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Judgmental Anchoring and Adjustment in Metacomprehension

Permanent Link: http://ufdc.ufl.edu/UFE0022325/00001

Material Information

Title: Judgmental Anchoring and Adjustment in Metacomprehension
Physical Description: 1 online resource (60 p.)
Language: english
Creator: Zhao, Qin
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: adjustment, anchoring, judgment, metacomprehension
Educational Psychology -- Dissertations, Academic -- UF
Genre: Educational Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: I conducted two experiments to test several hypotheses derived from the Anchoring and Adjustment Model of Metacomprehension Judgment (Zhao & Linderholm, 2008): (a) Making metacomprehension judgments should involve a process of anchoring and adjustment;(b) due to reduced judgmental uncertainty,the magnitude of anchoring should be smaller in retrospective than in prospective metacomprehension judgments; and (c) information that biases expectations of performance should serve as anchor information and influence metacomprehension judgments. As predicted,anchoring and adjustment occurred in metacomprehension judgments and the extent of anchoring was smaller in retrospective than in prospective judgments, which provides insight into why retrospective judgments are generally more accurate than prospective judgments. As for the anchor information, self-perceptions of ability and information about peer performance significantly influenced metacomprehension judgments. These results support the anchoring and adjustment account of metacomprehension judgments and have educational implications for the efforts to reduce the magnitude of anchoring and to improve students' metacomprehension accuracy.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Qin Zhao.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Linderholm, Tracy A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022325:00001

Permanent Link: http://ufdc.ufl.edu/UFE0022325/00001

Material Information

Title: Judgmental Anchoring and Adjustment in Metacomprehension
Physical Description: 1 online resource (60 p.)
Language: english
Creator: Zhao, Qin
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: adjustment, anchoring, judgment, metacomprehension
Educational Psychology -- Dissertations, Academic -- UF
Genre: Educational Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: I conducted two experiments to test several hypotheses derived from the Anchoring and Adjustment Model of Metacomprehension Judgment (Zhao & Linderholm, 2008): (a) Making metacomprehension judgments should involve a process of anchoring and adjustment;(b) due to reduced judgmental uncertainty,the magnitude of anchoring should be smaller in retrospective than in prospective metacomprehension judgments; and (c) information that biases expectations of performance should serve as anchor information and influence metacomprehension judgments. As predicted,anchoring and adjustment occurred in metacomprehension judgments and the extent of anchoring was smaller in retrospective than in prospective judgments, which provides insight into why retrospective judgments are generally more accurate than prospective judgments. As for the anchor information, self-perceptions of ability and information about peer performance significantly influenced metacomprehension judgments. These results support the anchoring and adjustment account of metacomprehension judgments and have educational implications for the efforts to reduce the magnitude of anchoring and to improve students' metacomprehension accuracy.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Qin Zhao.
Thesis: Thesis (Ph.D.)--University of Florida, 2008.
Local: Adviser: Linderholm, Tracy A.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0022325:00001


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JUDGMENTAL ANCHORING AND ADJUSTMENT IN METACOMPREHENSION


By

QIN ZHAO
















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

UNIVERSITY OF FLORIDA

2008



































2008 Qin Zhao




































To Tao Chen









ACKNOWLEDGMENTS

I wish to thank several individuals for their help and support concerning my dissertation

study. First, I thank Dr. Tracy Linderholm, my advisor and the chair of my dissertation

committee, for providing me with generous support throughout the process. Her constructive and

prompt feedback on my study design, data analyses, and writing was greatly appreciated. I also

thank the other members of my dissertation committee, Dr. David Therriault, Dr. David Miller,

and Dr. Zhihui Fang, for their service and helpful comments about my study. In addition, I wish

to thank Dr. Katherine Rawson at the Kent State University for kindly providing me with some

of the texts and tests used in this study and Dr. Wei Pan at the University of Cincinnati for his

helpful comments concerning my data analyses. Last but not least, I acknowledge Dr. Tao Chen,

my husband, for supporting and encouraging me as well as helping me with data analysis

software. All these individuals have helped me achieve a goal that seemed like a very daunting

challenge.










TABLE OF CONTENTS

page

A CK N O W LED G M EN T S .................................................................. ........... .............. .....

LIST OF TABLES ........... ... ...... ..................................................... 7

LIST OF FIGURES .................................. .. .... ... ...............8

A B S T R A C T ................................. .................. ......................................... .. 9

CHAPTER

1 IN TR O D U CTIO N ............... .............................. ..................... ........ .. 10

Bases of M etacom prehension Judgm ents ...................................... ..............................11
The Anchoring and Adjustment M odel ........................................................................... 15

2 OBJECTIVE AND OVERVIEW OF THE STUDY....... ........ ...............20

3 E X P E R IM E N T 1 ........................................ ............................................................. .. 2 4

M e th o d ................................................................................................................2 5
P articip an ts ................................................................2 5
M a te ria ls ..........................................................................................................2 5
P ro c e d u re s .................................................................................................................. 2 6
Results and Discussion ................................................. 27
Anchoring and Adjustment in Metacomprehension....................................27
Judgm mental A anchors ................................................................28
M etacomprehension Accuracy ......................................................... 30

4 EXPERIM ENT 2 ....................................................................... ........ 35

M e th o d ................................................................................................................3 6
P articip an ts ................................................................3 6
M a te ria ls ..........................................................................................................3 6
P ro c e d u re s .................................................................................................................. 3 7
Results and Discussion ................................................. 38
Anchoring and Adjustment in Metacomprehension....................................38
Judgm mental A anchors ................................................................40
M etacomprehension Accuracy ......................................................... 41

5 GENERAL DISCU SSION .....................................................................46









APPENDIX

A SAMPLE EXPERIMENTAL TEXTS ............................................................................52

B SAMPLE EXPERIMENTAL TESTS .............................................................................54

L IST O F R E F E R E N C E S ...................................................................................... ...................57

B IO G R A PH IC A L SK E T C H .............................................................................. .....................60














































6









LIST OF TABLES

Table page

3-1 T ext titles and grade lev els ........................................................................ .................. 3 1

3-2 Partial correlation coefficients (Experiment 1)...................................... ............... 32

4-1 Partial correlation coefficients (Experiment 2)...................................... ............... 43









LIST OF FIGURES


Figure page

1-1 The anchoring and adjustment model of metacomprehension judgment. ........................ 19

3-1 How prospective judgments changed with text difficulty (Experiment 1)......................33

3-2 How retrospective judgments changed with text difficulty (Experiment 1)....................33

3-3 How comprehension performance changed with text difficulty (Experiment 1) ..............34

4-1 How prospective judgments in percentage changed with text difficulty (Experiment
2 ) ............................................................ ................................ . 4 4

4-2 How prospective judgments in number changed with text difficulty (Experiment 2).......44

4-3 How retrospective judgments changed with text difficulty (Experiment 2)....................45

4-4 How comprehension performance changed with text difficulty (Experiment 2) .............45









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

JUDGMENTAL ANCHORING AND ADJUSTMENT IN METACOMPREHENSION

By

Qin Zhao

August 2008

Chair: Tracy Linderholm
Major: Educational Psychology

I conducted two experiments to test several hypotheses derived from the Anchoring and

Adjustment Model of Metacomprehension Judgment (Zhao & Linderholm, 2008): (a) Making

metacomprehension judgments should involve a process of anchoring and adjustment; (b) due to

reduced judgmental uncertainty, the magnitude of anchoring should be smaller in retrospective

than in prospective metacomprehension judgments; and (c) information that biases expectations

of performance should serve as anchor information and influence metacomprehension

judgments. As predicted, anchoring and adjustment occurred in metacomprehension judgments

and the extent of anchoring was smaller in retrospective than in prospective judgments, which

provides insight into why retrospective judgments are generally more accurate than prospective

judgments. As for the anchor information, self-perceptions of ability and information about peer

performance significantly influenced metacomprehension judgments. These results support the

anchoring and adjustment account of metacomprehension judgments and have educational

implications for the efforts to reduce the magnitude of anchoring and to improve students'

metacomprehension accuracy.









CHAPTER 1
INTRODUCTION

Who of us have not had the experience of reading a set of text materials and realizing that

we have comprehended some texts better than other ones? Suppose there is an upcoming exam

that tests our comprehension of the texts, we may judge better performance over some texts than

over others. The process of judging comprehension performance has been termed

metacomprehension in cognitive and educational psychology research (Maki, 1998). This is an

important process to study because metacomprehension accuracy, the ability to accurately judge

comprehension performance, is important for effective self-regulation of study (e.g., Thiede,

Anderson, & Therriault, 2003). For instance, if students are aware of their comprehension or lack

of it, they can focus time and effort on restudying the texts that they failed to comprehend during

the first read of the texts. One of the goals of education is to help students become life-long

learners who are able to self-regulate their learning. Hence, metacomprehension research in the

long run has significant educational implications.

Metacomprehension research often involves adult readers as participants. In a typical

research procedure, college-student participants are asked to read a set of text materials. After

finishing reading each text, they judge their comprehension performance over it on a percentage

scale or in terms of the number of test questions they could correctly answer. After all the texts

are read, they complete the comprehension tests that measure their actual understanding of the

texts. In the cognitive psychology literature, researchers have measured relative

metacomprehension accuracy by calculating the gamma or Pearson correlation between

metacomprehension judgments and actual comprehension performance across a set of text

materials (e.g., Glenberg & Epstein, 1985, 1987; Maki & Berry, 1984; Maki, Shields, Wheeler,

& Zacchilli, 2005; Rawson, Dunlosky, & Thiede, 2000; Weaver & Bryant, 1995). A gamma or









Pearson correlation coefficient ranges from -1 to +1 and describes the extent to which materials

that receive higher judgments are associated with higher performance within each individual.

Relative accuracy thus describes an individual's ability to discriminate between better-

understood texts and less-understood texts when making metacomprehension judgments.

Surprisingly, research has shown that adult readers typically have low relative

metacomprehension accuracy (e.g., Glenberg & Epstein, 1985, 1987; Maki, 1998; Maki & Berry,

1984). For example, Maki (1998) reported a mean gamma correlation of only +.27 between

judgments and comprehension performance across over 20 studies from her laboratory. To shed

light on why relative metacomprehension accuracy is commonly low among adult readers,

researchers have investigated the bases of metacomprehension judgments.

Bases of Metacomprehension Judgments

Research has suggested that adult readers base their metacomprehension judgments on

different types of information, including experiences with current tasks and pre-formed

expectations of performance. Specifically, some researchers have found that metacomprehension

judgments are influenced by experiences n i/h current tasks such as familiarity with the text topic

(e.g., Glenberg, Sanocki, Epstein, & Morris, 1987; Maki & Serra, 1992), ease of text processing

(e.g., Dunlosky, Baker, Rawson, & Hertzog, 2006; Rawson & Dunlosky, 2002), and ease of

immediate text recall (e.g., Morris, 1990). For example, Maki and Serra (1992) asked

participants to rate their familiarity with text topics based on text titles and descriptions. The

familiarity ratings were correlated with metacomprehension judgments made after reading the

full texts, which suggests that topic familiarity is a basis of metacomprehension judgments.

Metacomprehension judgments are also influenced by ease of text processing. Research has

shown that metacomprehension judgments are higher for intact texts than for texts with deleted-

letter words (Rawson & Dunlosky, 2002) and are higher for more coherent texts than for less









coherent ones (Dunlosky et al., 2006; Rawson & Dunlosky, 2002). Ease of immediate text recall

is another experiential basis of metacomprehension judgments. Morris (1990) instructed students

to perform speeded-recall tasks after reading brief expository passages. It was found that the

more recalled immediately, that is, within 15 seconds after reading, the higher the judgments;

and that the longer recall latency, that is, the more time lapsed before the first unit of information

was recalled, the lower the judgments. Hence, metacomprehension judgments are influenced by

a variety of individuals' experiences with current tasks such as how easy the text is to process

and how much they can immediately recall from the text.

There is also self-report evidence that experiences with current tasks serve as bases of

metacomprehension judgments (e.g., Linderholm, Zhao, Therriault, & Cordell-McNulty, under

review; Zhao et al., 2006). Linderholm et al. (under review) instructed college-student

participants to judge their comprehension performance over two expository texts that they read

and to report afterwards in writing how they made their judgments for each text. Participants'

self-reports were categorized and the frequency counts for each category were then calculated.

The self-report evidence revealed that readers used experiences with current tasks such as topic

familiarity (mentioned by 35% of participants), topic interest (32%), and text difficulty (18%) as

the bases of their metacomprehension judgments. In another study, Zhao et al. (2006) asked

college students to read several texts and to judge after reading each text their comprehension

performance over it. Students later described on a self-report questionnaire how they made their

judgments. A qualitative analysis of the self reports revealed experiential bases of

metacomprehension judgments such as topic familiarity, ease of immediate text recall, and topic

interest. For instance, one participant reported, "(I judged my performance) based on how

interested I was in the text and how closely I felt I focused on the physical statements." In all,









both quantitative and qualitative evidence has shown that metacomprehension judgments are

based on individuals' various cognitive experiences with the current reading task.

Interestingly, other research has suggested that metacomprehension judgments are

influenced by a different type of information: pre-formed expectations ofperformance (e.g.,

Hacker, Bol, Horgan, & Rakow, 2000; Moore, Lin, & Zabrucky, 2005). Expectations of

comprehension performance may be influenced by factors such as preexisting self-perceptions of

reading ability, that is, individuals' views about their own ability to understand text information.

For example, Hacker et al. (2000) examined how performance judgments and actual performance

on prior exams influenced performance judgments on subsequent exams in a college classroom.

Participants were students in an introductory educational psychology course. Across a semester

students were given three multiple-choice exams that tapped their understanding, integration, and

application of text concepts. Standard multiple regression analyses revealed that prior judgments,

not prior exam performance, significantly contributed to subsequent judgments. Judgments of

performance on Exam 1 significantly influenced judgments on Exam 2 (squared semi-partial

correlation = .06, p < .005); and judgments of performance on Exam 2 significantly influenced

judgments on Exam 3 (squared semi-partial correlation = .08, p < .005). Hacker et al. (2000)

discussed that students might have heavily relied on self-perceptions of ability based on their

history of performance to make performance judgments. As a result, judgments of performance

on prior exams significantly contributed to judgments on subsequent exams. In a more recent

study, Moore et al. (2005) used a path model analysis to examine the relations among judgments

of performance before and after test and actual comprehension performance within and across

three reading trials. Moore et al. (2005) found that despite nine practice trials and the

manipulation of text difficulty across all reading trials, judgments of future performance were









relatively stable across the three evaluated trials, which were trials 10, 11, and 12. The judgment

at trial 10 significantly influenced the judgments at both trial 11 (standardized path coefficient =

.65, p < .05) and trial 12 (standardized path coefficient = .42, p < .05). Moore et al. (2005)

explained that participants might have largely based metacomprehension judgments on self-

perceptions of ability shaped by prior task experience, which caused the relatively stable

judgments across texts.

Recent self-report evidence has revealed that self-perceptions of ability as well as several

other factors may contribute to pre-formed expectations of performance and affect

metacomprehension judgments (Linderholm et al., under review; Zhao et al., 2006). For instance,

a participant in Zhao et al. (2006) reported: "I used prior knowledge of how well I have done on

tests for comprehension in the past where I have read the information directly before the test, as

well as what I could understand and recall to myself from the texts right before taking the

experiment test (to make my judgment)." So, the participant based metacomprehension judgment

on self-perception of ability in addition to experiential cues. In addition, about 15% of

participants in Linderholm et al.'s study (under review) reported having different expectations of

performance on different test types. Here is an example, "I think that I would get a B on text 1 if

it was multiple-choice and an A on text 2 if it was multiple-choice .... However, if the test was

fill-in-the-blank or essay, I think I would do at least one or two letter grades worse on each test."

Self-perceptions of ability or performance expectations for different test types are likely

shaped by an accumulation of past test-taking experiences. Some not-so-distant prior task

experience or exposure, however, may also influence one's expectations of performance. About

41% of participants in Linderholm et al.'s study (under review) reported using the first text, in a

series of two texts, as a basis of comparison to judge their performance over the following text.









For example, one participant wrote, "Since I feel like I didn't learn as much information from

text 2 as I did from text 1, I guessed that I would receive a lower grade of C." So, exposure to the

first text served as a reference point for the subsequent judgment of performance. In all, there is

some indirect evidence that metacomprehension judgments are influenced by pre-formed

expectations of performance. Pre-formed expectations of performance may come from at least

two sources of information: self-perceptions of ability (on various test types) based on past task

experiences and not-so-distant prior task exposure.

To conclude, a review of both quantitative and qualitative research findings has revealed

two types of bases of metacomprehension judgments. Some research has shown that individuals

base metacomprehension judgments on their experiences with current tasks such as ease of text

processing and ease of immediate text recall. Other research has suggested that

metacomprehension judgments are influenced by individuals' pre-formed expectations of

performance contributed to by factors such as self-perceptions of ability and not-so-distant prior

task exposure. To account for the evidence and conceptualize the processes underlying

metacomprehension judgments, Zhao and Linderholm (2008) proposed the Anchoring and

Adjustment Model of Metacomprehension Judgment (Figure 1-1).

The Anchoring and Adjustment Model

The Anchoring and Adjustment Model of Metacomprehension Judgment (Zhao &

Linderholm, 2008) incorporated Tversky and Kahneman's (1974) anchoring and adjustment

heuristic that has been described in the decision-making literature (e.g., Epley & Gilovich, 2001;

2005). According to the anchoring and adjustment heuristic (Tversky & Kahneman, 1974), when

judging under uncertainty people start with an anchor and then deliberately adjust away from it

to reach a plausible final estimate, but adjustment requires mental effort and tends to be

insufficient, so the final estimate is biased toward the initial anchor. For example, Jacowitz and









Kahneman (1995) asked people to estimate the height of Mount Everest. They provided a

median estimate of 8000 feet after considering an anchor of 2000 feet, but provided a median

estimate of 42,500 feet after considering an anchor of 45,500 feet. This example illustrates how

individuals' estimates are biased toward anchor points.

Zhao and Linderholm (2008) proposed that the anchoring and adjustment processes are

involved in metacomprehension judgments. Individuals in metacomprehension research or in

educational settings usually have to judge their own test performance under uncertainty, for

example, under uncertainty about the contents and difficulty levels of the upcoming

comprehension tests. Hence, they may use pre-formed expectations of performance as the anchor

and then adjust away from it to account for experiences with current tasks, but the final

judgments tend to be biased in the direction of the anchor due to insufficient adjustments. The

anchoring and adjustment model of metacomprehension judgment sheds light on why students

typically have poor relative metacomprehension accuracy. Specifically, relative

metacomprehension accuracy describes one's ability to discriminate between better-understood

texts and less-understood texts when making metacomprehension judgments over a set of texts.

Probably due to judgmental anchoring, students provide relatively stable judgments of

performance across a set of text materials (see Hacker et al., 2000; Moore et al., 2005) and

insufficiently adjust their judgments to discriminate between better-understood texts and less-

understood ones in a set. Judgmental anchoring may thus significantly contribute to poor relative

metacomprehension accuracy.

The Anchoring and Adjustment Model of Metacomprehension Judgment (Zhao &

Linderholm, 2008) was proposed based on a review of the research evidence regarding the bases

of prospective metacomprehension judgments or judgments of future comprehension









performance. However, the basic anchoring and adjustment process may also be involved in

retrospective metacomprehension judgments or judgments of past comprehension performance,

that is, when people are asked to estimate how many comprehension test questions they

accurately answered. Although there is no test uncertainty when judging one's past performance,

there is still uncertainty in making this kind of judgment which involves recalling events

associated with answering specific test questions (Pierce & Smith, 2001). Research has also

shown that retrospective judgments of performance are influenced by self-perceptions of ability

(e.g., Dunning, Johnson, Ehrlinger, & Kruger, 2003; Ehrlinger & Dunning, 2003; Schraw &

Roedel, 1994). For example, Ehrlinger and Dunning (2003: Study 1) showed that participants'

self-perceptions of logical reasoning ability were significantly and positively correlated with

their judgments of past performance on a logical reasoning task, after controlling for actual

performance. In Study 3, Ehrlinger and Dunning (2003) manipulated participants' self-

perceptions of knowledge of geography using techniques such as asking questions that gave

participants favorable or unfavorable impressions of their own geographical knowledge. The

manipulation of self-perceptions influenced judgments of past performance, independent of

actual performance. So, even after taking the tests, individuals based their performance

judgments on preexisting ability perceptions. It is possible that when making retrospective

metacomprehension judgments, individuals rely on anchor information such as enduring sense of

reading ability and then adjust away from it based on current task experiences. The magnitude of

anchoring, however, may be smaller in retrospective than in prospective judgments because

without test uncertainty, individuals may rely less on the anchor when making retrospective

judgments. This could shed light on the research evidence that retrospective metacomprehension









judgments are more accurate than prospective ones (e.g., Glenberg & Epstein, 1985; Maki &

Serra, 1992; Pierce & Smith, 2001).









to reach
iV


metacomprehension
judgment


Theory-based inferential processes
SExperience-based inferential processes



Figure 1-1. The anchoring and adjustment model of metacomprehension judgment.









CHAPTER 2
OBJECTIVE AND OVERVIEW OF THE STUDY

The primary objective of the study was to empirically test the hypotheses discussed above

that are derived from the anchoring and adjustment account of metacomprehension judgments

(Zhao & Linderholm, 2008). Specifically, I conducted two experiments to test the following

hypotheses: (a) making prospective and retrospective metacomprehension judgments should

involve a process of anchoring and adjustment; (b) reducing judgmental uncertainty should

decrease the magnitude of judgmental anchoring. That is, the magnitude of anchoring should be

smaller in retrospective than in prospective metacomprehension judgments; and (c) information

that biases one's expectations of performance should serve as anchor information and influence

metacomprehension judgments.

To demonstrate judgmental anchoring and adjustment, I used the same methodology that

researchers used to show anchoring and/or monitoring involved in judgments-of-learning (JOLs)

of word pairs (Scheck, Meeter, & Nelson, 2004). Scheck et al. (2004) manipulated the difficulty

of the word pairs and examined how the magnitude of JOLs and the level of recall changed with

word-pair difficulty. Three hypotheses were evaluated. According to the Anchoring Hpl,h,,hwi%,

the magnitude of JOLs would be entirely affected by an anchor point and would not change with

word-pair difficulty. That is, the slope of the JOL line would be equal to zero and would be

smaller than the slope of the Recall line (|A Recall| > |A JOLI = 0). According to the Monitoring

Hypothesis, the magnitude of JOLs would change systematically with word-pair difficulty

corresponding to the change in the level of recall. In this case, the slopes of the lines of JOL and

Recall would be equal and greater than zero (|A RecallA = |A JOLI > 0). The third hypothesis, the

Dual-Factors Hplh,,Ic\i\, was that JOLs are influenced by both anchoring and monitoring of

word-pair difficulty. According to this hypothesis, the magnitude of JOLs would change with









word-pair difficulty, but to a lesser extent than would the level of recall (|A Recall] > |A JOL| >

0). Similar logic was used to generate hypotheses for the two studies I describe next.

In Experiments 1 and 2, I manipulated text difficulty and examined how the magnitude of

metacomprehension judgments and the level of comprehension performance changed with text

difficulty. Based on the Anchoring and Adjustment Model of Metacomprehension Judgment

(Zhao & Linderholm, 2008), I hypothesized that the magnitude of students' prospective and

retrospective metacomprehension judgments would change with text difficulty, but to a lesser

extent than would the level of comprehension performance (|A Performancel > |A Judgment] > 0).

That is, students would adjust their metacomprehension judgments based on text difficulty, but

they would also rely on a judgmental anchor. In addition, I hypothesized that due to reduced

uncertainty when making retrospective judgments, the magnitude of anchoring would be smaller

in retrospective than in prospective metacomprehension judgments. In this case, the slope of the

line of retrospective judgment would be greater than that of the line of prospective judgment (|A

Retrospective Judgment| > |A Prospective Judgment]).

To further study whether students anchor on pre-formed expectations of performance, I

examined information that may bias students' expectations of their own performance such as

self-perceptions of ability. As reviewed in the paper, self-perceptions of reading ability has been

suggested to be an important basis of prospective metacomprehension judgments (e.g., Hacker et

al., 2000; Moore et al., 2005; Zhao et al., 2006), but there has been no direct empirical support.

As for retrospective judgments, there is direct evidence that people's retrospective judgments of

performance on a logical reasoning task and a test of geography knowledge are influenced by

their self-perceptions of ability in these areas (Ehrlinger & Dunning, 2003). However, it is not

certain whether retrospective metacomprehension judgments would be influenced by self-









perceptions of ability in reading comprehension. Hence, I aimed to provide direct empirical

evidence that students use self-perceptions of reading ability to make prospective and

retrospective metacomprehension judgments (Experiments 1 and 2).

Self-perception of ability is unlikely the only factor that influences expectations of

performance, however. There is self-report evidence suggesting that exposure to the first text

affects prospective metacomprehension judgments for the subsequent text (Linderholm et al.,

under review). It is possible that prior task exposure serves as anchor information that affects

one's expectations of performance over subsequent tasks. Recall that researchers have suggested

that a plausible explanation for relatively stable metacomprehension judgments across texts of

varying difficulty is that students significantly base their judgments on enduring sense of ability

(e.g., Moore et al., 2005). However, an alternative explanation for the finding is that individuals

significantly base their judgments over subsequent texts on prior task exposure. Whereas self-

perceptions of ability are likely shaped by an accumulation of past task experiences, prior task

exposure refers to not-so-distant prior task experience. To investigate and compare the effects of

these two kinds of potential anchor information, I investigated how prior task exposure, in

addition to self-perceptions of ability, influences metacomprehension judgments (Experiment 1).

Individuals' expectations of their own performance can also be influenced by information

about others' performance. Learning situations are rarely isolated. For example, in a school

setting students have access to information about peer performance on a learning task. It is

important to study how such social information influences one's own metacomprehension

judgments. There is evidence that college students' prospective metamemory judgments over

word pairs, for instance, Judgments-of-Learning (JOL) and Ease-of-Learning (EOL) judgments,

were significantly affected by fictitious social cues about previous performance of other college









students (de Carvalho Filho & Yuzawa, 2001). Peer performance in the study was expressed in

terms of the range and the mean of the percentage of correct word-pair recall. In the case of JOL

judgments, for example, the social cues about peer performance particularly affected students

with low level of metacognitive knowledge. Metacognitive knowledge was assessed by tests that

tap one's metamemory knowledge about how person, task, and strategy variables affect recall. In

the low knowledge group, the JOL judgments of students who received high social cues were

significantly higher than the JOL judgments of students without social cues, which, in turn, were

significantly higher than those of students who received low social cues. In the case of EOL

judgments, the social cues affected all students. The pattern of results for the low knowledge

group was the same as that regarding JOL judgments. In the high knowledge group, the EOL

judgments of students who received high social cues were significantly higher than those of

students who received low social cues. To study the effects of social information on students'

expectations of their own comprehension performance, I investigated how information about

peer performance, in addition to self-perceptions of ability, influences metacomprehension

judgments (Experiment 2).

To summarize, I conducted two experiments to investigate whether: (a) making

prospective and retrospective metacomprehension judgments should involve a process of

anchoring and adjustment; (b) due to reduced judgmental uncertainty, the extent of anchoring

should be smaller in retrospective than in prospective metacomprehension judgments

(Experiments 1 and 2); and (c) anchor information that biases expectations of performance, for

instance, self-perceptions of ability (Experiments 1 and 2), prior task exposure (Experiment 1),

and information about peer performance (Experiment 2), should influence metacomprehension

judgments.









CHAPTER 3
EXPERIMENT 1

In Experiment 1, I asked participants to read a set of expository texts of varying difficulty.

After reading each text, participants were instructed to judge their future performance on a

comprehension test over it. After the reading and prospective judgment tasks were completed,

participants took the comprehension tests and judged their test performance after finishing each

test. I tested these hypotheses: First, the magnitude of prospective and retrospective

metacomprehension judgments would change with text difficulty, but to a lesser extent than

would the level of comprehension performance (|A Performancel > |A Judgmentl > 0). This

would show that the magnitude of metacomprehension judgments is influenced by anchoring as

well as adjustment based on text difficulty. Second, the magnitude of anchoring would be

smaller in retrospective than in prospective metacomprehension judgments (IA Retrospective

Judgment > |A Prospective Judgmentl).

To show whether self-perceptions of ability served as a judgmental anchor, I asked

participants to rate their own reading ability on a 5-point Likert scale. I hypothesized that

preexisting self-perceptions of ability would be significantly and positively correlated with

prospective and retrospective metacomprehension judgments. I also manipulated participants'

expectations of performance by influencing their prior task exposure. I gave participants easy or

difficult practice texts to shape their positive or negative expectations for subsequent

performance, respectively. My hypothesis was that those who read easy practice texts would give

higher prospective metacomprehension judgments over the experimental texts than those who

read hard practice texts.









Method


Participants

Sixty undergraduates from the Department of Educational Psychology human-participants

research pool at a large southeastern university participated to fulfill part of their course

requirements. They were tested in a group format, with six participants at a time. Before the

experiment, participants were randomly assigned to one of the two practice-text conditions: easy

or hard practice texts.

Materials

The materials included nine expository texts and the corresponding tests, five of which

were developed by Rawson et al. (2000) from materials in a Graduate Record Examination

(GRE) preparation manual (Branson, Selub, & Solomon, 1987 as cited in Rawson et al., 2000)

and four of which were taken from a GRE study guide (Educational Testing Service, 1994).

They were presented to participants in a text or test booklet form.

The text booklet included two easy or two hard practice texts and five experimental texts

of varying difficulty (Appendix A). Text difficulty was assessed using the Flesch-Kincaid (F-K)

grade level that measures the readability level of text materials in terms of U. S. grade level. The

formula for calculating the F-K grade level is: 0.39 (the number of words/the number of

sentences) + 11.8 (the number of syllables/the number of words) 15.59. The F-K grade level

has often been used to indicate text difficulty in metacomprehension research (e.g., Maki et al.,

2005; Moore et al., 2005; Rawson et al., 2000; Weaver & Bryant, 1995). The two easy practice

texts had F-K grade levels of 11.5 and 13.2 whereas the two hard practice texts had F-K grade

levels of 15.8 and 18.5. Participants in this study ranged from freshmen (13th grade level) to

seniors (16th grade level), so it is appropriate to consider the texts with F-K grade levels of 11.5

and 13.2 easy and those with F-K grade levels of 15.8 and 18.5 hard. The difficulty levels of the









five experimental texts ranged from approximately 11 to 19 in F-K grade levels. Table 3-1 shows

the title and F-K grade level of each text. As shown in Table 3-1, the five experimental texts

varied greatly in terms of difficulty.

The test booklet included multiple-choice comprehension tests over all the texts (Appendix

B). Each test consisted of six five-alternative multiple-choice questions, including approximately

half factual and half inferential types of questions. Factual questions prompted readers to retrieve

information explicitly stated in the text whereas inferential questions required readers to infer (a)

the main point of the text; (b) ideas implied in the text; or (c) the attitude, logic, or purpose of the

author. The advantage of using GRE testing materials is that they are standardized and have

similar structures, so readers will find the same types of reading comprehension questions to

answer across different texts.

Procedures

Participants were randomly assigned to the easy or the hard practice-text condition in order

to influence their expectations of performance. Before the experiment, participants were

informed of the main tasks involved in the study: read a set of expository texts for

comprehension in a self-paced manner, self-evaluate comprehension performance over the texts,

and take multiple-choice comprehension tests about the texts.

Before receiving the text booklet, participants were asked to rate their own reading ability

on a five-point Likert scale where 1 = very poor, 2 = poor, 3 = average, 4 = above average, and 5

= excellent. Then participants received the text booklet that consisted of two easy or hard

practice texts and five experimental texts. The order of text presentation was randomized for the

five experimental texts. Table 3-1 shows the order of text presentation. Before starting to read,

participants read the instructions: "Please read the following texts for comprehension. The first

two texts are practice texts. Read each text carefully as you will be asked questions about them









later. Read each text only one time but at a pace that is comfortable to you. After reading each

text, you will be asked to judge your comprehension performance over it." Each text was on a

separate page. After reading each text, participants turned to the next page and read: "You are

going to take a multiple-choice comprehension test over the text you just read. Please judge how

many of six multiple-choice test questions you could correctly answer over the text."

Once the reading and judgment tasks were completed, participants submitted the text

booklet and received the test booklet. The order of test presentation was the same as that of text

presentation, which was to create a similar length of delay between reading each text and taking

the corresponding test. Participants first read the instructions for the test booklet: "Please

complete the following comprehension test questions over the texts you just finished reading.

There are six multiple-choice questions in each test. Please be sure you answer each question.

The text titles are at the top of the pages to serve as memory cues. After finishing each test, you

will be asked to judge your test performance." After finishing each test, participants read:

"Please judge how many of the six test questions you correctly answered." After participants

completed all the tasks, they were debriefed and thanked.

Results and Discussion

Anchoring and Adjustment in Metacomprehension

To demonstrate the anchoring and adjustment process underlying metacomprehension

judgment, I first formed the lines of best fit by plotting mean metacomprehension judgments and

comprehension performance over the five experimental texts as a function of text difficulty

(Figures 3-1 3-3). Judgments of performance were made on how many of six multiple-choice

test questions participants could correctly answer (prospective) or believed that they correctly

answered (retrospective). Text difficulty levels ranged from approximately 11 to 19 in terms of









Flesch-Kincaid grade levels. The Figures 3-1, 3-2, and 3-3 show how prospective judgment,

retrospective judgment, and performance changed as a function of text difficulty, respectively.

Then I conducted a multivariate regression analysis to test two hypotheses: (a) the

magnitude of metacomprehension judgments would change with text difficulty, but to a lesser

extent than would the level of comprehension performance, indicating anchoring and adjustment

(based on text difficulty) in metacomprehension judgments; and (b) the magnitude of anchoring

would be smaller in retrospective than in prospective metacomprehension judgments. The

multivariate regression analysis supported the two hypotheses. The slope of the line of

prospective judgment (A= 0.1) was significantly different from zero, t (1) = 4.5, p < .01, so

were the slopes of the line of retrospective judgment (A= 0.15), t (1) = 6.47, p < .01 and the

line of performance (A= 0.24), t (1) = 8.64, p < .01. These results showed that the magnitude

of metacomprehension judgments and the level of comprehension performance significantly

changed with text difficulty. Additionally, the analysis showed that the slope of the performance

line was significantly greater than that of the retrospective judgment line, F (1, 298) = 10.15, p <

.01, which, in turn, was significantly greater than that of the prospective judgment line, F (1,

298) = 5.86, p < .05. These results demonstrated that metacomprehension judgments changed

with text difficulty to a significantly lesser extent than did actual comprehension performance

and that prospective judgments changed with text difficulty to a significantly lesser degree than

did retrospective judgments. In all, anchoring and adjustment was involved in making

metacomprehension judgments and the extent of anchoring was smaller in retrospective than in

prospective judgments.

Judgmental Anchors

To show whether or not enduring sense of ability served as a judgmental anchor, I

computed the partial correlation coefficients between self-ratings of reading ability and









metacomprehension judgments, controlling for actual performance (Table 3-2). The results

showed that participants' self-ratings of reading ability (M = 3.93 out of 5, SD = 0.8) were

significantly correlated with their prospective metacomprehension judgments (M = 4.42 out of 6,

SD = 0.71), r = 0.35, p < .01, and retrospective metacomprehension judgments (M= 3.55 out of

6, SD = 0.77), r = 0.26, p < .05, controlling for actual comprehension performance. Hence, this

study provided direct empirical evidence that people relied on self-perceptions of reading ability

to make judgments about their future and past comprehension performance. I also computed the

partial correlation coefficient between self-perceptions of reading ability and actual

comprehension performance (M= 3.01 out of 6, SD = 0.84), controlling for metacomprehension

judgments. They were not significantly correlated (r = 0.04, p > .05), which is in line with

previous research evidence that self-perceptions of abilities correlate modestly or not at all with

actual performance (Falchikov & Boud, 1989). Participants' self-perceptions of reading ability

thus seemed to be misperceptions about their true reading skills.

I examined next whether or not prior task exposure shaped by the practice texts served as

an anchor for metacomprehension judgments. T-tests were conducted to compare the

metacomprehension judgments over the experimental texts between the easy- and hard-practice-

text groups. There was no significant effect, p > .05. Those who read easy practice texts did not

give significantly different prospective or retrospective metacomprehension judgments over the

experimental texts compared to those who read hard practice texts. Prior task exposure

manipulated by easy or hard practice texts thus did not serve as an anchor for subsequent

judgments of performance. Additionally, comprehension performance over the experimental

texts between the two practice conditions did not differ either, p > .05.









Metacomprehension Accuracy

To show metacomprehension accuracy, I also reported the partial correlation coefficients

between prospective or retrospective metacomprehension judgments and actual comprehension

performance (Table 3-2). Prospective judgments were not significantly correlated with actual

performance, controlling for retrospective judgments, r = 0.12, p > .05. This showed that

participants had poor accuracy of judging their own future comprehension performance.

However, retrospective judgments were significantly correlated with comprehension

performance, controlling for prospective judgments, r = 0.35, p < .01. These findings are

consistent with current research evidence that adult readers typically have low accuracy of

judging future comprehension performance (e.g., Glenberg & Epstein, 1985, 1987; Maki, 1998;

Maki & Berry, 1984) and retrospective metacomprehension judgments are more accurate than

prospective ones (e.g., Glenberg & Epstein, 1985; Maki & Serra, 1992; Pierce & Smith, 2001).

Prospective and retrospective metacomprehension judgments were significantly correlated with

each other, controlling for actual performance, r = 0.64, p < .01.

To summarize, the findings in Experiment 1 demonstrated that the anchoring and

adjustment process occurred in metacomprehension judgments and that the magnitude of

anchoring was smaller in retrospective than in prospective metacomprehension judgments. These

findings shed light on why prospective judgments were less accurate than retrospective

judgments because greater reliance on a judgmental anchor likely causes poorer discrimination

between well-understood texts and less-understood ones in a set. As for the specific anchor

information, prior task exposure shaped by the practice texts did not serve as a judgmental

anchor whereas self-perceptions of ability did they influenced both prospective and

retrospective metacomprehension judgments.









Table 3-1. Text titles and grade levels
Easy Practice Texts
Guilt, Good, and Bad
Obesity
Hard Practice Texts
Intelligence and Measurement
History of the English Colonies
Experimental Texts
Affirmative Action
Inventions, Inventors, and Industry
Literature in the Classroom
The Culture of Colonial America
Parental Involvement in Education


F-K Grade Level
11.5
13.2

15.8
18.5


14.7
17.5
11.2
19.3
13.9









Table 3-2. Partial correlation coefficients (Experiment 1)
Variable 1 2 3 4
1. self-rating of ability
2. prospective judgment 0.35**
3. retrospective judgment 0.26* 0.64**
4. performance -0.04 -0.12 0.35**
Note: ** significant at .01 level. significant at .05 level.












y =-0.1024x + 5.9894

6 ** *
5 *
4
3
2- + *
1
0
10111213 14 15 16 17 18 19 20

Text Difficulty

Figure 3-1. How prospective judgments changed with text difficulty (Experiment 1)



y =-0.1528x + 5.8914

6- *
5 *



S2- ** *
1- + *
0 --
10 11 12 13 14 15 16 17 18 1920

Text Difficulty


Figure 3-2. How retrospective judgments changed with text difficulty (Experiment 1)












y = -0.2441x + 6.7495

6 *
61 **





i 2- --* ** *-
1- ** *
0 I I I I l i *-]
1011 1213 14 15 16 17 18 1920

Text Difficulty


Figure 3-3. How comprehension performance changed with text difficulty (Experiment 1)









CHAPTER 4
EXPERIMENT 2

Experiment 2 was conducted to replicate the findings of Experiment 1 with respect to the

anchoring and adjustment process involved in metacomprehension judgments and the different

extent of anchoring in prospective and retrospective judgments. I also asked participants to rate

their own reading ability on a 5-point Likert scale. As in Experiment 1, I hypothesized that self-

perceptions of ability would serve as a judgmental anchor and would be significantly and

positively correlated with metacomprehension judgments. In addition, I investigated whether or

not metacomprehension judgments would be affected by another kind of information that might

influence individuals' expectations of their own performance information about peer

performance.

In Experiment 2, I provided participants with fictitious positive or negative information

about the comprehension performance of their peers to shape positive or negative expectations of

their own performance, respectively. As previously reviewed, research has shown that fictitious

social cues about peer performance influenced college students' prospective metamemory

judgments over word pairs (de Carvalho Filho & Yuzawa, 2001). For example, de Carvalho

Filho and Yuzawa (2001) found that among students with low level of metacognitive knowledge,

those who received high social cues made significantly higher Ease-of-Learning (EOL)

judgments than those who received no social cues, who, in turn, made significantly higher

judgments than those who received low social cues; and among students with high level of

metacognitive knowledge, those who received high social cues made significantly higher EOL

judgments than those who received low social cues. I expected the social information to also

affect metacomprehension judgments, especially prospective metacomprehension judgments.

People have to make prospective metacomprehension judgments under much uncertainty such as









uncertainty about the upcoming comprehension tests. So, when receiving positive information

that their peers did very well on a comprehension task, they might perceive the task to be fairly

easy and expect their own performance on this task to be good. On the other hand, when

receiving negative information that their peers did poorly on the task, individuals may perceive

the task to be fairly difficult and have lowered expectations of their own performance. So, my

main hypothesis was that those who received positive social information would make

significantly higher prospective metacomprehension judgments than those who received negative

social information. I also included a group who received no social information and hypothesized

that the prospective metacomprehension judgments of this group would be lower than those of

the positive information group but higher than those of the negative information group.

Method

Participants

Ninety undergraduates from the Department of Educational Psychology human-

participants research pool at a large southeastern university participated to fulfill part of their

course requirements. They were tested in a group format, with six participants at a time. Before

the experiment, participants were randomly assigned to positive-, negative-, or no-social-

information groups.

Materials

The materials were the same as those in Experiment 1 except that the practice materials

were not included in Experiment 2. The text booklet included the five experimental texts of

varying difficulty (Table 3-1). The test booklet included the corresponding multiple-choice

comprehension tests. Each test consisted of six five-alternative multiple-choice questions,

including both factual and inferential types of questions.









Procedures

Participants were randomly assigned to positive-, negative, or no-information condition to

influence their expectations of their own performance. Before the experiment, participants were

informed that they would be asked to read a set of expository texts for comprehension in a self-

paced manner, judge their own comprehension performance over the texts, and take multiple-

choice comprehension tests.

Before giving participants the text booklet, I asked them to rate their own reading ability

on a five-point Likert scale (1 = very poor, 2 = poor, 3 = average, 4 = above average, and 5 =

excellent). Then participants received the text booklet that consisted of five expository texts of

varying difficulty. Each text was on a separate page and the order of text presentation was

randomized. Table 3-1 shows the order of text presentation. Participants read the instructions

before starting to read: "Please read the following texts for comprehension. Read each text

carefully as you will be asked questions about them later. Read each text only one time but at a

pace that is comfortable to you. After reading each text, you will be asked to judge your

comprehension performance over it."

After reading each text, participants turned to the next page. The instructions for those

participants in the positive-information group were: "In a previous experiment, college students'

multiple-choice test performance over the texts in our current study ranged from 80 to 90%, with

a mean of 85%. Please estimate on a scale from 0% to 100% how well YOU would do on a

multiple-choice comprehension test about the text you just read." The instructions for those

participants in the negative-information group were: "In a previous experiment, college students'

multiple-choice test performance over the texts in our current study ranged from 50 to 60%, with

a mean of 55%. Please estimate on a scale from 0% to 100% how well YOU would do on a

multiple-choice comprehension test about the text you just read." Participants in the no-









information group read: "Please estimate on a scale from 0% to 100% how well you would do on

a multiple-choice comprehension test about the text you just read." In addition, all participants

were asked to judge how many of six multiple-choice test questions they could correctly answer

over the text they just read. So, different from the participants in Experiment 1 who made

judgments only in numbers, the participants in Experiment 2 judged their future performance in

terms of both percentages and numbers. The reason for including a percentage scale was that the

social information that participants received was in percentages. And I expected the prospective

judgments in percentages and in numbers to be significantly correlated since both were about

future test performance.

After the reading and judgment tasks were done, participants submitted the text booklet

and received the test booklet. The order of test presentation was the same as that of text

presentation, so there was a similar length of delay between reading each text and taking the

corresponding test. Participants read the instructions for the test booklet: "Please complete the

following comprehension test questions over the texts you just finished reading. There are six

multiple-choice questions in each test. Please be sure you answer each question. The text titles

are at the top of the pages to serve as memory cues. After finishing each test, you will be asked

to judge your test performance." After finishing each test, participants judged how many of the

six test questions they correctly answered. After completing all the tasks, participants were

debriefed and thanked.

Results and Discussion

Anchoring and Adjustment in Metacomprehension

To replicate the Experiment 1 findings regarding the anchoring and adjustment process

involved in metacomprehension judgments, I formed the lines of best fit by plotting mean

metacomprehension judgments and comprehension performance over the texts as a function of









text difficulty (Figures 4-1 4-4). Text difficulty levels ranged from approximately 11 to 19 in

terms of Flesch-Kincaid grade levels. Participants judged their future comprehension

performance both in percentages (0% 100%) and in terms of the number of test questions they

could correctly answer. Figures 4-1 and 4-2 showed how prospective metacomprehension

judgments in terms of percentage and number changed with text difficulty, respectively. Figures

4-3 and 4-4 showed how retrospective metacomprehension judgment and comprehension

performance changed with text difficulty, respectively.

Next, I conducted a multivariate regression analysis to test whether (a) the magnitude of

prospective and retrospective metacomprehension judgments would significantly change with

text difficulty, but to a significantly lesser extent than would the level of comprehension

performance, which indicates judgmental anchoring and adjustment; and (b) the magnitude of

prospective judgments would change with text difficulty to a significantly lesser extent than

would the magnitude of retrospective judgments. The analysis confirmed these hypotheses. The

slope of the line of prospective judgment in percentage (A= 0.01) was significantly different

from zero, t (1) = 4.79, p < .01, so were the slopes of other lines, including the line of

prospective judgment in number (A= 0.09), t (1) = 5.59, p < .01, the line of retrospective

judgment (A= 0.17), t (1) = 9.39,p < .01, and the line of performance (A= 0.28), t (1) = -

11.39, p < .01. These results showed that individuals did adjust their metacomprehension

judgments based on text difficulty. However, the slope of the performance line was significantly

greater than that of the line of retrospective judgment, F (1, 448) = 22.32, p < .01 which, in turn,

was significantly greater than the slope of the line of prospective judgment, F (1, 448) = 19.81, p

< .01. These results replicated those in Experiment 1 that metacomprehension judgments were









influenced by anchoring and adjustment based on text difficulty and that the magnitude of

anchoring was smaller in retrospective than in prospective judgments.

Judgmental Anchors

I examined self-perceptions of ability as a judgmental anchor by computing the partial

correlation coefficients between self-ratings of reading ability and metacomprehension

judgments, controlling for actual comprehension performance (Table 4-1). Self-ratings of

reading ability (M= 3.89 out of 5, SD = 0.66) were significantly correlated with prospective

judgments in percentages (M= 78%, SD = 10%), r = 0.24, p < .05, prospective judgments in

numbers (M= 4.4 out of 6, SD = 0.57), r = 0.33,p < .01, and retrospective judgments (M= 3.63

out of 6, SD = 0.75), r = 0.27, p < .01, controlling for actual performance. Hence, the findings in

Experiment 1 were replicated that participants relied on self-perceptions of reading ability to

judge their future and past comprehension performance. Self-perceptions of reading ability,

however, were not significantly correlated with actual comprehension performance (M= 3.25 out

of 6, SD = 0.67), controlling for metacomprehension judgments, r = 0.18, p > .05. So,

participants' self-perceptions of reading ability were misperceptions about their true reading

ability.

To examine whether social information about peer performance served as a judgmental

anchor, I conducted an ANOVA and a series of t-tests to compare the metacomprehension

judgments among the three social-information groups (positive, negative, and no-information).

The prospective judgments in percentage were significantly affected by social information, F (2,

87) = 18.57, p < .01. Specifically, those who received positive information about the mean of

peer performance (85%) gave significantly higher prospective judgments (M = 82.9%, SD = 6%)

than did those who received negative information (55%) (M= 69.8%, SD = 12%), t (58) = 5.5, p

< .01. Those who received no information made significantly higher prospective judgments (M=









80.4%, SD = 8%) than those who received negative information, t (58) = 4.07, p <.01. However,

there was no significant difference between the positive information group and the no

information group in the magnitude of prospective judgments (p > .05). This was likely because

participants without the influence of positive social information already had "positive"

expectations of their own performance.

Interestingly, the social information did not significantly affect prospective or retrospective

judgments in terms of the number of questions they could correctly answer out of six (p > .05). I

inspected the mean prospective judgments in number in the positive, negative and no information

groups. They were 4.47 (equivalent to 74.5%), 4.25 (71%), and 4.47 (74.5%), respectively. As

previously reported, the mean prospective judgments in percentage in the positive, negative and

no information groups were 82.9%, 69.8%, and 80.4%, respectively. So, participants did not

make equivalent levels of prospective judgments on these two scales, which throws some light

on why the social information affected the judgments in percentages but not the judgments in

numbers. It is surprising that changing the scale of judgment made such a difference, but the

result coincidentally revealed the non-analytic nature of students' judgment process. Although

students first judged their performance on a percentage scale, they did not roughly convert the

percentages to numbers to make judgments on the subsequent numeric scale. In all, the results

showed that the percentage information about peer performance only influenced students'

prospective metacomprehension judgments made on a percentage scale.

Metacomprehension Accuracy

To show metacomprehension accuracy, I also reported partial correlation coefficients

between metacomprehension judgments and comprehension performance (Table 4-1).

Prospective judgments in percentage and in number were not significantly correlated with

comprehension performance, controlling for retrospective judgments, r = 0.13 and r = 0.04,









respectively, p > .05. However, retrospective judgments were significantly correlated with

comprehension performance, controlling for prospective judgments, r = 0.32, p < .01. These

findings were consistent with the those in Experiment 1 as well as in metacomprehension

research that students have poor accuracy of judging future comprehension performance (e.g.,

Glenberg & Epstein, 1985, 1987; Maki, 1998) and that retrospective metacomprehension

judgments are more accurate than prospective ones (e.g., Glenberg & Epstein, 1985; Maki &

Serra, 1992; Pierce & Smith, 2001). In addition, prospective judgments in percentage and in

number were significantly correlated, controlling for actual performance, r = 0.70, p < .01; and

prospective and retrospective judgments in number were significantly correlated, controlling for

actual performance, r = 0.43,p < .01.

In summary, in Experiment 2 I replicated the Experiment 1 findings that the anchoring and

adjustment process was involved in metacomprehension judgments and that the extent of

anchoring was smaller in retrospective than in prospective metacomprehension judgments.

Prospective metacomprehension judgments were found to be poor and less accurate than

retrospective metacomprehension judgments, probably due to students' greater reliance on a

judgmental anchor when making prospective judgments. As for the anchor information, I showed

that social information about peer performance influenced prospective metacomprehension

judgments whereas self-perceptions of ability influenced both prospective and retrospective

metacomprehension judgments.









Table 4-1. Partial correlation coefficients (Experiment 2)
Variable 1 2 3 4 5
1. self-rating of ability
2. prospective judgment (percentage) 0.24*
3. prospective judgment (number) 0.33** 0.70**
4. retrospective judgment 0.27** 0.1 0.43**
5. performance 0.18 0.13 0.04 0.32**
Note: ** significant at .01 level. significant at .05 level.













-0.0114x + 0.9513


1011 12131415161718 1920

Text Difficulty

Figure 4-1. How prospective judgments in percentage changed with text difficulty (Experiment
2)




y = -0.0903x + 5.7789


6T :


* *


5 *
4 -- *
3 *


* *


1 *
0
0 ------------------

10 11 12 13 14 15 16 17 18 19 20
Text Difficulty

Figure 4-2. How prospective judgments in number changed with text difficulty (Experiment 2)













y =-0.1702x + 6.2344

6 *
5 -- *
5 4- *


S2 *
1 *
0 *
10 11 12 13 14 15 16 17 18 1920

Text Difficulty

Figure 4-3. How retrospective judgments changed with text difficulty (Experiment 2)




y = -0.2771x + 7.4963

6 *
5 ** *
4 *
S3 *


1 *
0 -- -- I I II
10 11 12 13 14 15 16 17 181920

Text Difficulty

Figure 4-4. How comprehension performance changed with text difficulty (Experiment 2)









CHAPTER 5
GENERAL DISCUSSION

The ability to make accurate metacomprehension judgments is crucial to successful

learning. The more able learners are to discriminate between what they know and what they need

to study more, the more effectively they will be able to regulate their study, for example, by

restudying the less-understood materials. However, an accurate metacomprehension judgment is

harder to achieve than one may expect. Research has shown that adult learners typically have

poor metacomprehension accuracy, particularly poor accuracy of judging future comprehension

performance (e.g., Glenberg & Epstein, 1985, 1987; Maki, 1998). Zhao and Linderholm (2008)

proposed an Anchoring and Adjustment Model of Metacomprehension Judgment that illuminates

why metacomprehension accuracy is commonly poor. According to the model, people make

metacomprehension judgments by anchoring on their pre-formed expectations of performance

and then adjusting based on experiences with current tasks, but adjustment from the anchor

requires mental effort and tends to be insufficient. Zhao and Linderholm (2008) contended that

judgmental anchoring largely contributes to poor accuracy of metacomprehension judgments.

In this study I sought to empirically investigate the anchoring and adjustment process

involved in metacomprehension judgments. The results of my two experiments demonstrated

that the process of making prospective and retrospective metacomprehension judgments did

involve anchoring and adjustment based on text difficulty. In addition, both experiments showed

that the magnitude of anchoring was smaller in retrospective than in prospective

metacomprehension judgments. Thus, participants relied on the anchor less heavily when

judging past performance compared to when judging future performance. This finding may be

connected to the evidence that retrospective judgments are more accurate than prospective

judgments, which was shown in this study as well in previous research (e.g., Glenberg &









Epstein, 1985; Maki & Serra, 1992; Pierce & Smith, 2001). Likely because there is no

uncertainty about the test when making retrospective judgments, students rely less on an anchor

and instead, reflect more on their experiences with current tasks, which may explain better

accuracy in retrospective judgments than in prospective ones. Further research should be

conducted to investigate the idea that reducing the magnitude of anchoring directly improves

metacomprehension accuracy. In both experiments, the accuracy of students' prospective

judgments was poor. In fact, prospective judgments were not significantly correlated with actual

performance (r = .12 in Experiment 1; r = .04 in Experiment 2). The finding is consistent with

current research evidence that adult learners are not adept at judging future comprehension

performance (e.g., Glenberg & Epstein, 1985, 1987; Maki, 1998), but the judgment accuracy

shown in my study was even poorer than that demonstrated in current research. As reviewed

previously, Maki (1998) reported a mean gamma correlation of + .27 between prospective

judgments and performance across over 20 studies. One explanation for the extremely poor

prospective metacomprehension accuracy in my study is that prospective judgments were being

manipulated as part of the design. For instance, the participants in Experiment 2 were given

positive, negative, or no social information about peer performance to influence their own

metacomprehension judgments. The manipulation created more variability among participants'

prospective judgments.

As for the judgmental anchors, I expected information that may bias expectations of

performance to be potential anchors. The results of the study showed that students relied on their

self-perceptions of ability to make prospective and retrospective metacomprehension judgments

(Experiments 1 and 2). So, this study offers direct empirical evidence that self-perception of

reading ability is a major basis of metacomprehension judgments. The results are in line with









research findings that individuals' self-perceptions of ability in areas such as logical reasoning

influence their retrospective judgments of performance on the task (Ehrlinger & Dunning, 2003).

In contrast to self-perceptions of ability, prior task exposure manipulated with easy or hard

practice texts did not affect metacomprehension judgments (Experiment 1). It could be that adult

readers do not significantly base their metacomprehension judgments on not-so-distant prior

experiences with a task. Another explanation is that using two practice texts is not a strong

manipulation of prior task exposure. Students knew the first two texts in the booklet were

practice texts. There is the possibility that they read them for practice but did not regard their

experiences with them as relevant cues about the difficulty levels of the subsequent texts. It is

necessary to use other manipulations to further examine prior task exposure as potential anchor

information. The effects of prior task exposure on metacomprehension are indeed being

investigated in a study by Linderholm et al. (in progress) who manipulated the order of text

difficulty. Participants in the study read a series of texts in one of three order conditions: easy to

hard, hard to easy, and random. The preliminary results have shown that the difficulty levels of

initial texts in a series of texts affected relative metacomprehension accuracy. Specifically,

relative metacomprehension accuracy was significantly different between the easy-to-hard

condition and the hard-to-easy or the random condition. Further analysis is needed on how

metacomprehension judgments were affected by prior task exposure manipulated with the

different orders of text difficulty.

Finally, the information about peer performance (in terms of the mean percentages of

performance) influenced students' prospective metacomprehension judgments made on a

percentage scale (Experiment 2). Students who received positive or no information about peer

performance made significantly higher prospective judgments than did those who received









negative information, but there was no significant difference in the magnitude of prospective

judgments between the positive- and the no-information groups. Why would having no social

information be like having positive social information? It is likely because students, in general,

already had "positive" expectations of performance on the upcoming tests, rendering the effect of

positive social information insignificant. As shown in the study, students significantly relied on

their self-perceptions of ability to make metacomprehension judgments. For instance, the mean

of students' self-ratings of reading ability in Experiment 1 was 3.93 out of 5, which is equivalent

to 79%. A performance of 70% (equivalent to a C on a typical grading scale) is usually

considered average, so students seemed to perceive themselves to possess above-average reading

ability. This helps explain why students in the no information group gave a mean judgment of

80.4%, which is equivalent to a B grade, and why there is no difference in the levels of

judgments between the positive- and the no-information groups. Another interesting finding was

that participants made different levels of prospective judgments on the percentage scale and the

numeric scale. It is curious that a change in the scale made a significant difference in prospective

judgments. It may be that participants were fairly familiar with percentage scales but were not

used to make performance judgment on a 7-point (0-6) numeric scale. Future research could be

conducted to systematically investigate how the judgment scales influence metacomprehension

judgments.

The results of my investigation have both theoretical and practical implications.

Theoretically, my results provide empirical support and add to the Anchoring and Adjustment

Model of Metacomprehension Judgment (Zhao & Linderholm, 2008). This model was proposed

based on a review of the literature on the bases of prospective metacomprehension judgments.

Based on the results of this study, making both prospective and retrospective









metacomprehension judgments involves anchoring and adjustment. The extent of anchoring was

found to be smaller in retrospective than in prospective judgments, supporting the contention that

test uncertainty significantly contributes to anchoring (Zhao & Linderholm, 2008). Additionally,

the results support and add to the proposal that individuals anchor on their pre-established

expectations of performance (Zhao & Linderholm, 2008) by revealing that metacomprehension

judgments are influenced by different kinds of information that biases performance expectations,

including self-perceptions of ability and information about the performance of peers.

Practically, the results of the study have implications for understanding the processes

underlying metacomprehension judgments and for illuminating the ways of improving

metacomprehension accuracy. Learners rely on anchor information such as self-perceptions of

ability and information about peer performance when making judgments of their own

performance. However, self-perceptions of ability are not significantly correlated with actual

performance as shown in this study as well as in previous research (e.g., Falchikov & Boud,

1989). Peer performance is obviously not reliable as a basis of one's own performance judgment

either. Thus, to improve metacomprehension accuracy, we should seek to reduce learners'

reliance on judgmental anchors. Anchoring is significantly due to test uncertainty, so providing

learners with more information about the upcoming test, for example, what concepts or ideas will

be tapped by the test, may reduce anchoring and improve judgment accuracy.

The magnitude of anchoring may also be decreased by ways such as educating or

forewarning students about the anchoring phenomenon and motivating or encouraging them to

adjust away from the anchor. For example, it is important to educate students about the effects of

self-perceptions of ability on metacomprehension judgments and to forewarn students about the

social influences on metacomprehension judgments because it is alarming that fictitious social









information about peer performance has significant effects on students' judgments of their own

performance. There is also evidence from the decision-making literature that judgmental

anchoring decreases when people are provided with forewarning about anchoring or financial

incentives for accurate judgments (Epley, Boven, Keysar, & Gilovich, 2004; Epley & Gilovich,

2005). For example, Epley et al. (2004) reported that accuracy incentives motivated people to

decrease egocentric biases in perspective taking and to reflect more on the differences between

themselves and other people. Future research should be conducted to directly examine the

effectiveness of these approaches to reduce the magnitude of anchoring in metacomprehension

judgments. In all, the results reported in the study provide a further understanding of how

learners make metacomprehension judgments. This enhanced understanding, in turn, provides

important insight into how to improve learners' metacomprehension accuracy.









APPENDIX A
SAMPLE EXPERIMENTAL TEXTS

Affirmative Action

Reverse discrimination, minority recruitment, racial quotas, and, more generally, affirmative
action are phrases that carry powerful emotional charges. But why should affirmative action, of
all government policies, be so controversial? In a sense, affirmative action is like other
governmental programs, e.g., defense, conservation, and public schools. Affirmative action
programs are designed to achieve legitimate government objectives such as improved economic
efficiency, reduced social tension, and general betterment of the public welfare. While it cannot
be denied that there is no guarantee that affirmative action will achieve these results, neither can
it be denied that there are plausible, even powerful, sociological and economic arguments
pointing to its likely success.

Government programs, however, entail a cost, that is, the expenditure of social or economic
resources. Setting aside cases in which the specific user is charged a fee for service (toll roads
and tuition at state institutions), the burdens and benefits of publicly funded or mandated
programs are widely shared. When an individual benefits personally from a government
program, it is only because she or he is one member of a larger beneficiary class, e.g., a farmer;
and most government revenue is obtained through a scheme of general taxation to which all are
subject.

Affirmative action programs are exceptions to this general rule, though not, as might at first
seem, because the beneficiaries of the programs are specific individuals. It is still the case that
those who ultimately benefit from affirmative action do so only by virtue of their status as
members of a larger group, a particular minority. Rather, the difference is the location of the
burden. In affirmative action, the burden of "funding" the program is not shared universally, and
that is inherent in the nature of the case, as can be seen clearly in the case of affirmative action in
employment. Often job promotions are allocated along a single dimension, seniority; and when
an employer promotes a less senior worker from a minority group, the person disadvantaged by
the move is easily identified: the worker with greatest seniority on a combined minority--non-
minority list passed over for promotion.

Now we are confronted with two competing moral sentiments. On the one hand, there is the idea
that those who have been unfairly disadvantaged by past discriminatory practices are entitled to
some kind of assistance. On the other, there is the feeling that no person ought to be deprived of
what is rightfully his or hers, even for the worthwhile service of fellow humans. In this respect,
disability due to past racial discrimination, at least insofar as there is no connection to the
passed-over worker, is like a natural evil. When a villainous man willfully and without
provocation strikes and injures another, there is not only the feeling that the injured person ought
to be compensated but there is consensus that the appropriate party to bear the cost is the one
who inflicted the injury. Yet, if the same innocent man stumbled and injured himself, it would be
surprising to hear someone argue that the villainous man ought to be taxed for the injury simply
because he might have tripped the victim had he been given the opportunity. There may very
well be agreement that he should be aided in his recovery with money and personal assistance,









and many will give willingly; but there is also agreement that no one individual ought to be
singled out and forced to do what must ultimately be considered an act of charity.

Inventions, Inventors, and Industry

There is widespread belief that the emergence of giant industries has been accomplished by an
equivalent surge in industrial research. A recent study of important inventions made since the
turn of the century reveals that more than half were the product of individual inventors working
alone, independent of organized industrial research. Independent inventors have contributed such
products as air conditioning, the automatic transmission, the jet engine, and streptomycin.
Despite these findings, we are urged to support monopoly power on the grounds that such power
creates an environment supportive of innovation.

We are told that the independent inventor, along with the small firm, cannot afford to undertake
the important research needed to improve our standard of living while protecting our diminishing
resources. We are told that only the prodigious assets of the giant corporation or conglomerate
can afford the kind of expenditures that can produce the technological advances vital to
economic progress. But when we examine expenditures for research, we find that of the more
than $35 billion spent each year in this country, almost two-thirds is spent by the federal
government. More than half of this government expenditure is funneled into military research
and product development, accounting for the enormous increase in spending in such industries as
nuclear energy, aircraft, missiles, and electronics. There are those who consider it questionable
that these defense-linked research projects will account for an improvement in the standard of
living or, alternately, do much to protect our diminishing resources.

Recent history has demonstrated that we may have to alter our long-standing conception of the
process actuated by competition. The price variable, once perceived as the dominant aspect of
the competitive process is now subordinate to the competition of the new product, the new
business structure, and the new technology. It can be assumed that in a highly competitive
industry not dominated by a single corporation, investment in innovation--a risky and expensive
budget item--might meet resistance from management and stockholders who might be more
concerned with cost-cutting, efficient organization, and large advertising budgets. However, it
would be an egregious error to assume that the monopolistic producer should be equated with
bountiful expenditures for research. Large-scale enterprises tend to operate more comfortably in
stable and secure circumstances, and their managerial bureaucracies tend to promote the status
quo and resist the threat implicit in change.

Furthermore, the firm with a small share of the market will aggressively pursue new techniques
and different products, since with little vested interest in capital equipment or plant it is not
deterred from investment in innovation. In some cases, where inter-industry competition is
reduced or even entirely eliminated, the industrial giants may seek to avoid capital loss resulting
from obsolescence by deliberately obstructing technological progress.









APPENDIX B
SAMPLE EXPERIMENTAL TESTS

Affirmative Action

1. The passage is primarily concerned with

a. comparing affirmative action programs to other government programs
b. arguing that affirmative action programs are morally justified
c. analyzing the basis for moral judgments about affirmative action programs
d. introducing the reader to the importance of affirmative action as a social issue
e. describing the benefits that can be obtained through affirmative action programs

2. The author mentions toll roads and tuition at state institutions in order to

a. anticipate a possible objection based on counterexamples
b. avoid a contradiction between moral sentiments
c. provide illustrations of common government programs
d. voice doubts about the social and economic value of affirmative action
e. offer examples of government programs which are too costly

3. With which of the following statements would the author most likely agree?

a. affirmative action programs should be discontinued because they place an unfair
burden on non-minority persons who bear the cost of the programs.
b. affirmative action programs may be able to achieve legitimate social and
economic goals such as improved efficiency.
c. affirmative action programs are justified because they are the only way of
correcting injustices created by past discrimination.
d. affirmative action programs must be redesigned so that society as a whole rather
than particular individuals bears the cost of the programs.
e. affirmative action programs should be abandoned because they serve no useful
social function and place unfair burdens on particular individuals.

4. According to the passage, affirmative action programs are different from most other
government programs in which of the following ways?

I. the goals the programs are designed to achieve
II. the ways in which costs of the programs are distributed
III. the ways in which benefits of the programs are allocated

a. I only
b. II only
c. III only
d. II and III only
e. I, II, and III









5. It can be inferred that the author believes the reader will regard affirmative action
programs as

a. posing a moral dilemma
b. based on unsound premises
c. containing self-contradictions
d. creating needless suffering
e. offering a panacea

6. The primary purpose of the passage is to

a. reconcile two conflicting points of view
b. describe and refute a point of view
c. provide a historical context for a problem
d. suggest a new method for studying social problems
e. analyze the structure of an institution

Inventions, Inventors, and Industry

1. Management and stockholders might be deeply concerned with cost-cutting rather than
innovation if

a. their company is faced with strong competition in a field not dominated by one of
the industrial giants
b. they are very stable and secure and hold a monopoly position in the industry
c. they are part of the military-industry complex and are the recipients of federal
funds for product development
d. they have produced some of the important inventions of this century
e. they have little vested interest in capital equipment or plants

2. The author's purpose in this passage is to

a. advocate an increase in governmental support of organized industrial research
b. point out a common misconception about the relationship between the extent of
industrial research and the growth of monopolistic power in industry
c. describe the inadequacies of small firms in dealing with the important matter of
research and innovation
d. show that America's strength depends upon individual ingenuity and
resourcefulness
e. encourage free market competition among industrial giants

3. It can be inferred from the passage that the author

a. has little confidence in the ability of monopolistic industry to produce the
important inventions of the future
b. would rather see the federal government spend money on social services than on









the defense establishment
c. favors a conservative approach to innovation and places trust in conglomerates to
provide efficient production
d. while admitting that more than half the important inventions of the century were
produced by independent inventors, feels that the future lies in the hands of giant
industry
e. believes spin-offs from defense-linked research will account for an improvement
in future inventions

4. The amount of money spent in this country for research and product development is

a. approximately $24 billion each year
b. less than $24 billion each year
c. more than $18 billion each year
d. less than $35 billion each year
e. more than $35 billion each year

5. Which of the following products was NOT mentioned as having been developed by
independent inventors?

a. air conditioning
b. automatic transmission
c. transistors
d. jet engine
e. streptomycin

6. What was formerly perceived as the dominant aspect of the competitive process,
according to the passage?

a. the price variable
b. competition of the new product
c. new business structures
d. new technology
e. standard of living









LIST OF REFERENCES


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Psychology and Aging. 21, 390-400.

Dunning, D., Johnson, K., Ehrlinger, J., & Kruger, J. (2003). Why people fail to recognize their
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Educational Testing Service (1994). GRE, practicing to take the general test (9th edition).
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Ehrlinger, J., & Dunning, D. (2003). How chronic self-views influence (and potentially mislead)
estimates of performance. Journal of Personality and Social Psychology, 84, 5-17.

Epley, N., & Gilovich, T. (2001). Putting adjustment back in the anchoring and adjustment
heuristic: Differential processing of self-generated and experimenter-provided anchors.
Psychological Science, 12, 391-396.

Epley, N., & Gilovich, T. (2005). When effortful thinking influences judgmental anchoring:
Differential effects of forewarning and incentives on self-generated and externally
provided anchors. Journal of Behavioral Decision l, kiiln. 18, 199-212.

Epley, N., Keysar, B., Van Boven, L., & Gilovich, T. (2004). Perspective taking as egocentric
anchoring and adjustment. Journal ofPersonality and Social Psychology, 87, 327-339.

Falchikov, N., & Boud, D. (1989). Student self-assessment in higher education: A meta-analysis.
Review ofEducational Research, 59, 395-430.

Glenberg, A. M., & Epstein, W. (1985). Calibration of comprehension. Journal ofExperimental
Psychology: Learning. Memory, and Cognition, 11, 702-718.

Glenberg, A. M., & Epstein, W. (1987). Inexpert calibration of comprehension. Memory and
Cognition, 15, 84-93.

Glenberg, A. M., Sanocki, T., Epstein, W., & Morris, C. (1987). Enhancing calibration of
comprehension. Journal ofExperimental Psychology: General, 116, 119-136.

Hacker, D. J., Bol, L., Horgan, D. D., & Rakow, E. A. (2000). Test prediction and performance
in a classroom context. Journal ofEducational Psychology, 92, 160-170.

Jacowitz, K. E., & Kahneman, D. (1995). Measures of anchoring in estimation tasks. Personality
& Social Psychology Bulletin, 21, 1161-1166.









Linderholm, T., Zhao, Q., Therriault, D., & Cordell-McNulty, K. (under review).
Metacomprehension estimates investigated within an anchoring and adjustment framework.
Metacognition and Learning.

Maki, R. H., (1998). Test prediction over text materials. In D. J. Hacker, J. Dunlosky, & A. C.
Graesser (Eds.), Metacognition in educational theory and practice (pp.117-144). Hillsdale,
NJ: Erlbaum.

Maki, R. H. & Berry, S. L. (1984). Metacomprehension of text material. Journal ofExperimental
Psychology: Learniung. Memory, and Cognition, 10, 663-679.

Maki, R. H., & Serra, M. (1992). The basis of text predictions for text material. Journal of
Experimental Psychology: Learning. Memory, & Cognition, 18, 116-126.

Maki, R. H., Shields, M., Wheeler, A. E., & Zacchilli, T. L. (2005). Individual differences in
absolute and relative metacomprehension accuracy. Journal of Educational Psychology, 97,
723-731.

Moore, D., Lin, L., & Zabrucky, K. (2005). A source of metacomprehension inaccuracy.
Reading Psychology, 26, 251-265.

Morris, C. C. (1990). Retrieval processes underlying confidence in comprehension judgments.
Journal ofExperimental Psychology: Learning. Memory, & Cognition, 16, 223-232.

Pierce, B. H., & Smith, S. M. (2001). The postdiction superiority effect in metacomprehension of
text. Memory & Cognition, 29 (1), 62-67.

Rawson, K. A., & Dunlosky, J. (2002). Are performance predictions for text based on ease of
processing? Journal ofExperimental Psychology: Learning. Memory, & Cognition, 28, 69-
80.

Rawson, K. A., Dunlosky, J., & Thiede, K. W., (2000). The rereading effect: Metacomprehenion
accuracy improves across reading trials. Memory & Cognition, 28, 1004-1010.

Scheck, P., Meeter, M., & Nelson, T. 0. (2004). Anchoring effects in the absolute accuracy of
immediate versus delayed judgments of learning. Journal of Memory and language, 51,
71-79.

Schraw, G., & Roedel, T. D. (1994). Test difficulty and judgment bias. Memory & Cognition, 22,
63-69.

Thiede, K.W., Anderson, M.C.M., & Therriault, D., (2003). Accuracy of metacognitive
monitoring affects learning of texts. Journal of Educational Psychology, 95, 66-73.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases.
Science, 185, 1124-1130.









Weaver, C. A. III., & Bryant, D. S. (1995). Monitoring of comprehension: The role of text
difficulty in metamemory for narrative and expository text. Memory & Cognition, 23, 12-
22.

Zhao, Q., & Linderholm, T. (2008). Adult metacomprehension: Judgment processes and
accuracy constraints. Published online in Educational Psychology Review,
http://www.springerlink.com/content/104855

Zhao, Q., Linderholm, T., & Therriault, D. (2006, August). Absolute Metacomprehension
Accuracy: The Effects of Cue-Utilization Instruction and Working-Memory Capacity.
Poster presented at the 2006 American Psychological Association Convention, New
Orleans, Louisiana.









BIOGRAPHICAL SKETCH

Qin Zhao was born and grew up in Jiangxi, China. She earned her Bachelor of Arts in

English from the North China Electric Power University in China in 2001. In fall 2002 she began

her graduate program in the Department of Educational Psychology at the University of Florida

in Gainesville, Florida. She earned her Master of Arts in educational psychology in 2005 and

graduated with her Doctor of Philosophy in educational psychology in 2008.





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1 JUDGMENTAL ANCHORING AND ADJUSTM ENT IN METACOMPREHENSION By QIN ZHAO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2008

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2 2008 Qin Zhao

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3 To Tao Chen

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4 ACKNOWLEDGMENTS I wish to thank several indi viduals for their help and s upport concerning m y dissertation study. First, I thank Dr. Tracy Linderholm, my advisor and th e chair of my dissertation committee, for providing me with generous supp ort throughout the proces s. Her constructive and prompt feedback on my study design, data analys es, and writing was greatly appreciated. I also thank the other members of my dissertation comm ittee, Dr. David Therriau lt, Dr. David Miller, and Dr. Zhihui Fang, for their service and helpfu l comments about my study. In addition, I wish to thank Dr. Katherine Rawson at the Kent Stat e University for kindly providing me with some of the texts and tests used in this study and Dr Wei Pan at the University of Cincinnati for his helpful comments concerning my data analyses. La st but not least, I acknowledge Dr. Tao Chen, my husband, for supporting and encouraging me as well as helping me with data analysis software. All these individuals have helped me achieve a goal that seemed like a very daunting challenge.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................8 ABSTRACT.....................................................................................................................................9 CHAP TER 1 INTRODUCTION..................................................................................................................10 Bases of Metacomprehension Judgments............................................................................... 11 The Anchoring and Adjustment Model.................................................................................. 15 2 OBJECTIVE AND OVERVIEW OF THE STUDY.............................................................. 20 3 EXPERIMENT 1....................................................................................................................24 Method....................................................................................................................................25 Participants......................................................................................................................25 Materials..........................................................................................................................25 Procedures..................................................................................................................... ..26 Results and Discussion......................................................................................................... ..27 Anchoring and Adjustment in Metacom prehension........................................................ 27 Judgmental Anchors........................................................................................................ 28 Metacomprehension Accuracy........................................................................................30 4 EXPERIMENT 2....................................................................................................................35 Method....................................................................................................................................36 Participants......................................................................................................................36 Materials..........................................................................................................................36 Procedures..................................................................................................................... ..37 Results and Discussion......................................................................................................... ..38 Anchoring and Adjustment in Metacom prehension........................................................ 38 Judgmental Anchors........................................................................................................ 40 Metacomprehension Accuracy........................................................................................41 5 GENERAL DISCUSSION..................................................................................................... 46

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6 APPENDIX A SAMPLE EXPERIMENTAL TEXTS...................................................................................52 B SAMPLE EXPERIMENTAL TESTS....................................................................................54 LIST OF REFERENCES...............................................................................................................57 BIOGRAPHICAL SKETCH.........................................................................................................60

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7 LIST OF TABLES Table page 3-1 Text titles and grade levels............................................................................................... .31 3-2 Partial correlation coefficients (Experiment 1).................................................................. 32 4-1 Partial correlation coefficients (Experiment 2).................................................................. 43

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8 LIST OF FIGURES Figure page 1-1 The anchoring and adjustment mode l of m etacomprehe nsion judgment.......................... 19 3-1 How prospective judgments changed with text difficulty (Experim ent 1)........................ 33 3-2 How retrospective judgments changed with text difficulty (E xperiment 1)...................... 33 3-3 How comprehension performance changed with text difficulty (Experim ent 1).............. 34 4-1 How prospective judgments in percentage changed with text difficulty (E xperiment 2)........................................................................................................................................44 4-2 How prospective judgments in number changed with text difficulty (Experim ent 2)....... 44 4-3 How retrospective judgments changed with text difficulty (E xperiment 2)...................... 45 4-4 How comprehension performance changed with text difficulty (Experim ent 2).............. 45

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9 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy JUDGMENTAL ANCHORING AND ADJUSTM ENT IN METACOMPREHENSION By Qin Zhao August 2008 Chair: Tracy Linderholm Major: Educational Psychology I conducted two experiments to test severa l hypotheses derived from the Anchoring and Adjustment Model of Metacomprehension J udgment (Zhao & Linderholm, 2008): (a) Making metacomprehension judgments should involve a process of anchori ng and adjustment; (b) due to reduced judgmental uncertainty, the magnitude of anchoring should be smaller in retrospective than in prospective metacomprehension judgments; and (c) information that biases expectations of performance should serve as anchor in formation and influence metacomprehension judgments. As predicted, anchoring and adjust ment occurred in metaco mprehension judgments and the extent of anchoring was smaller in retrospec tive than in prospective judgments, which provides insight into why retrospective judgments are generally more accurate than prospective judgments. As for the anchor information, self-p erceptions of ability and information about peer performance significantly influe nced metacomprehension judgme nts. These results support the anchoring and adjustment accoun t of metacomprehension judg ments and have educational implications for the efforts to reduce the magn itude of anchoring and to improve students metacomprehension accuracy.

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10 CHAPTER 1 INTRODUCTION Who of us have not had the expe rien ce of reading a set of text materials and realizing that we have comprehended some texts better than other ones? Suppose there is an upcoming exam that tests our comprehension of the texts, we ma y judge better performance over some texts than over others. The process of judging compre hension performance has been termed metacomprehension in cognitive and educational psychology research (Maki, 1998). This is an important process to study because metacomprehension accuracy, the ability to accurately judge comprehension performance, is important for effective self-regulati on of study (e.g., Thiede, Anderson, & Therriault, 2003). For instance, if stude nts are aware of their comprehension or lack of it, they can focus time and effort on restudying the texts that they failed to comprehend during the first read of the texts. One of the goals of education is to help students become life-long learners who are able to self-re gulate their learning. Hence, met acomprehension research in the long run has significant educational implications. Metacomprehension research often involves ad ult readers as participants. In a typical research procedure, college-student participants are asked to read a set of text materials. After finishing reading each text, they judge their comprehension performance over it on a percentage scale or in terms of the number of test questions they could corre ctly answer. After all the texts are read, they complete the comprehension test s that measure their act ual understanding of the texts. In the cognitive psychology literatu re, researchers have measured relative metacomprehension accuracy by calculating the gamma or Pearson correlation between metacomprehension judgments and actual compre hension performance across a set of text materials (e.g., Glenberg & Epstein, 1985, 1987; Maki & Berry, 1984; Maki, Shields, Wheeler, & Zacchilli, 2005; Rawson, Dunlosky, & Thiede, 2000; Weaver & Bryant, 1995). A gamma or

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11 Pearson correlation coefficient ranges from -1 to +1 and describes the extent to which materials that receive higher judgments are associated with higher performance within each individual. Relative accuracy thus describe s an individuals ability to discriminate between betterunderstood texts and less-understood texts when making metacomprehension judgments. Surprisingly, research has shown that a dult readers typically have low relative metacomprehension accuracy (e.g., Glenberg & Epstein, 1985, 1987; Maki, 1998; Maki & Berry, 1984). For example, Maki (1998) reported a m ean gamma correlation of only +.27 between judgments and comprehension performance across over 20 studies from her laboratory. To shed light on why relative metacomprehension accuracy is commonly low among adult readers, researchers have investigated the ba ses of metacomprehension judgments. Bases of Metacomprehension Judgments Research has suggested that adult readers base their m etacomprehension judgments on different types of information, including expe riences with current tasks and pre-formed expectations of performance. Specifically, some researchers ha ve found that metacomprehension judgments are influenced by experiences with current tasks such as familiarity with the text topic (e.g., Glenberg, Sanocki, Epstein, & Morris, 1987; Maki & Serra, 1992), ease of text processing (e.g., Dunlosky, Baker, Rawson, & Hertzog, 2006; Rawson & Dunlosky, 2002), and ease of immediate text recall (e.g., Morris, 1990). For example, Maki and Serra (1992) asked participants to rate their familia rity with text topics based on text titles and descriptions. The familiarity ratings were correlated with metaco mprehension judgments made after reading the full texts, which suggests that topic familiarity is a basis of metacomprehension judgments. Metacomprehension judgments are also influenc ed by ease of text processing. Research has shown that metacomprehension judg ments are higher for intact text s than for texts with deletedletter words (Rawson & Dunlosky, 2002) and are hi gher for more coherent texts than for less

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12 coherent ones (Dunlosky et al., 2006; Rawson & Dunlosky, 2002). Ease of immediate text recall is another experiential basis of metacomprehension judgments. Morris (1990) instructed students to perform speeded-recall tasks after reading br ief expository passages. It was found that the more recalled immediately, that is, within 15 s econds after reading, the higher the judgments; and that the longer recall latency, that is, the more time lapsed before the first unit of information was recalled, the lower the judgments. Hence, metacomprehension judgments are influenced by a variety of individuals experiences with current tasks such as how easy the text is to process and how much they can immedi ately recall from the text. There is also self-report evidence that experi ences with current task s serve as bases of metacomprehension judgments (e .g., Linderholm, Zhao, Therriault, & Cordell-McNulty, under review; Zhao et al., 2006). Linderholm et al. (under review) instructed college-student participants to judge their comp rehension performance over two expository texts that they read and to report afterwards in writing how they ma de their judgments for each text. Participants self-reports were categorized a nd the frequency counts for each category were then calculated. The self-report evidence revealed that readers us ed experiences with current tasks such as topic familiarity (mentioned by 35% of participants), topi c interest (32%), and text difficulty (18%) as the bases of their metacomprehension judgments In another study, Zhao et al. (2006) asked college students to read several texts and to judge after reading each te xt their comprehension performance over it. Students later described on a self-report questionnaire how they made their judgments. A qualitative analysis of the self reports revealed experiential bases of metacomprehension judgments such as topic familia rity, ease of immediate text recall, and topic interest. For instance, one participant reported, (I judged my performance) based on how interested I was in the text and how closely I felt I focused on the physical statements. In all,

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13 both quantitative and qualitativ e evidence has shown that metacomprehension judgments are based on individuals various cognitive expe riences with the current reading task. Interestingly, other research has sugges ted that metacomprehension judgments are influenced by a different type of information: pre-formed expectations of performance (e.g., Hacker, Bol, Horgan, & Rakow, 2000; Moor e, Lin, & Zabrucky, 2005). Expectations of comprehension performance may be influenced by f actors such as preexisting self-perceptions of reading ability, that is, individua ls views about their own abilit y to understand text information. For example, Hacker et al. (2000) examined how performance judgments and actual performance on prior exams influenced performance judgments on subsequent exams in a college classroom. Participants were students in an introductory educational ps ychology course. Across a semester students were given three multiple-choice exams that tapped their understanding, integration, and application of text concepts. Standard multiple re gression analyses revealed that prior judgments, not prior exam performance, sign ificantly contributed to subse quent judgments. Judgments of performance on Exam 1 significantly influenced judgments on Exam 2 (squared semi-partial correlation = .06, p < .005); and judgments of performance on Exam 2 significantly influenced judgments on Exam 3 (squared semi-partial correlation = .08, p < .005). Hacker et al. (2000) discussed that students might ha ve heavily relied on self-percep tions of ability based on their history of performance to make performance judgments. As a re sult, judgments of performance on prior exams significantly contributed to judg ments on subsequent exams. In a more recent study, Moore et al. (2005) used a path model anal ysis to examine the relations among judgments of performance before and afte r test and actual comprehension performance within and across three reading trials. Moore et al. (2005) found that despite nine practice trials and the manipulation of text difficulty acr oss all reading trials, judgments of future performance were

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14 relatively stable across the thre e evaluated trials, which were trials 10, 11, and 12. The judgment at trial 10 significantly influenced the judgments at both trial 11 (standardiz ed path coefficient = .65, p < .05) and trial 12 (standard ized path coefficient = .42, p < .05). Moore et al. (2005) explained that participants might have larg ely based metacomprehension judgments on selfperceptions of ability shaped by prior task ex perience, which caused the relatively stable judgments across texts. Recent self-report evidence has revealed that self-perceptions of ability as well as several other factors may contribute to pre-formed expectations of performance and affect metacomprehension judgments (Linderholm et al ., under review; Zhao et al., 2006). For instance, a participant in Zhao et al. (2006) reported: I used prior knowledge of how well I have done on tests for comprehension in the past where I have read the informati on directly before the test, as well as what I could understand and recall to myself from the texts right before taking the experiment test (to make my judgment). So, the participant based me tacomprehension judgment on self-perception of ability in addition to experiential cu es. In addition, about 15% of participants in Linderholm et al .s study (under review) reported having differe nt expectations of performance on different test types. Here is an example, I think that I would get a B on text 1 if it was multiple-choice and an A on text 2 if it was multiple-choice. However, if the test was fill-in-the-blank or essay, I think I would do at least one or two letter grades worse on each test. Self-perceptions of ability or performance expectations for di fferent test types are likely shaped by an accumulation of past test-taking experiences. Some not-so-distant prior task experience or exposure, however, may also influence ones expect ations of performance. About 41% of participants in Linderholm et al.s study (under review) reported using the first text, in a series of two texts, as a basis of comparison to judge their performance over the following text.

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15 For example, one participant wrote, Since I feel like I didnt learn as much information from text 2 as I did from text 1, I guessed that I woul d receive a lower grade of C. So, exposure to the first text served as a reference point for the subsequent judgment of performance. In all, there is some indirect evidence that metacomprehens ion judgments are influenced by pre-formed expectations of performance. Pre-formed expect ations of performance ma y come from at least two sources of information: self-p erceptions of ability (on various test types) based on past task experiences and not-so-distant prior task exposure. To conclude, a review of both quantitative and qualitative re search findings has revealed two types of bases of metacomprehension judgmen ts. Some research has shown that individuals base metacomprehension judgments on their experien ces with current tasks such as ease of text processing and ease of immediate text reca ll. Other research has suggested that metacomprehension judgments are influenced by individuals pre-formed expectations of performance contributed to by factors such as se lf-perceptions of ability and not-so-distant prior task exposure. To account for the evidence and conceptualize the processes underlying metacomprehension judgments, Zhao and Li nderholm (2008) proposed the Anchoring and Adjustment Model of Metacomprehension Judgment (Figure 1-1). The Anchoring and Adjustment Model The Anchoring and Adjustm ent Model of Metacomprehension Judgment (Zhao & Linderholm, 2008) incorporated Tversky and Kahnemans (1974) anchoring and adjustment heuristic that has been described in the deci sion-making literature (e.g., Epley & Gilovich, 2001; 2005). According to the anchoring and adjustme nt heuristic (Tversky & Kahneman, 1974), when judging under uncertainty people start with an anc hor and then deliberately adjust away from it to reach a plausible final estimate, but adjust ment requires mental effort and tends to be insufficient, so the final estimate is biased towa rd the initial anchor. For example, Jacowitz and

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16 Kahneman (1995) asked people to estimate the height of Mount Everest. They provided a median estimate of 8000 feet after considering an anchor of 2000 feet, but provided a median estimate of 42,500 feet after considering an anc hor of 45,500 feet. This example illustrates how individuals estimates are biased toward anchor points. Zhao and Linderholm (2008) proposed that th e anchoring and adjustment processes are involved in metacomprehension ju dgments. Individuals in metaco mprehension research or in educational settings usually have to judge th eir own test performance under uncertainty, for example, under uncertainty about the conten ts and difficulty levels of the upcoming comprehension tests. Hence, they may use pre-formed expectations of performance as the anchor and then adjust away from it to account for e xperiences with current tasks, but the final judgments tend to be biased in the direction of the anchor due to insufficient adjustments. The anchoring and adjustment model of metacompre hension judgment sheds light on why students typically have poor relative metacomp rehension accuracy. Specifically, relative metacomprehension accuracy describes ones abili ty to discriminate between better-understood texts and less-understood texts when making met acomprehension judgments over a set of texts. Probably due to judgmental anchoring, students provide relatively stable judgments of performance across a set of text materials (s ee Hacker et al., 2000; Moore et al., 2005) and insufficiently adjust their judgments to disc riminate between betterunderstood texts and lessunderstood ones in a set. Judgmental anchoring may t hus significantly contribute to poor relative metacomprehension accuracy. The Anchoring and Adjustment Model of Metacomprehension Judgment (Zhao & Linderholm, 2008) was proposed based on a review of the research eviden ce regarding the bases of prospective metacomprehension judgments or j udgments of future comprehension

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17 performance. However, the basic anchoring and adjustment process may also be involved in retrospective metacomprehension judgment s or judgments of past comprehension performance, that is, when people are asked to estimate how many comprehension test questions they accurately answered. Although there is no test un certainty when judging ones past performance, there is still uncertainty in making this ki nd of judgment which involves recalling events associated with answering speci fic test questions (Pierce & Smith, 2001). Research has also shown that retrospective judgments of performance are influenced by self-perceptions of ability (e.g., Dunning, Johnson, Ehrlinger, & Kruger, 2 003; Ehrlinger & Dunning, 2003; Schraw & Roedel, 1994). For example, Ehrlinger and Dunni ng (2003: Study 1) showed that participants self-perceptions of logical reas oning ability were significantly a nd positively correlated with their judgments of past performance on a logica l reasoning task, after controlling for actual performance. In Study 3, Ehrlinger and D unning (2003) manipulated participants selfperceptions of knowledge of ge ography using techniques such as asking questions that gave participants favorable or unfavorable impre ssions of their own geographical knowledge. The manipulation of self-perceptions influenced judg ments of past performance, independent of actual performance. So, even after taking the tests, individuals ba sed their performance judgments on preexisting ability perceptions. It is possible th at when making retrospective metacomprehension judgments, indi viduals rely on anchor informa tion such as enduring sense of reading ability and then adjust away from it ba sed on current task experiences. The magnitude of anchoring, however, may be smaller in retrosp ective than in prospective judgments because without test uncertainty, indivi duals may rely less on the anc hor when making retrospective judgments. This could shed light on the research evidence that retrospe ctive metacomprehension

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18 judgments are more accurate than prospectiv e ones (e.g., Glenberg & Epstein, 1985; Maki & Serra, 1992; Pierce & Smith, 2001).

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19 Figure 1-1. The anchoring and adjustment model of metacomprehension judgment. basedon based on Theory-based inferential processes Experience-based inferential processes anchoring adjusting p re-formed performance expectation experiences with current tasks then to reach metacomprehension judgment

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20 CHAPTER 2 OBJECTIVE AND OVERVIEW OF THE STUDY The prim ary objective of the study was to empi rically test the hypotheses discussed above that are derived from the anchoring and adju stment account of metaco mprehension judgments (Zhao & Linderholm, 2008). Specifically, I conduct ed two experiments to test the following hypotheses: (a) making prospective and retros pective metacomprehension judgments should involve a process of anchoring and adjustmen t; (b) reducing judgmental uncertainty should decrease the magnitude of judgmental anchoring. That is, the ma gnitude of anchoring should be smaller in retrospective than in prospective metacomprehensi on judgments; and (c) information that biases ones expectations of performance should serve as anchor in formation and influence metacomprehension judgments. To demonstrate judgmental anchoring and ad justment, I used the same methodology that researchers used to show anchoring and/or mon itoring involved in judgments-of-learning (JOLs) of word pairs (Scheck, Meeter, & Nelson, 2004). Sc heck et al. (2004) manipulated the difficulty of the word pairs and examined how the magnitude of JOLs and the level of recall changed with word-pair difficulty. Three hypotheses were evaluated. According to the Anchoring Hypothesis the magnitude of JOLs would be entirely affected by an anchor point and would not change with word-pair difficulty. That is, the slope of the JOL line would be equal to zero and would be smaller than the slope of the Recall line (| Recall| > | JOL| = 0). According to the Monitoring Hypothesis, the magnitude of JOLs would change systematically with word-pair difficulty corresponding to the change in the level of recall. In this case, th e slopes of the lines of JOL and Recall would be equal a nd greater than zero (| Recall| = | JOL| > 0). The th ird hypothesis, the Dual-Factors Hypothesis was that JOLs are influenced by both anchoring and monitoring of word-pair difficulty. According to this hypothesis, the magnitude of JOLs would change with

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21 word-pair difficulty, but to a lesser exte nt than would the level of recall (| Recall| > | JOL| > 0). Similar logic was used to generate hypot heses for the two studies I describe next. In Experiments 1 and 2, I manipulated text di fficulty and examined how the magnitude of metacomprehension judgments and the level of comprehension performance changed with text difficulty. Based on the Anchoring and Adjustme nt Model of Metacomprehension Judgment (Zhao & Linderholm, 2008), I hypothesized that th e magnitude of students prospective and retrospective metacomprehension judgments would change with te xt difficulty, but to a lesser extent than would the level of comprehension performance (| Performance| > | Judgment| > 0). That is, students would adjust their metacompre hension judgments based on text difficulty, but they would also rely on a judgm ental anchor. In addition, I hypot hesized that due to reduced uncertainty when making retrospective judgments, the magnitude of anchoring would be smaller in retrospective than in prospect ive metacomprehension judgments. In this case, the slope of the line of retrospective judg ment would be greater than that of the line of prospective judgment (| Retrospective Judgment| > | Prospective Judgment|). To further study whether students anchor on pre-formed expectations of performance, I examined information that may bias students ex pectations of their own performance such as self-perceptions of ability. As reviewed in the paper, self-perceptions of reading ability has been suggested to be an important basis of prospective metacomprehens ion judgments (e.g., Hacker et al., 2000; Moore et al., 2005; Zhao et al., 2006), but there has been no direct empirical support. As for retrospective judgments, there is direct evidence that peoples retr ospective judgments of performance on a logical reasoning task and a test of geography knowledge are influenced by their self-perceptions of ability in these ar eas (Ehrlinger & Dunning, 2003). However, it is not certain whether retrospective metacomprehension judgments would be influenced by self-

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22 perceptions of ability in reading comprehension. Hence, I aimed to provide direct empirical evidence that students use self-perceptions of reading ability to make prospective and retrospective metacomprehension ju dgments (Experiments 1 and 2). Self-perception of ability is unlikely the only factor that influences expectations of performance, however. There is self-report eviden ce suggesting that exposure to the first text affects prospective metacomprehension judgments for the subsequent text (Linderholm et al., under review). It is possible that prior task exposure serves as anchor information that affects ones expectations of performance over subsequent tasks. Recall that researchers have suggested that a plausible explanation for relatively stab le metacomprehension ju dgments across texts of varying difficulty is that student s significantly base their judg ments on enduring sense of ability (e.g., Moore et al., 2005). However, an alternative explanation for the finding is that individuals significantly base their judgment s over subsequent texts on prior task exposure. Whereas selfperceptions of ability are likely shaped by an accu mulation of past task experiences, prior task exposure refers to not-so-distant prior task expe rience. To investigate a nd compare the effects of these two kinds of potential anc hor information, I investigated how prior task exposure, in addition to self-perceptions of ability, influences metacomprehension judgments (Experiment 1). Individuals expectations of their own performance can also be influenced by information about others performance. Learni ng situations are rarely isol ated. For example, in a school setting students have access to information a bout peer performance on a learning task. It is important to study how such social information influences ones own metacomprehension judgments. There is evidence that college stude nts prospective metamemory judgments over word pairs, for instance, Judgments-of-Learni ng (JOL) and Ease-of-Learning (EOL) judgments, were significantly affected by fict itious social cues about previ ous performance of other college

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23 students (de Carvalho Filho & Yuzawa, 2001). Peer performance in the study was expressed in terms of the range and the mean of the percentage of correct word -pair recall. In the case of JOL judgments, for example, the social cues about p eer performance particularly affected students with low level of metacognitive knowledge. Metac ognitive knowledge was assessed by tests that tap ones metamemory knowledge about how person, task, and strategy variab les affect recall. In the low knowledge group, the JOL judgments of students who received high social cues were significantly higher than the JOL judgments of students without social cues, which, in turn, were significantly higher than those of students who received low social cues. In the case of EOL judgments, the social cues affect ed all students. The pattern of results for the low knowledge group was the same as that regarding JOL judgments. In the high knowledge group, the EOL judgments of students who received high social cues were significantly higher than those of students who received low social cues. To study the effects of social information on students expectations of their own comprehension performance, I investigated how information about peer performance, in addition to self-perceptions of ability, influences metacomprehension judgments (Experiment 2). To summarize, I conducted two experiment s to investigate wh ether: (a) making prospective and retrospective metacomprehens ion judgments should involve a process of anchoring and adjustment; (b) due to reduced ju dgmental uncertainty, th e extent of anchoring should be smaller in retrospective than in prospective metacomprehension judgments (Experiments 1 and 2); and (c) anchor information that biases expectations of performance, for instance, self-perceptions of ability (Experiments 1 and 2), prior task exposure (Experiment 1), and information about peer pe rformance (Experiment 2), should influence metacomprehension judgments.

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24 CHAPTER 3 EXPERIMENT 1 In Experim ent 1, I asked participants to read a set of expository texts of varying difficulty. After reading each text, participants were inst ructed to judge their future performance on a comprehension test over it. Af ter the reading and prospective judgment tasks were completed, participants took the comprehensio n tests and judged their test pe rformance after finishing each test. I tested these hypotheses: First, the magnitude of prospective and retrospective metacomprehension judgments would change with text difficulty, but to a lesser extent than would the level of comprehension performance (| Performance| > | Judgment| > 0). This would show that the magnitude of metacomprehe nsion judgments is influenced by anchoring as well as adjustment based on text difficulty. Second, the magnitude of anchoring would be smaller in retrospective than in pros pective metacomprehension judgments (| Retrospective Judgment| > | Prospective Judgment|). To show whether self-perceptions of ability served as a judgmental anchor, I asked participants to rate their own reading ability on a 5-point Likert scale. I hypothesized that preexisting self-perceptions of ability would be significantly and positively correlated with prospective and retrospective metacomprehensio n judgments. I also manipulated participants expectations of performance by infl uencing their prior task exposure. I gave participants easy or difficult practice texts to shape their positive or negative expectations for subsequent performance, respectively. My hypothesis was that t hose who read easy prac tice texts would give higher prospective metacomprehension judgments over the experimental texts than those who read hard practice texts.

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25 Method Participants Sixty undergraduates from the Department of Educational Psychology human-participants research pool at a large southeastern university participated to fulfill part of their course requirements. They were tested in a group format, with six participants at a time. Before the experiment, participants were randomly assigned to one of the two practi ce-text conditions: easy or hard practice texts. Materials The m aterials included nine expository texts and the corresponding tests, five of which were developed by Rawson et al. (2000) from ma terials in a Graduate Record Examination (GRE) preparation manual (Branson, Selub, & Solomon, 1987 as cited in Rawson et al., 2000) and four of which were taken from a GRE study guide (Educational Testing Service, 1994). They were presented to participants in a text or test booklet form. The text booklet included two easy or two hard practice texts and five experimental texts of varying difficulty (Appendix A). Text difficulty was assessed using the Flesch-Kincaid (F-K) grade level that measures the readability level of te xt materials in terms of U. S. grade level. The formula for calculating the F-K grade level is : 0.39 (the number of words/the number of sentences) + 11.8 (the number of syllables/th e number of words) 15.59. The F-K grade level has often been used to indicate text difficulty in metacomprehension rese arch (e.g., Maki et al., 2005; Moore et al., 2005; Rawson et al., 2000; W eaver & Bryant, 1995). The two easy practice texts had F-K grade levels of 11.5 and 13.2 whereas the two hard practice texts had F-K grade levels of 15.8 and 18.5. Participants in this study ranged from freshmen (13th grade level) to seniors (16th grade level), so it is appropriate to consider the te xts with F-K grade levels of 11.5 and 13.2 easy and those with F-K grade levels of 15.8 and 18.5 hard. The difficulty levels of the

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26 five experimental texts ranged fr om approximately 11 to 19 in F-K grade levels. Table 3-1 shows the title and F-K grade level of each text. As sh own in Table 3-1, the five experimental texts varied greatly in terms of difficulty. The test booklet included multiple-choice comp rehension tests over all the texts (Appendix B). Each test consisted of six five-alternativ e multiple-choice questions, including approximately half factual and half inferential types of questions. Factual questions prompted readers to retrieve information explicitly stated in the text whereas inferential questions required readers to infer (a) the main point of the text; (b) ideas implied in the te xt; or (c) the attitude, l ogic, or purpose of the author. The advantage of using GRE testing materi als is that they are standardized and have similar structures, so readers will find the sa me types of reading comprehension questions to answer across different texts. Procedures Participants were random ly assigned to the easy or the hard practicetext condition in order to influence their expectations of performance. Before the experiment, participants were informed of the main tasks involved in th e study: read a set of expository texts for comprehension in a self-paced manner, self-evalu ate comprehension performance over the texts, and take multiple-choice comprehension tests about the texts. Before receiving the text booklet, participants were asked to ra te their own reading ability on a five-point Likert scale where 1 = very poor, 2 = poor, 3 = average, 4 = above average, and 5 = excellent. Then participants received the text booklet that consisted of two easy or hard practice texts and five experimental texts. The order of text presentation was randomized for the five experimental texts. Table 3-1 shows the order of text pres entation. Before starting to read, participants read the in structions: Please read the followi ng texts for comprehension. The first two texts are practice texts. Read each text carefully as you will be asked questions about them

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27 later. Read each text only one time but at a pace that is comfortable to you. After reading each text, you will be asked to judge your comprehension performance over it. Each text was on a separate page. After reading each text, participants turned to the next page and read: You are going to take a multiple-choice comprehension test over the text you just read. Please judge how many of six multiple-choice test questions you could correctly answer over the text. Once the reading and judgment tasks were completed, participants submitted the text booklet and received the test booklet. The order of test presentation was the same as that of text presentation, which was to create a similar lengt h of delay between reading each text and taking the corresponding test. Participan ts first read the instructions for the test booklet: Please complete the following comprehension test questions over the texts you ju st finished reading. There are six multiple-choice quest ions in each test. Please be sure you answer each question. The text titles are at th e top of the pages to serve as memory cues. After finishing each test, you will be asked to judge your test performance. After finishing each te st, participants read: Please judge how many of the six test questions you correctly answered. After participants completed all the tasks, they were debriefed and thanked. Results and Discussion Anchoring and Adjustment in Metacomprehension To dem onstrate the anchoring and adjust ment process underlying metacomprehension judgment, I first formed the lines of best fit by plotting mean metacomprehension judgments and comprehension performance over the five experime ntal texts as a function of text difficulty (Figures 3-1 3-3). Judgments of performan ce were made on how many of six multiple-choice test questions participants could correctly answer (prospective) or believed that they correctly answered (retrospective). Text difficulty levels ranged from approximately 11 to 19 in terms of

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28 Flesch-Kincaid grade levels. The Figures 3-1, 3-2, and 3-3 show how prospective judgment, retrospective judgment, and performance changed as a function of text difficulty, respectively. Then I conducted a multivariate regression analysis to test two hypotheses: (a) the magnitude of metacomprehension judgments would change with te xt difficulty, but to a lesser extent than would the level of comprehension performance, indicating anchoring and adjustment (based on text difficulty) in metacomprehension judgments; and (b) the magnitude of anchoring would be smaller in retrospective than in prospective metacomprehension judgments. The multivariate regression analysis supported th e two hypotheses. The slope of the line of prospective judgment ( = 0.1) was significantly different from zero, t (1) = 4.5, p < .01, so were the slopes of the line of retrospective judgment ( = 0.15), t (1) = 6.47, p < .01 and the line of performance ( = 0.24), t (1) = 8.64, p < .01. These results showed that the magnitude of metacomprehension judgment s and the level of comprehension performance significantly changed with text difficulty. Additionally, the analysis showed th at the slope of the performance line was significantly greater than that of the retrospective judgment line, F (1, 298) = 10.15, p < .01, which, in turn, was significantly greater than that of the prosp ective judgment line, F (1, 298) = 5.86, p < .05. These results demonstrated that metacomprehension judgments changed with text difficulty to a significantly lesser exte nt than did actual comprehension performance and that prospective judgments changed with text difficulty to a significan tly lesser degree than did retrospective judgments. In all, anchoring and adjust ment was involved in making metacomprehension judgments and the extent of an choring was smaller in retrospective than in prospective judgments. Judgmental Anchors To show whether or not endur ing sense of ability served as a judgm ental anchor, I computed the partial correlation coefficients between self-ratings of reading ability and

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29 metacomprehension judgments, controlling for actual performance (Table 3-2). The results showed that participants self -ratings of reading ability ( M = 3.93 out of 5, SD = 0.8) were significantly correlated with their prospective metacomprehension judgments ( M = 4.42 out of 6, SD = 0.71), r = 0.35, p < .01, and retrospective metacomprehension judgments (M = 3.55 out of 6, SD = 0.77), r = 0.26, p < .05, controlling for actual comprehension performance. Hence, this study provided direct empirical evidence that people relied on self-percepti ons of reading ability to make judgments about their future and past comprehension performance. I also computed the partial correlation coefficient between self-p erceptions of reading ability and actual comprehension performance ( M = 3.01 out of 6, SD = 0.84), controlling for metacomprehension judgments. They were not significantly correlated ( r = 0.04, p > .05), which is in line with previous research evidence that self-perceptions of abilities correlate modestly or not at all with actual performance (Falchikov & Boud, 1989). Participants self-perceptions of reading ability thus seemed to be misperceptions about their true reading skills. I examined next whether or not prior task expo sure shaped by the practice texts served as an anchor for metacomprehension judgments. T-tests were conducted to compare the metacomprehension judgments over the experiment al texts between the ea syand hard-practicetext groups. There was no significant effect, p > .05. Those who read easy practice texts did not give significantly different prospective or re trospective metacomprehension judgments over the experimental texts compared to those who r ead hard practice texts. Prior task exposure manipulated by easy or hard prac tice texts thus did not serve as an anchor for subsequent judgments of performance. Additionally, comprehension performance over the experimental texts between the two practice c onditions did not differ either, p > .05.

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30 Metacomprehension Accuracy To show m etacomprehension accuracy, I also reported the partial co rrelation coefficients between prospective or retros pective metacomprehension judg ments and actual comprehension performance (Table 3-2). Prospective judgments were not significantly correlated with actual performance, controlling for retrospective judgments, r = 0.12, p > .05. This showed that participants had poor accuracy of judging their own future comprehension performance. However, retrospective judgments were si gnificantly correlated with comprehension performance, controlling for prospective judgments, r = 0.35, p < .01. These findings are consistent with current research evidence that adult readers typically have low accuracy of judging future comprehension performance (e .g., Glenberg & Epstein, 1985, 1987; Maki, 1998; Maki & Berry, 1984) and retrospective metacomp rehension judgments are more accurate than prospective ones (e.g., Glenberg & Epstein, 1985 ; Maki & Serra, 1992; Pierce & Smith, 2001). Prospective and retrospective me tacomprehension judgments were significantly correlated with each other, controlling for actual performance, r = 0.64, p < .01. To summarize, the findings in Experiment 1 demonstrated that the anchoring and adjustment process occurred in metacomprehe nsion judgments and that the magnitude of anchoring was smaller in retrospective than in prospective metacomprehension judgments. These findings shed light on why prospective judgments were less accurate than retrospective judgments because greater reliance on a judgmen tal anchor likely causes poorer discrimination between well-understood texts a nd less-understood ones in a set. As for the specific anchor information, prior task exposure shaped by the practice texts did not serve as a judgmental anchor whereas self-perceptions of ability did they influenced both prospective and retrospective metacomprehension judgments.

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31 Table 3-1. Text titles and grade levels Easy Practice Texts F-K Grade Level Guilt, Good, and Bad 11.5 Obesity 13.2 Hard Practice Texts Intelligence and Measurement 15.8 History of the English Colonies 18.5 Experimental Texts Affirmative Action 14.7 Inventions, Inventors, and Industry 17.5 Literature in the Classroom 11.2 The Culture of Colonial America 19.3 Parental Involvement in Education 13.9

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32 Table 3-2. Partial correlation co efficients (Experiment 1) Variable 1234 1. self-rating of ability2. prospective judgment 0.35**3. retrospective judgment 0.26* 0.64**4. performance -0.04-0.120.35**Note: ** significant at .01 level. significant at .05 level.

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33 y = -0.1024x + 5.9894 0 1 2 3 4 5 6 1011121314151617181920 Text DifficultyJudgmen t Figure 3-1. How prospective judgments chan ged with text difficulty (Experiment 1) y = -0.1528x + 5.8914 0 1 2 3 4 5 6 1011121314151617181920 Text DifficultyJudgmen t Figure 3-2. How retrospective judgments changed with text difficulty (Experiment 1)

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34 y = -0.2441x + 6.7495 0 1 2 3 4 5 6 1011121314151617181920 Text DifficultyPerformance Figure 3-3. How comprehension performance cha nged with text difficulty (Experiment 1)

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35 CHAPTER 4 EXPERIMENT 2 Experim ent 2 was conducted to replicate the find ings of Experiment 1 with respect to the anchoring and adjustment proces s involved in metacomprehensi on judgments and the different extent of anchoring in prospective and retrospec tive judgments. I also aske d participants to rate their own reading ability on a 5-point Likert scale. As in Experi ment 1, I hypothesized that selfperceptions of ability would serve as a judgm ental anchor and would be significantly and positively correlated with metacomprehension judgme nts. In addition, I investigated whether or not metacomprehension judgments would be affect ed by another kind of information that might influence individuals expecta tions of their own performance information about peer performance. In Experiment 2, I provided participants with fictitious positive or negative information about the comprehension performan ce of their peers to shape positive or nega tive expectations of their own performance, respectively. As previously reviewed, research has shown that fictitious social cues about peer performance influen ced college students prospective metamemory judgments over word pairs (de Carvalho Fil ho & Yuzawa, 2001). For example, de Carvalho Filho and Yuzawa (2001) found that among student s with low level of metacognitive knowledge, those who received high social cues made significantly higher Ease-of-Learning (EOL) judgments than those who received no social cues, who, in turn, made significantly higher judgments than those who received low social cues; and among students with high level of metacognitive knowledge, those who received high social cues made significantly higher EOL judgments than those who received low social cu es. I expected the social information to also affect metacomprehension judgments, especially prospective metacomprehension judgments. People have to make prospective metacomprehension judgments under much uncertainty such as

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36 uncertainty about the upcoming comprehension te sts. So, when receiving positive information that their peers did very well on a comprehension task, they might pe rceive the task to be fairly easy and expect their own perf ormance on this task to be good. On the other hand, when receiving negative information that their peers did poorly on the task, individuals may perceive the task to be fairly difficult and have lowered expectations of their ow n performance. So, my main hypothesis was that those who received positive social information would make significantly higher prosp ective metacomprehension judgments than those who received negative social information. I also included a group who received no social information and hypothesized that the prospective metacomprehension judgments of this group would be lower than those of the positive information group but higher than those of the negative information group. Method Participants Ninety undergraduates from the Departme nt of Educational Psychology humanparticipants research pool at a large southeastern university participated to fulfill part of their course requirements. They were tested in a group format, with six participants at a time. Before the experiment, participants were randomly assigned to posi tive-, negative-, or no-socialinformation groups. Materials The m aterials were the same as those in E xperiment 1 except that the practice materials were not included in Experiment 2. The text book let included the five experimental texts of varying difficulty (Table 3-1). The test bookl et included the corresponding multiple-choice comprehension tests. Each test consisted of six five-alternative multiple-choice questions, including both factual and inferential types of questions.

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37 Procedures Participants were random ly assigned to positiv e-, negative, or no-in formation condition to influence their expectations of their own performance. Before th e experiment, participants were informed that they would be asked to read a se t of expository texts for comprehension in a selfpaced manner, judge their own comprehension pe rformance over the texts, and take multiplechoice comprehension tests. Before giving participants the text booklet, I asked them to rate their own reading ability on a five-point Likert scale (1 = very poor, 2 = poor, 3 = averag e, 4 = above average, and 5 = excellent). Then particip ants received the text booklet that co nsisted of five expository texts of varying difficulty. Each text wa s on a separate page and the or der of text presentation was randomized. Table 3-1 shows the order of text pr esentation. Participants read the instructions before starting to read: Please read the follo wing texts for comprehension. Read each text carefully as you will be asked ques tions about them later. Read each text only one time but at a pace that is comfortable to you. After reading each text, you will be asked to judge your comprehension performance over it. After reading each text, particip ants turned to the next page. The instructions for those participants in the positive-information group were: In a previous experiment, college students multiple-choice test performance over the texts in our current study ranged from 80 to 90%, with a mean of 85%. Please estimate on a scale from 0% to 100% how well YOU would do on a multiple-choice comprehension test about the text you just read. The instructions for those participants in the negative-inf ormation group were: In a previous experiment, college students multiple-choice test performance over the texts in our current study ranged from 50 to 60%, with a mean of 55%. Please estimate on a scale from 0% to 100% how well YOU would do on a multiple-choice comprehension test about the te xt you just read. Participants in the no-

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38 information group read: Please estimate on a s cale from 0% to 100% how well you would do on a multiple-choice comprehension test about the text you just read. In addition, all participants were asked to judge how many of six multiple-choi ce test questions they could correctly answer over the text they just read. So, different from the participants in Experiment 1 who made judgments only in numbers, the participants in E xperiment 2 judged their future performance in terms of both percentages and num bers. The reason for including a percentage scale was that the social information that participants received wa s in percentages. And I expected the prospective judgments in percentages and in numbers to be significantly correlated since both were about future test performance. After the reading and judgment tasks were done, participants submitted the text booklet and received the test booklet. Th e order of test presentation wa s the same as that of text presentation, so there was a similar length of delay between reading each text and taking the corresponding test. Participants read the instruct ions for the test booklet: Please complete the following comprehension test questions over the texts you just finished reading. There are six multiple-choice questions in each test. Please be sure you answer each question. The text titles are at the top of the pages to serve as memory cues. After finishing each test, you will be asked to judge your test performance. After finishi ng each test, participants judged how many of the six test questions they correctly answered. After completing all the tasks, participants were debriefed and thanked. Results and Discussion Anchoring and Adjustment in Metacomprehension To replicate the Experiment 1 findings rega rding the anchoring and adjustm ent process involved in metacomprehension ju dgments, I formed the lines of best fit by plotting mean metacomprehension judgments and comprehension performance over the texts as a function of

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39 text difficulty (Figures 4-1 44). Text difficulty levels ranged from approximately 11 to 19 in terms of Flesch-Kincaid grade levels. Part icipants judged their future comprehension performance both in percentages (0% 100%) and in terms of the number of test questions they could correctly answer. Figures 4-1 and 4-2 showed how prospective metacomprehension judgments in terms of percentage and number changed with text difficulty, respectively. Figures 4-3 and 4-4 showed how retrospective met acomprehension judgment and comprehension performance changed with text difficulty, respectively. Next, I conducted a multivariate regression analysis to test whether (a) the magnitude of prospective and retrospective metacomprehensio n judgments would significantly change with text difficulty, but to a significantly lesser ex tent than would the level of comprehension performance, which indicates judgmental anchor ing and adjustment; and (b) the magnitude of prospective judgments would change with text di fficulty to a significantly lesser extent than would the magnitude of retrospective judgments The analysis confirmed these hypotheses. The slope of the line of prospectiv e judgment in percentage ( = 0.01) was signifi cantly different from zero, t (1) = 4.79, p < .01, so were the slopes of ot her lines, including the line of prospective judgment in number ( = 0.09), t (1) = 5.59, p < .01, the line of retrospective judgment ( = 0.17), t (1) = 9.39, p < .01, and the line of performance ( = 0.28), t (1) = 11.39, p < .01. These results showed that individu als did adjust their metacomprehension judgments based on text difficulty. However, the slope of the performance line was significantly greater than that of the line of retrospective judgment, F (1, 448) = 22.32, p < .01 which, in turn, was significantly greater than the slop e of the line of pr ospective judgment, F (1, 448) = 19.81, p < .01. These results replicated those in Experime nt 1 that metacomprehension judgments were

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40 influenced by anchoring and adjustment based on text difficulty and that the magnitude of anchoring was smaller in retrospective than in prospective judgments. Judgmental Anchors I exam ined self-perceptions of ability as a judgmental an chor by computing the partial correlation coefficients between self-ratings of reading ability and metacomprehension judgments, controlling for actual comprehensi on performance (Table 4-1). Self-ratings of reading ability ( M = 3.89 out of 5, SD = 0.66) were significantly correlated with prospective judgments in percentages ( M = 78%, SD = 10%), r = 0.24, p < .05, prospective judgments in numbers ( M = 4.4 out of 6, SD = 0.57), r = 0.33, p < .01, and retrospective judgments (M = 3.63 out of 6, SD = 0.75), r = 0.27, p < .01, controlling for actual performance. Hence, the findings in Experiment 1 were replicated that participants relied on self-perceptions of reading ability to judge their future and past comprehension perf ormance. Self-perceptions of reading ability, however, were not significantly correlated with actual comprehension performance ( M = 3.25 out of 6, SD = 0.67), controlling for metacomprehension judgments, r = 0.18, p > .05. So, participants self-perceptions of reading ability were misperceptions about their true reading ability. To examine whether social information about peer performance served as a judgmental anchor, I conducted an ANOVA and a series of t-tests to compare the metacomprehension judgments among the three social-information groups (positive, negative, and no-information). The prospective judgments in percentage were significantly affected by social information, F (2, 87) = 18.57, p < .01. Specifically, those who received pos itive information about the mean of peer performance (85%) gave signifi cantly higher prospective judgments ( M = 82.9%, SD = 6%) than did those who received negative information (55%) ( M = 69.8%, SD = 12%), t (58) = 5.5, p < .01. Those who received no information made significantly higher prospective judgments ( M =

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41 80.4%, SD = 8%) than those who received negative information, t (58) = 4.07, p <.01. However, there was no significant difference between the positive information group and the no information group in the magnitude of prospective judgments ( p > .05). This was likely because participants without the influence of positive social information already had positive expectations of their own performance. Interestingly, the social inform ation did not significantly affect prospective or retrospective judgments in terms of the number of questions they could correctly answer out of six ( p > .05). I inspected the mean prospective judgments in num ber in the positive, negative and no information groups. They were 4.47 (equivalent to 74.5%), 4. 25 (71%), and 4.47 (74.5%), respectively. As previously reported, the mean prospective judgmen ts in percentage in the positive, negative and no information groups were 82.9%, 69.8%, and 8 0.4%, respectively. So, participants did not make equivalent levels of prospective judgments on these two scales, which throws some light on why the social information affected the judgm ents in percentages but not the judgments in numbers. It is surprising that changing the scale of judgment made such a difference, but the result coincidentally revealed the non-analytic nature of students judgment process. Although students first judged their perfor mance on a percentage scale, th ey did not roughly convert the percentages to numbers to make judgments on the subsequent numeric scale. In all, the results showed that the percentage information about peer performance only influenced students prospective metacomprehension judgme nts made on a percentage scale. Metacomprehension Accuracy To show m etacomprehension accuracy, I also reported partial correl ation coefficients between metacomprehension judgments and comprehension performance (Table 4-1). Prospective judgments in percentage and in number were not signifi cantly correlated with comprehension performance, controlling for retrospective judgments, r = 0.13 and r = 0.04,

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42 respectively, p > .05. However, retrospective judgment s were significantly correlated with comprehension performance, contro lling for prospective judgments, r = 0.32, p < .01. These findings were consistent with the those in E xperiment 1 as well as in metacomprehension research that students have poor accuracy of judging future comprehension performance (e.g., Glenberg & Epstein, 1985, 1987; Maki, 1998) an d that retrospective metacomprehension judgments are more accurate than prospectiv e ones (e.g., Glenberg & Epstein, 1985; Maki & Serra, 1992; Pierce & Smith, 2001). In addition, pros pective judgments in percentage and in number were significantly correlated, controlling for actual performance, r = 0.70, p < .01; and prospective and retrospective judgments in number were significantly correlated, controlling for actual performance, r = 0.43, p < .01. In summary, in Experiment 2 I replicated th e Experiment 1 findings that the anchoring and adjustment process was involved in metacompre hension judgments and that the extent of anchoring was smaller in retrospective than in prospective metacomprehension judgments. Prospective metacomprehension judgments were found to be poor and less accurate than retrospective metacomprehension judgments, probably due to st udents greater reliance on a judgmental anchor when making prospective judgme nts. As for the anchor information, I showed that social information about peer performance influenced prospective metacomprehension judgments whereas self-perceptions of ability influenced both prospective and retrospective metacomprehension judgments.

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43 Table 4-1. Partial correlation co efficients (Experiment 2) Variable 12345 1. self-rating of ability 2. prospective judgment (percentage)0.24*3. prospective judgment (number)0.33**0.70**4. retrospective judgment 0.27**0.10.43**5. performance 0.180.130.040.32**Note: ** significant at .01 level. significant at .05 level.

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44 y = -0.0114x + 0.9513 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1011121314151617181920 Text DifficultyJudgmen t Figure 4-1. How prospective judgm ents in percentage changed w ith text difficulty (Experiment 2) y = -0.0903x + 5.7789 0 1 2 3 4 5 6 1011121314151617181920 Text DifficultyJudgmen t Figure 4-2. How prospective judgments in number changed with text difficulty (Experiment 2)

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45 y = -0.1702x + 6.2344 0 1 2 3 4 5 6 1011121314151617181920 Text DifficultyJudgmen t Figure 4-3. How retrospective judgments changed with text difficulty (Experiment 2) y = -0.2771x + 7.4963 0 1 2 3 4 5 6 1011121314151617181920 Text DifficultyPerformance Figure 4-4. How comprehension performance cha nged with text difficulty (Experiment 2)

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46 CHAPTER 5 GENERAL DISCUSSION The ability to m ake accurate metacomprehe nsion judgments is crucial to successful learning. The more able learners ar e to discriminate between what they know and what they need to study more, the more effectively they will be able to regulate thei r study, for example, by restudying the less-understood materials. However, an accurate metacomprehension judgment is harder to achieve than one may expect. Research has shown that adult l earners typically have poor metacomprehension accuracy, particularly poor accuracy of judging future comprehension performance (e.g., Glenberg & Epstein, 1985, 1987; Maki, 1998). Zhao and Linderholm (2008) proposed an Anchoring and Adjustment Model of Metacomprehension Judgment that illuminates why metacomprehension accuracy is commonly poor. According to the model, people make metacomprehension judgments by anchoring on their pre-formed expectations of performance and then adjusting based on experiences with cu rrent tasks, but adjustment from the anchor requires mental effort and tends to be insuffici ent. Zhao and Linderholm (2008) contended that judgmental anchoring largely contributes to poo r accuracy of metacomprehension judgments. In this study I sought to empirically inves tigate the anchoring a nd adjustment process involved in metacomprehension ju dgments. The results of my two experiments demonstrated that the process of making pr ospective and retros pective metacomprehension judgments did involve anchoring and adjustment based on text difficulty. In addition, both experiments showed that the magnitude of anchoring was smalle r in retrospective than in prospective metacomprehension judgments. Thus, particip ants relied on the anc hor less heavily when judging past performance compared to when judg ing future performance. This finding may be connected to the evidence that retrospective judgments are more accurate than prospective judgments, which was shown in this study as well in previous research (e.g., Glenberg &

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47 Epstein, 1985; Maki & Serra 1992; Pierce & Smith, 2001). Li kely because there is no uncertainty about the test when making retrospe ctive judgments, students rely less on an anchor and instead, reflect more on their experiences with current tasks, which may explain better accuracy in retrospective judgments than in pr ospective ones. Further research should be conducted to investigate the idea that reducing the magnitude of anchoring directly improves metacomprehension accuracy. In both experime nts, the accuracy of students prospective judgments was poor. In fact, pros pective judgments were not signi ficantly correlated with actual performance (r = .12 in Experiment 1; r = .04 in Experiment 2). The finding is consistent with current research evidence that adult learners are not adept at judging future comprehension performance (e.g., Glenberg & Epstein, 1985, 1987; Maki, 1998), but the judgment accuracy shown in my study was even poorer than that de monstrated in current research. As reviewed previously, Maki (1998) reporte d a mean gamma correlation of + .27 between prospective judgments and performance across over 20 stud ies. One explanation for the extremely poor prospective metacomprehension accuracy in my study is that prospectiv e judgments were being manipulated as part of the design. For instance, the participants in Experiment 2 were given positive, negative, or no social information about peer performance to influence their own metacomprehension judgments. The manipulation created more variability among participants prospective judgments. As for the judgmental anchors, I expected information that may bias expectations of performance to be potential anchors. The results of the study showed that students relied on their self-perceptions of ability to make prospectiv e and retrospective met acomprehension judgments (Experiments 1 and 2). So, this study offers dir ect empirical evidence th at self-perception of reading ability is a major basis of metacomprehension judgments. The results are in line with

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48 research findings that individua ls self-perceptions of ability in areas such as logical reasoning influence their retrospective judgments of perf ormance on the task (Ehrlinger & Dunning, 2003). In contrast to self-perceptions of ability, pr ior task exposure manipulated with easy or hard practice texts did not affect met acomprehension judgments (Experiment 1). It could be that adult readers do not significantly base their metaco mprehension judgments on not-so-distant prior experiences with a task. Anothe r explanation is that using tw o practice texts is not a strong manipulation of prior task exposure. Students knew the first two text s in the booklet were practice texts. There is the possibi lity that they read them for practice but did not regard their experiences with them as relevant cues about the difficulty levels of the su bsequent texts. It is necessary to use other manipulati ons to further examine prior task exposure as potential anchor information. The effects of prior task expos ure on metacomprehension are indeed being investigated in a study by Linderholm et al. (i n progress) who manipulat ed the order of text difficulty. Participants in the study read a series of texts in one of three order conditions: easy to hard, hard to easy, and random. The preliminary re sults have shown that the difficulty levels of initial texts in a series of texts affected relative metacomp rehension accuracy. Specifically, relative metacomprehension accuracy was signi ficantly different between the easy-to-hard condition and the hard-to-easy or the random co ndition. Further analysis is needed on how metacomprehension judgments were affected by prior task exposure manipulated with the different orders of text difficulty. Finally, the information about peer performa nce (in terms of the mean percentages of performance) influenced students prospect ive metacomprehension judgments made on a percentage scale (Experiment 2) Students who received positive or no information about peer performance made significantly higher prospective judgments than did those who received

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49 negative information, but there was no significan t difference in the magnitude of prospective judgments between the positiveand the no-in formation groups. Why wo uld having no social information be like having positive social informatio n? It is likely because students, in general, already had positive expectations of performance on the upcoming tests, rendering the effect of positive social information insignificant. As shown in the study, students significantly relied on their self-perceptions of ability to make metaco mprehension judgments. For instance, the mean of students self-ratings of read ing ability in Experiment 1 was 3.93 out of 5, which is equivalent to 79%. A performance of 70% (equivalent to a C on a typical gradi ng scale) is usually considered average, so students seemed to percei ve themselves to possess above-average reading ability. This helps explain why students in the no information group gave a mean judgment of 80.4%, which is equivalent to a B grade, and why there is no difference in the levels of judgments between the positiveand the no-information groups. Another interesting finding was that participants made different levels of pros pective judgments on the percentage scale and the numeric scale. It is curious that a change in th e scale made a significant difference in prospective judgments. It may be that participants were fair ly familiar with percentage scales but were not used to make performance judgment on a 7-point (0 -6) numeric scale. Future research could be conducted to systematically inve stigate how the judgment scales influence metacomprehension judgments. The results of my investigation have bot h theoretical and practical implications. Theoretically, my results provide empirical sup port and add to the Anchoring and Adjustment Model of Metacomprehension Judgment (Zhao & Linderholm, 2008). This model was proposed based on a review of the literature on the bases of prospective metacomprehension judgments. Based on the results of this study, making both prospective and retrospective

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50 metacomprehension judgments involves anchoring and adjustment. The extent of anchoring was found to be smaller in retrospective than in pros pective judgments, supporting the contention that test uncertainty significantly contributes to anchoring (Zhao & Linderholm, 2008). Additionally, the results support and add to th e proposal that individuals an chor on their pre-established expectations of performance (Zhao & Linderhol m, 2008) by revealing that metacomprehension judgments are influenced by different kinds of in formation that biases performance expectations, including self-perceptions of ability and info rmation about the perf ormance of peers. Practically, the results of the study have im plications for understanding the processes underlying metacomprehension judgments a nd for illuminating the ways of improving metacomprehension accuracy. Learners rely on anc hor information such as self-perceptions of ability and information about peer perfor mance when making judgments of their own performance. However, self-perce ptions of ability are not signifi cantly correlated with actual performance as shown in this study as well as in previous research (e.g., Falchikov & Boud, 1989). Peer performance is obviously not reliable as a basis of one s own performance judgment either. Thus, to improve metacomprehension a ccuracy, we should seek to reduce learners reliance on judgmental anchors. An choring is significantly due to test uncertainty, so providing learners with more information about the upcoming test, for example, what concepts or ideas will be tapped by the test, may reduce anc horing and improve judgment accuracy. The magnitude of anchoring may also be decreased by ways such as educating or forewarning students about the anchoring pheno menon and motivating or encouraging them to adjust away from the anchor. For example, it is im portant to educate studen ts about the effects of self-perceptions of ability on me tacomprehension judgments and to forewarn students about the social influences on metacomprehension judgments because it is alarming that fictitious social

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51 information about peer performance has significant effects on students judgments of their own performance. There is also evidence from the decision-making literature that judgmental anchoring decreases when people are provided w ith forewarning about anchoring or financial incentives for accurate judgments (Epley, Boven, Keysar, & Gilovich, 2004; Epley & Gilovich, 2005). For example, Epley et al. (2004) reported that accuracy incentives motivated people to decrease egocentric biases in perspective taking and to reflect more on the differences between themselves and other people. Future research should be conducted to directly examine the effectiveness of these approach es to reduce the magnitude of anchoring in metacomprehension judgments. In all, the results reported in th e study provide a furthe r understanding of how learners make metacomprehension judgments. This enhanced understanding, in turn, provides important insight into how to improve learners metacomprehension accuracy.

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52 APPENDIX A SAMPLE EXPERIMENTAL TEXTS Affirma tive Action Reverse discrimination, minority recruitment, ra cial quotas, and, more generally, affirmative action are phrases that carry powerful emotional charges. But why should affirmative action, of all government policies, be so controversial? In a sense, a ffirmative action is like other governmental programs, e.g., defense, conser vation, and public schools. Affirmative action programs are designed to achieve legitimate gov ernment objectives such as improved economic efficiency, reduced social tension, and general betterment of the public welfare. While it cannot be denied that there is no guarantee that affirm ative action will achieve these results, neither can it be denied that there are plausible, even powerful, sociological and economic arguments pointing to its likely success. Government programs, however, en tail a cost, that is, the expend iture of social or economic resources. Setting aside cases in which the specif ic user is charged a fee for service (toll roads and tuition at state institutions), the burden s and benefits of publicly funded or mandated programs are widely shared. When an indivi dual benefits persona lly from a government program, it is only because she or he is one member of a larger beneficiary class, e.g., a farmer; and most government revenue is obtained throug h a scheme of general taxation to which all are subject. Affirmative action programs are exceptions to this general rule, though not, as might at first seem, because the beneficiaries of the programs are specific individuals. It is still the case that those who ultimately benefit from affirmative ac tion do so only by virtue of their status as members of a larger group, a pa rticular minority. Rather, the di fference is the location of the burden. In affirmative action, the burden of fundi ng the program is not shared universally, and that is inherent in the nature of the case, as can be seen clearly in the case of affirmative action in employment. Often job promotions are allocated along a single dimensi on, seniority; and when an employer promotes a less senior worker fr om a minority group, the person disadvantaged by the move is easily identified: the worker with greatest seniority on a combined minority--nonminority list passed over for promotion. Now we are confronted with two competing moral sentiments. On the one hand, there is the idea that those who have been unfairl y disadvantaged by past discriminatory practices are entitled to some kind of assistance. On the other, there is th e feeling that no person ought to be deprived of what is rightfully his or hers, even for the worthw hile service of fellow humans. In this respect, disability due to past racial discrimination, at least insofar as there is no connection to the passed-over worker, is like a natural evil. When a villainous man willfully and without provocation strikes and injures anot her, there is not only the fee ling that the injured person ought to be compensated but there is consensus that th e appropriate party to b ear the cost is the one who inflicted the injury. Yet, if the same innocen t man stumbled and injured himself, it would be surprising to hear someone argue that the villainous man ought to be taxed for the injury simply because he might have tripped the victim had he been given the opportunity. There may very well be agreement that he should be aided in hi s recovery with money and personal assistance,

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53 and many will give willingly; but there is also agreement that no one individual ought to be singled out and forced to do what must ulti mately be considered an act of charity. Inventions, Inventors, and Industry There is widespread belief that the emergence of giant industries has been accomplished by an equivalent surge in industrial research. A recent study of important inventions made since the turn of the century reveals that more than half were the product of individual inventors working alone, independent of organized in dustrial research. Independent i nventors have contributed such products as air conditioning, th e automatic transmission, the jet engine, and streptomycin. Despite these findings, we are urged to suppor t monopoly power on the grounds that such power creates an environment supportive of innovation. We are told that the independent inventor, along with the small firm, cannot afford to undertake the important research needed to improve our st andard of living while protecting our diminishing resources. We are told that only the prodigious assets of the gi ant corporation or conglomerate can afford the kind of expenditures that can produce the technologica l advances vital to economic progress. But when we examine expenditures for research, we find that of the more than $35 billion spent each year in this country, almost twothirds is spent by the federal government. More than half of this government expenditure is funneled into military research and product development, accounting for the enormous increase in spending in such industries as nuclear energy, aircraft, missiles, and electronics. Ther e are those who consider it questionable that these defense-linked research projects will account for an im provement in the standard of living or, alternately, do much to protect our diminishing resources. Recent history has demonstrated that we may ha ve to alter our long-standing conception of the process actuated by competition. The price variab le, once perceived as th e dominant aspect of the competitive process is now subordinate to the competition of the new product, the new business structure, and the new technology. It can be assumed that in a highly competitive industry not dominated by a singl e corporation, investment in i nnovation--a risky and expensive budget item--might meet resistance from mana gement and stockholders who might be more concerned with cost-cutting, effi cient organization, and large a dvertising budgets. However, it would be an egregious error to assume that the monopolistic producer should be equated with bountiful expenditures for researc h. Large-scale enterprises tend to operate more comfortably in stable and secure circumstances, and their manage rial bureaucracies tend to promote the status quo and resist the threat implicit in change. Furthermore, the firm with a small share of th e market will aggressively pursue new techniques and different products, since with little vested in terest in capital equipment or plant it is not deterred from investment in innovation. In so me cases, where inter-industry competition is reduced or even entirely eliminated, the industrial giants may seek to avoid capital loss resulting from obsolescence by deliberately ob structing technological progress.

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54 APPENDIX B SAMPLE EXPERIMENTAL TESTS Affirma tive Action 1. The passage is primarily concerned with a. comparing affirmative action programs to other government programs b. arguing that affirmative action programs are morally justified c. analyzing the basis for moral judg ments about affirmative action programs d. introducing the reader to the importance of affirmative action as a social issue e. describing the benefits that can be obtained through affirmative action programs 2. The author mentions toll roads and tuit ion at state instituti ons in order to a. anticipate a possi ble objection based on counterexamples b. avoid a contradiction be tween moral sentiments c. provide illustrations of common government programs d. voice doubts about the social and economic value of affirmative action e. offer examples of government programs which are too costly 3. With which of the following statements would the author most likely agree? a. affirmative action programs should be di scontinued because they place an unfair burden on non-minority persons who bear the cost of the programs. b. affirmative action programs may be ab le to achieve legitimate social and economic goals such as improved efficiency. c. affirmative action programs are justif ied because they are the only way of correcting in justices created by past discrimination. d. affirmative action programs must be redesi gned so that society as a whole rather than particular individuals bears the co st of the programs. e. affirmative action programs should be abandoned because they serve no useful social function and pl ace unfair burdens on particular individuals. 4. According to the passage, affirmative ac tion programs are different from most other government programs in which of the following ways? I. the goals the programs are designed to achieve II. the ways in which costs of the programs are distributed III. the ways in which benefits of the programs are allocated a. I only b. II only c. III only d. II and III only e. I, II, and III

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55 5. It can be inferred that the author belie ves the reader will rega rd affirmative action programs as a. posing a moral dilemma b. based on unsound premises c. containing self-contradictions d. creating needless suffering e. offering a panacea 6. The primary purpose of the passage is to a. reconcile two conflic ting points of view b. describe and refute a point of view c. provide a historical context for a problem d. suggest a new method fo r studying social problems e. analyze the structure of an institution Inventions, Inventors, and Industry 1. Management and stockholders might be deeply concerned with cost-cutting rather than innovation if a. their company is faced with strong comp etition in a field not dominated by one of the industrial giants b. they are very stable and secure and hold a monopoly position in the industry c. they are part of the military-industry complex and are the recipients of federal funds for product development d. they have produced some of the im portant inventions of this century e. they have little vested interest in capital equipment or plants 2. The author's purpose in this passage is to a. advocate an increase in governmental support of organized industrial research b. point out a common miscon ception about the relationship between the extent of industrial research a nd the growth of monopolis tic power in industry c. describe the inadequacies of small firm s in dealing with th e important matter of research and innovation d. show that America's strength de pends upon individual ingenuity and resourcefulness e. encourage free market competition among industrial giants 3. It can be inferred from th e passage that the author a. has little confidence in the ability of monopolistic industry to produce the important inventions of the future b. would rather see the federal government spend money on social services than on

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56 the defense establishment c. favors a conservative approach to innovation and places trust in conglomerates to provide efficient production d. while admitting that more than half the important inventions of the century were produced by independent invent ors, feels that the future lies in the hands of giant industry e. believes spin-offs from defense-linked research will account for an improvement in future inventions 4. The amount of money spent in this country for research and product development is a. approximately $24 billion each year b. less than $24 billion each year c. more than $18 billion each year d. less than $35 billion each year e. more than $35 billion each year 5. Which of the following products was NOT mentioned as having been developed by independent inventors? a. air conditioning b. automatic transmission c. transistors d. jet engine e. streptomycin 6. What was formerly perceived as the dominant aspect of the competitive process, according to the passage? a. the price variable b. competition of the new product c. new business structures d. new technology e. standard of living

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57 LIST OF REFERENCES de Carvalho Filho, M. K. & Yuzawa, M. (2001). The effect of social influences and general m etacogntive knowledge on metamemory judgments. Contemporary Educational Psychology, 26, 571-587. Dunlosky, J., Baker, J. M. C., Rawson, K. A ., & Hertzog, C. (2006). Does aging influence peoples metacomprehension? Effects of pro cessing ease on judgments of text learning. Psychology and Aging, 21 390-400. Dunning, D., Johnson, K., Ehrlinger, J., & Kruger, J. (2003). Why people fail to recognize their own incompetence. Current Directions in Psychological Science, 12 83-97. Educational Testi ng Service (1994). GRE, practicing to take the general test (9th edition). Princeton, NJ: Educational Testing Service for the Graduate Record Examinations Board. Ehrlinger, J., & Dunning, D. (2003). How chronic se lf-views influence (and potentially mislead) estimates of performance. Journal of Personality and Social Psychology, 84 5-17. Epley, N., & Gilovich, T. (2001). Putting adjust ment back in the anchoring and adjustment heuristic: Differential processing of self-gen erated and experimenter-provided anchors. Psychological Science, 12, 391-396. Epley, N., & Gilovich, T. (2005). When effortfu l thinking influences judgmental anchoring: Differential effects of fore warning and incentives on self -generated and externally provided anchors. Journal of Behavioral Decision Making, 18 199-212. Epley, N., Keysar, B., Van Boven, L., & Gilovic h, T. (2004). Perspective taking as egocentric anchoring and adjustment. Journal of Personality and Social Psychology, 87, 327-339. Falchikov, N., & Boud, D. (1989). St udent self-assessment in higher education: A meta-analysis. Review of Educational Research, 59 395-430. Glenberg, A. M., & Epstein, W. (1985). Calibration of comprehension. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11 702-718. Glenberg, A. M., & Epstein, W. (1987). Inexpert calibration of comprehension. Memory and Cognition, 15 84-93. Glenberg, A. M., Sanocki, T., Epstein, W., & Mo rris, C. (1987). Enhancing calibration of comprehension. Journal of Experimental Psychology: General, 116 119-136. Hacker, D. J., Bol, L., Horgan, D. D., & Rakow E. A. (2000). Test prediction and performance in a classroom context. Journal of Educational Psychology, 92 160-170. Jacowitz, K. E., & Kahneman, D. (1995). Meas ures of anchoring in estimation tasks. Personality & Social Psychology Bulletin, 21 1161-1166.

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58 Linderholm, T., Zhao, Q., Therriault, D., & Cordell-McNulty, K. (under review). Metacomprehension estimates investigated with in an anchoring and adjustment framework. Metacognition and Learning. Maki, R. H., (1998). Test predicti on over text materials. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educati onal theory and practice (pp.117-144). Hillsdale, NJ: Erlbaum. Maki, R. H. & Berry, S. L. (1984). Me tacomprehension of text material. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10 663-679. Maki, R. H., & Serra, M. (1992). The basis of text predictions for text material. Journal of Experimental Psychology: Le arning, Memory, & Cognition, 18 116-126. Maki, R. H., Shields, M., Wheeler, A. E., & Z acchilli, T. L. (2005). Individual differences in absolute and relative metacomprehension accuracy. Journal of Educational Psychology, 97 723-731. Moore, D., Lin, L., & Zabrucky, K. (2005). A source of metacomprehension inaccuracy. Reading Psychology, 26 251-265. Morris, C. C. (1990). Retrieval processes underlying confidence in comprehension judgments. Journal of Experimental Psychol ogy: Learning, Memory, & Cognition, 16 223-232. Pierce, B. H., & Smith, S. M. (2001). The postdiction superiority effect in metacomprehension of text. Memory & Cognition, 29 (1) 62-67. Rawson, K. A., & Dunlosky, J. (2002). Are perfor mance predictions for text based on ease of processing? Journal of Experimental Psychol ogy: Learning, Memory, & Cognition, 28 6980. Rawson, K. A., Dunlosky, J., & Thiede, K. W., (2000). The rereading e ffect: Metacomprehenion accuracy improves acro ss reading trials. Memory & Cognition, 28 1004-1010. Scheck, P., Meeter, M., & Nelson, T. O. (2004). An choring effects in the absolute accuracy of immediate versus delayed judgments of learning. Journal of Memory and language, 51 71-79. Schraw, G., & Roedel, T. D. (1994). Test difficulty and judgment bias. Memory & Cognition, 22 63-69. Thiede, K.W., Anderson, M.C.M., & Therriault, D., (2003). Accuracy of metacognitive monitoring affects learning of texts. Journal of Educational Psychology, 95 66-73. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185 1124-1130.

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59 Weaver, C. A. III., & Bryant, D. S. (1995). Moni toring of comprehension: The role of text difficulty in metamemory for narrative and expository text. Memory & Cognition, 23 1222. Zhao, Q., & Linderholm, T. (2008). Adult me tacomprehension: Judgment processes and accuracy constraints. Published online in Educational Psychology Review http://www.springerlink.com/content/104855 Zhao, Q., Linderholm, T., & Therriault, D. (2006, August). Absolute Metacomprehension Accuracy: The Effects of Cue-Utilizati on Instruction and Working-Memory Capacity Poster presented at the 2006 American Psychological Association Convention, New Orleans, Louisiana.

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BIOGRAPHICAL SKETCH Qin Zhao was born and grew up in Jiangxi, Ch ina. She earned her B achelor of Arts in English from the North China Electric Power Univ ersity in China in 2001 In fall 2002 she began her graduate program in the Department of Edu cational Psychology at the University of Florida in Gainesville, Florida. She earned her Master of Arts in educational psychology in 2005 and graduated with her Doctor of Philo sophy in educational psychology in 2008.