The Good Judge of Meta-perception

by

Norhan Elsaadawy

A thesis submitted in conformity with the requirements

for the degree of Master of Arts

Department of Psychology

University of Toronto

© Copyright by Norhan Elsaadawy 2018

ii

The Good Judge of Meta-perception

Norhan Elsaadawy

Master of Arts

Department of Psychology

University of Toronto

2018

Abstract

Is meta-accuracy an individual difference that people carry with them wherever they go, or is it

context-specific? Previous research assumes that meta-accuracy is an individual difference, but

has not explicitly tested this assumption. Using seven samples, I tested whether meta-accuracy is

an individual difference by conducting three tests that examined whether there is a good judge of

meta-perception across different 1) contexts, 2) traits, and 3) targets. The Good Judge Hypothesis

predicts that there are individuals who are consistently meta-accurate across contexts, traits, and

targets. In contrast, the Context Hypothesis predicts that there are no consistently meta-accurate

individuals across contexts, traits, or targets. The present results support the Good Judge

Hypothesis. However, while there was a good judge across various traits and targets, and within

specific contexts, there was no evidence of a good judge across contexts. Theoretical

implications, practical applications, and future directions of the current work are discussed.

iii

Acknowledgments

Thank you to my advisor, Erika Carlson, for her endless support, insight, and guidance

throughout the year. Erika, I’ve learned so much from you – not only about how to conduct

research, but also about how to take on every new challenge with excitement and curiosity.

Thank you for always encouraging me to pursue the questions I am most curious about.

Thank you as well to my lab-mate, Max Barranti, for his input and constructive feedback on this

project, and for suffering through many of my most ridiculous statistics questions with kindness

and minimal complaint.

I would also like to extend my immense gratitude to Lauren Human for allowing me to analyze

data from the Social Interaction & Perception Lab and for her invaluable feedback on my

analyses, as well as to Elizabeth Page-Gould and Marc Fournier for being on my committee.

Finally, thank you to my family.

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Table of Contents

Acknowledgments ........................................................................................................................ iii

Table of Contents ......................................................................................................................... iv

List of Tables ................................................................................................................................ vi

List of Figures .............................................................................................................................. vii

List of Appendices ...................................................................................................................... viii

The Good Judge of Meta-perception........................................................................................... 1

The Formation of Accurate Meta-perceptions ........................................................................... 2

The Good Judge ............................................................................................................................ 5

Test 1: Cross-context Accuracy ................................................................................................... 7

Methods ....................................................................................................................................................8

Participants ...........................................................................................................................................9

Measures ...............................................................................................................................................9

Procedures...........................................................................................................................................10

Analyses ..............................................................................................................................................11

Results ....................................................................................................................................................14

The Good Judge and the Context hypotheses .....................................................................................15

Discussion ...............................................................................................................................................26

Test 2: Cross-trait Accuracy ...................................................................................................... 27

Methods ..................................................................................................................................................27

Participants .........................................................................................................................................28

Measures .............................................................................................................................................28

Procedures...........................................................................................................................................29

Analyses..................................................................................................................................................29

Results ....................................................................................................................................................30

Discussion ...............................................................................................................................................36

Test 3: Cross-target Accuracy ................................................................................................... 38

Methods ..................................................................................................................................................39

v

Participants .........................................................................................................................................40

Measures .............................................................................................................................................40

Procedures...........................................................................................................................................41

Analyses..................................................................................................................................................42

Results ....................................................................................................................................................43

Discussion ...............................................................................................................................................46

General Discussion ...................................................................................................................... 48

References .................................................................................................................................... 51

Appendices ................................................................................................................................... 58

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List of Tables

Table 1. Summary of all samples .................................................................................................... 8

Table 2. Accuracy, positivity, transparency, and cross-context correlations in Test 1 ................. 20

Table 3. Descriptive statistics for self-, meta-, and others' perceptions for the Big Five traits in

Test 2. ............................................................................................................................................ 29

Table 4. Meta-accuracy and meta-insight for the Big Five traits in Test 2. ................................. 33

Table 5. Correlations between differential meta-accuracy scores of the Big Five traits in Test 2.

....................................................................................................................................................... 34

Table 6. Accuracy, positivity, and transparency in Test 3. ........................................................... 44

Table 7. Judge, target, and dyadic variance in Test 3. .................................................................. 45

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List of Figures

Figure 1. Distributions of accuracy and bias scores and results of Shapiro-Wilk normality test in

the first impression and close other contexts in Test 1. ................................................................ 25

Figure 2. Distribution of differential meta-accuracy scores for the Big Five traits and the

distribution of the latent variable scores of the Good Judge in Test 2. ......................................... 34

Figure 3. Models 1, 2, and 3 of the good judge across traits tested in Test 2 ............................... 35

viii

List of Appendices

Appendix A .............................................................................................................................................58

Appendix B .............................................................................................................................................59

Appendix C .............................................................................................................................................60

1

The Good Judge of Meta-perception

The impressions we make on others are important and have consequences for different

aspects of our lives, ranging from the personal to the professional. For example, we are more

likely to make friends if people think we are kind and interesting, and we are more likely to

succeed at a job interview if we appear competent and accomplished. As such, we often wonder

about the impressions we make on others. Our beliefs about how other people perceive us are

called meta-perceptions, and the degree to which these beliefs align with people’s actual

impressions of us is called meta-accuracy (Kenny & DePaulo, 1993).

Past work has shown that we are aware of our reputation, or the general impression we

make on others (Levesque, 1997). Although it is easier to know our reputation, several studies

have demonstrated that we are also able to detect how specific others perceive us, including new

acquaintances and close others (Carlson & Furr, 2009; Kenny & DePaulo, 1993; Levesque,

1997). Further, this ability has been linked to various consequential individual differences, such

as depression and self-esteem (Moritz & Roberts, 2017), personality pathology (Carlson &

Oltmanns, 2015; Carlson, Wright, & Imam, 2017), and social and psychological adjustment

(Carlson, 2016b). This cross-sectional work suggests that, due to its association with several

individual differences, meta-accuracy is itself an individual difference. Indeed, there is

variability in people’s ability to accurately judge others, suggesting that being a “good judge” of

personality is an individual difference (Biesanz, 2010; Human & Biesanz, 2013). However, to

our knowledge, this has never been explicitly tested in the context of meta-perceptions. In the

current literature, it is unclear if there are good judges of meta-perception. In other words, is

meta-accuracy an individual difference that people take with them across interactions or, instead,

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is meta-accuracy something that arises within a specific context? The goal of the current project

was to determine if there are good judges of meta-perception by testing whether meta-accuracy is

a form of self-knowledge that operates like a skill that people carry with them across contexts,

traits, and targets.

The Formation of Accurate Meta-perceptions

Like other social judgments, people (i.e., meta-perceivers) form meta-perceptions by

detecting and utilizing cues that are relevant and available to them. In terms of the Realistic

Accuracy Model (RAM; Funder, 1995), the meta-perceiver is the judge, whereas the subject of

the meta-perception is the target. According to the RAM, four steps lead to meta-accuracy. First,

there must be cues that are relevant to the target’s impression of the judge (relevance). Second,

the relevant cues for the target’s impression must be available to the judge (availability). Third,

the judge must detect the relevant and available cues of the target’s impression (detection).

Fourth, the judge must correctly utilize and interpret the relevant, available, and detected cues

(utilization). Errors at any step can reduce or hinder meta-accuracy. For instance, if the judge

fails to detect some cues, meta-accuracy will be lower. Likewise, if cues are simply not

available, the judge cannot detect or utilize meaningful information and meta-accuracy will be

lower. Thus, when forming meta-perceptions, each step of the RAM is critical for achieving

accuracy.

The strong correlation between meta-perceptions and self-perceptions (r = .87; Kenny &

DePaulo, 1993) has sometimes brought into question whether meta-perceptions are distinct from

self-perceptions. Indeed, several studies suggest that people rely largely on their self-views when

forming meta-perceptions and often overestimate how transparent they are, or how much others

see them as they see themselves (Cameron & Vorauer, 2008; Kaplan, Santuzzi, & Ruscher,

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2009; Kenny & DePaulo, 1993). However, some work suggests that, people realize that they

make unique impressions on others in different social contexts (Carlson & Furr, 2009), insight

that requires using information beyond a global self-view. Further, several studies demonstrate

that, when forming meta-perceptions, people can achieve meta-insight, or accuracy beyond their

own self-perceptions (Carlson, Vazire, & Furr, 2011; Gallrein et al., 2013, 2016; Oltmanns &

Turkheimer, 2009). Specifically, controlling for self-perceptions, people are still able to detect

how others perceive them with some degree of accuracy (Carlson et al., 2011). Thus, it appears

that meta-perceptions are distinct from self-perceptions, and that global self-views are one of

many cues that are relevant, available, detected, and utilized in the formation of meta-

perceptions.

Another cue that judges rely on when forming meta-perceptions is normative knowledge,

or information about the type of impression that the average person makes (Human & Biesanz,

2011). Interestingly, the average person is typically seen in positive ways (Wood & Furr, 2016).

As such, relying on normative knowledge can lead to accuracy when judges are seen in

normative or positive ways. However, overestimating how positively others see them can lead

judges to be inaccurate. Similarly, relying on self-perceptions as a cue can lead to accuracy when

judges’ self-perceptions align with others’ impressions of them (i.e., when self-other agreement

is high; Carlson & Kenny, 2012). However, relying on self-perceptions can lead judges astray

when there is self-other disagreement, such as with new acquaintances (Connelly & Ones, 2010)

or for individuals with personality disorders (Carlson & Oltmanns, 2015). Thus, while normative

knowledge and self-perceptions are valid cues often used in the formation of meta-perceptions,

both positivity (i.e., the degree to which people think they are seen in especially positive ways;

Borkenau & Zaltauskas, 2009; Carlson 2016) and transparency (i.e., the degree to which people

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overestimate how much others share their self-perceptions; Cameron & Vorauer, 2008) are

considered biases.

In addition to global self-perceptions and normative knowledge, people can and do

successfully use other cues to form meta-perceptions. These cues include self-observations of

behaviour (Kenny & DePaulo, 1993), context-specific self-perceptions (Carlson et al., 2011), a

target’s direct and indirect feedback (Langer & Wurf, 1999), and meta-stereotypes about the

judge or the target (Malloy & Janowski, 1992; Vorauer, Main, & O’Connell, 1998). In sum,

while there are several cues that are relevant, available, detected, and utilized in the formation of

accurate meta-perceptions, there are also factors that can influence, or moderate, meta-accuracy

at each stage of the RAM.

According to the RAM (Funder, 1995), there are at least four types of moderators that

might influence meta-accuracy. The first is information, which refers to the quality and quantity

of cues and moderates the relevance and availability stages of the RAM, respectively.

Specifically, judges are more accurate when there are more cues or higher quality cues about a

target’s impression of them. As such, judges are more accurate in contexts where they have more

relevant and available information (i.e., with close others) than in contexts where they have less

(i.e., with new acquaintances; Carlson & Kenny, 2012; Kenny & DePaulo, 1993). The second

moderator is traits, which refers to the idea that some trait impressions are more available to the

judge and easier to detect. Indeed, meta-accuracy is higher for more observable or neutral traits

(e.g., extraversion) than internal or evaluative traits (Carlson & Kenny, 2012; Vazire, 2010). The

third moderator is the target, which refers to the idea that some targets are more expressive,

transparent, or easier to read than others (Human & Biesanz, 2013). As a result, good targets

likely make more cues and better quality cues available to judges, moderating the relevance and

5

availability stages of the RAM. Lastly, the fourth moderator is the judge, which refers to the idea

that some judges are more accurate than others. A good judge may influence each stage of the

RAM: a good judge may elicit more cues (availability) or more honest feedback (relevance) from

targets, they may be better at observing cues (detection), or be less defensive when interpreting

cues (utilization; Letzring, 2008). In sum, there are several potential moderators of meta-

accuracy, but because judges can potentially moderate meta-accuracy at any of the stages of the

RAM, there are many ways that some judges might be better than others at knowing the

impressions they make. As such, the focus of the current project was on the good judge

moderator.

The Good Judge

As discussed above, good judges are better able to detect and utilize information as well

as elicit more cues about how they are perceived. Importantly, good judges are able to do this

across contexts, traits, and targets. Good judges of meta-perception know how they are seen in

contexts where they have a lot or limited information, for traits that vary in observability and

evaluativeness, and for targets that are difficult and easy to read. Thus, if meta-accuracy is an

individual difference, it should be a consistent skill, or trait-like ability, that people carry with

them everywhere they go, for all types of impressions, and for everyone they interact with.

There are many ways to conceptualize and measure individual differences, but in the

current research, consistency in meta-accuracy is indexed as a correlation, or the rank order

stability of accuracy across contexts, traits, or targets. Thus, consistency in meta-accuracy was

not operationalized as similar absolute levels of meta-accuracy in two different situations, for

two traits, or for two targets (Clarke & Clarke, 1984). Indeed, I expected that there would be

mean level differences in meta-accuracy for different levels of information (e.g., people are more

6

accurate about people they know and like than they are about people they just met), different

traits (e.g., people are more accurate about observable and neutral traits than they are about

evaluative traits), and different targets (e.g., people are more accurate about expressive targets

than reserved targets). However, if meta-accuracy is an individual difference, I expected that

some people would be consistently better than others at meta-accuracy across contexts, traits, or

targets, independent of their absolute accuracy in any one situation.

Based on the definition of consistency as rank order stability, there are at least two

competing hypotheses about the nature of meta-accuracy. On one hand, meta-accuracy might be

an individual difference that people take with them across contexts, traits, and targets. This

possibility, which I call the Good Judge Hypothesis, predicts that judges’ meta-accuracy in one

context, for one trait, or for one target should predict their accuracy in another context, for

another trait, or another target, respectively. On the other hand, meta-accuracy might be driven

by information-specific, trait-specific, or target-specific factors, a possibility I call the Context

Hypothesis. The Context Hypothesis predicts that there is no consistent good judge of meta-

perception across contexts, traits, or targets. To test whether there is a good judge of meta-

perception or if instead, meta-accuracy is contextual, the current research conducted three tests:

Test 1, which examines if judges’ meta-accuracy in one context predicts their meta-accuracy in

another (cross-context accuracy), Test 2, which examines if judges’ meta-accuracy for one trait

predicts their accuracy for another (cross-trait accuracy), and Test 3, which examines if some

targets are easier to be meta-accurate about compared to others (cross-target accuracy).

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Test 1: Cross-context Accuracy

Test 1 assessed if people’s meta-accuracy in a first impression context predicts their

meta-accuracy for a close other. A correlation between a first impression and a close other

context is a strong test of cross-context accuracy because these two contexts differ significantly

in length of acquaintanceship and liking, both of which influence meta-accuracy (Carlson &

Kenny, 2012; Levesque, 1997; Ohtsubo, Takezawa, & Fukuno, 2009). I expected that there

would be a mean-level difference in meta-accuracy between contexts such that people would be

more accurate for close others than for new acquaintances. Despite this expected mean-level

difference, the Good Judge Hypothesis predicts that people maintain their rank order stability in

meta-accuracy across first impressions and close others, such that meta-accuracy in a first

impression context will be positively correlated with meta-accuracy in a close other context. In

contrast, the Context Hypothesis predicts that meta-accuracy in a close other context is unrelated

to meta-accuracy in a first impression context. Perhaps good judges are just especially good at

knowing how they are seen in specific contexts, for specific traits, or specific types of targets, or

perhaps the cues that facilitate meta-accuracy in one context are not necessarily useful in other

contexts. For example, transparency bias (Cameron & Vorauer, 2008) may facilitate accuracy in

a close other context but not in a first impression, while positivity bias (i.e., normative

knowledge; Human & Biesanz, 2011) may facilitate accuracy in a first impression but not in a

close other context.

Importantly, accuracy and bias are independent such that people can be both accurate and biased

(Human & Biesanz, 2011). For example, people can guess how others see them with some

degree of accuracy while also assuming that others see them positively (i.e., positivity bias) and

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that others’ share their self-perceptions (i.e., transparency bias). In other words, we can tease

positivity, transparency, and accuracy apart. Therefore, whether judges are accurate across

contexts is independent of whether or not judges are biased across contexts. Perhaps people carry

both their accuracy and biases across contexts. Alternatively, perhaps there is no good judge

across contexts and people only carry their biases from one context to another. To address these

possibilities along with the Good Judge Hypothesis and the Context Hypothesis, Test 1 assessed

whether people carry their accuracy, positivity bias, and/or transparency bias across contexts.

Methods

Test 1 assessed cross-context accuracy using two archival datasets. Each dataset included

two contexts per judge: first impressions and close others. The datasets are summarized in Table

1 in terms of sample size, the personality measures used, and the average number of close others

per judge. Two samples (Samples 1 and 2) examine judges’ meta-perceptions for specific targets

across contexts, and one sample (Sample 3) examines judges’ meta-perceptions of their

reputations across contexts.

Table 1. Summary of all samples

Sample

Name

Judges

(N)

Big Five

Measures

First

impression

partners

per judge

(M)

Close others

per judge

(M)

Test

Sample 1 221 BFI 1 1.86 1, 2

Sample 2 122 BFI 1 3.96 1

Sample 3 186 TIPI 2.35 3.33 1

Sample 4 296 BFI - 3.74 2

Sample 5 240 TIPI 4.43 - 3

Sample 6 547 BFI 5.56 - 3

Sample 7 172 BFI-10 13.63 - 3

Total N 1784

Note. BFI = Big Five Inventory (John & Strivastava, 1999). TIPI = Ten Item Personality

Inventory (Gosling, Rentfrow, & Swann, 2003). BFI-10 = Short version of the Big Five

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Inventory (Rammstedt & John, 2007). Total N for Test 1 = 440, Total N for Test 2 = 517, Total

N for Test 3 = 959.

Participants

Although there were 221 participants in Sample 1, only 132 of those participants took

part in the in-lab first impression portion of the study and could be included in Test 1. As a

result, the three datasets used in Test 1 (i.e., Samples 1, 2, and 3) have a total sample size of 440

judges (61% female; M age = 19.74, SD = 1.16). Of this total sample, 62% identified as White,

24% as Asian or Asian American, 11% as Black or African American, 2% as Latin American or

Hispanic, and 1% as Other. In all three samples, participants were undergraduate students at a

mid-western university and were paid $20 or received course credit for their participation.

Participants in Sample 3 could earn an additional $30 for completing a subsequent lab session

unrelated to the current project.

I used only informants who participants identified as their friends to ensure that the close

other context was comprised of targets with whom judges had a comparable relationship type

(i.e., friendship) and level of closeness. There were 1,320 total informants, specifically 222

informants in Sample 1, 479 informants in Sample 2, and 619 informants in Sample 3.

Informants were not paid for their participation.

Measures

In each sample, judges’ self-perceptions and meta-perceptions as well as targets’

personality judgments of the judges were reported on the Big Five traits. In Sample 1, these

perceptions were reported for the 44-item Big Five Inventory (BFI; John & Strivastava, 1999) on

a 1 (strongly disagree) to 7 (strongly agree) scale. In Sample 2, a subset of 17 items of the BFI

was used (see Appendix A), and items were reported on a 1 (strongly disagree) to 15 (strongly

10

agree) scale. In Sample 3, perceptions were reported on the Ten Item Personality Inventory

(TIPI; Gosling, Rentfrow, & Swann, 2003) using a 1 (strongly disagree) to 15 (strongly agree)

scale. However, in Sample 3, judges provided one meta-reputation per context, specifically how

they thought the people in that context perceived them in general. To parallel these meta-

reputations, I calculated the average impression a judge made on each trait (i.e., the target’s

reputation) across new acquaintances met in the lab and across friends. This resulted in an

aggregated impression (i.e., reputation) per judge in each context.

Procedures

In Samples 1 and 2, participants came to the lab in unacquainted pairs and completed a

short battery of personality measures, one of which was a self-report of the BFI. Next,

participants were instructed to have a brief (i.e., 5 minute) interaction with their new

acquaintance. The research assistant told participants to discuss whatever topics they wished and

then left them alone for the duration of the conversation. Immediately after, participants rated

each other’s personality and guessed how their interaction partner would rate them on the BFI as

well as on items unrelated to the current project. In Sample 3, participants came to the lab in

unacquainted groups of 5-6 members and had a leaderless group discussion. After the discussion,

they rated each group member’s personality and reported on their meta-reputation (i.e., what they

thought their group as a whole thought of them) on the TIPI as well as other items unrelated to

the current project. Thus, all participants served as a judge (meta-perceiver) and a target (rater of

personality) in the first impression context.

After the first impression activity, participants were asked to nominate close others (e.g.,

friends, family members, romantic partners) to describe their personality. In Samples 1 and 2,

participants guessed how each of these close others would rate them on the BFI. In Sample 3,

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participants reported their meta-reputation among people who know them well on the TIPI.

Close others provided their personality judgments of the participant using an online

questionnaire at a later date. Thus, in the close other context, the main participants were judges

and the informants were targets.

Analyses

To test cross-context accuracy and cross-context bias, each participant was given a meta-

accuracy score, a positivity score, and a transparency score for a first impression and a close

other context. These scores were correlated to index the degree to which people who tended to be

accurate or biased in one context tended to be accurate or biased in the other.

Meta-accuracy scores

Meta-accuracy scores were computed using the profile-based approach to meta-accuracy

(e.g., Carlson & Furr, 2013; Carlson, 2016). Conceptually, profile-based meta-accuracy is the

profile correlation between a judge’s meta-perception across several traits and the actual

impression they make on a target on those same traits. For example, Judy’s meta-accuracy is the

correlation between her guess about how Tom perceives her on the 44-item BFI (John &

Strivastava, 1999) and Tom’s actual ratings of her on these same items. This profile correlation

reflects the degree to which Judy knows how Tom perceives her characteristic pattern of traits

(e.g., Does Judy know that Tom sees her as more agreeable than extraverted or conscientious?).

Distinctive meta-accuracy and positivity bias

While the basic profile meta-accuracy score reflects accuracy, the raw correlations

between meta-perceptions and impressions can overestimate accuracy because some of the

agreement between profiles is driven by agreement about normative standing on traits (e.g., most

people are more kind than cruel; Wood & Furr, 2016). As such, a judge may achieve meta-

12

accuracy when guessing how a target perceives them simply by assuming that they are perceived

similarly to the average person. A more stringent model of meta-accuracy teases apart this

normative information to index the degree to which judges are aware of how a target sees their

distinctive characteristics, or what distinguishes them from the typical person (i.e., distinctive

meta-accuracy). Specifically, the average impression profile from the entire sample (i.e.,

normative profile) is subtracted from each target’s impression profile, and the association

between meta-perceptions and distinctive impression profiles indexes distinctive meta-accuracy.

Importantly, the normative profile, or the way people are typically seen, is also highly socially

desirable (Wood & Furr, 2016). As a result, the normative profile is also used as an index of

positivity bias, or the degree to which people think they are seen in especially positive ways

(Borkenau & Zaltauskas, 2009; Carlson, 2016). Thus, the normative profile is included as a

second predictor of meta-perceptions to explore the degree to which judges assume others see

them in positive ways. In sum, the distinctive meta-accuracy model provides distinctive meta-

accuracy scores as well as positivity bias scores that can be correlated across contexts.

Meta-insight and transparency bias

In general, people seem to know how they are seen when others see them differently from

how they see themselves (i.e., meta-insight; Carlson, Vazire, & Furr, 2011). Yet, people often

assume that others see them similarly to how they see themselves more so than they really do

(i.e., transparency; Cameron & Vorauer, 2008). Do the people who assume they are more

transparent than they really are in one context do so in others (i.e., Is assuming transparency an

individual difference)? To better understand the degree to which meta-insight and transparency

bias are consistent across contexts, a second model was computed that included judges’ self-

perceptions as a third predictor. In addition to subtracting the normative profile from the raw

impression profile to obtain a distinctive impression profile, the normative profile was also

13

subtracted from participants’ self-perceptions to capture distinctive self-perceptions, or the

unique ways in which judges see themselves. The distinctive impression profile, the normative

profile, and the distinctive self-perception profile were then all entered as predictors of

participants’ meta-perceptions to index distinctive meta-insight (i.e., the degree to which

participants accurately judge the unique ways in which they are seen compared to the typical

person and their own self-perceptions), positivity (i.e., the degree to which participants assume

they are seen in positive ways), and distinctive transparency (i.e., the degree to which

participants overestimate how much others’ share their self-perceptions, controlling for

positivity).

Profile correlations for meta-accuracy, distinctive-meta-accuracy, positivity, meta-

insight, and transparency were all calculated using a modified version of the Social Accuracy

Model (SAM; Biesanz, 2010), which models associations between profiles using multi-level

modeling. The profile correlations were computed in R (version 3.5.0) using the lme4 package

(Bates & Sarker, 2007). Items were modeled at Level 1 and the judge and target were modeled at

Level 2. There was only one judge meta-perception profile and one target impression profile for

the close other contexts and in both contexts of Sample 3; however, in Samples 1 and 2, the first

impression contexts were dyadic and, as such, judges and targets were nested within dyads.

Accuracy and biases across contexts

To test cross-context accuracy, in both the first impression and close other context, I

exported Empirical Bayes estimates of the Level 2 slopes to obtain measures of judges’ meta-

accuracy and positivity bias (distinctive meta-accuracy model) as well as meta-insight, positivity

bias, and transparency bias (meta-insight model). To test whether there is a good judge across

contexts, I correlated judges’ meta-accuracy profile scores from a first impression context with

14

their scores from a close other context. Similarly, to test whether judges carry their biases with

them across contexts, I correlated judges’ positivity (or transparency) bias scores from a first

impression context with their positivity (or transparency) bias scores from a close other context.

Because there were multiple friends per judge in Samples 1 and 2, each judge had more than one

meta-accuracy profile score in the close other context (i.e., one score per friend). Bootstrapping

was used to derive the best estimate of the correlation coefficient between a judge’s meta-

accuracy profile score in a first impression and their meta-accuracy profile scores in a close other

context.

Accuracy and biases within contexts

A first impression and close other context are vastly different; they differ in length of

acquaintanceship, quantity and quality of interactions, feelings of closeness, and liking. Thus, it

is possible that the skills necessary to achieve accuracy in a first impression differ from those in

a close other context, and that people may not carry their accuracy from a first impression to a

close other context. However, perhaps meta-accuracy is a skill that people carry with them

within a single context. To assess this possibility, I correlated participants’ meta-accuracy scores

for one friend with their meta-accuracy scores for another friend in Samples 1 and 2. To examine

if participants carried their positivity and transparency biases across interactions, participants’

positivity bias scores (or transparency bias scores) for one friend were correlated with their

biases for other friends.

Results

As shown in Table 2, participants generally knew how they were seen (meta-accuracy),

they knew the distinctive impressions they made (distinctive meta-accuracy), and they knew how

they were seen as unique from how they see themselves (meta-insight). This is line with findings

15

from past work (e.g., Carlson & Furr, 2013; Carlson, 2016). Further, in all three samples,

participants were, on average, more meta-accurate in a close other than a first impression context

(Sample 1: t = 8.850, p < .001; Sample 2: t = 8.114, p < .001; Sample 3: t = 9.753, p < .001). In

Sample 3, meta-accuracy was descriptively higher than in the Samples 1 and 2, which is in line

with the expectation that it is easier for judges to guess their reputation than the unique way in

which they are seen by a single target. As demonstrated by the score distributions and the results

of the Shapiro-Wilk normality test (Figure 1), meta-accuracy, distinctive-meta-accuracy, and

distinctive meta-insight scores were largely normally distributed in the first impression context.

In the close other context, there were a few deviations from normality in the distributions of

these scores. However, none of these deviations were extreme (i.e., |skew| < 1).

Also similar to past work (e.g., Carlson & Furr, 2013), participants demonstrated

positivity and transparency bias in all samples (Table 2), suggesting that although they were

aware of the impressions they made on others, participants tended to overestimate how positively

others saw them (positivity) and the extent to which others’ shared their self-perceptions

(transparency). There were no significant deviations from normality in the distributions of

positivity and transparency scores in the first impression context (Figure 1). However, in the

close other context, there were deviations from normality in the positivity scores of both Samples

1 and 2, as well as in the transparency scores of Samples 2 and 3. These deviations were not

drastic (i.e., |skew| <1), except in the transparency scores of Sample 2, which were highly

negatively skewed (skew = -1.72).

The Good Judge and the Context hypotheses

As shown in Table 2, in Samples 2 and 3, participants’ meta-accuracy scores in a first

impression predicted their meta-accuracy scores in a close other context. However, these meta-

16

accuracy scores do not tease apart whether participants are carrying their accuracy or biases (i.e.,

positivity and transparency) across contexts. Indeed, there were non-significant correlations for

distinctive meta-accuracy and meta-insight across a first impression and close other context

(Table 2).

While participants’ accuracy in a first impression context did not predict their accuracy in

a close other context, there was a moderate positive correlation between participants’ positivity

bias in a first impression and their positivity bias with a friend in Samples 2 and 3, although this

correlation was near zero in Sample 1. Interestingly, the correlation between participants’

transparency bias in a first impression and close other context was negative in the Sample 1,

positive in Sample 2, and near zero in Sample 3.

To address the possibility that the null correlations across contexts were due to low

power, I conducted a post-hoc test of cross-context accuracy on a larger merged sample

comprised of Samples 1 and 2. First, I standardized the accuracy and bias scores within each

sample separately by converting them into Z-scores. Second, I merged Samples 1 and 2 together

to create a large sample of 281 judges. Finally, I used bootstrapping to obtain the best estimate of

a correlation between accuracy (or bias) in a first impression and accuracy (or bias) with a friend.

The results of this post-hoc test demonstrated that participants’ meta-accuracy scores in a first

impression predict their meta-accuracy scores with a friend (r = 0.233, 95%CI [0.211, 0.286]).

However, when this raw meta-accuracy score is teased apart into accuracy and biases,

participants’ distinctive meta-accuracy (r = 0.093, 95%CI [-0.006, 0.102]) and distinctive meta-

insight scores (r = -0.029, 95%CI [-0.098, 0.031]) do not correlate across contexts. In contrast,

participants’ positivity bias (r = .536, 95%CI [0.496, 0.544]) and transparency bias scores (r =

.208, 95%CI [0.179, 0.249]) correlate across contexts. Together, the results of this post-hoc test

17

support the results of the initial Test 1 analyses and suggest that, while people appear to carry

their positivity and transparency biases with them, there is either no cross-context accuracy, or it

is a very weak effect such that I am unable to detect it in a reasonably large sample.

The cross-context test revealed that, overall, people do not appear to carry their accuracy

with them from a first impression to a close other context in our sample. However, based on this

test alone, I could not rule out the possibility that there was an unmeasured moderator, or more

specifically, a subset of people who do carry their accuracy from one context to another or a

subset of people who is especially inaccurate across contexts. For example, on one hand, people

who make the same impression on both new acquaintances and friends might be more able to

accurately guess how they are perceived in both contexts. This is because people do have a

tendency to think they are seen in similar ways (Kenny & DePaulo, 1993), suggesting that

people who actually are seen in similar ways would be more accurate across contexts. Further

people who actually do make the same impressions across contexts are likely more consistent,

which makes it easier to guess how others perceive the self regardless of the source of

information people use to form meta-perceptions (e.g., self-observation of behavior). On the

other hand, people who assume others see them in the same way might be blind to the distinct

impressions they make on others, which might make them especially inaccurate across contexts

(Kenny & DePaulo, 1993). Put another way, it might be that people who know the impressions

they make across contexts are the individuals who form distinct, person-specific meta-

perceptions in each context. To examine these two possibilities, I tested whether the consistency

of participants’ impressions (i.e., consensus) or meta-perceptions across contexts moderated

whether people carried their accuracy from a first impression to a close other context.

18

To test the first possibility, I set up a multilevel model in which the impressions

participants made on a friend were a predictor of the impressions they made on the new lab

acquaintance. I exported the Empirical Bayes estimates of the Level 2 slopes of this model to

obtain a consensus score (i.e., the extent to which they made a consistent impression across

contexts) for each participant. Then, I conducted a hierarchical multiple regression analysis.

First, two variables were mean-centered and included in the model: close other distinctive meta-

accuracy and consensus score. These variables accounted for a significant amount of variance in

participants’ first impression distinctive meta-accuracy in Sample 3 (R2 = 10.65, F (2,154) = 0.1,

p < 0.001), but not Sample 1 (R2 = 0.04, F (2,154) = 3.10, p = 0.05) or Sample 2 (R2 = 0.02, F

(2,121) = 1.35, p = 0.26). Next, an interaction term between the two variables was added to the

regression model. This interaction variable did not account for a significant proportion of the

variance in participants’ first impression distinctive meta-accuracy in Sample 1 (b = 0.00, t(156)

= -0.26, p = 0.80), Sample 2 (b = 0.20, t(121) = 0.36, p = 0.72), or Sample 3 (b = 0.02, t(185) =

1.54, p = 0.13). Thus, it appears that people do not carry their meta-accuracy with them from a

first impression to a close other context, regardless of whether or not they make the same

impression in both contexts.

To examine the second possibility, I set up a multilevel model in which participants’

meta-perceptions of a friend were a predictor of participants’ meta-perceptions of the new lab

acquaintance. I exported the Empirical Bayes estimates of the Level 2 slopes of this model to get

a meta-perception consistency score for each participant. Again, I conducted a hierarchical

multiple regression analysis. I mean-centered participants’ close other distinctive meta-accuracy

and meta-perception consistency scores then added them as predictors in a linear model. These

two variables explained a significant amount of variance in participants’ distinctive meta-

accuracy in a first impression Sample 3 (R2 =0.07, F (2,183) = 7.29, p < 0.001), but not in

19

Sample 1 (R2 = 0.04, F (2,154) = 3.29, p = 0.04) or Sample 2 (R2 = 0.05, F (2,121) = 2.97, p =

0.06). Next, an interaction term between the two variables was added to the regression model.

This interaction variable did not account for a significant proportion of the variance in

participants’ first impression distinctive meta-accuracy in Sample 1 (b = 0.01, t(156) = 0.56 , p =

0.57 ), Sample 2 (b = 0.01, t(121) = 1.79, p = 0.08), or Sample 3 (b = 0.01, t(185) = 0.50, p =

0.62). These results suggest that even participants that formed distinct meta-perceptions in each

context did not carry their meta-accuracy with them from a first impression to a close other

context. In sum, the results of the moderation analyses suggest that there is no subset of people

who do carry their meta-accuracy from one context to another.

Notably, while there was no good judge across contexts, it appeared that there was a good

judge within contexts. Specifically, in Samples 1 and 2, participants’ meta-accuracy scores

(Sample 1: r = 0.511, 95%CI [0.509, 0.624]; Sample 2: r = 0.559, 95%CI [0.506, 0.612]),

distinctive meta-accuracy scores (Sample 1: r = 0.413, 95%CI [0.361, 0.467]; Sample 2: r =

0.298, 95%CI [0.147, 0.318]), and distinctive meta-insight scores (Sample 1: r = 0.162, 95%CI

[0.115, 0.255]; Sample 2: r = 0.199, 95%CI [0.071, 0.218]) with one friend predicted their scores

with other friends. Further, in both samples, there were strong positive correlations between

participants’ positivity bias scores among their friends (Sample 1: r = 0.824, 95%CI [0.783,

0.850]; Sample 2: r = 0.783, 95%CI [0.755, 0.814]) and transparency bias scores among friends

(Sample 1: r = 0.676, 95%CI [0.677, 0.999]; Sample 2: r = 0.683, 95%CI [0.655, 0.740]).

20

Table 2. Accuracy, positivity, transparency, and cross-context correlations in Test 1

Sample 1 Sample 2 Sample 3

First

Impression

Close

Other

Cross-

Context

First

Impression

Close

Other

Cross-

Context

First

Impression

Close

Other

Cross-

Context

b

[95% CI]

b

[95% CI]

r

[95% CI]

b

[95% CI]

b

[95% CI]

r

[95% CI]

b

[95% CI]

b

[95% CI]

r

[95% CI]

Meta-accuracy

.401***

[.361, .440]

.517***

[.451, .583]

.026

[-.023, .056]

.459***

[.406, .513]

.518***

[.450, .586]

.330*

[.231, .350]

.685***

[.633, .737]

.689***

[.645, .733]

.342*

[.209, .463]

Distinctive

meta-accuracy

.161***

[.119, .202]

.352***

[.293, .412]

-.004

[-.088, .032]

.135***

[.076, .193]

.306***

[.226, .386]

.073

[-.047, .163]

.408***

[.337, .479]

.609***

[.540, .680]

.010

[.134, .154]

Positivity

.924***

[.844, 1.01]

.806***

[.661, .951]

-.018

[-.059, .069]

.849***

[.766, .932]

.839***

[.735, .943]

.443*

[.373, .528]

.850***

[.779, .921]

.777***

[.709, .845]

.388*

[.258, .504]

Distinctive

meta-insight

.117***

[.079, .153]

.176***

[.130, .221]

.0675

[-.066, .080]

.097***

[.050, .145]

.174***

[.105, .243]

-.139

[-.176, .058]

.291***

[.224, .358]

.264***

[.206, 322]

-.053

[-.195, .092]

Distinctive

transparency

.283***

[.250, .317]

.517***

[.469, .566]

-.072*

[-.107, -.029]

.291***

[.247, .335]

.495***

[.418, .573]

.058*

[.029, .139]

.306***

[.262, .351]

.579***

[.538, .621]

.011

[.133, .154]

*p < .05 ,**p < .01, ***p < .001

21

First Impression Close Other

Meta-accuracy scores

Sample 1

W = 0.990, p = 0.473

W = 0.992, p = 0.672

Sample 2

W = 0.987, p = 0.297

W = 0.991, p = 0.622

Sample 3

W = 0.991, p = 0.309

W = 0.981, p = 0.011*

Distinctive meta-accuracy scores

22

Sample 1

W = 0.995, p = 0.935

W = 0.962, p = 0.0009*

Sample 2

W = 0.989, p = 0.435

W = 0.979, p = 0.056

Sample 3

W = 0.978, p = 0.005*

W = 0.982, p = 0.015*

Positivity scores

23

Sample 1

W = 0.987, p = 0.296

W = 0.956, p = 0.0003*

Sample 2

W = 0.992, p = 0.740

W = 0.969, p = 0.008*

Sample 3

W = 0.993, p = 0.459

W = 0.992, p = 0.412

Distinctive meta-insight scores

24

Sample 1

W = 0.989, p = 0.477

W = 0.978, p = 0.038*

Sample 2

W = 0.977, p= 0.0399*

W = 0.843, p = 0.668

Sample 3

W = 0.964, p = 9.594e-05*

W = 0.989, p = 0.195

Distinctive transparency scores

25

Sample 1

W = 0.991, p = 0.557

W = 0.992, p = 0.718

Sample 2

W = 0.986, p = 0.243

W = 0.895, p = 1.224e-07*

Sample 3

W = 0.988, p = 0.114

W = 0.984, p = 0.029*

Figure 1. Distributions of accuracy and bias scores and results of Shapiro-Wilk normality

test in the first impression and close other contexts in Test 1. *p< .05

26

Discussion

Overall, results from Test 1 suggest that there is no good judge across contexts.

Specifically, participants’ distinctive meta-accuracy and meta-insight scores in a first impression

context were not predictive of scores in a close other context. On one hand, it is possible that the

true correlation was too small to detect. For example, if the effect is weak (e.g., r = 0.15), a

sample size of 273 is required to achieve power of 0.80. On the other hand, if the true correlation

is close to the average effect size in social and personality psychology (r = 0.21; Richard, Bond,

& Stokes-Zoota, 2003), Samples 1 and 2 had a 78% chance and a 75% chance respectively of

detecting the effect. Further, no correlation was observed in Sample 3 where participants’ task

was to know their reputation rather than know how a specific person viewed them. Similarly, no

correlation was observed in the larger merged sample (Sample 1 + Sample 2; N =281). Finally,

meta-accuracy scores within a close other context were positively correlated. Taken together, the

lack of a significant positive correlation between participants’ meta-accuracy scores in a first

impression and a close other context suggests that meta-accuracy is likely not an individual

difference that people carry with them from a new acquaintance context to a friend context.

Two of the three samples, as well as the larger merged sample, suggested that people do

seem to carry positivity biases with them across contexts, while evidence for transparency bias

was mixed across the three samples and suggested no clear link across contexts. It is possible

that the null effects for transparency were due to slight deviation from normality, but given that

biases were correlated within contexts and that transparency scores were positively correlated

across contexts in the larger merged sample, it is more likely that the effect was too small to

detect. In sum, Test 1 suggests that positivity bias is a cross-context trait, but does not point to a

consistent finding regarding the cross-context nature of the transparency bias.

27

Test 2: Cross-trait Accuracy

Test 2 of the Good Judge Hypothesis vs. the Context Hypothesis assessed whether there

is rank order stability in meta-accuracy across core personality traits (i.e., the Big Five traits).

Past research has demonstrated that overall people are generally more accurate at knowing the

impressions they make on certain traits (e.g., extraversion) compared to others (e.g., neuroticism;

Levesque, 1997), but no study has tested whether the people who are the most accurate on

certain traits are the same people who are the most accurate on other traits. The Good Judge

Hypothesis predicts that meta-accuracy scores for one trait (e.g., extraversion) predict meta-

accuracy scores for other traits (e.g., agreeableness, openness). According to the Good Judge

Hypothesis, meta-accuracy is a general skill that allows judges to understand how they are

perceived on many traits. On the other hand, perhaps some judges are good at judging certain

traits, but bad at judging others, which might be because judges are more experienced with or

interested in certain traits (Funder, 1995), or because judges are especially good at knowing the

impressions they make on their defining, their most salient, or their most extreme traits. Thus,

the Context Hypothesis predicts that judges’ meta-accuracy on one trait will not correlate with

their accuracy on other traits and that self-knowledge is trait-specific.

Methods

Test 2 assessed cross-trait accuracy in a large sample (N = 517) that combined Samples 1

and 4, the characteristics of which are summarized in Table 1. In both studies, a given judge was

rated by and guessed how they were rated by up to six close others on the 44-item Big Five

Inventory (BFI; John & Strivastava, 1999). The profile approach used in Test 1 provided a

general sense of accuracy about one’s personality; however, information regarding whether

accuracy is consistent across traits was lost in this approach. In contrast, the within-person design

28

of Test 2 allowed us to index within-person meta-accuracy scores for each Big Five trait

separately.

Participants

The sample used in Test 2 has a total sample size of 517 judges (38% male; M age =

19.68 years, SD = 1.44). 60% of participants identified as White, 25% as Asian or Asian

American, 11% as African American or Black, 2% as Latin-American, and 2% as Other. All

participants were students attending a mid-western university at the undergraduate level and

were paid $20 or received course credit for participation. Informants (N = 1457; M = 2.8

informants per judge) who described participants were not paid for their participation.

Informants knew judges on average for 8.25 years.

Measures

In the Test 2 sample, judges reported their self-perceptions and meta-perceptions for

several close others and informants reported personality judgments about the judge on a 1

(strongly disagree) to 7 (strongly agree) scale of the 44-item Big Five Inventory (BFI; John &

Strivastava, 1999). The means, standard deviations, and alpha reliabilities of the self-perceptions,

meta-perceptions, and personality judgments (i.e., others’ perceptions) reported on the BFI are

presented in Table 3.

29

Table 3. Descriptive statistics for self-, meta-, and others' perceptions for the Big Five traits

in Test 2.

Self-perception Meta-perception Others’ perceptions

M (SD) α M (SD) α M (SD) α

Extraversion 4.62 (1.14) 0.90 5.02 (1.14) 0.88 5.03 (1.12) 0.86

Agreeableness 5.16 (0.86) 0.81 5.22 (0.98) 0.84 5.43 (1.08) 0.87

Emotional Stability 4.37 (1.09) 0.85 4.40 (1.16) 0.85 4.53 (1.20) 0.85

Conscientiousness 4.80 (0.94) 0.85 4.83 (1.05) 0.87 5.20 (1.10) 0.87

Openness 5.09 (0.85) 0.82 5.13 (0.89) 0.83 5.22 (0.95) 0.81

Procedures

See Test 1 procedures of Sample 1 for a description of the procedures used.

Analyses

Analyses were conducted in two phases: a) computing meta-accuracy scores and b)

fitting a model for the good judge. To compute meta-accuracy scores, I conducted a two-level

model for each Big Five trait using lme4 (Bates & Sarkar, 2007) in R (version 3.5.0). At Level 1,

a judge’s meta-perceptions for each close other on a given trait were predicted by the actual

impressions the judge made on each close other. These models were person-centered;

specifically, within-person reputations for each judge were calculated as the average impression

a judge made on a specific trait across informants, and each impression was centered on that

reputation by subtracting the trait-specific reputation from each actual impression. Therefore, the

close other impression included in the model captured deviations from the judge’s reputation,

and each judge’s Level 1 slope represented that individual’s differential meta-accuracy, or their

ability to detect which informant saw them as higher or lower on a trait. Level 2 indexed the

average person’s ability to differentiate among informants’ impressions, and the intercept and

30

slope of this model were modeled as random. As in past work, I predicted that there would be

individual differences in differential meta-accuracy (slopes; Carlson & Furr, 2009). Further, to

account for generalized meta-accuracy, or the degree to which judges’ perceived reputation (i.e.,

their average meta-perception) was associated with their actual reputation (i.e., the average

impression they made), and meta-insight, or the degree to which judges’ meta-perceptions were

associated with their reputation beyond their self-perception, judges’ reputations and self-

perceptions were added as Level 2 predictors of the random intercept. All predictors were grand-

mean centered. Meta-accuracy scores for each trait were exported using the Empirical Bayes

estimates of Level 1 slopes. Thus, each judge had five slopes, one for each Big Five trait.

The underlying theory of the good judge, in the context of Test 2, is that there is an

underlying “good judge” attribute in people that causes them to be accurate on any given trait.

Thus, I used confirmatory factor analysis (CFA) using maximum likelihood estimation to test if

one latent trait (i.e., the good judge across traits) causes associations among meta-accuracy

scores across the Big Five traits. All factor analysis was conducted using lavaan (R package;

Rosseel, 2012). Following the recommendations of Hu and Bentler (1999), the cut off values

applied to the fit indices were as follows: Comparative Fit Index (CFI) > 0.90, Tucker Lewis

Index (TLI) > 0.90, Root Mean Square Error of Approximation (RMSEA) < 0.05, and

Standardized Root Mean Square Residual (SRMR) < 0.08.

Results

In line with past work (e.g., Carlson & Furr, 2009), both generalized meta-accuracy and

differential meta-accuracy were significant for all Big Five traits (Table 4). That is, overall,

participants knew their reputation among close others and were able to detect which informants

saw them as higher or lower on each Big Five trait. Meta-insight was also significant for all Big

31

five traits, suggesting people were generally able to detect who saw them as higher or lower on

traits above and beyond how they saw their own personality. For both differential meta-accuracy

and meta-insight, the random slope variance was significant for extraversion, emotional stability,

and openness but was not significant for agreeableness and conscientiousness (Table 4). Thus,

while there appear to be individual differences in accuracy for most of the Big Five traits, there is

not enough variance to suggest individual differences in the accuracy of agreeableness and

conscientiousness.

The correlation matrix among all possible pairwise correlations between differential

meta-accuracy scores (Table 5) shows that there were weak but statistically significant pairwise

correlations among the meta-accuracy scores of all Big Five traits, suggesting that an

individual’s meta-accuracy score on one trait is somewhat predictive of their meta-accuracy

score on another trait. Prior to conducting the CFA, the distributions of meta-accuracy scores for

each Big Five trait were checked for deviations from univariate normality using the Shapiro-

Wilk test in the MVN package (R package; Korkmaz, Goksuluk, & Zararsiz, 2014). According

to this test, none of the variables were normally distributed but there were no drastic deviations

from normality (e.g., outliers, ceiling effect; see Figure 2). As such, I used the five differential

meta-accuracy scores to build a single factor model (see Model 1 in Figure 3). However, model

fit was poor (CFI = 0.895; RMSEA = 0.083, 90%CI [0.050, 0.119], p = 0.048; SRMR = 0.038).

Modification indices suggested correlating the residuals of agreeableness and emotional stability,

which fit significantly better than the original model (χ2 (1)=17.41, p<. 001), and had good fit

(CFI = 0.992, TLI = 0.980, RMSEA = 0.026, 90%CI [0.00, 0.075], p = 0.736; SRMR = 0.020).

As expected, the indicators all show positive factor loadings with standardized coefficients

ranging from 0.25 to 0.54 (see Model 2 in Figure 3). The factor loadings demonstrate that the

latent variable (i.e., the good judge across traits) is more predictive of people’s meta-accuracy

32

scores for conscientiousness and openness, and less predictive of people’s meta-accuracy scores

for emotional stability and extraversion. Further, the variance in the latent variable, while small,

was statistically significant (Z = 2.958, p = 0.003; see Figure 2). When meta-insight scores were

used instead of differential meta-accuracy scores, modification indices once again suggested

correlating the residuals of agreeableness and emotional stability, which produced a model with

good fit (CFI = 0.969, TLI = 0.923, RMSEA = 0.036, 90%CI [0.00, 0.083], p = 0.620; SRMR =

0.025), positive factor loadings with standardized coefficients ranging from 0.08 to 0.70 (see

Model 3 in Figure 3), and very little variance in the latent variable (Z= 2.002, p = 0.045). In this

model, the factor loadings demonstrate that the latent variable is very weakly predictive of

people’s accuracy on emotional stability.

33

Table 4. Meta-accuracy and meta-insight for the Big Five traits in Test 2.

Generalized meta-

accuracy

b

[.95% CI]

Differential

meta-accuracy

b

[.95% CI]

Meta-

insight

b

[.95% CI]

Random slope

value

(variance)

Extraversion .787***

[.718, .857]

.191 ***

[.146, .236]

.191 ***

[.146, .237]

0.036***

Agreeableness .527***

[.454, .598]

.138**

[.099, .178]

.139***

[.099, .178]

0.011

Emotional

Stability

.683***

[.608, .758]

.173***

[.128, .218]

.178***

[.131, .223]

0.031***

Conscientiousness .667***

[.594, .741]

.155***

[.112, .197]

.155***

[.112, .198]

0.014

Openness .638***

[.562, .714]

.182***

[.141, .222]

.184***

[.143, .225]

0.026**

* p <.05, **p <.01, ***p <.001

34

Table 5. Correlations between differential meta-accuracy scores of the Big Five traits in Test 2.

Extraversion Agreeableness Emotional

Stability

Conscientiousness Openness

Extraversion

Agreeableness .161*

[.075, .243]

Emotional Stability .169*

[.084, .252]

.299*

[.218, .375]

Conscientiousness .173*

[.088, .255]

.224*

[.140, .304]

.112*

[.026, .196]

Openness .189*

[.105, .271]

.217*

[.133, .297]

.107*

[.021, .191]

.299*

[.218, .375]

* p <.05

Figure 2. Distribution of differential meta-accuracy scores for the Big Five traits and the

distribution of the latent variable scores of the Good Judge in Test 2.

35

Model 1

Model 2

Model 3

Figure 3. Models 1, 2, and 3 of the good judge across traits tested in Test 2 using

confirmatory factor analysis. ‘MA’ and ‘MI’ refer to meta-accuracy and meta-insight, and

represent the latent variables of cross-trait meta-accuracy and cross-trait meta-insight,

respectively. ‘E’, ‘A’, ‘ES’, ‘C’, and ‘O’ refer to the observed variables for the meta-

accuracy scores of extraversion, agreeableness, emotional stability, conscientiousness, and

openness, respectively. Factor loadings and variances are all standardized.

36

Discussion

The results from Test 2 suggest that there is a good judge of meta-perception across traits.

Specifically, the good fit of the theoretical model suggests that there is shared variance among

people’s meta-accuracy scores on the Big Five traits that can be explained by one latent variable

that I would call the good judge of meta-perception. Additionally, the statistically significant

variance in the latent variable suggests that there is some variability in the good judge of meta-

perception. In other words, Test 3 suggests that the good judge of meta-perception is an

individual difference, such that some judges are better and some are worse at accurately judging

meta-perceptions. However, the variability is quite small, and whether it is a meaningful amount

of variance is unclear.

While there is shared variance among people’s meta-accuracy scores on all Big Five

traits that can be explained by the latent variable, the low factor loadings for emotional stability

and extraversion suggest that the latent variable explains less of the variance in the meta-

accuracy scores of these traits compared to the other Big Five traits. Further, the large specific

variances (i.e., item residuals) suggest that there are other, unmeasured predictors of meta-

accuracy for each trait, or a great deal of error in meta-accuracy measurement. In sum, meta-

accuracy appears to be an individual difference that predicts accuracy across traits, but this

individual difference is more predictive of the degree to which people know how they are seen

on some traits (i.e., agreeableness, conscientiousness, openness) than others (i.e., emotional

stability, extraversion).

Model 2 also demonstrates that there is additional covariance between people’s meta-

accuracy scores on agreeableness and emotional stability beyond their relationship to the latent

variable. Thus, it appears that there is something unique about the relationship between people’s

37

meta-accuracy scores on agreeableness and their meta-accuracy scores on emotional stability that

is not captured by the general ability factor. However, it is important to note that Model 2 was

not driven by an a priori hypothesis, but rather by modifications made to the original model

(Model 1) based on post hoc analysis of the modification indices. As such, it will be necessary to

test this model in future studies to confirm that the results are not unique to our sample.

38

Test 3: Cross-target Accuracy

Test 3 of the Good Judge Hypothesis assessed the cross-target consistency of meta-

accuracy, or whether good judges of meta-perception are accurate across a set of targets. Past

work suggests that people who are accurate about one target tend to be accurate about another

(e.g., Carlson & Furr, 2009, 2013). However, these findings are based on non-overlapping

groups whereby each judge nominates their own targets (i.e., friends, family members). This is

problematic given that close others tend to see us in especially positive ways (Leising, Erbs, &

Fritz, 2010) and might have been chosen in ways that artificially boosted meta-accuracy. In sum,

there has been no systematic test of whether good judges of meta-perception know how they are

seen by a set of targets not selected by the judge.

Test 3 addresses this issue by testing whether judges are accurate across the same set of

targets in a round robin design. This paradigm is especially powerful since judges did not pick

their targets and all judges in a group interacted with the same set of targets, reducing the

possibility that meta-accuracy was artificially inflated for some judges as a result of the targets

they chose. The Good Judge Hypothesis predicts that when rating the same set of targets, judges’

accuracy for one target predicts their accuracy for other targets. In other words, meta-accuracy is

a skill that judges can use with every target they meet. In contrast, the Context Hypothesis

predicts that there are no good judges across targets. Instead, there may only be good targets;

some targets may just be easier to read than others (Biesanz, 2010; Human & Biesanz, 2013).

Another possibility is that judges are more accurate at judging certain targets over others; certain

relationship-specific factors may influence meta-accuracy, including liking (Levesque, 1997;

Ohtsubo et al., 2009), assumed similarity (Human & Biesanz, 2011), a discrepancy in social

status (Miller & Malloy, 2003; Santuzzi, 2007), and a focus on performance (Gilovich, Kruger,

39

& Medvec, 2002). Finally, it is possible that there are both good judges and good targets (Rogers

& Biesanz, 2018). As such, Test 3 assessed if there are good judges, good targets, or both.

To accomplish this, Test 3 analyzed three samples that came from three very different

contexts: 1) a group of students meeting in class, 2) brief one-on-one interactions between two

unacquainted students in the lab, and 3) one-on-one interactions between potential dating

partners at a speed-dating event. The motivations and emotions associated with these contexts

can affect meta-accuracy in distinct ways. For example, it is possible that judges in a dating

context may be motivated to perceive potential partners’ thoughts and feelings inaccurately to

protect their hope of a future relationship (Ickes, 1993). Further, meeting a potential dating

partner might make participants more anxious than meeting classmates or new acquaintances,

causing judges to be more self-focused and miss detecting relevant cues (Mellings & Alden,

2000) and targets to be more withdrawn and make fewer cues available (Meleshko & Alden,

1993). In contrast, when meeting with a group of classmates working together towards a

common goal, participants may be motivated by outcome-dependency and, as such, judges and

targets might perceive one another more accurately (Neuberg & Fiske, 1987). Therefore, because

it uses samples from different contexts, Test 3 is a more robust test of the presence of good

judges and/or good targets.

Methods

In Test 3, Samples 5, 6, and 7 (see Table 1) were used to analyze the presence of a good

judge across targets. Together, the three samples had a total sample size of 959 participants. In

each sample, participants engaged in round robin ratings, in which each person was rated by

multiple targets in a group and guessed how they were seen by each target. In Sample 5, ratings

40

occurred among group members in a class setting. In Samples 6 and 7, ratings occurred after

short, dyadic (i.e., one-on-one) interactions.

Participants

Participants in Sample 5 (62% female; M age = 19.79, SD = 0.98) were students enrolled

in the same class at a mid-western university. 67.5% of participants identified as White or

Caucasian, 5.5% as Black or African-American, 22.5% as Asian or Asian American, 3% as

Hispanic or Latin American, and 1.5% as other.

In Sample 6, participants (85% female; M age =20.42, SD = 2.14) were undergraduate

students attending a Canadian university. 56% of participants identified as Caucasian or

European, 26% as East Asian or Southeast Asian, 2.2 % as Black or African, 2% as Middle

Eastern, and 14% as mixed race or other. Participants were offered $20 or 2 extra course credits

as compensation.

In Sample 7, participants (54% female, M age = 21.42, SD = 2.58) were university

students that attended one of six speed-dating events at an on-campus bar at a Canadian

university. All participants were between the ages of 18-28 and identified as heterosexual.

Additionally, 47% of participants identified as Caucasian, 33% as Asian, 2% as African

American, 16% as Hispanic, and 2% as other. Participants received $20 for participating in the

speed-dating event and could receive an additional $10 for completing follow-up questionnaires

as part of a larger study.

Measures

In each sample, participants’ self-perceptions, meta-perceptions, and their judgments of

group members’ personalities were reported on the Big Five traits. In Sample 5, these

41

perceptions were reported using the Ten Item Personality Inventory (TIPI; Gosling, Rentfrow, &

Swann, 2003) on a scale of 1 (strongly disagree) to 15 (strongly agree). In Sample 6, perceptions

were reported on 10 items from the 44-item Big Five Inventory (BFI; John & Strivastava, 1999)

and two intelligence items (see Appendix B) on a scale of 1 (strongly disagree) to 7 (strongly

agree). Finally, in Sample 7, perceptions were reported on 5-items from the short version of the

Big Five Inventory (BFI-10; Rammstedt & John, 2007) and 2 attraction items (see Appendix C)

on a sale of 1 (strongly disagree) to 7 (strongly agree).

Procedures

At Time 1, participants in Sample 5 met for the first time in groups of 6-8 in a classroom

setting. Following an icebreaker activity (e.g., Two Truths and a Lie), participants reported their

self-perceptions, their meta-perceptions, and their judgments’ of other group members’

personalities. Participants met weekly in these groups for four months. At the end of the four

months (Time 2), participants once again reported on their self-perceptions, meta-perceptions,

and group members’ personalities.

In Sample 6, participants came to the lab in unacquainted groups of 4-7 and had brief,

one-on-one interactions with each of the other group members. Following each one-on-one

interaction, participants rated their meta-perceptions and the group member’s personality.

In Sample 7, participants attended a speed-dating event. At the event, participants rated

their personalities and attractiveness. Afterwards, participants had 3-minute dyadic interactions

with participants of the opposite sex. After each dyadic interaction, participants completed a

short questionnaire that included meta-perceptions and personality ratings of their interaction

partner, as well as ratings of attraction.

42

Analyses

To conduct a test of cross-target accuracy in Test 3, I used the profile approach and the

Social Accuracy Model (SAM; Biesanz, 2010) as described in the analyses section for Test 1. In

addition to indexing profile agreement, this model can test questions about good judges and good

targets interacting in groups. Specifically, the SAM decomposes variance in accuracy in terms of

judge variance, target variance, and dyadic variance.

In full round robin designs, each person acts as a judge and a target, but within a group,

judges interact with the same set of targets (excluding themselves). This allowed for a test of

whether the variance in meta-accuracy is the product of good judges, good targets, or both. If

variance is entirely driven by judges, meta-accuracy is in the direct control of the judge

regardless of the target in that interaction. According to the Good Judge Hypothesis, the variance

explained by judges would be significant, suggesting that there is a good judge of meta-

perception across targets. Support for the Good Judge Hypothesis would also be observed if both

judge and target variance is significant. This would mean that some judges are more accurate

than others and that some targets are easier to read than others (i.e., there are both good judges

and good targets). Likewise, there would be support for the Good Judge Hypothesis if both judge

variance and dyadic variance is significant. This would suggest that there are good judges across

targets, but that the specific judge-target relationship also influences meta-accuracy. However, if

the Context Hypothesis is true, target variance or dyadic variance would be significant while

judge variance would not be significant, suggesting that judges achieve meta-accuracy simply

because some targets are easier to read than others or because of relationship factors specific to a

judge-target dyad, respectively.

43

Results

Consistent with the results from Test 1, participants in all three samples of Test 3 were

aware of how they were seen in general (meta-accuracy), they knew the distinctive impressions

they made on others (distinctive meta-accuracy), and they knew how they were seen as unique

from how they see themselves (meta-insight). In addition to being accurate, participants were

also biased (Table 6); specifically, participants overestimated how positively others saw them

(positivity) and the extent to which others’ saw them as they see themselves (transparency).

In all three samples, the variance explained by judges was significant for meta-accuracy,

distinctive meta-accuracy, positivity, distinctive meta-insight, and distinctive transparency

(Table 7). This suggests that accuracy and bias are individual differences that judges carry with

them across targets. In Sample 5, only the variance explained by judges is significant at Time 1,

but at Time 2, the variance explained by targets is also significant for meta-accuracy and

distinctive meta-accuracy. In Sample 6, the variance explained by judges, targets, and dyads is

significant for meta-accuracy, distinctive meta-accuracy, positivity, distinctive meta-insight, and

distinctive transparency, suggesting that judges, targets, and dyads all influenced judgments in

this sample. In Sample 7, in addition to significant judge variance, target variance was also

significant for meta-accuracy and positivity, and dyadic variance was significant for positivity.

44

Table 6. Accuracy, positivity, and transparency in Test 3.

Sample 5

(Classroom Groups)

Sample 6

(Platonic Dyads)

Sample 7

(Dating Dyads)

Time 1

b[.95% CI]

Time 2

b[.95% CI]

b[.95% CI]

b[.95% CI]

Meta-accuracy .428***

[.395, .461]

.474***

[.437, .511]

.448***

[.426, .470]

.327***

[.290, .364]

Distinctive

meta-accuracy

.169***

[.140, .198]

.211***

[.180, .242]

.102***

[.086, .118]

.081***

[.061, .101]

Positivity .911***

[.838, .984]

.921***

[.837, 1.001]

.832***

[.795, .870]

1.01***

[.896, 1.124]

Distinctive

meta-insight

.096***

[.072, .119]

.119***

[.097, .141]

.074***

[.060, .088]

.063***

[.047, .079]

Distinctive

Transparency

.356***

[.319, .393]

.426***

[.385, .467]

.208***

[.175, .241]

.177***

[.075, .279]

* p <.05, **p <.01, ***p <.001

45

Table 7. Judge, target, and dyadic variance in Test 3.

Judge Variance Target Variance Dyadic Variance

Sample 5: Classroom groups

Time 1 Time 2 Time 1 Time 2 Time 1 Time 2

Meta-accuracy .046*** .044*** .007*** .012*** .000 .000

Distinctive meta-

accuracy

.032*** .025*** .002 .006**

.003 .002

Positivity .289*** .313*** .000 .004 .000 .000

Distinctive meta-

insight

.019*** .011***

.001 .001 .002 .002

Distinctive

transparency

.069*** .073*** .000 .000 .000 .000

Sample 6: Platonic dyads

Meta-accuracy .044*** .007*** .004

Distinctive meta-

accuracy

.013***

.002***

.017***

Positivity .180*** .002*** .008*

Distinctive meta-

insight

.008***

.002***

.011***

Distinctive

transparency

.034***

.001*** .003***

Sample 7: Dating dyads

Meta-accuracy .038*** .014*** .006

Distinctive meta-

accuracy

.003*** .002 .008

Positivity .527*** .010*** .036*

Distinctive meta-

insight

.000 .001 .006

Distinctive

transparency

.074*** .000 .000

* p <.05, **p <.01, ***p <.001

46

Discussion

Overall, the results from Test 3 support the Good Judge Hypothesis. Specifically, a

significant proportion of variance in accuracy and bias was explained by judges who met with

targets in a group classroom context, a platonic dyadic interaction context, and a dyadic dating

context, suggesting that meta-accuracy as well as positivity and transparency biases are

individual differences that judges carry with them across targets, regardless of context.

While the presence of significant judge variance is consistent across samples, the extent

to which targets explained a significant proportion of variance differed. In Sample 5, while the

variance explained by targets was significant for only meta-accuracy at Time 1, it was significant

for both meta-accuracy and distinctive meta-accuracy at Time 2, suggesting that some of the

characteristics or behaviors of specific targets influenced judges’ accuracy after people were

acquainted. Thus, in a group context, accuracy and bias are initially in the control of the judge,

regardless of the target they are interacting with, but after getting to know one another over a

period of 4 months, some targets become easier to read than others. However, when participants

interacted in platonic, one-on-one meetings (Sample 6), both judge and target variance were

significant for accuracy (i.e., distinctive meta-accuracy, meta-insight) and bias (i.e., positivity,

transparency) at a first impression, suggesting that some judges were more accurate (and biased)

than others and some targets were easier to read than others or elicited more biased judgments

than others. Yet, in the dating context (Sample 7), meta-accuracy and positivity were more likely

with some targets than others but distinctive meta-accuracy, meta-insight, and transparency were

not. This pattern suggests that some targets made judges feel as though they made a positive

impression (e.g., Tom made all judges feel like he thought they had a desirable personality), but

some targets were not necessarily easier to read when it came to being accurate. This might be

47

due to the fact that in a dating context, targets might be open and expressive only with the

specific judges they are interested in, which would result in low target variance for accuracy. In

sum, results suggest that the extent to which targets influence judges’ accuracy may depend on

context; specifically, some targets are easier to read than others in platonic group and one-on-one

contexts, but not in dating contexts.

While there is evidence to suggest that some judges can be accurate with everyone, and

that in some contexts, some targets are easy to read for everyone, there could also be certain

pairings, or dyads, that lead to greater accuracy than others. This was examined by testing

whether there was significant dyadic variance in each sample. The only sample that showed

significant dyadic variance was Sample 6 where participants interacted in platonic dyadic

interactions. Dyadic interactions in a platonic, one-on-one context might have allowed dyad-

specific factors to be more prominent and influence participants’ accuracy and bias more than a

group interaction context (Sample 5) or an evaluative dating context (Sample 7). Another reason

for low dyadic variance in the dating context (Sample 7) is that certain judge characteristics, such

as attractiveness and likeability, may have been especially powerful in targets’ impression

formation process (Fletcher, Kerr, Li, & Valentine, 2014) such that targets were likely to

perceive them more accurately (Human & Biesanz, 2013). In other words, attractive and likeable

judges could just use their self-perceptions to be accurate. Interestingly, dyadic variance for

positivity in the dating context (Sample 7) was significant, suggesting that in certain dyads,

judges tended to think that they were seen in especially positive ways. Thus, there might have

been some kind of interpersonal dynamic in some dyads that made judges think they made an

especially positive impression, but this dynamic did not foster their accuracy. Of course, the low

dyadic variance in Sample 7 might also have been due to a smaller sample size (i.e., Sample 6

N= 547, Sample 7 N=172).

48

General Discussion

The underlying assumption in the meta-accuracy literature is that meta-accuracy is an

individual difference, or trait-like skill, that people carry with them wherever they go. However,

this assumption is based primarily on cross-sectional work that links meta-accuracy to a variety

of individual differences (e.g., Carlson & Oltmanns, 2015; Moritz & Roberts, 2017). While this

work implicitly suggests that meta-accuracy is also an individual difference, it has often only

tested meta-accuracy in one context, for one trait, or for a single target. No studies have formally

tested the cross-context, cross-trait, or cross-target consistency of meta-accuracy. To directly

address the question of whether there are good judges of meta-perception, the current project

conducted three tests that explicitly examined whether or not meta-accuracy is a skill that people

take with them across contexts (Test 1), traits (Test 2), and targets (Test 3). The results of this

project suggest that meta-accuracy is a trait-like skill and that there is indeed a good judge of

meta-perception, but that there are constraints to this skill.

Specifically, it appears that there is a good judge of meta-perception across targets, across

traits, and within contexts, but not across contexts. While people carry their meta-accuracy with

them across their friends (Test 1) and across new acquaintances (Test 3), I did not find any

evidence that people carry their meta-accuracy with them from a new acquaintance context to a

friend context. The absence of a good judge of meta-perception across a first impression and

close other context does not challenge the conclusion that good judges exist, rather it suggests

that the good judges in a first impression context are not the same good judges in a close other

context. This is likely because a first impression and close other context are at the opposite

extremes in terms of length of acquaintanceship, quantity and quality of interactions, feelings of

closeness, and liking. As such, it is likely that the factors that facilitate accuracy in a first

impression context differ from those in a close other context, and that the judges that are accurate

49

with a new acquaintance are therefore no more or less likely to be accurate with a close friend.

Future work might examine if certain traits drive accuracy in one context or the other.

The existence of the good judge of meta-perception suggests that there might be an

attribute or set of attributes that fosters self-knowledge, and that targeting this set of attributes

may be a successful intervention for improving people’s accuracy across different relationships.

However, the absence of a good judge across contexts suggests that the set of accuracy-

enhancing attributes may differ from one context to another. Thus, future research should

investigate the attributes or individual differences that promote accuracy in a first impression and

close other context separately. This future work would help shed light on which attributes people

can change in order to garner self-knowledge and reap the benefits (e.g., high relationship

satisfaction, social and psychological adjustment).

In addition to judges, results from Test 2 and Test 3 suggest that there are other factors

that influence meta-accuracy. In Test 2, while the latent factor of a good judge explained some of

the variance in trait-specific meta-accuracy scores, a significant proportion of the variance

remained unexplained, suggesting that there are other factors besides judges that predict meta-

accuracy. Similarly, in Test 3, the presence of target and dyadic variance in meta-accuracy

implies that meta-accuracy is not entirely in the control of the judge; rather, there may also be

target- and dyad-specific factors at play. In other words, there may be both good judges and good

targets of meta-perception. An important avenue for future research is to examine whether good

judges within a context are good judges regardless of whether they are interacting with good or

bad targets, or if good judges only emerge in the presence of good targets (Rogers & Biesanz,

2018).

50

One of the limitations of the current research is the use of different types of scores to

index meta-accuracy across tests. Specifically, I used profile meta-accuracy in Tests 1 and 3 and

differential meta-accuracy in Test 2. The type of meta-accuracy score used may have affected the

results of the test. For example, the profile meta-accuracy scores used in Test 1 demonstrated

that there is no good judge across contexts when perceptions are about judges’ overall

personalities (i.e., across traits). However, it is possible that there are good judges across

contexts for specific traits. Perhaps good judges are accurate about their extraversion, but not

other traits, across a first impression and close other context. This possibility could not be tested

using the profile approach in Test 1.

Another limitation of the current research is the use of archival data that were not

collected with the Good Judge and the Context hypotheses in mind. Therefore, the three tests of

the good judge of meta-perception were limited by the data that existed and the measures that

were administered in previous studies. For instance, different measures of personality traits (e.g.,

BFI-44, BFI-10, TIPI) were used across tests. However, while the use of different measures may

have introduced additional noise, the general consistency of results across samples that used

different measures suggests that the observed trends are robust. Additionally, the use of multiple

archival datasets allowed for the analysis of large samples for each of the three tests of the good

judge of meta-perception, reducing the possibility that results were due to chance. Nonetheless,

future work should use a within-person design to conduct all three tests on one large sample.

In sum, evidence for the good judge has been largely absent in the interpersonal

perception literature. However, the current project has identified one example of the good judge:

the good judge of meta-perception. Specifically, it appears that there is a good judge of meta-

perception within certain contexts, across various traits, and across different targets.

51

References

Bates, D., & Sarkar, D. (2007). lme4: Linear mixed-effects models using S4 classes (R package

Version 0.9975-12) [Computer soft- ware]. Retrieved from http://cran.r-project.org/

web/packages/lme4/index.html

Biesanz, J. C. (2010). The social accuracy model of interpersonal perception: Assessing

individual differences in perceptive and expressive accuracy. Multivariate Behavioral

Research, 45(5), 853–885. http://doi.org/10.1080/00273171.2010.519262

Borkenau, P., & Zaltauskas, K. (2009). Effects of Self-enhancement on Agreement on

Personality Profiles. European Journal of Personality, 23, 107–123.

http://doi.org/10.1002/per

Cameron, J. J., & Vorauer, J. D. (2008). Feeling transparent: On metaperceptions and

miscommunications. Social and Personality Psychology Compass, 2, 1093–1108.

http://doi.org/10.1111/j.1751-9004.2008.00096.x

Carlson, E. N. (2016a). Do psychologically adjusted individuals know what other people really

think about them? The link between psychological adjustment and meta-accuracy. Social

Psychological and Personality Science, 7, 717-725.

http://doi.org/10.1177/1948550616646424

Carlson, E. N. (2016b). Meta-Accuracy and Relationship Quality : Weighing the Costs and

Benefits of Knowing What People Really Think About You, Journal of Personality and

Social Psychology, 111(2), 250–264.

52

Carlson, E. N., & Furr, M. M. (2013). Resolution of Meta-Accuracy: Should People Trust Their

Beliefs About How Others See Them? Social Psychological and Personality Science, 4(4),

419–426. http://doi.org/10.1177/1948550612461653

Carlson, E. N., & Furr, R. M. (2009). Evidence of differential meta-accuracy: People understand

the different impressions they make. Psychological Science, 20(8), 1033–1039.

http://doi.org/10.1111/j.1467-9280.2009.02409.x

Carlson, E. N., & Kenny, D. A. (2012). Meta-accuracy: Do we know how others see us? In S.

Vazire & T. D. Wilson (Eds.), Handbook of self-knowledge (pp. 242-257). New York, NY:

Guilford Press.

Carlson, E. N., & Oltmanns, T. F. (2015). The role of metaperception in personality disorders:

Do people with personality problems know how others experience their personality?

Journal of Personality Disorders, 29(1), 449–467.

http://doi.org/10.1521/pedi.2015.29.4.449

Carlson, E. N., Vazire, S., & Furr, R. M. (2011). Meta-Insight: Do People Really Know How

Others See Them? Journal of Personality and Social Psychology, 101(4), 831–846.

http://doi.org/10.1037/a0024297

Carlson, E. N., Wright, A. G. C., & Imam, H. (2017). Blissfully Blind or Painfully Aware?

Exploring the Beliefs People With Interpersonal Problems Have About Their Reputation.

Journal of Personality, 85(6), 757–768. http://doi.org/10.1111/jopy.12284

53

Clarke, A. D. B., & Clarke, A. M. (1984). Constancy and Change in the Growth of Human

Characteristics. Journal of Child Psychology and Psychiatry, 25(2), 191–210.

http://doi.org/10.1111/j.1469-7610.1984.tb00144.x

Connelly, B. S., & Ones, D. S. (2010). Another perspective on personality: Meta-analytic

integration of observers’ accuracy and predictive validity. Psychological Bulletin, 136(6),

1092–1122. doi:10.1037/a0021212

Fletcher, G. O., Kerr, P. G., Li, N. P., & Valentine, K. A. (2014). Predicting romantic interest

and decisions in the very early stages of mate selection: Standards, accuracy, and sex

differences. Personality and Social Psychology Bulletin, 40(4), 540–550.

doi:10.1177/0146167213519481

Funder, D. C. (1995). On the Accuracy of Personality Judgement: A Realistic Approach.

Psychological Review, 102(4), 652–670.

Gallrein, A. M. B., Carlson, E. N., Holstein, M., & Leising, D. (2013). You spy with your little

eye: People are “blind” to some of the ways in which they are consensually seen by others.

Journal of Research in Personality, 47(5), 464–471.

http://doi.org/10.1016/j.jrp.2013.04.001

Gallrein, A. M. B., Weßels, N. M., Carlson, E. N., & Leising, D. (2016). I still cannot see it - A

replication of blind spots in self-perception. Journal of Research in Personality, 60, 1–7.

http://doi.org/10.1016/j.jrp.2015.10.002

54

Gilovich, T., Kruger, J., & Medvec, V. H. (2002). The spotlight effect revisited: Overestimating

the manifest variability of our actions and appearance. Journal of Experimental Social

Psychology, 38(1), 93–99. http://doi.org/10.1006/jesp.2001.1490

Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the Big-Five

personality domains. Journal of Research in Personality, 37(6), 504–528.

http://doi.org/10.1016/S0092-6566(03)00046-1

Human, L. J., & Biesanz, J. C. (2013). Targeting the good target: An integrative review of the

characteristics and consequences of being accurately perceived. Personality and Social

Psychology Review, 17, 248-272. http://doi.org/10.1177/1088868313495593.

Human, L. J., & Biesanz, J. C. (2011). Through the Looking Glass Clearly: Accuracy and

Assumed Similarity in Well-Adjusted Individuals’ First Impressions. Journal of Personality

and Social Psychology, 100(2), 349–364. http://doi.org/10.1037/a0021850

Hu, L., & Bentler, P. M. (1999) Cutoff criteria for fit indexes in covariance structure analysis:

Conventional criteria versus new alternatives, Structural Equation Modeling: A

Multidisciplinary Journal, 6(1), 1–55. doi: 10.1080/10705519909540118

Ickes, W. (1993). Empathic accuracy. Journal of Personality, 61(4), 587–610.

doi:10.1111/j.1467-6494.1993.tb00783.x.

John, O. P., & Srivastava, S. (1999). The Big Five taxonomy: History, measurement, and

theoretical perspectives. In L.A. Pervin & O. P. John (Eds), Handbook of personality:

Theory and research (pp. 102–138). New York, NY: Guilford.

55

Kaplan, S. A., Santuzzi, A., & Ruscher, J. (2009). Elaborative Metaperceptions in Outcome-

Dependent Situations: The Diluted Relationship Between Default Self-perceptions and

Meta-perceptions. Social Cognition, 27(4), 601–614.

Kenny, D. A., & Depaulo, B. M. (1993). Do People Know How Others View Them: An

Empirical and Theoretical Account. Psychological Bulletin, 114(1), 145–161.

http://doi.org/10.1037//0033-2909.114.1.145

Korkmaz, S., Goksuluk, D. & Zararsiz, G. (2014). MVN: An R Package for Assessing

Multivariate Normality. The R Journal, 6(2), 151–162.

Langer, S. L., & Wurf, E. (1999). The effects of channel-consistent and channel-inconsistent

interpersonal feedback on the formation of metaperceptions. Journal of Nonverbal

Behavior, 23(1), 43– 65.

Leising, D., Erbs, J., & Fritz, U. (2010). The letter of recommendation effect in informant ratings

of personality. Journal of personality and social psychology, 98(4), 668–682.

Letzring, T. D. (2008). The Good Judge of Personality: Characteristics, Behaviors, and Observer

Accuracy. Journal of Research in Personality, 42(4), 914–932.

http://doi.org/10.1016/j.jrp.2007.12.003.The

Levesque, M. J. (1997). Meta-accuracy among acquainted individuals: A social relations analysis

of interpersonal perception and metaperception. Journal of Personality and Social

Psychology, 72(1), 66–74. http://doi.org/10.1037/0022-3514.72.1.66

56

Malloy, T. E., & Janowski, C. L. (1992). Perceptions and Metaperceptions of Leadership:

Components, Accuracy, and Dispositional Correlates. Personality and Social Psychology

Bulletin, 18(6), 700–708. http://doi.org/10.1177/0146167292186006

Meleshko, K. G. A., & Alden, L. E. (1993). Anxiety and self-disclosure: Toward a motivational

model. Journal of Personality and Social Psychology, 64(6), 1000–1009.

Mellings, M. B., & Alden, L. E. (2000). Cognitive processes in social anxiety: The effects of

self-focus, rumination, and anticipatory processing. Behaviour Research and Therapy, 38,

243−257.

Miller, S., & Malloy, T. E. (2003). Interpersonal Behavior, Perception, and Affect in Status-

Discrepant Dyads: Social Interaction of Gay and Heterosexual Men. Psychology of Men and

Masculinity, 4(2), 121–135. http://doi.org/10.1037/1524-9220.4.2.121

Ohtsubo, Y., Takezawa, M., & Fukuno, M. (2009). Mutual liking and meta-perception accuracy.

European Journal of Social Psychology, 39, 707–718. http://doi.org/10.1002/ejsp

Oltmanns, T. F., & Turkheimer, E. (2009). Person Perception and Personality Pathology. Current

Directions in Psychological Science, 18(1), 32–26.

http://doi.org/10.1016/j.jacc.2007.01.076

Richard, F. D., Bond, C. F., Jr, & Stokes-Zoota, J. J. (2003). One hundred years of social

psychology quantitatively described. Review of General Psychology, 7(4), 331-363. doi:

10.1037/1089-2680.7.4.331

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical

Software, 48(2), 1-36.

57

Santuzzi, A. M. (2007). Perceptions and metaperceptions of negative evaluation: Group

composition and meta-accuracy in a social relations model. Group Processes and

Intergroup Relations, 10(3), 383–398. http://doi.org/10.1177/1368430207078700

Vazire, S. (2010). Who Knows What About a Person? The Self-Other Knowledge Asymmetry

(SOKA) Model. Journal of Personality and Social Psychology, 98(2), 281–300.

http://doi.org/10.1037/a0017908

Vorauer, J. D., Main, K. J., & O’Connell, G. B. (1998). How do individuals expect to be viewed

by members of lower status groups? Content and implications of meta-stereotypes. Journal

of Personality and Social Psychology, 75(4), 917–937. http://doi.org/10.1037/0022-

3514.75.4.917

Wood, D., & Furr, R. M. (2016). The Correlates of Similarity Estimates Are Often Misleadingly

Positive: The Nature and Scope of the Problem, and Some Solutions. Personality and Social

Psychology Review, 20(2), 79–99. http://doi.org/10.1177/1088868315581119

58

Appendices

Appendix A

Subset of Big Five Inventory (BFI-44; John & Strivastava, 1999) items used in Sample 2

1. Is talkative

2. Tends to find fault with others

3. Is original, comes up with new ideas

4. Is depressed, blue

5. Is helpful and unselfish with others

6. Is relaxed, handles stress well

7. Is curious about many different things

8. Has a forgiving nature

9. Worries a lot

10. Gets nervous easily

11. Likes to reflect, play with ideas

12. Tends to be lazy

13. Has an assertive personality

14. Can be cold and aloof

15. Is moody

16. Is considerate and kind to almost everyone

17. Is easily distracted

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Appendix B

Subset of Big Five Inventory (BFI-44; John & Strivastava, 1999) and intelligence items used in

Sample 6

1. Is intelligent

2. Tends to be quiet

3. Makes plans and follows through with them

4. Is outgoing, sociable

5. Tends to find fault with others

6. Is depressed, blue

7. Is original, comes up with new ides

8. Is helpful and unselfish with others

9. Can be somewhat careless

10. Is relaxed, handles stress well

11. Is ingenious, a deep thinker

12. Is bright

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Appendix C

Subset of Big Five Inventory (BFI-10; Rammstedt & John, 2007) and attraction items used in

Sample 7

1. Is outgoing, sociable

2. Tends to find fault with others

3. Does a thorough job

4. Gets nervous easily

5. Has an active imagination

6. Is engaging and interesting

7. Is physically attractive