On the Competing Roles of Attractiveness and Group Membership in
Person Evaluations
by
Laura Tian
A thesis submitted in conformity with the requirements for the degree of Master of Arts
Department of Psychology
University of Toronto
© Copyright by Laura Tian 2017
ii
On the Competing Roles of Attractiveness and Group Membership in Person Evaluations
Laura Tian
Master of Arts
Department of Psychology
University of Toronto
2017
Abstract Individual preferences notwithstanding, studies on physical attractiveness have suggested that
people largely agree about others’ attractiveness and favor attractive individuals. Though this
attractiveness halo represents one of the strongest influences over social behavior,
psychological literature has documented other robust biases as well. For instance, favoritism
towards members of one’s own group guide much of a person’s thoughts and actions. Here, I
investigated what happens when these two biases collide by examining how attractiveness
affects implicit and explicit evaluations of ingroup and outgroup members. I hypothesized that
group membership biases would cede to attractiveness biases; participants would prefer
attractive individuals irrespective of group membership. However, whereas the results of
Implicit Association Tests showed that participants’ evaluations of ingroup and outgroup
targets differed more by group membership, semantic differential scales showed that explicit
evaluations differed more by attractiveness levels. A person’s attractiveness and group
membership therefore seem to separately affect others’ evaluations.
iii
Acknowledgements
I would like to thank: my supervisor, Nick Rule, whom I will never feel deserving of; my lab
mates, for their support day in and day out; my graduate cohort, for sharing my successes and
struggles; my parents, for everything; my family and friends, who often feel like one and the
same; Kirsti Toivonen, the rock on which I have built my achievements; my thesis committee,
for their encouraging, kind words and guidance; and all the teachers, professors, teaching
assistants, and faculty members that have allowed me to pursue my academic dreams.
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Table of Contents Abstract…………………………………………………………………………………….. ii Acknowledgements………………………………………………………………………… iii Table of Contents…………………………………………………………………………... iv List of Tables………………………………………………………………………………. v List of Figures……………………………………………………………………………… vi List of Appendices…………………………………………………………………………. vii Chapter 1. Introduction…………………………………………………………..………… 1
Models of Impression Formation……………………………………………………….. 3 Group Membership Biases……………………………………………………………... 3 The Malleability of Group Membership Biases………………………………………... 5 Physical Attractiveness Biases…………………………………………………………. 6 Combining Physical Attractiveness Biases and Group Membership Biases…………... 8 The Present Study………………………………………………………………………. 9
Chapter 2. Study 1A………………………………………………………………..……… 10 Method………………………………………………………………………………….. 10
Participants and Design……………………………………………………………. 10 Stimuli……………………………………………………………………………... 11 Data Analysis……………………………………………………………………… 12 Procedure………………………………………………………………………….. 12
Results………………………………………………………………………………..… 13 Implicit Evaluations……………………………………………………………….. 13 Explicit Evaluations……………………………………………………………….. 14 Discussion……………………………………………………………………………… 14
Chapter 3. Study 1B……………………………………………………………...………... 15 Method…………………………………………………………………………………. 15 Participants and Design……………………………………………………………. 15 Procedure………………………………………………………………………..… 15 Results……………………………………………………………………………….…. 15 Discussion…………………………………………………………………………….... 16
Chapter 4. Study 2…………………………………………………………………..…….. 18 Method…………………………………………………………………………………. 18
Participants and Design…………………………………………………………… 18 Stimuli…………………………………………………………………………….. 19 Data Analysis……………………………………………………………………... 19 Procedure…………………………………………………………………………. 19
Results…………………………………………………………………………………. 20 Implicit Evaluations………………………………………………………………. 20 Explicit Evaluations………………………………………………………………. 21 Discussion……………………………………………………………………………... 21
Chapter 5. General Discussion………………………………………………..…………... 22 Implications……………………………………………………………………………. 23 Limitations and Future Directions……………………………………………………... 24 Conclusion……………………………………………………………………………... 25
References………………………………………………………………………………… 27 Supplemental Materials…………………………………………………………………… 49
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List of Tables Table 1. Target Attractiveness Ratings…………………………………………………… 40 Table 2. IAT Blocks for Study 1A and 2…………………………………………………. 41 Table 3. Words Used in IAT……………………………………………………………… 42
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List of Figures Figure 1. …………………………………………………………………………………… 43 Figure 2. …………………………………………………………………………………… 44 Figure 3. …………………………………………………………………………………… 45 Figure 4. …………………………………………………………………………………… 46 Figure 5. …………………………………………………………………………………… 47
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List of Appendices Appendix A………………………………………………………………………………… 48
1
Chapter 1
Introduction
In the ancient Greek tragedy Medea, Euripides dramatizes the harrowing story of a
handsome barbarian princess, who seeks revenge after her husband, the Greek hero Jason,
abandons her to marry a proper Greek bride. Many centuries later, the Italian composer
Puccini would tell a strikingly similar story. In Madama Butterfly, a naïve Japanese girl
awaits the return of her American husband, only to discover that—during his absence—he
has taken an American wife.
As with all great works of art, these stories continue to resonate with audiences because
they reveal a fundamental truth about human nature. Thematically, these works suggest that—
despite their beauty and youth—neither heroine could overcome her husband’s deep-rooted
contempt for her foreign heritage. Yet, oft-repeated legends of women like Helen of Troy would
maintain that with great beauty comes great power. These stories therefore raise the question of
whether powerful traits, such as attractiveness, can ever counteract the effects of group
membership biases, or whether group membership reigns supremely over social judgment.
Numerous psychology studies attest to the power of beauty. Rather than being in the eye
of the beholder, for instance, research suggests that most people agree on who they deem to be
physically attractive (Coetzee, Greeff, Stephen, & Perrett, 2014; Langlois et al., 2000; Maret &
Harling, 1985). Moreover, those considered attractive by others reap tremendous social,
financial, and health benefits (Dion & Berscheid, 1974; Frieze, Olson, & Russell, 1991; Farina et
al., 1977), making the attractiveness halo effect one of the most powerful governing influences
of interpersonal behavior (for reviews, see Eagly, Ashmore, Makhijani, & Longo, 1991; Langlois
et al., 2000; Ritts, Patterson, & Tubbs, 1992).
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Yet, people show heady preferences for those who share their group membership. Group
membership biases—perceptual and evaluative biases based on social categories such as race,
gender, age, religiosity, and political affiliation—take root quickly and resist disconfirmation
(Allport & Ross, 1967; Brewer, 1999; Cohen, 2013; Nelson, 2005; Swim, Aikin, Hall, & Hunter,
1995). People attach strong positive associations to those who share similar social identities and
disparage those who do not (Hewstone, Rubin, Willis, & 2002; Mullen, Brown, & Smith, 1992).
Regrettably, these biases are the basis of much intergroup conflict and have immeasurable social
consequences—prompting many researchers to search for ways in which these biases might be
mitigated.
Given the importance of attractiveness to person perception and the strength of its biases,
attractiveness may counteract biases associated with group membership. I therefore investigated
the intersection of physical attractiveness and group membership biases to rank the influence of
one over the other. Whereas models of impression formation and the literature on intergroup
biases have traditionally suggested that group membership biases are salient and resilient to
change (Brewer, 1988; Dovidio & Fazio, 1992), more recent findings suggest that they are more
malleable than previously believed (Blair, 2002). Thus, I hypothesized that the attractiveness
biases would overpower intergroup biases, leading people to evaluate attractive individuals more
positively regardless of their group membership.
However, this may depend on the nature of that evaluation. Although contemporary
society admonishes negative attitudes towards others based on both how they look and the
groups to which they belong, the latter typically carries greater censure because it can lead to
systematic discrimination and social unrest (Beck, Reitz, & Weiner, 2002; Devine, Plant,
Amodio, Harmon-Jones, & Vance, 2002). Participants may therefore reveal biases favoring
3
attractive people in their explicit reports but nevertheless demonstrate favoritism towards
ingroup members when evaluating them implicitly. Hence, I measured both explicit and implicit
evaluations to explore potential ranking differences as a function of its level of disclosure.
Models of Impression Formation
Brewer (1988) theorized that people use two processes to form impressions of others.
Upon automatically identifying targets’ social dimensions (gender, age and skin color), the
perceiver processes the target in either a top-down (i.e., according to the person’s social
category) or bottom-up fashion (i.e., according to the individual’s social attributes). Fiske and
Neuberg (1990) offered an alternative roadmap for how impressions form. They suggested that
Brewer’s dual processes might span a continuum that begins with categorization and ends with
analyses of the target’s specific attributes.
Both Brewer’s dual-process model and Fiske and Neuberg’s continuum model stress that
category-based processes generally take priority over attribute-oriented processes. Accordingly,
they agree that people automatically and implicitly extract social category information pertaining
to “privileged” dimensions (e.g., age, race, and gender) during the earliest stages of impression
formation. The result of this perception then determines their motivation to form a more detailed
impression. Thus, social category biases should affect impression formation more than
individuating information (such as an individual’s level of attractiveness) unless the perceiver is
motivated to develop a deeper understanding of the target, or if the targets’ attributes cannot be
pigeonholed into any known categories.
Group Membership Biases
Until recently, literature on group membership has agreed with impression formation
models. Most evidence suggests that group membership biases appear early in the person
4
perception process. People often instantly and automatically extract social category or group
membership information when perceiving others (Neuberg & Sng, 2013), which allows this
information to influence how subsequent cues—such as facial affect (Hugenberg &
Bodenhausen, 2003), speech (Popp, Donovan, Crawford, Marsh, & Peele, 2003), and behavior
(Sagar & Schofield, 1980)—are processed and interpreted (Dovidio & Fazio, 1992; Greenwald,
Oakes, & Hoffman, 2003). Early findings also show that people are more likely to recall
individuating information consistent with social group biases than to recall inconsistent
information (Lui & Brewer, 1983), implying that group stereotypes may be difficult to discount
once activated. And, as demonstrated by the fact that group membership biases can
spontaneously arise for novel and arbitrary groups (Billig & Tajfel, 1973; Brewer, 1979), people
seem cognitively ready to identify and use group membership information when forming
impressions of others.
Furthermore, differentiating between ingroup members (those who belong to the same
social groups) and outgroup members (those who belong to different social groups) is a key
component of group membership processes. This distinction leads people to form beliefs and
attitudes that paint ingroup members in a favorable light and derogate outgroup members
through negative biases and stereotypes (Hewstone, Rubin, & Willis, 2002; Tajfel, 1979; Riek,
Mania, & Gaertner, 2006). Once formed, these intergroup biases can be difficult to undermine.
Incredibly, Urada, Stenstrom, and Miller (2007) found that the effects of outgroup derogation are
so great that two ingroup memberships are often required to offset a negative bias from one
outgroup membership. Moreover, ingroups and outgroups respectively take on strong positive
and negative affective associations. Upon meeting ingroup members, people feel more
empathetic, trusting, and friendly (Brewer, 1999; Brown, Bradley, & Lang 2005; Stürmer,
5
Snyder, Kropp, & Siem, 2006), whereas outgroup members often evoke feelings of anxiety,
disgust, and fear (Buckels & Trapnell, 2013; Stephen & Stephen, 1985). Taken together, these
findings have led to researchers deduce that—because individuals quickly become invested in
meaningful and arbitrary group memberships—group membership biases must be highly resilient
to change.
The Malleability of Group Membership Biases
Yet, in spite of impression formation models and intergroup literature, more recent
research has argued that group membership biases are more malleable than previously believed
(Blair, 2002; Dasgupta & Greenwald, 2001; Devos, 2008; Echabe, 2013). Far from being
unyielding, several studies have demonstrated that situational context, perceiver motivations, and
perceiver traits are all factors that can influence and mitigate intergroup biases (Devine, 1989;
Lepore & Brown, 1997). More important, however, not all group members evoke the same
biases, or evoke these biases to the same degree.
For instance, intergroup biases differ depending on whether experimenters use positive or
negative exemplars to represent the group. Specifically, Govan and Williams (2004) found that
they could effectively eliminate implicit racial biases if they asked White participants to
categorize names of admired Black individuals (e.g., Michael Jordan, Eddie Murphy) and
disliked White individuals (e.g., Charles Manson, Hannibal Lechter). Likewise, Richeson and
Trawalter (2005) reported that White participants were slower to categorize admired Black
individuals and disliked White individuals by race—implying that a group exemplar’s likeability
may help or hinder social categorization.
Therefore, although intergroup biases may wield a large influence on impressions, these
biases do not always dominate evaluations. This is especially evident in cases where targets
6
belong to multiple social categories—some of which elicit conflicting stereotypes. For instance,
people may feel conflicted by the contradictory gender and race stereotypes associated with
Black females. Although Black individuals are often negatively stereotyped as “aggressive,”
“brutish,” and “dominant” (Amodio & Devine, 2006; Gaertner & McLaughlin, 1983), females
are positively characterized as “gentle,” “refined,” and “submissive” (Eagly & Mladinic, 1989).
Since race and gender are both perceptually obvious, “privileged” categories, it is not
immediately clear how perceivers reconcile these contradictory biases.
On one hand, one cue may dominate evaluations (Macrae, Bodenhausen, & Milne, 1995):
If gender dominates impressions, perceivers would evaluate Black females positively, whereas if
race dominates impressions, Black females would elicit mostly negative evaluations.
Alternatively, perceivers may also treat multiple cues additively (Anderson, 1965, 1971, 1974).
As proposed by the double jeopardy hypothesis, people doubly penalize individuals who belong
to multiple outgroups (Berdahl & Moore, 2006; Chappell & Havens, 1980; Dowd & Bengton,
1978; Kinzler, Shutts, & Correll, 2010; Stangor, Lynch, Duan, & Glas, 1992; for a review, see
Kang and Bohenhausen, 2014).
However, few studies have looked at whether “individualistic” traits can negate group
membership biases. This is perhaps because, according to impression formation models,
perceivers should disregard traits such as physical attractiveness when they are not motivated to
form in-depth impressions—which is often the case when encountering outgroup members
(Judd, Park, Yzerbyt, Gordijn, & Muller, 2005; Park & Rothbart, 1982).
Physical Attractiveness Biases
Unlike other traits, however, attractiveness exerts robust and widespread effects on our
impressions of others. Just as people often automatically categorize individuals by group
7
membership, people cannot help but judge the attractiveness of others (Van Leeuwen & Macrae,
2004; Zhang, Zheng, & Wang, 2016). Olson and Marshuetz (2005), for example, observed that
participants judged facial attractiveness in 100 ms, and that these judgments subsequently biased
perceptions of gender typicality, employability, and cooperativeness (Locher, Unger, Sociedad,
& Wahl,1993). Similarly, even when people are presented with severely degraded facial
information (e.g., blurred photos or concealed facial features), their attractiveness ratings are
surprisingly consistent with ratings of high-resolution face photos shown in their entirety (Sadr,
Fatke, Massay, & Sinha, 2002). Infants also show preferences for more attractive faces, which
suggests that attractiveness standards and biases are less socio-culturally dependent than
commonly believed (Coetzee et al., 2014; Cunningham, Roberts, Barbee, Druen, & Wu, 1995;
Ramsey, Langlois, Hoss, Rubenstein, & Griffin, 2004; Slater et al.,1998). Indeed, the mounting
social, cognitive, and neurobiological evidence suggests that attractiveness may be one of the
few individualistic traits salient enough to contend with intergroup biases.
Moreover, a wealth of literature suggests that attractiveness biases are by and large halo
effects: Langlois and colleagues’ (2000) compilation of 11 meta-analyses concluded that people
judge and treat attractive children and adults more favorably. The widely held belief that
attractive individuals are more sociable, intelligent, moral, and honest leads to countless social
consequences in everyday life and in society writ large (Eagly et al., 1991; Paunonen, 2006;
Tsukiura & Cabeza, 2010; Zebrowitz, Hall, Murphy, & Rhodes, 2002). Attractiveness predicts
the success of electoral candidates (Verhulst, Lodge, & Lavine, 2010; White, Kenrick, &
Neuberg, 2013; Zebrowitz, Franklin, & Palumbo, 2015), evaluations in pedagogical contexts
(Lerner & Lerner, 1977; Salvia, Algozzine, & Sheare, 1977), peer preferences in children
(Langlois, & Stephan, 1997), and criminal sentencing decisions (Stewart, 1980). More important
8
still, the positive biases associated with attractiveness directly contradict the negative biases
associated with many outgroups.
Combining Physical Attractiveness Biases and Group Membership Biases
As with multiple group memberships, indirect evidence suggests that attractiveness may
interact additively with group membership (Park & Kennedy, 2007), and in some cases,
supersede group membership altogether. When Benson, Karabenick, and Lerner (1976) observed
the behavior of White adults who happened upon misplaced graduate school applications, they
found that strangers were more likely to help attractive Black applicants than to help unattractive
White applicants. Similarly, Maruyama and Miller (1980) discovered that when participants
scored the essays of Black and White students, the students’ facial attractiveness, but not race,
predicted essay scores. Although the authors may have not found an effect of race because the
participants wanted to appear unprejudiced (i.e., social desirability biases; Maruyama & Miller,
1980), racial biases may also have ceded to the powerful effects of attractiveness.
More recently, however, Agthe, Strobel, Spörrle, Pfundmair, and Maner (2016) reported
that attractiveness halos only affected people’s desire to interact with opposite-sex, own-race
individuals. The authors ground their findings in an evolutionary framework, arguing that only
individuals of the same race activate attractiveness biases because these biases stem from
intragroup mating motivations. Yet, considering that people automatically judge the
attractiveness of outgroup members, and exhibit attractiveness biases in nonromantic situations
(Benson, Karabenick, & Lerner, 1976; Locher et al., 1993, Maruyama & Miller, 1980), I contend
that attractiveness biases generalize widely and extend to outgroup members as well.
9
The Present Study
Thus far, literature from person perception research suggests that attractiveness biases
may be able to compete with intergroup biases. To investigate this possibility, I examined how
people evaluate ingroup and outgroup members of differing attractiveness levels. Given that
people may be reluctant to explicitly derogate outgroup members—especially those of a different
race—I asked participants to complete an Implicit Association Test (IAT) to probe whether their
evaluations of Black and White targets differed as a function of attractiveness level. As a
secondary, exploratory analysis, participants rated the same targets on a more explicit measure
(i.e., semantic differential scales; Study 1a and Study 1b). To generalize these results to social
groups beyond racial groups, I also conducted the study using experimentally created groups
(Study 2). I hypothesized that attractiveness would take precedence over group membership
biases, such that perceivers would evaluate attractive outgroup members more positively than
unattractive ingroup members. Moreover, I also hypothesized that attractiveness would have a
larger effect on evaluations than group membership.
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Chapter 2 Study 1A
As demonstrated in the literature, attractiveness biases are nearly ubiquitous. They
influence perceptions of job applicants, peers, students, politicians, and romantic interests
(Benson, Karabenick, & Lerner, 1976; Heilman & Saruwatari, 1979; Langlois et al., 2000).
Thus, it is somewhat surprising that studies on attractiveness biases on outgroup members have
yielded mixed results. Whereas indirect evidence suggests that participants apply attractiveness
biases to racial outgroup members (Benson, Karabenick, & Lerner, 1976; Maruyama & Miller,
1980), Agthe and her colleagues (2016) have recently found that attractiveness biases are
activated exclusively for those of the same race.
If physical attractiveness biases do outrank group membership biases, this may indicate
that an individualistic trait, such as attractiveness, can mitigate automatic intergroup biases.
Alternatively, if automatic racial biases are not mitigated by attractiveness, then this would
suggest that—despite the robust halo effects of attractiveness—group membership information is
ultimately prioritized during impression formation. In Study 1A, I investigated these questions
by examining how White participants implicitly and explicitly evaluate White and Black targets
of differing attractiveness levels.
Method
Participants and Design. I recruited 92 White participants (50 men, 42 women; Mage =
38.03 years, SD = 10.83) residing in the U.S. from Amazon Mechanical Turk (MTurk). Given
that previous race-based IAT studies have shown large effect sizes for White perceivers (r = .46;
Sabin, Rivara, & Greenwald, 2008), a power analysis indicated that the sample size was
11
sufficient to achieve more than 95% power for a two-way repeated-measures analysis of variance
with a 5% false-positive rate.
I compensated participants $2.50 USD for completing the task, which took approximately
half an hour and followed a 2 (Target Race: Black, White) × 3 (Target Attractiveness Level:
attractive, average, unattractive) fully within-subjects design.
Stimuli. Targets consisted of 30 Black and 30 White headshot photographs of men and
women posing neutral expressions without accessories (e.g., eyeglasses or piercings) from the
Chicago Face Database (Ma, Correll, & Wittenbrink, 2015). Using the database’s norming data, I
selected targets perceived to be between 18 to 36 years old to prevent age from confounding
attractiveness, as previous studies have shown that youth positively relates to attractiveness
(Henss, 1991; Jones & Hill, 1993; Sutherland et al., 2013).
Each level of Target Attractiveness consisted of 10 Black targets and 10 White targets,
categorized according to the mean attractiveness data included in the database for each racial and
gender group. Specifically, attractive targets consisted of those rated as at least one standard
deviation above the group mean, average targets consisted of those within half of a standard
deviation from the mean, and unattractive targets consisted of those at least one standard
deviation below the mean (see Table 1 for descriptive statistics of target attractiveness ratings by
race).
I cropped the target photos at the top of the head, bottom of the chin, and extremes of the
ears, and then proportionally resized the photos to 250 pixels wide. The attractiveness ratings did
not significantly differ between the Black and White targets overall, t(58) = 0.69, p = .50, d =
0.18, or between Black and White targets at each Target Attractiveness level, F(2, 54) = 0.12, p
= .89, ηp2 = .05.
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Data Analysis. To measure the participants’ implicit evaluations, I calculated the IAT D
scores following Greenwald, Nosek, and Banaji’s (2003) suggestions—deleting trials with
response latencies greater than 10,000 ms and excluding altogether participants who responded
faster than 300 ms on more than 10% of trials. I then computed mean latency differences
between the positive and negative blocks, such that positive D scores indicated more positive
valence evaluations and negative D scores indicated more negative valence evaluations, and
divided the mean latency differences by the standard deviations inclusive across all of the
positive and negative blocks to produce D scores for each Target Race × Target Attractiveness
condition. I conducted all main data analyses using participants as the unit of analysis (see
Supplemental Materials for alternative multilevel mixed-effects analyses in which targets
constitute the unit of analysis and attractiveness ratings substitute for attractiveness categories).
To measure the participants’ explicit evaluations, I averaged the four semantic
differentiation scales of perceived goodness, pleasantness, honesty, and niceness to create a
Likeability measure (Cronbach’s α = .99). I then standardized the ratings to a normal distribution
such that negative values represented stronger negative associations whereas positive values
represented stronger positive associations.
Procedure. Participants first completed a screening questionnaire on Qualtrics that
included demographics questions asking for age, race, gender, sexual orientation, socioeconomic
status (household income and subjective social class), and country of residence. The survey
deemed participants eligible for the study if they identified as White and resided in the US. After
completing the questionnaire, the survey directed participants to the race IAT via a Web link
hosted by Inquisit v4.0.
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Following standard procedures (e.g,, Greenwald, McGee, & Schwatz, 1998), the IAT
asked participants to categorize targets as Black or White and words as positive or negative.
Participants first completed two practice blocks in which they categorized only targets or words
before completing two test blocks in which they categorized both (see Table 2). The first test
block consisted of 30 target faces (15 Black, 15 White) and 30 written attributes (15 positive,
e.g., marvelous, amazing; 15 negative, e.g., horrible, dreadful; see Table 3 for a complete list of
attributes) whereas the second test block consisted of 60 target faces and 60 attributes.
Participants then practiced categorizing the stimuli with reversed key mappings and completed a
second pair of test blocks using the reversed response keys. I randomly assigned participants to
first complete the compatible test block (i.e., Black faces and negative words sharing the same
response key) or to first complete the incompatible test block (i.e., Black faces and positive
words sharing the same response key).
After the IAT, participants explicitly evaluated each target by indicating the degree to
which they believed the target was “Good–Bad”, “Pleasant–Unpleasant”, “Honest–Dishonest”,
and “Nice–Awful” along 7-point Likert-type scales.
Results
Implicit evaluations. The participants’ IAT D scores showed significant main effects of
Target Race, F(1, 91) = 47.04, p < .001, ηp2 = .34, and Target Attractiveness, F(2, 182) = 3.52, p
= .03, ηp2 = .04, but no interaction between the two, F(2, 182) = 0.83, p = .44, ηp
2 = .01.
Specifically, participants evaluated White targets (M = 0.30, SD = 0.54) more positively than
Black targets (M = -0.29, SD = 0.56), and attractive targets (M = 0.06, SD = 0.55) more
positively than average targets (M = 0.00, SD = 0.52) and unattractive targets (M = -0.04, SD =
0.58).
14
Explicit evaluations. The participants’ explicit evaluations significantly differed
according to Target Attractiveness, F(2, 182) = 166.54, p < .001, ηp2 = .65, but not Target Race,
F(1, 91) = 1.52, p = .22, ηp2 = .02. Moreover, Target Race and Target Attractiveness significantly
interacted, F(2, 182) = 13.15, p < .001, ηp2 = .13. Sidak corrected simple effects tests showed
that participants evaluated attractive Black targets (M = 0.62, SD = 0.79) more favorably than
attractive White targets (M = 0.45, SD = 0.86; t(91) = 1.98, p = .05, d = 0.42), and average Black
targets (M = 0.23, SD = 0.92) more favorably than average White targets (M = 0.01, SD = 0.78;
t(91) = 2.45, p = .02, d = 0.51). But participants did not discriminate between unattractive Black
(M = -0.71, SD = 0.94) and unattractive White targets (M = -0.61, SD = 0.91; t(91) = -1.29, p =
.20, d = -0.27).
To explicate the relative contributions of Target Race and Target Attractiveness more
clearly, I subsequently compared the effect sizes of each factor. This showed that the difference
based on Target Race significantly exceeded that based on Target Attractiveness for implicit
evaluations, z = 4.62, p < .001, but that the difference based on Target Attractiveness
significantly exceeded that based on Target Race for explicit evaluations, z = 9.65, p < .001.
Discussion
Because Study 1A and Study 1B used similar methods and showed a similar pattern of
results, the discussion of both studies follows Study 1B.
15
Chapter 3 Study 1B
Given that I initially included the explicit measures as an exploratory measure,
participants completed the implicit measure before the explicit measure. Recognizing that task
order effects may have influenced explicit evaluations, I replicated the explicit evaluations with
another participant sample. Because participants completed the IAT first, task order could not
have influenced the results and consequently I did not replicate the race IAT.
Method
Participants and design. I recruited 80 U.S. raters (34 men, 46 women; Mage = 35.33
years, SD = 11.43) from MTurk. Participants were compensated $1.50 USD and completed the
task in approximately 15 minutes. As in Study 1A, participants rated 30 Black and 30 White
targets on four semantic differentiation scales. Given the typical effect sizes in social psychology
research (r = .21; Richard, Bond, & Stokes-Zoota, 2003), I conducted a power analysis that
indicated that the sample size was sufficient to achieve more than 95% power for detecting an
interaction with a two-way repeated-measures analysis of variance with a 5% false-positive rate.
Procedure. For Study 1B, I asked an independent set of participants to explicitly
evaluate the targets from Study 1A. Participants rated each target as “Good–Bad,”, “Pleasant–
Unpleasant,” “Honest–Dishonest,” and “Nice–Awful” along 7-point Likert-type scales.
Results
As in Study 1A, I standardized the Likeability scores to a normal distribution. Using
participants as the unit of analysis, I found no significant effect of Target Race, F(1, 79) = 0.49,
p = .49, ηp2 = .01, but a significant effect of Target Attractiveness, F(2, 158) = 146.22, p < .001,
ηp2 = .65. I also found a significant interaction between Target Race and Target Attractiveness,
16
F(2, 158) = 7.88, p < .01, ηp2 = .09. Simple main effect tests with Sidak corrections revealed that
participants showed a marginal preference for average Black targets (M = 0.14, SD = 0.91) over
average White targets (M = -0.05, SD = 0.81; t(79) = 1.95, p = .06, d = 0.44), but that
participants did not discriminate between attractive Black (M = 0.55, SD = 0.96) and attractive
White targets (M = 0.52, SD = 0.80; t(79) = 0.32, p = .75, d = 0.07), or between unattractive
Black (M = -0.60, SD = 0.96) and unattractive White targets (M = -0.56, SD = 0.90; t(79) = -
0.50, p = .62, d = 0.11).
Although the analyses showed only one significant main effect of Target Attractiveness, I
nevertheless compared the effect sizes of Target Race and Target Attractiveness to determine if
they significantly differed. As expected, Target Attractiveness accounted for a significantly
larger effect than Target Race in the Likeability ratings, z = 9.32, p < .001.
Discussion
The results of Study 1A and 1B suggest that attractiveness biases can overcome racial
group membership biases, but this effect is contingent on how one measures person evaluations.
Whereas participants evaluate ingroup and outgroup members similarly by attractiveness level,
they nevertheless implicitly prefer ingroup members over outgroup members—regardless of
target attractiveness. This implies that race plays a larger role in determining implicit
evaluations, yet attractiveness dominates explicit evaluations.
One possible explanation is that attractiveness accounts for a larger effect in explicit
evaluations because people are less aware of attractiveness biases. Consequently, attractiveness
biases are less prone to self-correction and may go unfettered when people express their
judgments explicitly. In contrast, people’s heightened sensitivity to racial biases allows them to
readily censor their explicit evaluations of intergroup members (Devine, 1989; Lepore & Brown,
17
1997). Indeed, the interaction between race and attractiveness in explicit evaluations in Studies
1A and 1B (see Figure 1 and Figure 3), where White participants report more favorable
evaluations for Black targets than White targets, may result from prejudice-correction contrast
effects (Carver, Glass, & Katz, 1978; Carver, Glass, Snyder, & Katz, 1977; Lepore & Brown,
2002). Interestingly, however, the results here would suggest this only occurs for targets of
average attractiveness: Explicit preferences for Black targets over White targets disappear for
attractive and unattractive targets. This may suggest that attractiveness biases mitigate prejudice-
correction contrast effects—and perhaps, by extension, social desirability biases—when
participants are evaluating attractive or unattractive ingroup and outgroup members.
18
Chapter 4 Study 2
By using Black and White targets, Studies 1A and 1B capitalized on real-world group
biases to investigate how attractiveness biases interact with group membership biases. However,
the complexity of real-world groups introduces possible confounds that may obscure the true
effect of attractiveness. One possibility, for instance, is that participants may have preferred
attractive Black targets over other Black targets because attractive Black faces are also low in
racial phenotypic prototypicality, and therefore less likely to activate negative group membership
biases (Blair, Judd, & Fallman, 2004; Livington & Brewer, 2002). Another possibility is that—as
suggested by the explicit preference for other-race targets over own-race targets in Studies 1A
and 1B—perceivers may have been especially motivated to censor racial prejudice when
explicitly evaluating Black targets (Fazio & Dunton, 1997). Thus, participants may be more
willing to express explicit prejudice towards outgroups based age or gender differences (Officer
et al., 2016).
To address these possibilities and test if the results from Study 1A and 1B generalize to
other types of outgroups, I used a minimal group paradigm in Study 2 to investigate how
attractiveness interacts with group membership for other types of social groups (Bernstein,
Young, & Hugenberg, 2007; Billig & Tajfel, 1973; Brewer, 1979).
Method
Participants and Design. One hundred twenty White participants (63 males, 57 females;
Mage = 41.96 years, SD = 13.07) from the MTurk’s U.S. worker pool participated for $2.50 USD
compensation. The study took approximately 40 minutes to complete. As in Study 1A, Study 2
19
used a 2 (Target Group: ingroup, outgroup) × 3 (Target Attractiveness: attractive, average,
unattractive) fully within-subject design.
Based on effect sizes typically found in social and personality psychology (r = .21;
Richard et al., 2003) and the smallest significant effect size found in Study 1A (ηp2 = .02), I
determined that the sample size was sufficient for over 99% power for a two-way repeated-
measure analysis of variance with a 5% false positive rate. I excluded participants who did not
reside in the US, and participants who failed the attention check at the end of the task.
Stimuli. Targets consisted of 60 headshots of White individuals from the Chicago Face
Database (Ma et al., 2015). Study 2 used the same target inclusion criteria as Study 1A, and each
level of Target Attractiveness (as defined by Study 1A) was represented by 20 faces. Using
Adobe Photoshop CS4, I manipulated the background color of each target photo to change
targets’ group membership for a fully crossed design, and resized photo dimensions to a height
of 280 px and a width of 400 px.
Data Analysis. Data analysis for Study 1 followed the same analysis procedures as
Study 1A.
Procedure. As in Study 1, participants first completed a pre-screening demographics
questionnaire in Qualtrics. The survey deemed them eligible if they were U.S. residents and
identified as White/Caucasian. To create minimal groups, the survey assigned participants to
experimentally created groups in the manner laid out by Bernstein et al. (2007): Participants
completed a bogus personality test based on 20 randomly selected statements from the Big Five
Personality Inventory (see Appendix A for the statements used in the study; Goldberg, 1993).
On-screen instructions described the personality test as a good predicator of future social,
romantic, and career success. I included all personality inventory items on one page, and asked
20
participants to indicate on a 7-point Likert-type scale from 1 (Inaccurate) to 7 (Accurate) the
degree to which they agreed with each statement. Although the survey recorded participants’
responses, I did not analyze the data from the bogus personality inventory. After completing the
personality inventory, the survey randomly sorted participants into an “orange” personality group
or “purple” personality group. Additional instructions informed participants that, in the second
phase of the study, they would view photos of individuals with a similar or different personality,
and that their personality type would be indicated by the photo’s background color.
In the second phase of the study, participants completed an IAT through Inquisit v4.0.
Before the task began, participants were reminded of their assigned group color. Participants
then completed the 7 discrimination task blocks (see Table 3), where they categorized positive
and negative valence words as Good or Bad, and photos of White targets with orange and purple
background colors as Orange or Purple. An attention-check question at the end of the
experiment asked participants which group they had been assigned to the beginning of the
experiment.
Results
Implicit evaluations. Using participants as the unit of analysis, I found that when
participants implicitly evaluated targets, their evaluations differed by Target Group, F(1, 119) =
34.23, p < .001, ηp2 = .22, and Target Attractiveness, F(2, 238) = 3.18, p = .04, ηp
2 = .03.
However, Target Group evaluations did not differ as a function of Target Attractiveness, F(2,
238) = 0.04, p = .97, ηp2 < .001. Specifically, participants favored ingroup members (M = 0.16,
SD = 0.50) over outgroup members (M = -0.21, SD = 0.59), and marginally preferred attractive
targets (M = .03, SD = 0.50) over average targets (M = -0.06, SD = 0.63) and over unattractive
targets (M = -0.04, SD = 0.50).
21
Explicit evaluations. When participants explicitly evaluated the targets, their evaluations
differed by Target Group, F(1, 119) = 18.27, p < .001, ηp2 = .13, and by Target Attractiveness,
F(2, 238) = 266.38, p < .001, ηp2 = .69. I found no significant interaction between Target Group
and Target Attractiveness, F(2, 238) = 0.60, p = .55, ηp2 = .01. Participants preferred ingroup
targets (M = 0.14, SD = 0.82) over outgroup targets (M = -0.14, SD = 0.83). Participants
preferred attractive targets (M = 0.66, SD = 0.78) over average targets (M = 0.00, SD = 0.81) and,
in turn, average targets over unattractive targets (M = -0.66, SD = 0.91).
I also compared the effect sizes for the implicit and explicit evaluations to examine the
relative importance of each target cue during evaluation formation. I found that Target Group
influenced evaluations more when participants implicitly evaluated targets, z = 3.75, p < .001,
whereas Target Attractiveness influenced evaluations more when participants explicitly
evaluated targets, z = -9.59, p < .001.
Discussion
Similar to Study 1, results from Study 2 showed that group membership influenced
participants’ implicit evaluations of targets more than attractiveness. In contrast, participants
weighed attractiveness more heavily when explicitly evaluating targets. However, by using the
experimentally created groups, Study 2 generalized these findings to other types of social groups
and ruled out possible confounds associated with racial biases.
22
Chapter 5 General Discussion
In spite of being classified as an individuating trait, attractiveness can improve
evaluations of outgroup members. However, whether attractiveness biases dominate impressions
depends on how people express their judgments. Whereas people explicitly expressed preference
for those of greater attractiveness, they implicitly preferred members of the same race (Studies
1A and 1B). Moreover, these results extend beyond racial groups. Even when evaluating
members of experimentally constructed minimal groups, participants demonstrated a similar
pattern of preferences for ingroup and outgroup members (Study 2).
If, as many dual-process models propose, implicit processes represent earlier stages
of social judgment (Morewedge & Kahneman, 2010), then group membership’s dominance
over implicit evaluations would lend support to impression formation models; this would
suggest that people do indeed prioritize social categories over individuating traits when
forming impressions. Yet, the results found here also departed from impression formation
models in an important way. Specifically, attractiveness still affected implicit evaluations—
indicating that attractiveness did not necessarily follow social categorization in a serial
manner. Rather, like multiple social categories, attractiveness and group membership
influenced impressions additively and separately (Anderson, 1965; Berdahl, & Moore, 2006;
Dowd & Bengtson, 1978; Cummings, Kropf, & Weaver, 2000). When combined with studies
that show that people may prioritize familiarity and likeability cues over group membership
information (Govan & Williams, 2004; Quinn, Mason, & Macrae, 2009), the results here
imply that impression formation may not necessarily start with social categorization and end
with individuation. Instead, even when people are tasked with categorizing others by group
23
membership and unmotivated to form indepth social judgments, they incorporate individualistic
traits, such as physical attractiveness, into their judgments of other individuals.
In the context of studies examining attractiveness and racial biases, the discrepancy
between implicit and explicit evaluations may provide insight as to why results in the
literature have been mixed. Whereas Maruyama and Miller (1980) found that students’
attractiveness, but not race, predicted essay scores, Agthe and her colleagues (2016)
discovered that people only applied attractiveness biases to same-race others. These
differences likely reflect the fact that, despite probing similar constructs (e.g., affective
evaluations), different measures are sensitive to different biases.
Implications
Accordingly, researchers have theorized that implicit and explicit measures account
for unique effects in attitudes and behavior. Whereas implicit evaluations better predict
nonverbal and affective responses, explicit evaluations better predict verbal behavior and
stereotype endorsement (Dovidio, Kawakami, & Beach, 2001; Dovidio, Kawakami, Johnson,
Johnson & Howard, 1997; Gawronski & Bodenausen, 2011). Thus, implicit measures may
detect subtle discrimination, even when participants express no overt prejudice. In the
context of these implicit-explicit frameworks, the results of this thesis may imply that
individuating traits, such as attractiveness, better predict overt, verbal behaviors, whereas
group membership may better predict subtle, nonverbal discrimination.
This distinction may shed light on why—despite the social progress witnessed by the
Western world over the last century—discrimination continues to run rampant in many
private sectors today (Brief, Dietz, Cohen, Pugh, & Vaslow, 2000). For instance, although
women like Grace Jones and Naomi Campbell are now canonized icons, fashion and beauty
24
industries still afford women of color far fewer opportunities than their White counterparts
(Freeman, 2014; Graham, 2017; Hoskins, 2014). Thus, although industry moguls often
publicly denounce discrimination (Larkin, 2017; Ryan, 2016), group membership biases may
nevertheless govern their actions—preventing them from installing truly progressive policies
and reforms.
Limitations and Future Directions
As with many social cues, there may be a question of whether group membership
outranked attractiveness because of bottom-up processes (e.g., visual salience) or because of
top-down processes (e.g., conceptual importance). Although group membership may appear
more salient than attractiveness, it is unlikely that it dominated implicit evaluations because
targets’ attractiveness escaped notice. Whereas participants often took longer than 600 ms to
respond to the race IAT’s target trials, studies have shown that facial attractiveness biases
can be activated within 100 ms (Locher et al., 1993). Thus, participants had ample time to
consider and integrate attractiveness information into their impressions.
Another possibility is that task demands also exercised a top-down influence. Given
that the IAT—by nature—emphasizes social categorization, this may prime the concept of
group membership, rendering it more salient than it otherwise would be. However, the fact
that Govan and Williams (2004) eliminated racial bias in IAT results by varying the
likeability of group exemplars suggests that group membership need not always drive racial
IAT results. Nevertheless, future research may wish to address this confound by using other
implicit evaluation tasks.
Finally, the target attractiveness ratings may also have influenced attractiveness halo
effects. According to the Chicago Face Database’s norming data (Ma et al., 2015), raters
25
based their evaluations of each target on other targets of the same race and gender.
Consequently, although raters gave similar attractiveness ratings to attractive Black targets
and attractive White targets, raters did not necessarily judge the attractive Black targets to be
equally as attractive as the attractive White targets. Put differently, intergroup biases may
have influenced attractiveness ratings; attractiveness halos for White targets may have been
more robust than attractiveness halos for attractive Black targets (Moss, Miller, & Page,
1975).
Whereas attractiveness biases play an important role in social cognition and behavior,
future research should also examine how other important social traits influence perceptions
of outgroup members. Recently proposed three-dimensional models of social inference
suggest that attractiveness, along with warmth and competence, is one of the three principle
dimensions of social judgment (Hehman et al., 2017; Sutherland et al., 2013). It is possible
that these three traits can influence intergroup biases in ways that most individuating traits
cannot. For instance, despite being recognized by laymen and researchers as a ubiquitous
trait that transcends group membership (Cunningham et al., 1995; Sutherland et la., 2013),
people may form subgroups within superordinate outgroups using attractiveness information.
As such, investigating how warmth and competence cues interact with group membership
may provide insight into the relationship between “privileged” social group categories (e.g.,
age, gender, race) and “privileged” social traits.
Conclusion
Given the many forces that guide people’s beliefs, attitudes, and behaviors,
psychology researchers are tasked with the challenge of determining which forces dominate,
and when they do so. Literature has shown, for instance, that the compelling forces of
26
attractiveness biases and group membership biases wield great influence over social
judgment and behavior. The social consequences of attractiveness biases notwithstanding,
attractiveness can serve as a powerful individuating trait that improves evaluations of
outgroup members and, at times, even supersedes group membership biases altogether. This
suggests that people are neither blinded by beauty nor group membership, but instead
capitalize on myriad available social cues to form individuated impressions of others.
27
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Table 1 Target Attractiveness Ratings Studies 1A and 1B Study 2 Black
(n = 30)
White (n = 30)
White (n = 60)
Attractiveness Level M SD M SD M SD Attractive 4.47 0.51 4.36 0.41 4.42 0.54 Average 3.24 0.39 3.02 0.29 3.14 0.26 Unattractive 2.38 0.38 2.18 0.30 2.18 0.28
Note. M = Mean, SD = Standard Deviation.
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Table 2 IAT Blocks for Study 1A and Study 2
Study 1A Response key assignment
Block N trials Discrimination task Left key Right key
1 24 Target practice White Black
2 24 Attribute practice Good Bad
3 24 Combined compatible test 1 White Good
Black Bad
4 48 Combined compatible test 2 White Black Bad
5 24 Reverse target practice Black White
6 24 Combined incompatible test 1 Black Good
White Bad
7 48 Combined incompatible test 2 Black Good
White Bad
Study 2 1 60 Target practice Ingroup Outgroup
2 60 Attribute practice Good Bad
3 60 Combined compatible test 1 Ingroup Good
Outgroup Bad
4 120 Combined compatible test 2 Ingroup Good
Outgroup Bad
5 60 Reverse target practice Outgroup Ingroup
6 60 Combined incompatible test 1 Outgroup Good
Ingroup Bad
7 120 Combined incompatible test 2 Outgroup Good
Ingroup Bad
Note. In lieu of “Ingroup” and “Outgroup” in Study 2, participants categorized images based on colors associated with group membership.
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Table 3 Words Used in the IAT Positive Negative Amazing Beautiful Excellent Glorious Joyful Lovely Marvelous Pleasure Success Superb Triumph Wonderful
Agony Awful Dreadful Horrible Humiliate Grief Nasty Painful Repulsive Terrible Tragic Vile
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Figures
Figure 1. Implicit target evaluations for Study 1A. IAT D scores for targets by Target Race and Target Attractiveness. Results revealed a significant main effect of Target Race and a significant main effect of Target Attractiveness. Error bars represent ± 1 standard error.
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Figure 2. Explicit target evaluations for Study 1A. Standardized Likeability ratings of targets by Target Race and Target Attractiveness. Results showed a significant main effect of Target Attractiveness and a significant interaction between Target Attractiveness and Target Race. Error bars represent ± 1 standard error.
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Figure 3. Explicit target evaluations for Study 1B. Standardized Likeability ratings of targets by Target Race and Target Attractiveness. Results showed a significant main effect of Target Attractiveness and a significant interaction between Target Attractiveness and Target Race. Error bars represent ± 1 standard error.
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Figure 4. Implicit target evaluations for Study 2. IAT D scores for targets by Target Group and Target Attractiveness. Results showed a significant main effect of Target Group and a significant main effect of Target Attractiveness. Error bars represent ± 1 standard error.
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Figure 5. Explicit target evaluations for Study 2. Standardized Likeability ratings of targets by Target Group and Target Attractiveness. Results showed a significant main effect of Target Attractiveness, a significant main effect of Target Group, and no interaction. Error bars represent ± 1 standard error.
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Appendix A
Bogus Personality Test Items in Study 2 1. I often feel blue. 2. I feel comfortable around people. 3. I believe in the importance of art. 4. I have a good word for everyone. 5. I am often down in the dumps. 6. I make friends easily. 7. I tend to vote for liberal political candidates. 8. I believe that others have good intentions. 9. I am always prepared. 10. I dislike myself. 11. I don’t talk a lot. 12. I have a vivid imagination. 13. I make people feel at ease. 14. I pay attention to details. 15. I have frequent mood swings. 16. I am skilled in handling social situations. 17. I carry the conversation to a higher level. 18. I respect others. 19. I get chores done right away. 20. I panic easily.
49
Supplemental Materials
For each study, I have provided alternative target-level analyses and multilevel
mixed-effects analyses where appropriate. For explicit evaluation analysis in Study 1A,
Study 1B, and Study 2, models treat targets and participants as random effects. For implicit
evaluation results in Study 2, the model only treats targets as random effects. All analyses
use centered attractiveness ratings in lieu of attractiveness categories. As a note, the
supplemental analyses provided here are largely consistent with participant-level analyses
provided in the main text. Moreover, for Studies 1A and 1B, the target-level results are also
consistent when controlling for each target’s racial prototypicality.
Study 1A
Implicit evaluations. Using targets as the level of analysis, I found a significant main
effect of Target Race, such that White targets (M = 0.20, SD = 0.12) were evaluated more
favorably than Black targets (M = -0.23, SD = 0.09; F(1, 54) = 232.81, p < .001, ηp2 = .81). I also
found a significant main effect of Target Attractiveness, F(1, 54) = 4.25, p = .04, ηp2 = .07.
Target Race did not interact with Target Attractiveness, F(1, 54) = .01, p = .92, ηp2 = .00.
Explicit evaluations. In contrast to the implicit evaluations, I found no significant main
effect of Target Race for explicit evaluations: Participants did not evaluate Black Targets (M =
4.56, SD = 1.45) differently from White targets (M = 4.46, SD = 1.41; b = 0.00, SE = 0.11, t(56)
= -0.03, p = .98, R2semi-partial = .00. However, a significant main effect of Target Attractiveness
showed Target Attractiveness predicted Likeability ratings, b = 0.63, SE = 0.08, t(56) = 7.90, p <
.001, R2semi-partial = .11. Finally, Target Race did not interact with Target Attractiveness, b = -.13,
SE = 0.11, t(56) = -1.13, p = .26, R2semi-partial = .002.
50
Study 1B
As in Study 1A, I standardized the Likeability scores for Study 1B. Results showed no
effect of Target Race. Participants did not evaluate Black targets (M = 4.25, SD = 1.57)
differently from White targets (M = 4.18, SD = 1.51; b = 0.03, SE = 0.09, t(56) = 0.30, p = .76,
R2semi-partial = .00). However, Target Attractiveness ratings did significantly predict Likeability
ratings, b = 0.60, SE = 0.07, t(56) = 9.19, p < .001, R2semi-partial = .08. There was no significant
interaction between Target Race and Target Attractiveness, b = -0.03, SE = 0.09, t(56) = -0.35, p
= .73, R2semi-partial = .00.
Study 2
Implicit evaluations. Participants evaluated ingroup targets (M = 0.15, SD = 0.16) more
favorably than outgroup targets (M = -0.12, SD = 0.15; b = 0.14, SE = 0.01, t(116) = 9.97, p <
.001, R2semi-partial = .47) and evaluated targets differently by Target Attractiveness, b = 0.04, SE =
0.01, t(116) = 2.98, p < .01, R2semi-partial = .05. There was no interaction between Target Group
and Target Attractiveness, b = 0.00, SE = .01, t(116) = 0.01, p = .99, R2semi-partial = .00.
Explicit evaluations For explicit evaluations, participants evaluated ingroup members
(M = 4.43, SD = 1.34) significantly more favorably than outgroup members (M = 4.17, SD =
1.36; B = 0.26, SE = 0.05, t(236) = 4.95, p < .001, R2semi-partial = .01. Target Attractiveness also
significantly predicted Likeability ratings, b = 0.56, SE = 0.04, t(236) = 14.95, p < .001, R2semi-
partial = .09. Lastly, Target Group did not interact with Target Attractiveness, b = 0.01, SE = 0.05,
t(236) = 0.28, p < .001, R2semi-partial = .00.
