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Meta-accuracy as a moderator of the association between social experience and emotional adjustment during childhood
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Meta-accuracy as a moderator of the association between social experience and emotional adjustment during childhood
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Content
Meta-accuracy as a Moderator of the Association Between Social Experience and Emotional
Adjustment During Childhood
by
Luiza Vianna Mali
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF PHILOSOPHY
(PSYCHOLOGY)
August 2020
Copyright 2020 Luiza Vianna Mali
ii
Acknowledgements
I am grateful to all of those with whom I have had the pleasure to work during this and
other related projects. My former and current labmates Annemarie Kelleghan, Tana Luo, Sarah
Malamut, Skye Parral, and Yana Ryjova, who have spent countless hours providing feedback
and were instrumental in helping me collect data for this study. My cohort, Laura Garcia, Sohyun
Han, Hannah Khoddam, and Gabrielle Lewine, for being amazing friends and role models. Each
of the members of my Dissertation Committee, who have provided me with guidance and
support to conduct a complex project and taught me a great deal about scientific research.
I am especially indebted to my mentor, Dr. David Schwartz, who seven years ago
believed in my potential as a scholar and welcomed me to his lab. He has shown me how to be a
rigorous scientist and a caring teacher and has inspired me to be a fierce advocate for children
who are struggling socially, emotionally, and academically. I learned more from you than I could
ever give you credit for. I would also like express my heartiest gratitude and sincere thanks to the
administrators, teachers, and students who took part in this study for generously sharing their
time and providing me with helpful insights about child development and school adjustment.
Finally, nobody has been more important to me in the pursuit of this project than the
members of my family. I would like to thank my parents, who provided me with unwavering
love and support. I would not have gotten this far without you. I am also extremely thankful to
my husband, Karim, who cheered me on and was patient and understanding about the many
nights and weekends I spent working. My special gratitude goes to my two wonderful children,
Enzo and Sophia, who everyday give me the love and energy needed to believe that my research
and clinical efforts are contributing to a better world.
iii
TABLE OF CONTENTS
Page
Acknowledgements……………………..........................................................................................ii
List of Tables……………………...................................................................................................v
List of Figures…………………….................................................................................................vi
Abstract…………………..............................................................................................................vii
Introduction………………………..................................................................................................1
Meta-Perceptions as a Type of Interpersonal Perspective...................................................2
Implications of Meta-Accuracy for Adjustment ……….....................................................3
Social Experience and Emotional Functioning....................................................................5
Meta-Accuracy and Related Social-Cognitive Skills..........................................................7
Accounting for Individual Differences in Meta-Accuracy………....................................12
The Current Study………………………………………..................................................13
Method...........................................................................................................................................14
Participants .......................................................................................................................14
Procedure ..........................................................................................................................16
Measures............................................................................................................................16
Results…………...………….........................................................................................................23
Univariate Analyses and Bivariate Relations....................................................................23
Meta-Accuracy as a Moderator of the Link between Social Experience and Emotional
Functioning........................................................................................................................25
Meta-Accuracy and Social-Cognitive Skills…..................................................................26
Discussion……..……………………............................................................................................26
Meta-Accuracy of Social Competence during the Elementary School Years...................27
The Role of Meta-Accuracy as a Moderator......................................................................28
Meta-Accuracy and Related Socio-Cognitive Skills…………………….........................35
Limitations, Strengths and Future Directions....................................................................38
Conclusions........................................................................................................................40
References......................................................................................................................................42
Appendices.....................................................................................................................................67
Appendix A .......................................................................................................................67
Appendix B .......................................................................................................................69
Appendix C .......................................................................................................................73
Appendix D .......................................................................................................................74
Appendix E .......................................................................................................................76
Appendix F.........................................................................................................................78
iv
Appendix G .......................................................................................................................81
Appendix H .......................................................................................................................83
Appendix I ........................................................................................................................84
Appendix J ........................................................................................................................86
v
List of Tables
Table 1 ..............................................................................................................................60
Table 2 ..............................................................................................................................61
Table 3 ..............................................................................................................................62
Table 4 ..............................................................................................................................63
Table 5 ..............................................................................................................................64
Table 6 ..............................................................................................................................65
vi
List of Figures
Figure 1 .............................................................................................................................66
vii
Abstract
Despite its critical implications for social behavior and self-concept formation, meta-accuracy
(i.e., the ability to correctly identify others’ perceptions of one’s social competence) has not
received a great deal of attention in the interpersonal perception and developmental literature.
This cross-sectional study examined the interaction between social experience (i.e., liking by
peers) and meta-accuracy on emotional adjustment. The study also examined the association
between related social-cognitive skills and meta-accuracy. Participants were 147 ethnically
diverse elementary school children in grades three to five (Mage = 9.37 years, SD = 1.12, range 8-
12; 40.8% female). Students completed a peer nomination inventory assessing liking by peers
and peer- and meta-perceptions of liking and disliking. Participants self-reported perceptions of
social competence, depressive symptoms, and anxiety. Cognitive performance tasks indexed
theory of mind, switching, attention, and working memory abilities. Scores on standardized
achievement tests were obtained from school records. Among children who had high meta-
accuracy of liking, liking by peers was negatively associated with anxiety, but not with
depressive symptoms. Disliking meta-accuracy did not moderate the association between peer
liking and emotional functioning. Meta-accuracy of liking was significantly related to self-
perceptions and marginally associated with theory of mind, illustrating the influences of self-
concept and mentalizing ability on this type of meta-perception. Results suggest that the
dynamics of meta-accuracy are considerably more nuanced and complex than once thought, and
that future studies would benefit from considering methodological and theoretical factors
influencing meta-accuracy.
Keywords: peer liking, meta-accuracy, cognitive skills, emotional functioning
1
Meta-accuracy as a Moderator of the Association Between Social Experience and Emotional
Adjustment During Childhood
Introduction
Interpersonal perceptions have long been considered a central process in developmental,
social, and cognitive theories of behavior (e.g., Bandura, 1986; Heider, 1958; Selman, 1980).
Social judgments are made from an early age (e.g., Colwell & Lindsey, 2003; Wellman, Phillips,
Dunphy-Lelli, & LaLonde, 2004) and are especially important during the school years. During
this period, children begin to spend a significant portion of their time with peers and to develop
and refine their social competence (Spence, 2003). Although significant strides have been made
in clarifying children’s processing of their social context (e.g., Carpendale & Lewis, 2004),
implicit assumptions continue to be made about children’s knowledge and awareness of others’
judgments (Bellmore & Cillessen, 2006).
When a child experiences relational difficulties, several questions can be asked to assess
the child’s impressions of his or her social situation. Does the child believe he or she is having
social problems? Is the child’s perception congruent with that of his or her peers? Is the child
aware of how he or she is seen by peers? The first two questions have been widely explored in
the developmental literature. They address self-other agreement, with a focus on how the child’s
self-perceptions match others’ judgments. The latter question, which is the focus of the present
study, refers to the understudied construct of meta-accuracy, a sophisticated perceptual ability
that has been deemed an important aspect of social-cognitive competence (Ohtsubo, Takezawa,
& Fukuno, 2009).
Past studies investigating the implications of meta-accuracy for adjustment have treated
meta-accuracy as a predictor of youths’ functioning. Notably, accurate meta-perceptions appear
2
to be associated with both positive and negative socioemotional outcomes (e.g., Bellmore &
Cillessen, 2003; Schiff, 1954). Such findings highlight the need to consider interactional models
that carefully explore when accurate meta-perceptions are protective as well as the circumstances
under which they exacerbate risk.
The present investigation examines how the accuracy of interpersonal perceptions
interacts with peer experiences to predict adjustment during childhood. The study also examines
the association between core social-cognitive skills and meta-accuracy. A nuanced understanding
of meta-accuracy and its implications may guide research on the development and refinement of
social-cognitive interventions aimed at addressing social competence and emotional functioning
during the school years.
Meta-Perceptions as a Type of Interpersonal Perspective
Researchers have conceptualized meta-accuracy as the correctness of an individual’s
meta-perceptions, that is, the extent to which a person’s impressions of how he or she is seen by
others are congruent with others’ evaluations of him or her (Kenny & DePaulo, 1993). In past
developmental research, this unique type of interpersonal perception has been assessed using
peer ratings and self-report questionnaires. First, all children in a classroom are asked to rate how
they think or feel about each peer. Next, children are asked to indicate what they believe each
peer said about them. These two perspectives are then evaluated for agreement using a range of
statistical methods, such as correlations (Bruinikins, 1978), mean discrepancy (Boor-Klip,
Cillessen, & Van Hell, 2014), sum of discrepancy scores (Ausubel & Schiff, 1955), and kappa
scores (Badaly, Schwartz, & Gorman, 2012).
Meta-perceptions can be evaluated for veridicality, as levels of a perceived ability or
affective state can be validly contrasted with an objective standard of competence. The accuracy
3
of such perceptions can be assessed for a variety of traits and competencies, including
intellectual ability, behavioral adequacy, and emotional functioning. Given the salience of peer
relationships during the childhood years (Rubin, Bukowski, & Parker, 2006), increased attention
has recently been devoted to meta-accuracy of social competence and its implications.
Researchers have suggested that this type of meta-accuracy may influence key areas of youths’
lives, such as social behavior, self-concept formation, and self-esteem (Bellmore & Cillessen,
2006; MacDonald & Cohen, 1995; Zakriski & Coie, 1996).
Implications of Meta-Accuracy for Adjustment
Despite its theoretical and practical importance, only a small number of studies have
explored the link between meta-accuracy and adjustment. These studies have yielded an
interesting pattern of results. In some studies, lack of accuracy has been associated with negative
consequences, such as loneliness (Cillessen & Bellmore, 1999), depression (Kistner, David-
Ferdon, Repper, & Joiner, 2006), difficulty adjusting aspirations after experiences of failure
(Schiff, 1954), and social problems (i.e., decreased number of friendships, low peer group
acceptance; Bellmore & Cillessen, 2003), whereas other studies have highlighted the advantages
of inaccurate meta-perceptions. Schiff (1954) reported that biased perceptions tended to be
associated with increased self-regard and positive social adjustment (i.e., high likelihood of
being nominated as a friend over time). Similarly, Brendgen and colleagues (2004) described a
link between poor accuracy and increased social acceptance.
Such results are consistent with two perspectives in social-cognitive biases. The first
perspective proposes that inaccurate meta-perceptions may have a social cost (MacDonald &
Cohen, 1995). That is, when children act based on misperceptions, they risk behaving in
inappropriate ways and damaging social connections. Consider, for example, Jackie, a third
4
grader who is unaware of how she is perceived by her peers. Because of this lack of awareness,
she is more likely to approach peers that have a history of disliking her. She may also have a
tendency to avoid playing with classmates that actually enjoy her company. In this scenario, we
can see that accurate perceptions may have an important role in guiding appropriate behavior, by
determining whom to approach and how to behave in social situations (MacDonald & Cohen,
1995). Misperceptions, on the other hand, may jeopardize the formation of healthy friendships
and could put the child at risk for peer harassment.
The second perspective emphasizes the potential protective role of perceptual distortions
(Taylor & Brown, 1988; Zakriski & Coie, 1996). According to this perspective, children who are
not aware of the critical views that their peers have of them fail to internalize harmful feedback,
which protects their self-worth from being negatively affected. Consider Zack, a fourth grader
who is highly rejected and made fun of by most of his peers, but lacks awareness of his rejected
and victimized status. If Zack had a high degree of accuracy when inferring others’ impressions
of him, his self-esteem would likely suffer. In this case, his lack of awareness serves an adaptive
function, as it protects his self-concept from being damaged by negative feedback.
The latter perspective has also found some support in the developmental
psychopathology literature. Research on children with high-functioning autism (i.e., a form of
this neurodevelopmental disorder marked by relatively normal intellectual functioning but
varying degrees of social difficulties) suggests that increased awareness of social deficits may
place youths at a greater risk for depressive symptoms (Vickerstaff, Heriot, Wong, Lopes, &
Dossetor, 2006; Wing, 1992). For these children, a desire to establish social connections with
peers, combined with a reasonable ability to assess their social situation, is likely to result in
emotional suffering (i.e., feelings of loneliness; Bauminger & Kasari, 2000). Similarly, studies
5
examining the positive illusory bias (i.e., a form of perceptual distortion in which individuals
make overly positive reports of their competence) in children with Attention-
Deficit/Hyperactivity Disorder (ADHD) indicate that biased perceptions may serve as a coping
mechanism. That is, inaccurate impressions may work to protect the self from threatening
information (e.g., problematic social relations) that could negatively impact self-esteem (Ohan &
Johnston, 2002; Owens, Goldfine, Evangelista, Hoza, & Kaiser, 2007).
As suggested by meta-accuracy research and the developmental psychopathology
literature, lack of awareness of others’ feelings and impressions may serve as both a risk and a
protective factor for children’s development. This pattern of findings suggests that meta-
accuracy may not necessarily be beneficial or detrimental when considered in isolation. Rather, it
may be the interaction between social experience and meta-accuracy that impacts youths’
functioning (see Figure 1). We hypothesize that for children who are socially well-adjusted (i.e.,
well-liked), poor meta-accuracy is likely to confer risks for emotional outcomes, as children are
likely to fail to take advantage of their positive social context. In contrast, a protective advantage
would emerge when children who have difficulty adjusting socially are not aware of their
shortcomings. Thus, we hypothesize that for children who experience problematic peer relations,
poor meta-accuracy may act as a buffer, protecting children from emotional distress.
Social Experience and Emotional Functioning
Children's peer relationships serve as a context for the formation and refinement of
adaptive behavior and regulation of emotions (Eisenberg, Fabes, Guthrie, & Reiser, 2000;
Hubbard & Coie, 1994; Kim & Cicchetti, 2010). These relationships provide youths with
validation and information that fosters self-concept formation and self-esteem development
(Harter, 1999; Harter, Stocker, & Robinson, 1996). Indeed, research has demonstrated the
6
importance of children’s social environment for emotional adjustment (e.g., Kingery, Erdley, &
Marshall, 2011).
The present study examines the extent to which children are liked by their peers (i.e., peer
liking) as a proxy of children’s broad experiences in the peer group. For the past three decades,
peer liking and disliking (sometimes termed peer acceptance and rejection) have been among the
most widely studied interpersonal functioning indicators in the social development literature
(Rubin et al., 2006). Children who are highly disliked by their peers are at a heightened risk for
internalizing difficulties including depressed mood and loneliness (Lopez & DuBois, 2005;
McDougall, Hymel, Vaillancourt, & Mercer, 2001; Pedersen, Vitaro, Barker, & Borge, 2007)
and are often the target of peer mistreatment (Buhs & Ladd, 2001; Hodges & Perry, 1999). In
contrast, children who are well-liked by their classmates are usually socially competent and tend
to display low levels of dysphoria and few somatic complains (Kim & Cicchetti, 2010; Kistner,
Balthazor, Risi, & Burton, 1999).
Although there is evidence to suggest that negative affective judgments made by peers
represent a problematic relational context with important implications for emotional functioning
(Pedersen et al., 2007), research has indicated that elementary school children show significant
variability in their developmental trajectories in the face of peer dislike (e.g., Lopez & DuBois,
2005; Prinstein, Borelli, Cheah, Simon, & Aikins, 2005). The current investigation seeks to
understand why some children manage to navigate these negative experiences in adaptive ways,
whereas others are likely to endure significant emotional distress. A number of studies have
investigated the importance of social cognition for appropriate social behavior (see Crick &
Dodge, 1994; Heleniak & McLaughlin, 2019; Quan et al., 2019; Rubin & Rose-Krasnor, 1992).
Nonetheless, existing models fail to consider the role of meta-evaluations as a moderator of the
7
link between peer experiences and emotional adjustment. We hypothesize that, beyond their
direct effects on emotional functioning, children’s interactions with peers represent a key
interpersonal context, which, in combination with social cognitive interpretations, may either
contribute to or serve a protective function against adjustment problems. Consistent with
research on social-cognitive impressions, the present study investigates the moderating role of
two types of affective meta-accuracy (i.e., dyadic meta-accuracy for liking and disliking). Poor
social perceptions in affective domains have been linked to important socioemotional outcomes
during the elementary school years (Bellmore & Cillessen, 2003; Cillessen & Bellmore, 1999;
Kistner et al., 2006).
Meta-Accuracy and Related Social-Cognitive Skills
Insofar as meta-accuracy is predictive of children’s social and emotional adjustment, it is
important to understand how core social-cognitive factors might be related this unique perceptual
skill. Research has proposed links between deficits in social understanding, broader patterns of
cognitive function, and problematic peer relations (Hay, Payne, & Chadwick, 2004). These
associations, however, have rarely been empirically tested. To our knowledge, only one study
has examined the association between meta-accuracy and social-cognitive skills. Boor-Klip and
colleagues (2014) reported that theory of mind in children grades 4-6 was positively related to
liking meta-accuracy. In contrast, the authors did not find an association between accurate meta-
perceptions and cognitive ability (as indexed by a composite of abstract reasoning and academic
achievement). The present study addresses this gap in the literature by investigating how social-
reasoning abilities are associated with meta-accuracy. Specifically, we examine how theory of
mind, aspects of executive functions (i.e., shifting, attention, working memory), and self-
perceptions are related to this sophisticated perceptual ability.
8
Theory of Mind. Research on cognitive perspective-taking (i.e., capacity to demonstrate
awareness of informational states in oneself and others), and more specifically, theory of mind
(i.e., knowledge that each person has their own beliefs, desires, and emotions; Baron-Cohen,
Leslie, & Frith, 1985; Premack & Woodruff, 1978), has burgeoned in recent years, bringing to
light some important insights about the development of social cognition (e.g., Bianco, Lecce, &
Banerjee, 2016). An understanding of the mental world typically emerges during infancy (Yott &
Poulin-Dubois, 2016). Between the ages of three and five years old, normally developing
children display dramatic improvements in their ability to recognize that others have mental
states that may be distinct from their own (Flavell, 2004; Yirmiya, Erel, Shaked, & Solomonica-
Levi, 1998). Theory of mind continues to improve throughout late adolescence and even into
early adulthood (e.g., Dumontheil, Apperly, & Blakemore, 2010; Keysar, Lin & Barr, 2003).
The ability to make inferences about others is a crucial component of social skills.
Rehfeldt, Dillen, Ziomek, and Kowalchuk (2007) proposed that perspective-taking abilities
might contribute greatly to an individual’s success in social situations. The capacity to
successfully engage in reciprocal interactions (e.g., turn-taking) and to demonstrate empathy for
others requires considerable levels of perspective-taking (Baron-Cohen et al., 1985). Thus, in
order to reason about peers, make inferences about how they think or feel, and predict or explain
their behavior, children have to be able to put themselves into their peers’ shoes. Consistent with
this view, studies of young children have described associations between social understanding
and positive social skills (e.g., Watson, Nixon, Wilson, & Capage, 1999). It follows then that
when children display deficits in theory of mind, they will likely also experience difficulty
making correct inferences about their peers’ impressions of them.
9
Executive Functions. Difficulties with comprehending others’ perspectives may also
occur because of limitations in executive functioning, the cognitive control of thoughts and
behavior (Flavell, 2004). Associations have been found between metacognitive and executive
control functions (Fernandez-Duque, Baird, &Posner, 2000; Roebers, Cimeli, Röthlisberger, &
Neuenschwander, 2012), with researchers arguing that the ability to accurately determine others’
perceptions could be compromised by executive dysfunction (Owens et al., 2007). Research has
also consistently demonstrated the influence that social interaction exerts over executive
functioning during childhood, with more recent evidence suggesting that executive functions can
facilitate cognitive skills that are important in social interaction as well (see Moriguchi, 2014 for
a brief review).
Broadly, executive functions refer to the ability to control, direct, or coordinate cognitive
processes in order to execute and/or inhibit purposeful, goal-directed behavior (Gioia, Isquith, &
Guy, 2001; Lee, Bull, & Ho, 2013; Moriguchi, 2014). This theoretical term has been widely
utilized in cognitive and neuroscience research, yet there is considerable disagreement about how
to best conceptualize it (Welsh, Friedman, & Spieker, 2006). One of the central divergences,
which becomes particularly salient in childhood, concerns its factor structure (Anderson, 2002;
Lehto, Juujärvi, Kooistra, & Pulkkinen, 2003). Developmental researchers often uncover a range
of factors (between one and four), which at times overlap, and at other times do not (Anderson,
2002; Messer et al., 2018). Despite divergent theories and conceptualizations, most definitions
consider executive functioning processes to be multidimensional and to encompass a variety of
correlated but distinct skills, including switching, attentional control, and working memory
(Anderson, 2002; Jacobson, Williford, & Pianta, 2011; Miyake, Friedman, Emerson, Witzki,
Howerter, & Wager, 2000). The present study carefully investigates how each of the three
10
aforementioned aspects of executive functioning are related to meta-accuracy. Below, we briefly
overview each skill and outline what is known about its relation to social perception and
reasoning.
Switching. For effective social communication, individuals have to think flexibly:
dividing their attention, processing multiple sources of information concurrently, and alternating
between sources when necessary (e.g., the self and the other). Consider, for example, how in
order to form a meta-perception, a child must effortfully inhibit the natural and automatic
tendency to assume that others share their perspective (Fenigstein & Abrams, 1993) and adopt
the perspective of the others instead (Ruby & Decety, 2003). This child will have to move
between alternating sets of mental operations (i.e., think about how the other sees me and how it
is different than how I see myself) in order to make an inference, an executive ability known as
switching (an aspect of cognitive flexibility; Lee, Bull Ho, 2013, Miyake et al., 2000). Although
no studies have directly investigated the role of switching capacity in relation to perception
accuracy, links between cognitive flexibility and perspective taking have been suggested (Riggs,
Jahromi, Razza, Dillworth-Bart, & Mueller, 2006). Thus, the present study will be the first to
carefully examine the association between children’s switching ability and meta-accuracy.
Attention. In order to correctly understand other’s impressions and intentions, individuals
must perceive and attend to the available social cues, both verbal and non-verbal (Bodenhausen
& Hugenberg, 2009). Whereas perception is generally believed to be the first step in social
cognition, attention is commonly considered the first step in perception (Bodenhausen &
Hugenberg, 2009). Attention control refers to the ability to selectively attend to specific stimuli
and focus for a period of time, while regulating/monitoring actions and inhibiting prepotent
responses (Anderson, 2002). Indeed, attention deficits (e.g., joint attention) are thought to
11
underlie some of the key social deficits observed in children with autism, such as the ability to
understand that others have mental states that might be different from one’s own (Langton, Watt,
& Bruce, 2000; Mundy & Sigman, 2015; Tomasello, 1995). Impairments in attention have also
been linked with difficulties in self-regulation (Anderson, 2002). Despite the relevance of
attention for perception formation, the extant literature has not considered the implications of
attentional capacity for meta-accuracy. Thus, the present study investigates potential associations
between attention control and meta-accuracy.
Working Memory. Before forming an impression, an individual has to integrate
information that was previously known with information obtained during a given social
interaction, infer underlying intentions behind others’ acts, and decide how to evaluate others’
and their attitudes towards the individual. Working memory is likely to influence all of these
steps, through the processes of behavioral identification (i.e., organizing behavioral actions such
as muscle movements into a recognizable behavioral act, such as a smile), attributional inference
(i.e., inferring the cause of the identified behavioral acts), and correction (i.e., forming an
impression about others while considering alternatives for behaviors, such as situational causes;
see Barrett, Tugade & Engle, 2004). Working memory is conceptualized as an individual’s
ability to maintain and manipulate information that is relevant to achieving a particular goal
(Baddeley & Hitch, 1974). To our knowledge, research on meta-accuracy has largely ignored the
potential relevance of this construct for perception formation. This will be the first known study
to consider the role of working memory for meta-accuracy.
Self-Perceptions of Social Competence. In an effort to further explore the link between
related perceptual phenomena and meta-accuracy, the present study also examines self-
perceptions in our predictive models. Self-perceptions can be defined as the impressions that an
12
individual has of his or her own ability in a given domain. Self-perceptions differ from meta-
perceptions in that no inferences about others’ opinions are made, that is, the individual is
typically not required to look outside of the self to gather information about his levels of ability
or performance. Insofar as we are aware, no studies have taken this approach in the past.
Nonetheless, previous research with adults has found that self- and meta-perceptions tend to be
highly correlated (Kenny & DePaulo, 1993), suggesting that they may be partially overlapping
constructs. Although recent evidence suggests that self- and meta-perceptions are distinct
concepts (Carlson & Furr, 2009; Carlson, Vazire, & Furr, 2011), this proposition remains a
controversial point in meta-accuracy research, and is therefore worthy of exploration.
Accounting for Individual Differences in Meta-Accuracy
Extant research has not directly investigated when the ability to accurately infer another
person’s feelings emerges and solidifies. Older children typically have better metacognitive skills
than younger children (Mohamed, 2012), although there are individual differences in when
children become able to accurately infer another person’s knowledge and intentions. Research
examining the effects of age on meta-accuracy has produced somewhat inconsistent findings;
with some studies reporting that older children display more accurate perceptions than their
younger peers (Boor Klip, et al., 2014; Kistner et al., 2006; Malloy, Albright, & Scarpati, 2007),
and others failing to demonstrate such association (Brendgen et al., 2004; Cleary, Ray, LoBello,
& Zachar, 2002). The present study includes age as a covariate in our analyses, due to the
potential relation between age and meta-accuracy, as well as the well-documented effects of age
on social-cognitive performance tasks (Happé, 1995; Salthouse, 2011).
A large body of literature has also examined gender differences in meta-accuracy and
socioemotional functioning. There is some indication that girls are more accurate than boys in
13
perceiving liking by peers (Cillessen & Bellmore, 1999) and evaluating friendships (Badaly et
al., 2012; Brendgen et al. 2004; Morrow et al., 2015); nonetheless, some studies have failed to
document gender differences in meta-accuracy (David & Kistner, 2000; Kistner et al., 2006,
2007). Girls are also known to experience depressive symptoms at a higher rate and are
diagnosed with clinical depression more often than boys (Essau, Lewinsohn, Seeley, &
Sasagawa, 2010; Hankin & Abramson, 2001; Salk, Hyde, & Abramson, 2017). In addition,
significant gender differences have been observed in children's social goals (e.g., Rose & Asher,
2004), with girls placing greater value on peer liking and close friendships than boys
(Oldehinkel, Rosmalen, Veenstra, Dijkstra, & Ormel, 2007; Rose & Rudolph, 2006). As such,
girls have been found to be particularly vulnerable to emotional distress related to social stressors
(Hankin, Mermelstein, & Roesch, 2007). Given the aforementioned associations, the present
study accounts for the effects of gender in models examining meta-accuracy as a moderator.
Finally, we recognize that the association between cognition (especially aspects of
executive functions) and academic ability has been well-established (e.g., Deary et al., 2007;
Jarvis & Gathercole, 2003; Thorell, Veleiro, Siu, Mohammadi, 2013). For example, basic
reading and sequencing abilities are an essential component of many instruments commonly used
to assess cognitive functioning (Bowie & Harvey, 2006; Happé, 1995). Because of its potential
to influence performance in social-cognitive tasks, the current study adjusts for the effects of
academic ability in models examining the association between social-cognitive abilities and
perceptual accuracy.
The Current Study
The central goal of the present study is to examine the moderating role of meta-accuracy
in the association between social experience and emotional adjustment. We evaluate the
14
interaction between broad affective reactions by the peer group (i.e., peer ratings of liking) and
two types of meta-perception accuracy (i.e., liking and disliking). We hypothesize that children
who experience social difficulties and have high meta-accuracy will experience increased
emotional suffering (as indexed by depressive symptoms and anxiety) as compared to children
who experience social difficulties but are unaware of their shortcomings. In contrast, we expect
that children who are well adjusted socially will derive benefits from displaying high levels of
meta-accuracy, experiencing low levels of emotional difficulties as a consequence. We also
explore the relation between meta-accuracy and core social-cognitive skills (theory of mind,
aspects of executive functioning and self-perceptions). We predict that meta-accuracy will be
significantly associated with theory of mind and self-perceptions. We don’t have a priori
hypotheses with regard to the other cognitive skills, given the dearth of research in this area.
Method
Participants
The current study was completed in collaboration with two public elementary schools
located in the Los Angeles metropolitan area. In the spring of 2018, 239 third, fourth, and fifth
graders attending a K-6 school were invited to take part in the study. Of these, 109 students
received positive parental consent and provided assent to participate. Four students were absent
during data collection, resulting in a sample of 105 students (40 third graders, 38 fourth graders,
and 37 fifth graders). The ethnic/racial composition of the sample was 81% White, 6.7%
Asian/Pacific Islander, 4.8% Latino/Hispanic, 1.9% African American, 3.8% mixed and 1.9%
not classified. During the period of data collection the median household income was
approximately $107,000 (U.S. Census Bureau, 2010), with less than five percent of individuals
in the surrounding community living below the poverty line. Few students at this elementary
15
school (15%) qualified for free or reduced-cost meals (California Department of Education,
2018). The majority of the students met standards for Language Arts/Literacy and Mathematics
(about 71% and 57%, respectively), and fewer than 8% were classified as English Language
Learners (ELL; EdData, 2018).
The subsequent fall, an additional 130 third grade students attending a computer science
immersion program (grades K-3) were invited to take part in the project. A total of 42 students
returned positive parental consent and assent and were also present the day of data collection.
The ethnic/racial composition of the sample was 76.2% Asian, 9.5% White, 2.4%African
American, 2.4% Latino/Hispanic, 2.4% American Indian, and 7.1% mixed. During the period of
data collection twelve percent of individuals in the surrounding community lived below the
poverty line, and the median household income was approximately $70,000 (U.S. Census
Bureau, 2010). About 40% of students were eligible for free or reduced-price meals (California
Department of Education, 2018). The majority of students at this school met or exceeded grade
level standards in Language Arts/Literacy and Mathematics (63.4% and 75%, respectively) and
31.8% were classified as ELL (EdData, 2018).
Analyses for the present study were based on 147 participants, across 14 classrooms
(average classroom size 26.4; range 20–34), who had full data for some or all of the items
relevant to the analyses reported (Mage = 9.37 years, SD = 1.12, range 8-12 years; 40.8% female).
The ethnic composition of this subgroup was 60.5% White, 26.5% Asian, 4.1% Latino/Hispanic,
2.0% African American, 0.1% American Indian, 4.8% mixed, and 1.9% not classified. Nineteen
students in the subsample (12.9%) were classified as ELL.
16
Procedure
Trained graduate and undergraduate researchers group-administered measures at the end
of the semester during which the participants were recruited. Three to six administrators were
assigned to each classroom based on the number of participating students (maximum ratio: 1
administrator per 8 students). The lead administrator read standardized instructions out loud and
the remaining administrators walked around and answered students’ questions. Testing sessions
lasted approximately 75 minutes. Participants first completed cognitive performance measures.
In order to ensure familiarity with cognitive tasks, demonstration items were printed in large
posters that were displayed in front of the classroom. The lead administrator explained the rules
for the tasks and guided students as they completed sample items. Remaining administrators
answered students’ questions and provided corrective feedback as needed. Next, participants
completed a peer nomination inventory assessing affective feelings toward peers and impressions
of peers’ perceptions. This inventory was followed by self-report questionnaires of emotional
functioning. At the conclusion of the school year, participants’ date of birth, race/ethnicity,
grades, statewide standardized achievement test scores, absences, and disciplinary records were
obtained from the school. This project was conducted in compliance with the ethical standards of
the American Psychological Association and was approved by the university’s Internal Review
Board (IRB # UP-17-00597 “Peer Experiences and Academic Success Project”).
Measures
Predictor: Social experience. We assessed social experience using a peer nomination
inventory (see Appendix A). Children were provided with a roster of students from their class
who also consented to participating in the study. Students were asked to rate how much they
liked each peer on a 1-to-5 scale (“don’t like that much” to “like a lot”), with higher ratings
17
indicating greater liking. The ratings were summed and divided by the number of raters,
generating a total peer liking score for each child. Peer-nomination and rating approaches have
been widely used and validated in the peer relations literature (e.g. Schwartz, Gorman, Dodge,
Pettit, & Bates, 2008; Schwartz, Gorman, Nakamoto, & McKay, 2006).
Hypothesized moderators: Meta-accuracy of liking and disliking. Using the same
roster list described above, we assessed peer perceptions of liking and disliking. Students were
asked to nominate an unlimited number of peers from this roster that fit certain descriptors. Items
queried liking by peers (i.e., “students that you really like”) and peer disliking (i.e., “students that
you don’t like that much”). Each child was also asked to indicate which specific peers from the
aforementioned roster nominated the child as someone they like or dislike (dyadic meta-
perceptions; see Appendix B). The values obtained for peer perceptions and dyadic meta-
perceptions were evaluated for agreement using Cohen’s κ (Badaly et al., 2012; Bellmore &
Cillessen, 2003). Matrices of actual and perceived choices were compared, and a 2 X 2
correspondence table was constructed for each child. The table contained the number of hits (i.e.,
peers that the child thought nominated him and actually nominated him), false alarms (i.e., peers
that the child thought nominated him, but did not nominate him), misses (i.e., peers that the child
did not think nominated him, but actually nominated him), and correct rejections (i.e., peers that
the child did not think nominated him and did not nominate him). Chance agreements were
corrected based on marginal frequencies. Although past research has generated meta-accuracy
scores utilizing a variety of different statistics (e.g., correlations, mean discrepancy, sum of
discrepancy scores; Ausubel & Schiff, 1955; Boor-Klip et al., 2014; Bruinikins, 1978), kappa
scores have been considered the least biased estimators of perception accuracy (Cillessen &
Bellmore, 1999).
18
Covariate: Theory of mind. Theory of mind was assessed using two instruments. The
Reading the Eyes in the Mind task (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001; see
Appendix C) assessed perceptual aspects of theory of mind, namely, children’s ability to identify
what a person is thinking or feeling based on a picture of their eyes. This 28-item test has been
adapted from an adult version and has been validated for use with children (Baron-Cohen et al.,
2001). After completing a sample item guided by the lead examiner, children were allowed to
pace themselves for the rest of activity. No time limits were imposed for the activity. The
number of correct responses on the Reading the Eyes in the Mind task was summed and then
divided by the number of items, creating a mean score for each participant. The internal
consistency of the scale in the present study was high (α = .98).
The Strange Stories measure (Fletcher et al., 1995) tapped cognitive aspects of theory of
mind through examination of a series of vignettes in which the participant was expected to
attribute lower and higher order mental states to the characters of a story. This measure has been
shown to validly capture theory of mind difficulties in elementary school children (White,
Happé, Hill, and Frith, 2009). Children were given a set of four stories. Three stories assessed
mental state reasoning, and one three-part comprehension story developed from the stories in
White et al. (2009) controlled for comprehension ability (see Appendix D). Answers to
mentalizing stories were scored 0 (irrelevant or incorrect answers), 1 (references to traits or facts
without mention of thoughts or feelings), or 2 (references to mental state reasoning), according to
a key provided by White and colleagues (2009). Similarly, responses to the comprehension story
questions were scored 0 (irrelevant or incorrect answers), 1 (partially correct answers), or 2 (fully
correct answers). Two highly trained undergraduate research assistants scored participants’
answers independently. Interrater reliability was substantial (Mentalizing Story 1 κ = .80;
19
Mentalizing Story 2 κ = .70; Mentalizing Story 3 κ = .81; Comprehension Story Answer 1 κ =
1.0; Comprehension Story Answer 2 κ = .82; Comprehension Story Answer 3 κ = .68).
Discrepancies between coders were discussed with the lead researcher until agreement was
reached. To increase construct validity, a score on the Strange Stories task (average score across
the three mentalizing items) was generated only for students who obtained a mean score of one
or greater on the comprehension questions (N=116; 79% of the students who had complete
scores on the task).
In order to construct a composite theory of mind score, the average scores the participants
obtained in the Eyes in the Mind measure and the Strange Stories task were first standardized,
with a mean of 0 and a standard deviation of 1. Next, a composite score was computed by
calculating the mean of the standardized scores.
Covariate: Executive functions. Students completed three cognitive tasks tapping
different aspects of executive function (switching, working memory, and attention). Instructions
for the tasks were adapted in order to fit group administration and were read aloud to participants
by the lead examiner. Participants were given an opportunity to complete sample items to
practice the tasks and to ask questions. Although tests of cognitive skills are generally
administered on an individual basis, previous work with adults demonstrated the validity of
administration in a group format (Nation, Bondi, Gayles, & Delis, 2017). Before data collection,
the materials and procedures were piloted with 75 children in grades three to five and were
adapted/refined as needed.
Switching. Trail Making Test - Black & White (TMT-B&W; Kim, Baek, & Kim, 2014;
see Appendix E) is a measure of cognitive flexibility and inhibition in which the child shifts from
connecting numbers in ascending order (through 25; Part A) to sequencing numbers while
20
alternating between color sets (e.g., from white one, to black two, to white three and so forth;
Part B). The reliability and validity of the measure has been demonstrated in a study evaluating
the usefulness of the TMT-B&W as a neuropsychological assessment tool for individuals with
cognitive impairment (Kim et al., 2014). The TMT-B&W overcomes important shortcomings of
other trail making tests (e.g., utilization of alphabet sequencing which puts those with lower
education levels or non-English native speakers at a disadvantage), minimizing cultural bias and
increasing validity (Kim et al., 2014). Children were given 45 seconds to complete Part A of the
task and 60 seconds to complete Part B. “Switching cost” was calculated by subtracting the
number of correct lines linked in Part B from the number of corrects links in Part A of the task.
As such, higher scores (larger discrepancies) are indicative of poorer switching. Total number of
self-corrected (children were instructed to correct mistakes by crossing out wrong links) and
uncorrected errors were also computed for the switching portion of the task. Switching cost, self-
corrected errors, and uncorrected errors were standardized and a composite switching score was
generated by calculating the mean of the standardized scores. A high value on the switching
composite variable indicates switching difficulties.
Attention. The Digit Vigilance Task (Lewis, 1995) measured vigilance, sustained
attention, and accuracy during rapid visual tracking. Children were asked to cross out as many
number 6s as they could find in a page with numbers 1 to 9 arranged randomly in rows (30 digits
per row and 50 rows per sheet; see APPENDIX F). If they made a mistake, there were
encouraged to draw a circle around the number they mistakenly crossed out and to proceed with
the task, as quickly as possible. Students completed two trials of 60 seconds. Total number of
correct responses and errors were averaged across both trials. Given the low average number of
self-corrected (M=.04, SD=.14, range 0-1) and uncorrected (M=.01, SD=.06, range 0-.5) errors in
21
the present study, these variables were not included in any of the subsequent analyses. Sustained
attention was thus, indexed solely by the average number of correct responses.
Working Memory. The California Verbal Learning Test – Children’s Version (CVLT-C;
Delis, Kramer, Kaplan, & Ober, 1994) assessed working memory in children within the context
of an everyday shopping task. Children were read aloud a 15-item list (see APPENDIX G) and
were given ample time (90 seconds, of which they typically used less than 60 seconds) to write
down the words they remembered from the list immediately after presentation). The CVLT-C
demonstrates adequate test–retest reliability and internal consistency, as well as strong construct
validity (Delis et al., 1994). The total number of recalled words and the total number of errors the
child made were computed and used as variables. A composite working memory score was
generated by subtracting the total number of errors from the total number of correctly recalled
words. Although children were also asked to recall the shopping list after a 20-minute delay as
well as recognize the words on the list among a series of distractor words, performance on these
components of the task were not included in the working memory composite variable. A study
examining the factor structure of the CVLT-II among adolescents found that the immediate
recall portion of the task loads on a distinct factor then the delayed recall and recognition
portions of the task and should thus be treated as a separate construct (Donders, 2008).
Covariate: Self-perceptions of social competence. Self-perceptions of social
experiences were assessed using the 6-item Social Competence scale of the Self-perception
Profile for Children (Harter, 2012; see Appendix H). Children were asked to indicate which of
two statements (e.g., “Some kids find it hard to make friends”; “Other kids find it pretty easy to
make friends”) applied to them and to what extent (i.e., “Sort of true for me”; “Really true for
me”). Items were scored on a scale from 1-4, with higher values indicating more positive self-
22
perceptions. Items were then averaged to create a single self-perception score. The psychometric
adequacy of this scale has been demonstrated in children grades three to eight, from lower
middle class to upper middle class backgrounds (Harter, 2012). The internal consistency of the
scale in the present study was high (α = .98).
Covariate: Academic ability. At the end of each academic year students completed the
California Assessment of Student Performance and Progress (CAASPP), a computer adaptive
test designed to estimate mastery of core state standards for English language arts (ELA) and
mathematics, and to assess students’ ability to integrate knowledge and skills. Scaled scores for
grades three to five range between the low to the mid 2000s, and increase with each grade level
(i.e., 2114-2623 in grade three, 2131-2663 in grade four, and 2201-2701 in grade five). Because
the range of scores that fall within each category depends on grade level (i.e., a student who
earned a score of 2470 in third grade will be classified as meeting standards, whereas a student
who earned the same score in fifth grade will be classified as nearly meeting standards), we
utilized achievement levels to index academic achievement. Achievement levels are calculated
by CAASPP utilizing a standard-setting process, and range from 1 (“Standard not met”) to 4
(“Standard Exceeded”). In addition to the theoretical reasons outlined earlier, achievement levels
in ELA and math were used as indicators of academic ability and entered as covariates in our
models in order to account for possible deficits in reading and sequencing ability, which were
necessary to complete some of the cognitive tasks administered in the present study.
Outcome: Emotional adjustment. Depressive symptoms were assessed using the
Children’s Depression Inventory (CDI; Kovacs, 1985; see Appendix I), a 27-item self-report
measure that assesses the severity of depressive symptoms in children and adolescents. Per IRB
guidelines, one item assessing suicidality was excluded, resulting in a 26-item measure. Items
23
were rated on a 3-point scale from 0-2, with higher scores indicating greater symptom severity.
Items were averaged to create a mean depressive symptoms score (α = .98). Anxiety levels were
evaluated using a short version of the Revised Children’s Manifest Anxiety Scale (RCMAS-2;
Reynolds & Richmond, 2000; see Appendix J), a 10-item self-report measure designed to assess
the level and nature of anxiety problems in children. Children answered Yes or No to indicate if
each outlined symptom was consistent with their experience. Items were averaged to create a
mean anxiety score (α = .98).
Results
Univariate Analyses and Bivariate Relations
Before beginning inferential analyses, we examined variable distributions graphically and
with descriptive statistics. Although not normally distributed (Shapiro-Wilk tests of
distributional adequacy p<.0001 for nearly all variables), most variables had skewness and
kurtosis values between -1 and 1. As might be expected in a community sample, children tended
to endorse low levels of depressive symptoms, with only 12.5% of the sample surpassing the
recommended cutoff score for depressive symptoms (guidelines suggest a mean > .70 in
community-based samples; Birmaher et al., 1996; Comer & Kendall, 2005). Similarly, around
10% of the sample scored in the moderately problematic or extremely problematic range of
anxiety symptoms (10.5% of children aged 8 years and 10.8% of children aged 9 or older;
Reynolds & Richmond, 2000). Means and standard deviations for study variables (including
variables that were used to generate composite scores) are summarized in Table 1. A series of
independent samples comparisons revealed that girls earned higher scores on all theory of mind
tasks, and displayed overall better switching abilities and working memory. Significant gender
differences were not found for the study predictor, moderators, or outcomes.
24
A closer examination of meta-accuracy variables revealed that, on average, children were
significantly accurate in perceiving whom they believed identified them as someone they liked,
t(139) = 10.29, p < .0001, and in identifying who nominated them as someone they disliked,
t(133) = 4.24, p < .0001, at a better than chance level for an alpha of .05. Students were more
accurate perceivers of liking than of disliking t(129) = 5.02, p < .0001. Additional information
regarding mean proportions for accuracy across indicators of perception match/mismatch is
detailed in Table 2.
Table 3 presents bivariate correlations for study variables, including age (in months).
Children who were better liked by peers perceived themselves to have higher levels of social
competence and displayed lower levels of depressive symptoms. Children who reported higher
levels of anxiety also indicated experiencing more depressive symptoms and reported lower self-
perceptions of social competence. Younger children were better liked, and were less accurate in
identifying which peers nominated them as a liked individual. Older children displayed better
theory of mind, attention, and working memory. Social-cognitive variables were interrelated.
Children who had higher levels of theory of mind were higher in liking meta-accuracy, and had
more positive self-perceptions of competence and higher working memory compared to students
with lower levels of theory of mind. There was also a positive association between working
memory and attention. No association was observed between meta-accuracy for disliking and
any of the study variables. Finally, children who earned higher scores in standardized tests of
English and Language Arts (ELA) were better liked by peers and experienced lower levels of
depressive symptoms. Performance on the standardized ELA test was also positively associated
with theory of mind and self-perceptions. Performance on ELA and mathematics tests was highly
intercorrelated.
25
Meta-Accuracy as a Moderator of the Link between Social Experience and Emotional
Functioning
To examine the role of meta-accuracy as a moderator of the association between social
experience and emotional functioning, we conducted a series of regression analyses. Each
outcome (i.e., depressive symptoms and anxiety symptoms) was predicted from the main effects
of peer liking, meta-accuracy, gender, and age (entered on Step1), and the two-way interactions
between peer liking and meta-accuracy (entered on Step 2). The process was conducted
separately for each type of meta-accuracy (i.e., liking meta-accuracy and disliking meta-
accuracy). Variables were entered simultaneously at each step, and the steps were entered
sequentially. Interaction terms were calculated based on mean centered values (Aiken & West,
1991).
The prediction of depressive symptoms from social experience with meta-accuracy of
liking and disliking as moderators is summarized in Table 4. The overall models were
significant, with F(4, 135) = 2.69, p < .05, R
2
= .07 for liking meta-accuracy and F(4, 129) =
3.67, p < .01, R
2
= .10 for meta-accuracy of disliking. Peer liking was significantly predictive of
depressive symptoms on Step 1 in both models, with moderate effect sizes. Neither type of meta-
accuracy was associated with depressive symptoms. No significant two-way interactions were
found on Step 2, with the overall model reaching significance for disliking meta-accuracy, F(5,
128) = 2.21, p < .05, R
2
= .11, but not for meta-accuracy of liking. In analyses predicting anxiety,
a significant two-way interaction was found for peer liking by meta-accuracy of liking (see Table
5). No moderated effects emerged for peer liking by disliking meta-accuracy. The full models
were not significant.
26
To decompose the two-way interaction reported above, we adhered to Aiken and West’s
(1991) recommendations. We algebraically fixed meta-accuracy of liking at high (one SD above
the mean), medium (the mean), and low (one SD below the mean) cutoffs. We then examined
associations between peer liking and symptoms of anxiety at each level of the moderator. Peer
liking was negatively associated with anxiety symptoms at high levels of liking meta-accuracy (β
= -0.30, p < 0.05, sr
2
= 0.04). The corresponding effects did not approach significance at
medium (β = -0.13, ns, sr
2
= 0.02) or low (β = 0.03, ns, sr
2
= 0.00) levels of meta-accuracy of
liking.
Meta-Accuracy and Social-Cognitive Skills
In order to explore the link between social-cognitive abilities (including academic
achievement) and meta-accuracy, we conducted two regression analyses in which social-
cognitive skills predicted each type of meta-accuracy separately (see Table 6). Age was included
as a covariate in each of our models. Self-perceptions and age were significantly predictive of
liking meta-accuracy. There was also a marginal association between theory of mind and meta-
accuracy of liking. None of the skills entered in the model were predictive of meta-accuracy of
disliking. The overall model predicting liking meta-accuracy was significant F(8, 126) = 2.47, p
< 0.05, R
2
= 0.1356, whereas the model predicting meta-accuracy of disliking was not.
Discussion
Although substantial attention has been devoted to the study of social cognition during
childhood (e.g., Carpendale & Lewis, 2004; Mayeux & Cillessen, 2003; Sharp, Fonagy, &
Goodyer, 2008), few studies have carefully examined the implications of meta-accuracy of
social competence (the ability to correctly identify others’ perceptions of one’s social ability;
Kenny & DePaulo, 1993; Malloy et al., 2007) for emotional functioning. Collectively, these
27
studies suggest that meta-accuracy influences youths’ social behavior, self-concept formation,
and self-esteem (Bellmore & Cillessen, 2006; MacDonald & Cohen, 1995; Zakriski & Coie,
1996). Extant research has conceptualized meta-accuracy as a predictor of psychosocial
difficulties, without taking into account the importance of children’s social context (e.g.,
Bellmore & Cillessen, 2003; Kistner et al., 2006; Schiff, 1954). The present study was the first to
consider the nuances of meta-accuracy by investigating a model in which social experience and
meta-accuracy interact to predict emotional adjustment. This project also examined the
association between related social-cognitive skills (theory of mind, aspects of executive
functions, self-perceptions) and meta-accuracy.
Meta-accuracy did not moderate the association between social experience and the
indicators of emotional adjustments in most of our analyses. However, there was one notable
exception. For children with high levels of meta-accuracy, having more positive social
experiences was associated with fewer anxiety symptoms. As hypothesized, meta-accuracy of
liking was significantly associated with self-perceptions and marginally associated with theory of
mind, highlighting the potential influence of self-concept and mentalizing abilities in the
development of this unique type of meta-perception. Taken together, the results of this study
provide preliminary support for the moderating role of meta-accuracy, but suggest that an active
consideration of how research methodology might have contributed to our findings is warranted
in order to clarify the implications of meta-accuracy for emotional development.
Meta-Accuracy of Social Competence during the Elementary School Years
Research on self-other agreement abounds in the extant literature (e.g., Hoffman, Cole,
Martin, Tram, & Seroczyinski, 2000), yet comparatively little is known about meta-perception
accuracy. As such, we began the current study by attempting to replicate previous findings
28
regarding meta-accuracy. Consistent with past research utilizing kappa scores as an index of
accuracy (e.g., Cillessen & Bellmore, 1999), we found that elementary school children are able
to correctly determine which peers like and dislike them at a better-than chance level. This
finding builds on a body of research utilizing other, less stringent operational definitions of meta-
accuracy (e.g., mean discrepancy; Boor-Klip et al., 2014), and suggests that children are able to
make accurate discriminations of their peers’ perceptions, even when accounting for chance.
Children in our sample were more accurate perceivers of liking than of disliking, a finding
compatible with past work (Bellmore & Cillessen, 2003). Finally, similar to previous research
(Bellmore & Cillessen, 2003), we found that older children were more accurate in identifying
peers that nominated them as someone they liked (although this did not hold for nominations of
disliking). Bellmore and Cillessen (2003) suggested disliking accuracy scores tend to be low in
general, which may explain why we failed to find significant age differences with regard to
disliking meta-accuracy.
The Role of Meta-Accuracy as a Moderator
Consistent with our hypothesis, when children displayed high levels of liking meta-
accuracy, peer liking was negatively associated with anxiety symptoms. That is, when children
experienced social difficulties and were capable of identifying those peers who liked them (thus
demonstrating an awareness of their low levels of acceptance), they experienced increased
anxiety. The association between peer liking and anxiety symptoms was not significant at
average and at low levels of accuracy, suggesting that when children are only somewhat aware or
mostly unaware of their shortcomings, their social experience is not associated with symptoms of
anxiety. On the other hand, when children are well adjusted socially and are aware of the positive
perceptions peers have of them, they experience lower levels of anxiety.
29
It is not immediately clear why meta-accuracy of liking interacted with social experience
to predict anxiety symptoms, but not symptoms of depression. It is widely accepted that youth
depression tends to occur within an interpersonal context (Kochel, Ladd, &Rudolph, 2013;
Rudolph, Flynn, & Abaied, 2008). In contrast, social factors are not necessarily incorporated in
theories of anxiety (with the exception of literature on social anxiety; Starr & Davila, 2008).
Nonetheless, it is noteworthy that about half of the items included in the measure of anxiety
utilized in this study referenced interpersonal challenges that youth may experience (e.g., “I
worry that others do not like me”, “I fear other kids will laugh at me in class”; see Appendix J
for details), whereas fewer than a quarter of the items included in our measure of depressive
symptoms tapped relational problems (e.g., “I do not want to be with people at all”, “I do not
have any friends”; see Appendix I). As such, it is possible that the children only experienced
emotional distress to the extent that their suffering was related to their perceptions of social
competence. It will be important for future research to carefully account for differences in
assessments of emotional functioning (e.g., emphasis in relational challenges) when examining
the impact of interpersonal cognitions and stressors on emotional well-being.
We also found that whereas meta-accuracy of liking moderated the hypothesized
association, disliking meta-accuracy did not. Meta-accuracy research has typically utilized a
single indicator of perception accuracy (Kistner et al., 2006) or has not directly examined how
perception valence influences the link with emotional adjustment (Cillessen & Bellmore, 1999).
In the present study, we did not attempt to directly compare and contrast the effects of meta-
accuracy of liking and disliking as moderators. Instead, we assumed that a somewhat similar
pattern of results would emerge across models, even if the magnitude of the effects varied. This
30
approach has been adopted by research investigating the correlates of meta-accuracy (Cillessen
& Bellmore, 1999).
Nonetheless, there is some support in the literature for the notion that meta-accuracy of
liking could be more strongly related to emotional functioning. Bellmore and Cillessen (2003)
found that liking meta-accuracy was a main effect predictor of acceptance by same–sex peers
and overall number of friends. In contrast, the associations between disliking meta-accuracy and
social functioning did not reach significance. It is possible that this pattern may be in part
explained by the low dyadic correspondence rates that are typically present for negative
perceptions (Bellmore & Cillessen, 2003; Cillessen & Bellmore, 1999), which limits variability
and reduces power. There is also a more theory-driven explanation - that children might
experience greater emotional suffering when they accurately determine their lack of visibility
among their peers (i.e., that peers failed to nominate them as someone they like) than when they
correctly identify their peers’ negative views of them. Consistent with this perspective, research
with early adolescent samples has found a negative association between liking by peers and
internalizing symptoms, but no association between peer disliking and internalizing (Sentse,
Lindenberg, Omvlee, Ormel & Veenstra, 2010). This perspective is also supported by research
suggesting that a person’s feelings might be less hurt and the person might experience less
emotional toil when rejection is more explicit than when it is associated with greater ambiguity
or ostracism (Freedman, Williams, & Beer, 2006).
Our speculations notwithstanding, we acknowledge that the hypothesized pattern of
results was only present in one of our models. Thus, we outline several methodological and
theoretical reasons why we may have failed to consistently detect the hypothesized effect across
all of our models, despite having a strong theoretical rationale for our hypotheses.
31
In line with the majority of the published work on meta-accuracy and guided by
theoretical frameworks in social cognition, the present study considered dyadic perceptions of
social competence, examining children’s ability to infer how they are seen by particular others.
However, meta-accuracy can also be investigated at the group level, by exploring children’s
ability to determine how their peers see them in general (Kenny & DePaulo, 1993). It is possible,
for example, that if a child’s overall predictions are in line with peer group’s feelings, it is not
crucial for the child to know specifically who feels a certain way about him or her. That is, it
could be that group-level perceptions are more likely to interact with social experience to predict
adjustment than dyadic impressions (which are more likely to be inaccurate, thus complicating
the pattern of results). To our knowledge, only two studies have utilized generalized meta-
perceptions accuracy scores (Ausubel & Schiff, 1952; Malloy et al., 2007), and neither of them
explored the relation between meta-accuracy and adjustment. Although this investigation is
beyond the scope of the present project, future studies would benefit from considering the
similarities and divergences in generalized and dyadic forms of meta-accuracy and their
implications for emotional functioning.
The distinction between group- and individual-level impressions also underscores another
important methodological consideration - that meta-accuracy might be best conceptualized as a
dimensional construct. The present study took a categorical approach, asking children to indicate
whether or not a specific peer thought of them in a certain way. However, most affective
reactions are best represented on a continuum. Individuals don’t just like or dislike their peers,
but like or dislike them a little, some, quite a bit, or even a lot. A dimensional approach might
reflect important nuances, generating greater variability in scores and allowing for finer
distinctions with regard to inaccuracy. Indeed, some researchers have advocated for the use of
32
dimensional scales (see Malloy et al., 2007 for an example), highlighting the weaknesses of a
categorical approach. For the present study we were unable to adopt a dimensional perspective
due to limited resources (e.g., not enough time with students). Nonetheless, this methodological
consideration remains an important area of investigation that is worthy of future research.
The categorical nature of our data necessitated the use of kappa scores as indicators of
meta-perception accuracy. Kappa scores are the least biased estimators of accuracy when data
are categorical (Cillessen & Bellmore, 1999). However, this statistic is also highly conservative.
Past studies relying on similar methodology had sample sizes of nearly 700 students (Bellmore &
Cillessen, 2003; Cillessen & Bellmore, 1999), which is far larger than the sample utilized in the
current project. Thus, it is possible that when using such a conservative estimator of accuracy, a
much larger sample size is needed to detect an effect. This suggestion is also in line with an
identified challenge faced by social and developmental psychology researchers: the small
magnitude of effect sizes in social cognition/perception research, especially in research utilizing
multi-informant designs (Cameron, Brown-Iannuzzi, & Payne, 2012; Hawker & Boulton, 2000;
Schäfer & Schwarz, 2019).
Other factors that the present study did not consider, but could potentially underlie the
pattern of findings discussed above, concern the role of social motivation and causal attributions.
Human beings are motivated perceivers, that is, they are driven by core social motives in the
perceptions they form, such as to belong, to understand, and to maintain self-esteem (Stevens &
Fiske, 1995). Individuals also tend to search for meaning behind actions in order to organize
impressions (Reeder, 2013), and as such are prone to making attributions of cause in order to
make sense of their social experiences. However, not all individuals are equally socially
motivated across settings and the attributions they make can vary widely. As such, the impact of
33
the correctness of the impressions they form may not be consistent. Consider for example, a fifth
grade student named Johnny who is heavily disliked by most of his peers, and is accurate about
identifying his peers’ feelings. Johnny does not exhibit emotional distress because he does not
“care” about being perceived by his classmates in this way, that is, he is not particularly
motivated to establish a positive relationship with his peers. Johnny has a number of friends
outside of school, whose perceptions carry significantly more weight for him, and Johnny
believes that his classmates’ feelings are the result of prejudice (not a reflection of who he is;
e.g., external attributions). As illustrated in this example, factors beyond social context and
perception accuracy can influence the social experiences of children. Research has begun to
focus on the intersection between social motivation and interpersonal perceptions (e.g.,
Salmivalli, Ojanen, Haanpää, & Peets, 2005) to obtain a more comprehensive understanding of
youths’ social functioning. Similarly, a number of studies have examined the role of attributions,
noting that certain types of attributions (e.g., those that are internal, stable and uncontrollable)
are more likely to be associated with a pessimistic outlook and subsequent emotional suffering
(e.g., Graham, Bellmore, Nishina, & Juvonen, 2009).
An important final consideration is that children’s ability to make accurate predictions
about how they are viewed by others as well as their emotional reactions to such predictions may
vary greatly depending on the closeness of the relationships they have established with their
peers. Close relationships are characterized by a high degree of similarity and shared knowledge
(Hartup, 1996), factors that are likely to enhance accuracy. Indeed, associations between the
length and duration of children’s peer relationships and reciprocity in friends’ knowledge of each
other’s social and personal characteristics have been noted in peer relations studies for decades
(Ladd & Emerson, 1984). Research on meta-perceptions has also underscored the influence of
34
close relationships and reference group on perception accuracy. For example, Cleary (2002)
reported that children who are best friends demonstrate more accurate meta-perceptions than
those who consider each other to be “just” friends. Relatedly, Bellmore and Cillessen (2003)
found that students were more accurate perceivers of relationships with same-sex peers than with
other sex peers, a finding that is not surprising given the dynamics of same-sex preference
among school-age children (Maccoby, 1990). It has also been well-documented that close
relationships are important sources of social support and positive emotional adjustment, with
studies showing that when children perceive these relationships in a negative light (high conflict,
low quality) they are more likely to experience a range of detrimental outcomes (e.g., Burk &
Laursen, 2005). Despite its potential relevance, the present study neglected to consider the
implications of relationship closeness or reference group in our models. It is possible that
children are more susceptible to emotional distress if they are accurate about negative
interpersonal perceptions by peers whom they like, perceive themselves to be similar to, or
believe to have a close relationship with. That is, it is important to recognize that one’s social
context is not homogeneous and that different relationships carry different weights. This area of
investigation has not yet been explored but remains a promising line of inquiry for future
research.
In sum, we believe that the inconsistent pattern of results observed in the present study
should not deter researchers from continuing to explore the role of meta-accuracy in social and
emotional adjustment. We have identified important limitations in the current study design as
well as a number of fruitful areas of inquiry that are likely to enhance our understanding of meta-
cognition more generally and meta-accuracy more specifically.
35
Meta-Accuracy and Related Socio-Cognitive Skills
Given the theoretical importance of meta-accuracy for children’s social and emotional
adjustment (e.g., Bellmore & Cillessen, 2003; Kistner et al., 2006), it is also fundamental to
consider how core social-cognitive factors might be related to this unique perceptual skill. Links
between deficits in social understanding, broader patterns of cognitive function, and problematic
peer relations have been proposed (Hay, Payne, & Chadwick, 2004), although these associations
have rarely been empirically tested. A final and innovative goal of the current investigation was
to carefully investigate the association between four of these skills (i.e., theory of mind,
switching, attention, working memory, and self-perception) and meta-accuracy, given their
hypothesized relevance for meta-cognition and interpersonal perception (Boor-Klip et al., 2014;
Kenny & DePaulo, 1993; Owens et al., 2007). At the bivariate level, theory of mind was
correlated with meta-accuracy of liking. In multivariate models, however, only self-perceptions
significantly predicted liking meta-accuracy, with the predictive value of theory of mind being
reduced to marginally significant levels. Notably, none of the social cognitive skills included in
the present study were associated with disliking meta-accuracy.
The association between self-perceptions and meta-cognitive processes has been reported
in adult samples (e.g., Kenny & DePaulo, 1993) but had not been replicated with children. Our
results are consistent with the concept of the “looking-glass self”, the idea that a person’s self-
concept is formed and refined in the social world (Cooley, 1902). As proposed by Cooley (1902),
it is through interactions and relationships with others that individuals reason about how they are
seen, become aware of the other's evaluative judgments, and form self-perceptions.
In the present study, the association between self-perceptions and liking meta-accuracy was
negative, indicating that children who evaluated themselves as highly socially competent were
36
less accurate in identifying peers’ affective perceptions of them. Although counterintuitive, this
finding is consistent with the depressive realism hypothesis (Alloy & Abramson, 1979), which
posits that individuals who hold a negative cognitive bias about one’s functioning have a more
accurate appraisal of the world. Our results are also in line with more recent work documenting
fewer biased perceptions among dysphoric adolescents girls (Kistner. Balthazor, Risi, & David,
2001).
We are cautious about drawing strong conclusions given that some of our effects were
only marginally significant. Nonetheless, our results appear to suggest that the ability to
accurately identify others’ positive judgments of ourselves might require somewhat greater
mentalizing ability as well as more negative self-perceptions than accurately perceiving
judgments that are negative in nature. It is possible that, even though we tend to be open about
our positive feelings towards others, we do not necessarily show our lack of regard for those that
we dislike. In fact, it is not uncommon for children to experience sanctions for expressing
negative feelings towards others. As such, it may be much harder for children to accurately
identify their peers’ negative perceptions unless they are highly visible (as in the case with
physical aggression), which could potentially explain why accuracy of disliking was unrelated to
perspective-taking skills.
We hypothesized that aspects of executive functioning would be linked to meta-accuracy,
but these associations were not reflected in our analyses. Though there is reason to believe
executive abilities may contribute to children’s growing awareness of other people’s mental
states (Carlson & Moses, 2001), the trends observed in the current study are consistent with
research describing executive functions as falling into a hot-cool continuum, relative to the
setting and level of affective demand in which the specific skill is operating (Jacobson et al.,
37
2011; Peterson & Welsh, 2014). In this conceptual distinction, cool executive functions refer to
psychological processes involving mechanistic higher-order operations (e.g., working memory),
whereas hot executive functions refer to abilities supported by emotional awareness and social
perception (Zimmerman, Ownsworth, O’Donovan, Roberts, & Gullo, 2016). Research has shown
that not only hot and cool executive functions are associated with different brain regions (hot
aspects of executive functioning are associated with ventral and medial areas of the prefrontal
cortex whereas cool aspects are associated with the dorsolateral prefrontal cortex), but they
exhibit different patterns of age-related growth and have different developmental correlates, with
cool executive functions being a better predictor of academic performance and hot executive
functions being uniquely related to emotional problems (Poon, 2018; Zelazo, Qu, & Müller,
2005).
Thus, it could be that the general pattern of findings observed in the current investigation
reflects actual differences in how these skills contribute to social perception more generally, and
meta-accuracy in particular. The tasks of executive function administered to children in our study
were abstract, decontextualized problems, whereas the tasks tapping theory of mind required
children to reason about affect and motivation, relying on different brain regions and an entirely
different set of skills. Therefore, it makes sense that performance in theory of mind (but not
executive functioning) tasks in our study were marginally associated with interpersonal
perception accuracy. Indeed, a growing number of researchers has put forward the notion that
theory of mind represents an integral part of executive functioning, more specifically, a
manifestation of hot executive functions in the content domain of self and social understanding
(Perner, Stummer, & Lang, 1999; Zelazo et al., 2005). Nonetheless, regardless of whether theory
of mind is a manifestation of executive functioning or simply one class of problems for which
38
executive functioning is required, the present study aligns with previous work (Boor-Klip et al.,
2014) and suggests a possible link between meta-accuracy and theory of mind even when other
cognitive skills are taken into account.
Limitations, Strengths and Future Directions
Before we move on to our concluding comments, we acknowledge some important
limitations of the current project. Because of challenges with our community partnerships, the
current study relied on a small sample of slightly less than 150 elementary school students,
across two schools and three grade levels. Although power analyses suggested the presence of
enough power to conduct most of the analysis included in the present report, the sample size
likely impacted our ability to detect an effect, especially given that effect sizes in this area of
research are typically small in magnitude (Cameron, Brown-Iannuzzi, & Payne, 2012; Hawker &
Boulton, 2000; Schäfer & Schwarz, 2019). Given the small sample size, our ability to include a
comprehensive comparison between schools and comment on school effects was also limited.
Nonetheless, the present study included important methodological strengths. As discussed
earlier, we utilized kappa scores as an indicator of meta-accuracy, which allowed us to account
for chance agreement in all our predictive models. Previous research has often used less stringent
operational definitions of meta-accuracy (e.g., mean discrepancy; Boor-Klip et al., 2014), which
do not account for chance. We also made unique contributions by investigating the influence of
other types of cognitive and social reasoning for meta-accuracy development and including
relevant covariates like academic ability, gender, and age (in months, allowing for considerably
more variance).
Although we were able to estimate broad associations between social experience and
meta-accuracy, we were not in a position to evaluate how children react in particular situations.
39
We acknowledge that it is possible that a student may be accurate in some situations but
inaccurate in others. That is, accuracy may depend on the specific situation as well as the overall
social context in which children are immersed. Thus, we believe that an important future
direction for meta-accuracy research would be to explore accuracy levels for each child across
different situations and contexts.
Future studies could also examine meta-accuracy in greater depth by using round-robin
designs as described by Malloy and colleagues (2007). Despite being resource intensive, this
type of enterprise benefits greatly from the utilization of dimensional data (i.e., how much a
certain peer dislikes you) as opposed to the standard categorical data (i.e., did a certain peer
nominate you as someone he or she dislikes or not) that is derived from peer nominations. The
investigation of other types of meta-accuracy (e.g., peer mistreatment) would also be a
worthwhile endeavor. Distinct dimensions of social functioning are known to be differentially
related to emotional adjustment (La Greca & Harrison, 2005), and as such it is possible that
certain types of perceptions (e.g., those that challenge an early adolescent’s social status) might
contribute to emotional functioning to a greater extent. In the present study we were limited by
our goal to ensure that predictors and moderators assessed the same construct (i.e., liking by
peers) and weren’t computed based on the same questions of the peer nomination inventory (i.e.,
a dimensional liking item for social experience vs. categorical liking and disliking items for
meta-accuracy). Future work assessing social context from a third informant perspective (e.g.,
teachers) could also improve study design, as data for predictor and moderators would be based
on distinct informants.
We also acknowledge that social experience and emotional adjustment are not
unidirectionally linked. For example, research on the associations between peer liking and
40
depressive symptoms suggests that these constructs are reciprocally related, influencing each
other over time (Kochel, Ladd, & Rudolph, 2012). Whereas the current study captures only a
segment of a complex cycle of influence, we were interested in specifically examining emotional
risks associated with problematic social experiences. Future investigations that take into account
the complex relations between indicators of social functioning and emotional adjustment are
warranted.
Lastly, in the proposed study, we focus on childhood, a developmental period during
which youths’ interpersonal behaviors take center stage. The cross-sectional nature of this study
did not allow for conclusions regarding temporal order. Longer-term approaches may allow for
more inferences regarding causality and more effective detection of changes in our variables of
interest over time.
Conclusions
The findings of this study suggest that the dynamics of meta-accuracy are considerably
more nuanced and complex than the extant literature seems to indicate. There was preliminary
support for the moderating role of liking meta-accuracy, with highly accurate children who
experienced social challenges endorsing increased anxiety symptoms. Self-perception, a key
social-cognitive ability, was significantly associated with meta-accuracy of liking, illustrating the
influences of self-concept formation on correctness of interpersonal perceptions. General trends
also appeared to indicate that meta-accuracy is more closely associated with hot aspects of
executive functioning than with psychological processes involving mechanistic higher-order
operations (i.e., cool executive functions). Future work on this topic should aim to clarify the
moderating role of meta-accuracy with larger samples, actively considering methodological
41
influences as well as exploring alternative variables that may intensify or mitigate the effects of
meta-accuracy (e.g., social motivation) on adjustment.
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60
Note. With the exception of composite variables, descriptive statistics reflect raw, untransformed values.
*
p < .05.
**
p < .01.
***
p
<.001.
Table 1
Means and Standard Deviations
Gender
Full Sample Boys Girls
Variable M (SD) Min Max M (SD) M (SD)
Predictor
Peer Liking 3.56 (0.59) 1.67 4.67 3.53 (0.62) 3.61 (0.54)
Moderators
Liking Meta-Accuracy 0.27 (0.31) -0.28 1 0.25 (0.31) 0.30 (0.32)
Disliking Meta-Accuracy 0.10 (0.28) -0.8 1 0.11 (0.29) 0.09 (0.27)
Covariates
Theory of Mind Composite -0.04 (0.91) -3.58 1.80 -.23 (0.97) 0.25 (0.75)
**
Reading the Eyes 0.67 (0.11) 0.29 0.89 0.66 (0.12) .71 (0.08)
**
Strange Stories 1.18 (0.42) 0 2 1.10 (0.41) 1.30 (0.40)
*
Switching Composite 0.00 (0.66) -1.23 3.66 0.10 (0.72) -0.14 (0.54)
*
Switching Cost 10.07 (5.12) -5 22 10.41 (4.95) 9.57 (5.36)
Self-Corrected Errors 0.32 (0.72) 0 5 0.39 (0.78) 0.20 (0.61)
Uncorrected Errors 0.25 (0.78) 0 5 0.34 (0.94) 0.12 (0.45)
Attention – Number Correct 27.17 (5.23) 15 42 26.6 (5.03) 28.07 (5.42)
Working Memory Composite 5.84 (2.48) -5 11 5.47 (2.52) 6.40 (2.33)
*
Number Correct Words 6.51 (1.87) 1 11 6.31 (1.89) 6.80 (1.82)
Number of Errors 0.66 (1.11) 0 7 0.84 (1.18) 0.41 (0.95)
*
Self-Perceptions 2.82 (0.69) 1 4 2.85 (0.67) 2.77 (0.72)
Academic Achievement ELA 3.08 (1.05) 1 4 2.99 (1.06) 3.21 (0.99)
Academic Achievement Math 2.92 (1.00) 1 4 2.99 (1.02) 2.79 (0.94)
Outcomes
Depressive Symptoms 0.30 (0.29) 0 1.25 0.30 (0.29) 0.31 (0.30)
Anxiety Symptoms 0.34 (0.25) 0 1 0.30 (0.24) 0.38 (0.26)
61
Table 2
Correspondence Tables for Meta-Accuracy of Liking and Disliking
Liking Meta-
Accuracy
A thinks B nominated A A does not think B nominated A
n=140 B nominates A Hits (M=16.6%) Misses (M=24.6%)
B does not nominate A False Alarms (M=7.0%) Correct Rejections (M=51.9%)
Disliking Meta-
Accuracy
A thinks B nominated A A does not think B nominated A
n=134 B nominates A Hits (M=8.3%) Misses (M=20.2%)
B does not nominate A
False Alarms (M=10.3%) Correct Rejections (M=61.2%)
62
Note.
*
p < .05.
**
p < .01.
***
p <.001.
Table 3
Bivariate Correlations between Study Variables
Variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
1. Peer Liking --
2. Liking MA -.14 --
3. Disliking MA .06 .03 --
4. Theory of Mind .09 .17
*
.13 --
5. Switching .09 .02 .02 -.02 --
6. Attention -.02 .08 .04 .08 -.07 --
7. Working Memory .05 .13 .04 .19
*
-.01 .23
**
--
8. Self-Perceptions .19
*
-.14 -.04 .23
**
.09 .07 .16 --
9. Depression -.28
***
.06 .06 -.11 -.09 .01 -.10 -.55
***
--
10. Anxiety -.09 -.07 .06 .02 -.01 -.02 -.02 -.53
***
.63
***
--
11. Age (in months) -.27
**
.27
**
-.05 .27
***
-.13 .18
*
.27
**
.14 .04 .01 --
12. ELA Perf. .37
***
-.04 .11 .37
***
-.04 .03 .14 .17
*
-.28
***
-.14 -.10 --
13. Math Perf. .31
***
-.04 .13 .16 .06 .07 .06 .08 -.22
**
-.12 -.04 .75
***
--
63
Note. Gender was coded as 0 = male and 1 = female. sr
2
is the semi-partial correlation coefficient, which is the percent of variance in
the outcome that is accounted for uniquely by the predictor.
*
p < .05.
**
p < .01.
***
p < .001.
Table 4
Summary of Hierarchical Regression Analyses Predicting Depressive Symptoms from Meta-
Accuracy of Liking and Disliking
Liking Meta-Accuracy
Step Variables entered B SE β sr
2
1 Peer Liking
Liking Meta-accuracy
Gender
Age
-.14
.02
.04
-.00
.05
.08
.05
.00
-.28
.02
.06
-.03
.06
**
.00
.00
.00
2 Peer Liking by Liking Meta-accuracy
.02 .13
.01
.00
Disliking Meta-Accuracy
Step Variables entered B SE β sr
2
1 Peer Liking
Disliking Meta-accuracy
Gender
Age
-.16
.08
.05
-.00
.04
.09
.05
.00
-.31
.08
.09
-.01
.09
***
.01
.01
.00
2 Peer Liking by Disliking Meta-accuracy -.14 .14
-.09
.01
64
Note.
†
p < .1.
. *
p < .05.
**
p < .01.
***
p < .001
Table 5
Summary of Hierarchical Regression Analyses Predicting Anxiety Symptoms from Meta-
Accuracy of Liking and Disliking
Liking Meta-accuracy
Step Variables entered B SE β sr
2
1 Peer Liking
Liking Meta-accuracy
Gender
Age
-.05
-.08
.08
.00
.04
.07
.04
.00
-.11
-.10
.16
.01
.01
.01
.02
†
.00
2 Peer Liking by Liking Meta-accuracy
-.24
.12
-.18
.03
*
Disliking Meta-accuracy
Step Variables entered B SE β sr
2
1 Peer
Disliking Meta-accuracy
Gender
Age
-.06
.06
.08
.00
.04
.08
.04
.00
-.14
.07
.16
.02
.02
†
.01
.03
†
.00
2 Peer Liking by Disliking Meta-accuracy -.06 .13 -.05 .00
65
Note. Due to the small sample size, marginal effects are reported to illustrate trends.
†
p < .1.
. *
p < .05.
**
p < .01.
***
p < .001
Table 6
Summary of Regression Analyses Predicting Meta-Accuracy of Liking and Disliking from
Social-Cognitive Skills
Outcome: Liking meta-accuracy
Predictors B SE β sr
2
Theory of Mind
Switching
Attention
Working Memory
Self-Perception
Academic Achievement ELA
Academic Achievement Math
Age
.05
.04
.00
.01
-.10
-.01
.02
.01
.03
.04
.01
.01
.04
.04
.04
.00
.14
.08
.02
.07
-.23
-.02
.06
.25
.01
†
.01
.00
.00
.05
**
.00
.00
.04
*
Outcome: Disliking meta-accuracy
Predictors B SE β sr
2
Theory of Mind
Switching
Attention
Working Memory
Self-Perception
Academic Achievement ELA
Academic Achievement Math
Age
.04
.00
.00
.01
-.03
.00
.01
.00
.03
.04
.00
.01
.04
.04
.04
.00
.12
.00
.03
.06
-.07
.00
.04
-.13
.01
.00
.00
.00
.00
.00
.00
.01
66
Figure 1
Representation of an interactive model in which meta-accuracy influences the association between youths’ social experience and
emotional adjustment.
Emotional
Adjustment
Social
Experience
Meta-Accuracy
67
Appendix A
Others at School
Rate how much you like each student in your classroom
Don’t like
that much
Don’t like a
little bit
Don’t like
or dislike
Like a little
bit
Like a lot
Adam Apple (126)
Avery Apricot (187)
Billy Banana (190)
Bianca Blueberry (208)
Brandon Blackberry (235)
Cameron Coconut (267)
Charlotte Cherry (293)
Claire Cranberry (315)
Daniel Date (328)
Dina Dragonfruit (340)
Finnie Fig (386)
Gabriella Grape (411)
George Guava (449)
Harry Huckleberry (470)
Kate Kiwi (484)
Lara Lemon (501)
Linda Lime (529)
Maria Mango (535)
Mike Melon (581)
Nancy Nectarine (622)
68
Omar Orange (628)
Patricia Papaya (644)
Paula Peach (687)
Pedro Plum (733)
Perry Pomegranate (754)
Randy Raspberry (796)
Sandy Strawberry (861)
Walter Watermelon (873)
69
Appendix B
Others at School (Continued)
Fill in the bubbles next to the names of the students that you really like.
O Adam Apple (126)
O Avery Apricot (187)
O Billy Banana (190)
O Bianca Blueberry (208)
O Brandon Blackberry (235)
O Cameron Coconut (267)
O Charlotte Cherry (293)
O Claire Cranberry (315)
O Daniel Date (328)
O Dina Dragonfruit (340)
O Finnie Fig (386)
O Gabriella Grape (411)
O George Guava (449)
O Harry Huckleberry (470)
O Kate Kiwi (484)
O Lara Lemon (501)
O Linda Lime (529)
O Maria Mango (535)
O Mike Melon (581)
O Nancy Nectarine (622)
O Omar Orange (628)
O Patricia Papaya (644)
O Paula Peach (687)
O Pedro Plum (733)
O Perry Pomegranate (754)
O Randy Raspberry (796)
O Sandy Strawberry (861)
O Walter Watermelon (873)
70
Fill in the bubbles next to the names of the students that you don’t like that much.
O Adam Apple (126)
O Avery Apricot (187)
O Billy Banana (190)
O Bianca Blueberry (208)
O Brandon Blackberry (235)
O Cameron Coconut (267)
O Charlotte Cherry (293)
O Claire Cranberry (315)
O Daniel Date (328)
O Dina Dragonfruit (340)
O Finnie Fig (386)
O Gabriella Grape (411)
O George Guava (449)
O Harry Huckleberry (470)
O Kate Kiwi (484)
O Lara Lemon (501)
O Linda Lime (529)
O Maria Mango (535)
O Mike Melon (581)
O Nancy Nectarine (622)
O Omar Orange (628)
O Patricia Papaya (644)
O Paula Peach (687)
O Pedro Plum (733)
O Perry Pomegranate (754)
O Randy Raspberry (796)
O Sandy Strawberry (861)
O Walter Watermelon (873)
71
The following statements are about how other students think and feel about you. For each statement, fill
in the bubbles next to the names of students that you believe said each of the following things about you.
Which students do you think said that you are someone they really like?
O Adam Apple (126)
O Avery Apricot (187)
O Billy Banana (190)
O Bianca Blueberry (208)
O Brandon Blackberry (235)
O Cameron Coconut (267)
O Charlotte Cherry (293)
O Claire Cranberry (315)
O Daniel Date (328)
O Dina Dragonfruit (340)
O Finnie Fig (386)
O Gabriella Grape (411)
O George Guava (449)
O Harry Huckleberry (470)
O Kate Kiwi (484)
O Lara Lemon (501)
O Linda Lime (529)
O Maria Mango (535)
O Mike Melon (581)
O Nancy Nectarine (622)
O Omar Orange (628)
O Patricia Papaya (644)
O Paula Peach (687)
O Pedro Plum (733)
O Perry Pomegranate (754)
O Randy Raspberry (796)
O Sandy Strawberry (861)
O Walter Watermelon (873)
72
Which students do you think said that you are someone they don’t like that much?
O Adam Apple (126)
O Avery Apricot (187)
O Billy Banana (190)
O Bianca Blueberry (208)
O Brandon Blackberry (235)
O Cameron Coconut (267)
O Charlotte Cherry (293)
O Claire Cranberry (315)
O Daniel Date (328)
O Dina Dragonfruit (340)
O Finnie Fig (386)
O Gabriella Grape (411)
O George Guava (449)
O Harry Huckleberry (470)
O Kate Kiwi (484)
O Lara Lemon (501)
O Linda Lime (529)
O Maria Mango (535)
O Mike Melon (581)
O Nancy Nectarine (622)
O Omar Orange (628)
O Patricia Papaya (644)
O Paula Peach (687)
O Pedro Plum (733)
O Perry Pomegranate (754)
O Randy Raspberry (796)
O Sandy Strawberry (861)
O Walter Watermelon (873)
73
Appendix C
Eyes Task
Look carefully at the pictures and then choose the word that best describes what the person in the picture
is thinking or feeling. You might find some of the pictures quite easy and some of them quite hard, so
don’t worry if it’s not always easy to choose the best word. If you really can’t choose the best word, you
can have a guess.
What do you think the person in the picture is thinking or feeling?
74
Appendix D
Strange Stories Wave 1
One day Aunt Jane came to visit Peter. Peter loves his aunt very much, but today she is wearing
a new hat; a new hat which Peter thinks is very ugly. Peter thinks his aunt looks silly in it, and
she looked much better in her old hat. But when Aunt Jane asks Peter, “How do you like my new
hat?” Peter says, “Oh, it’s very nice.”
Q: Why does he say that?
A robber who has just robbed a shop is making his getaway. As he is running home, a policeman
sees him drop his glove. He doesn't know the man is a robber, he just wants to tell him he
dropped his glove. But when the policeman shouts out to the robber, "Hey, you! Stop!," the
robber turns around, sees the policeman and turns himself in. He puts his hands up and admits
that he robbed the local shop.
Q: Why did the robber do that?
Simon is a big liar. Simon's brother Jim knows this, he knows that Simon never tells the truth!
Yesterday Simon stole Jim's ping-pong paddle, and Jim knows Simon has hidden it somewhere,
though he can't find it. He's very annoyed. So he finds Simon and he says, "Where is my ping
pong paddle? You must have hidden it either in the cabinet or under your bed, because I've
looked everywhere else. Where is it, in the cabinet or under your bed"? Simon tells him the
paddle is under his bed.
Q: Why will Jim look in the cabinet for the paddle?
75
Animals that live in groups often have an order of importance within the group. The strongest
male is the leader of the group. This leader will often attack other animals in the group who are
not as strong as this leader. This shows the other animals how important the leader is.
Q1: Do animals have an order of importance within their groups?
Q2: Who is the leader of a group?
Q3: What does the leader do to show other animals how important it is?
John takes the special butter from the refrigerator. Each boxer has won several fights. He
carefully picks out the broken items. They are either in Boston or in New York. She has to cut
the grass and find somewhere to plant the fig tree. The conductor sees that the violin player has
broken a string. Tracy took the bus to the station.
Q1: Did Tracy take the bus?
Q2: Where did Tracy take the bus to?
76
Appendix E
Circles Task – Part A
In this box there are different black and white circles with numbers in them. When I say “Go” I want you
to take your pen and connect circles by going from 1 to 2 to 3 and so on, until you reach the number 8
[demonstrate by going over sample item].
I want you to connect the circles as fast as you can without lifting the pen off the paper. However, if you
make a mistake [demonstrate making mistake while tracing], lift the pen cross that line out, go back to the
last correct circle and continue from there. The line that you draw must touch the circles in the correct
order.
Let’s practice. Put you pen where the hand is telling you to start. When I say “Go”, connect the circles in
order as fast as you can until you reach the number 8 next to the hand telling you to stop. Once you are
done, put your pen down. We will be walking around so that we can help you if you are not sure how to
do the activity. Ready? Go”.
Soon, you will see a sheet with a lot more numbers and circles. You will connect the circles just like you
did a minute ago. Again, work as fast as you can, and do not lift the pen off the paper as you go. Make
sure that your lines touch the circles. You will have 45 seconds to do that. You will start where the hand
tells you to start, and, if you have enough time, you will end where the hand tells you to stop. You might
not have enough time to get to the end, and that is ok. When I tell you to stop, put your pen down, even if
you have not reached the end.
77
Circles Task – Part B
In this box there are different black and white circles with numbers in them. This time, I want you to take
the pen and connect the circles in order, but you need to switch from white 1 to black 2, to white 3, and so
on, until you get to the last number next to the hand telling you to stop. Notice that the color changes each
time you go to the next number [demonstrate by going over sample item].
I want you to work as fast as you can. Don’t lift the pen off the paper once you have started. However, if
you make a mistake, lift the pen off the paper, cross that line out, go back to the last correct circle and
continue from there. Just like before, the line that you draw must touch the circles in the correct order.
Let’s practice. Put you pen where the hand is telling you to start. When I say “Go”, connect the circles in
order as fast as you can, changing from one color to the next, until you get to the hand telling you to stop.
Once you are done, put your pen down. We will be walking around so that we can help you if you are not
sure how to do the activity. Ready? Go”.
Soon, you will see a sheet with a lot more numbers and black and white circles. You will connect the
circles just like you did a minute ago. Again, work as fast as you can, and do not lift the pen off the paper
as you go. Make sure that your lines touch the circles. You will have 60 seconds to do that. You will start
where the hand tells you to start, and, if you have enough time, you will end where the hand tells you to
stop. You might not have enough time to get to the end, and that is ok. When I tell you to stop, put your
pen down, even if you have not reached the end.
78
Appendix F
79
Trial 1
80
Trial 2
81
Appendix G
Shopping List
Let’s pretend that you are going shopping on Monday. I am going to read a list of things for you to buy.
Listen carefully, because when I am through, I want you to turn the page and write down as many of the
things as you can. Don’t turn the page until I tell you to. You will have 45 second to write as many of the
things as you can remember and you can write them in any order. When I say stop, stop writing and turn
the page immediately.
(Read:
bananas
sweater
puzzle
jacket
grapes
blocks
watermelon
shorts
crayons
peaches
balloons
hat
strawberries
belt
marbles)
Now turn the page and write all the things you can remember from the list.
82
Shopping List
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
______________________
83
Appendix H
What I Am Like
Check ONE box on the side that is most like you, DO NOT CHECK BOTH SIDES.
Really
True for
me
Sort of
True for
me
Sort of
True for
me
Really
True for
me
Sample Sentence
a.
✔
Some kids would
rather play outdoors in
their spare time
BUT Other kids would rather
watch T.V.
1. Some kids find it hard
to make friends
BUT Other kids find it pretty
easy to make friends
2. Some kids know how
to make classmates
like them
BUT Other kids don’t know
how to make
classmates like them
3. Some kids don’t have
the social skills to
make friends
BUT
Other kids do have the
social skills to make
friends
4. Some kids understand
how to get peers to
accept them
BUT
Other kids don’t
understand how to get
peers to accept them
5. Some kids wish they
knew how to make
more friends
BUT Other kids know how
to make as many
friends as they want
6. Some kids know how
to become popular
BUT Other kids do not know
how to become popular
84
Appendix I
How I Feel
These questions ask about how you’ve been feeling for the past 2 weeks. Please circle one sentence from
each list that best describes how you have been feeling for the past two weeks.
List 1
A. I am sad once in a while.
B. I am sad many times.
C. I am sad all the time.
List 2
A. Things will work out for me O.K.
B. I am not sure if things will work out for
me.
C. Nothing will ever work out for me.
List 3
A. I do most things O.K.
B. I do many things wrong.
C. I do everything wrong.
List 4
A. I have fun in many things.
B. I have fun in some things.
C. Nothing is fun at all.
List 5
A. I am bad once in a while.
B. I am bad many times.
C. I am bad all the time.
List 6
A. I think about bad things happening to
me once in a while.
B. I worry that bad things will happen to
me.
C. I am sure that terrible things will
happen to me.
List 7
A. I like myself.
B. I do not like myself
C. I hate myself.
List 8
A. Bad things are not usually my fault.
B. Many bad things are my fault.
C. All bad things are my fault
List 9
A. I feel like crying once in a while.
B. I feel like crying many days.
C. I feel like crying every day.
List 10
A. Things bother me once in a while.
B. Things bother me many times.
C. Things bother me all the time.
List 11
A. I like being with people.
B. I do not like being with people many
times.
C. I do not want to be with people at all.
List 12
A. I make up my mind about things easily.
B. It is hard to make up my mind about
things.
C. I cannot make up my mind about
things.
85
List 13
A. I look O.K.
B. There are some bad things about my
looks.
C. I look ugly.
List 14
A. Doing schoolwork is not a big problem.
B. I have to push myself many times to do
my work.
C. I have to push myself all the time to do
my work.
List 15
A. I sleep pretty well.
B. I have trouble sleeping many nights.
C. I have trouble sleeping every night.
List 16
A. I am tired once in a while.
B. I am tired many days.
C. I am tired all the time.
List 17
A. I eat pretty well.
B. Many days I do not feel like eating.
C. Most days I do not feel like eating.
List 18
A. I do not worry about aches and pains.
B. I worry about aches and pains many
times.
C. I worry about aches and pains all the
time.
List 19
A. I do not feel alone.
B. I feel alone many times.
C. I feel alone all the time.
List 20
A. I have fun at school many times.
B. I have fun at school only once in a
while.
C. I never have fun at school .
List 21
A. I have plenty of friends.
B. I have some friends, but I wish I had
more.
C. I do not have any friends.
List 22
A. My schoolwork is alright.
B. My schoolwork is not as good as
before.
C. I do very badly in subjects I used to be
good in.
List 23
A. I am just as good as other kids.
B. I can be as good as other kids if I want
to.
C. I can never be as good as other kids.
List 24
A. I am sure that somebody loves me.
B. I am not sure if anybody loves me.
C. Nobody really loves me.
List 25
A. I usually do what I am told.
B. I do not do what I am told most times.
C. I never do what I am told.
List 26
A. I get along with people.
B. I get into fights many times.
C. I get into fights all the time.
86
Appendix J
How I Feel (Part 2)
These sentences on this form tell how some people think and feel about themselves. Read the sentences
carefully, then circle the word that shows your answer. Circle Yes if you think the sentence is true about
you. Circle No if you think it is not true about you. Give an answer for every sentence, even if it’s hard to
choose one that fits you.
1. Often I feel sick in my stomach. …………………………………… Yes No
2. I am nervous. …………………………………………………..…… Yes No
3. I often worry about something bad happening to me. .……….…….. Yes No
4. I fear other kids will laugh at me in class. ……….…………..……... Yes No
5. I have too many headaches. …………………...……………..……... Yes No
6. I worry that others do not like me. ………………………..…..……. Yes No
7. I wake up scared sometimes. ……………………..…………..….…. Yes No
8. I get nervous around people. …………………...……………..……. Yes No
9. I feel someone will tell me I do things the wrong way. …..……….... Yes No
10. I fear other people will laugh at me. ………………………………. Yes No
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Asset Metadata
Creator
Mali, Luiza Vianna
(author)
Core Title
Meta-accuracy as a moderator of the association between social experience and emotional adjustment during childhood
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Psychology
Publication Date
07/31/2020
Defense Date
06/11/2020
Publisher
University of Southern California
(original),
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Tag
cognitive skills,emotional functioning,meta-accuracy,OAI-PMH Harvest,peer liking
Language
English
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Electronically uploaded by the author
(provenance)
Advisor
Schwartz, David (
committee chair
), Cederbaum, Julie (
committee member
), Manis, Franklin (
committee member
), Nation, Daniel (
committee member
), Williams, Marian (
committee member
)
Creator Email
tillyzinha@hotmail.com,vianna.luizam@gmail.com
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