, 1916. Cohen, J., & Cohen, P. Applied multiple regression/correlation analysis for the behavioral sciences . New York: Wiley & Sons, 1975. Corliss, W. S. Relationships in achievement by sex and number of children in the family. Michigan Education Review , 1964, 42, 28-29. Cunningham, W. R. , Clayton, V., & Overton, W. Fluid and crystallized intelligence in young adulthood and old age. Journal of Gerontology , 1975, 30, 53-55. Dunn, H. L. (Ed.), Vital statistics of the United States , 1952 (Vol. 1). Washington, D.C.: U.S. Government Printing Office, 1956. Ellis, H. A study of British genius . London: Hurst & Blackett, 1904. Eysenck, H.J., & Cookson, D. Personality in primary school children: Family background. British Journal of Educational Psychology , 1970, 40', 117-131. Forer, L. K. The birth order factor: How your personality is influenced by your place in the family . New York: McKay, 1976. French, J. W., Ekstrom, R. B., & Price, L. A. Manual for kit of r eference tests for cognitive factors . Princeton: Educational Testing Service, 1963. Gal ton, F. English men of science . London: MacMillan, 1874. Glass, D. C. , Neulinger, J., & Brim, C. G. Birth order, verbal intelligence, and educational aspirations. Child Development , 1974, 45, 807-811. Guilford, J. P. A new revision of the Army Alpha Examination and a weighted scoring for three primary factors. Journal of Applied Psychology , 1938, 5, 239-246. Guilford, J. P., & Hoepfner, R. The analysis of intelligence . New York: McGraw-Hill, 1971. Hall, E. E. Psychological correlates of ordinal position in the two-child family . Unpublished doctoral dissertation, University of Florida, 1963.

PAGE 78

70 Hoepfner, R. , & Guilford, J. P. Structure of Intellect Tests . Orange, California: Sheridan Psychological Services, 1969. Horn, J. L. Integration of structural and developmental concepts in the theory of fluid and crystallized intelligence. In R. B. Cattel 1 (Ed.), Handbook of multivariate experimental psychology . Chicago: Rand McNally, 1966. Horn, J. L. Organization of abilities and the development of intelligence. Psychological Review , 1968, 75_, 242-259. Horn, J. L. Organization of data on lifespan development of human abilities. In L. R. Goulet & P. B. Baltes (Eds.), Li fespan develop mental psychology . New York: Academic Press, 1970. Horn, J. L. Psychometric studies of aging and intelligence. In S. Gershon & A. Ruskin (Eds.), Aging: Genesis and treatment of psychologic disorders in the elderly . New York: Raven Press, 1975. Horn, J. L., & Cattel! , R. B. Refinement and test of the theory of fluid and crystallized intelligence. Journal of Educational Psychology , 1966, 57_, 252-270. Horn, J. L., & Cattell, R. B. Age differences in fluid and crystallized intelligence. Acta Psychol ogica , 1967, 26_, 107-129. Huntington, E. Season of birth . New York: Wiley & Sons, 1928. Jureskog, K. G., & Sorbom, D. EFAP: Exploratory factor analysis program . Chicago: National Educational Resources, 1976. Ca) Joreskog, K. G. , & Sorbom, D. LISRELL III: Estimation of linear structural equation systems by maximum likelihood methods . Chicago: National Educational Resources, 1976. (b) Kennett, K. F. Measured intelligence, family size, and socio-economic status. Alberta Journal of Educational Research , 1973, ]_9, 314-320. Lees, J. P., & Stewart, A. H. Family or sibship position and scholastic ability. Sociological Review (British) , 1957 (July), 85-106. Marjori banks, K. Birth order, family environment, and mental abilities: A regression surface analysis. Psychological Reports , 1976, 39_, 759-765. (a) Marjoribanks, K. Sibsize, family environment, cognitive performance, and affective characteristics. Journal of Psychology , 1976, 94, 195-204. (b) Marjoribanks, K. , & Walberg, H. J. Ordinal position, family environment, and mental abilities, Journal of Social Psychology , 1975, 9_5, 77-84.

PAGE 79

71 Marjoribanks, K., Walberg, H. J., & Bargen, M. Mental abilities: Sibling constellations and social class correlations. British Journal of Social and Clinical Psychology , 1975, 1_4, 109-116. Maxwell, J., & Pilliner, A. E. The intellectual resemblance between sibs. Annals of Human Genetics , 1960, 24, 23-32. McCutcheon, L. E. Dumber by the dozen? Not necessarily! Psychological Reports , 1977, 40_, 109-110. Murray, C. The effects of ordinal position on measured intelligence and peer acceptance in adolescence. British Journal of Social Psychology, 1971, 10, 221-227. Nichols, R. G. Birth order and intelligenc e. Unpublished manuscript, 1964. (Available from the National Merit Scholarship Corporation.) Nie, N. H., Hull, C. H., Jenkins, J. G., Steinbrenner, K., & Bent, D. H. (Eds.) SPSS: Statistical package for the social sciences (2nd ed.). New York: McGraw-Hill, 1975. Nisbet, J. D., & Entwistle, N. J. Intelligence and family size, 1949-1965. British Journal of Educational Psychology , 1967, 37_, 188-193. NuttalU E. V., Nuttall, R. L., Polit, D., & Hunter, J. B. The effects of family size, birth order, sibling separation and crowding on the academic achievement of boys and girls. American Educational Research Journal , 1976, 13., 217-223. Record, R. G., McKeown, T. , & Edwards, J. H. The relation of measured intelligence to birth order and maternal age. Annals of Human Genetics , 1969, 33, 61-69. Rosenberg, B. G., & Sutton-Smith, B. The relationship of ordinal position and sibling sex status to cognitive abilities. Psychonomic Science , 1964, 1, 81-82. Schachter, S. Birth order, eminence, and higher education. American Sociological Review , 1963, 28, 757-768. Schooler, C. Birth order effects: Not here, not now! Psychological Bulletin , 1972, 78, 161-175. Schooler, C. Birth order effects: A reply to Breland. Psychological Bulletin , 1973, 80, 213-214. Start, A., & Start, K. B. The relation between birth order and effort or conscientiousness among primary school children. Research in Education , 1974, 9_, 1-8. Terman, L. M. Genetic studies of genius. The mental and physical traits of a thousand gifted children . Stanford, California: Stanford University Press, 1925.

PAGE 80

72 Thurstone, L. L.", & Thurstone, T. G. SRA primary mental abilities . Chicago: Science Research Associates, 1949. Treiman, M. Occupational prestige in comparative perspective . New York: Academic Press, 1977. Wark, D. M., Swanson, E. D., & Mack, J. More on birth order: Intelligence and college plans. Journal of Indivi dual Psychology, 1974, 30, 221-226. Warren, J. R. Birth order and social behavior. Psycholog ical Bulletin, 1966, 65, 38-49. Weller, G. M. Arousal effects of the newborn infant of being first or later born. American Journal of Orthopsychiatry , 1965, 35, 341-342. Yoder, A. H. Birth order. Pedagogical Seminary , 1894, 3, 146. Zajonc, R. B. Family configuration and intelligence. Science , 1976, 192 , 227-136. Zajonc, R. B., & Markus, G. B. Birth order and intellectual development, Psychological Review , 1975, 82, 74-88.

PAGE 81

BIOGRAPHICAL SKETCH Sandra Johnson Witt was born in Sanford, Florida, on July 17, 1946, the daughter of Betty Beeler Johnson and Elmer Hunter Johnson. She graduated from Hialeah Senior High School in Hialeah, Florida. A German major at the University of Florida, she graduated in 1968 with high honors and was elected to Phi Beta Kappa. She received her master's degree in German and French from the University of Florida in 1969. She worked for Trans World Airlines in New York until 1971 as an international airline hostess and in-flight instructor. She married William Morris Witt in 1971. In 1973 s,he received a master's degree in psychology from Emory University under the direction of Dr. Philip Dreyer and Dr. Boyd McCandless. She worked as staff psychologist and director of adult remedial education at the Key Training Center for Mentally Retarded and Physically Handicapped Adults in Lecanto, Florida, from 1973 until 1974. In 1975 she entered the Ph.D. program in developmental psychology at the University of Florida. While in this program, she received a pre-doctoral fellowship from the Center for Gerontological Studies and worked as a research assistant for Dr. Walter R. Cunningham, who is also the chairman of her doctoral committee. She has two daughters, Amanda and Katherine. 73

PAGE 82

I certify bhat I have reed this study and that ' : w : n Perrv Professor of C I inical Psy< h< 1< v

PAGE 83

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. E. Wfrorton ;ociate Professor of Special Education This dissertation was submitted to the Graduate Faculty of the Department of Psychology in the College of Arts and Sciences and the Graduate Council, and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy. June 1978 Dean, Graduate School

PAGE 84

[II $Vl2 4 7 8. 2 3 4!•


xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E87MN8I0U_6CV28F INGEST_TIME 2017-07-14T22:06:42Z PACKAGE UF00097478_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES