Archives of Sexual Behavior

, Volume 45, Issue 3, pp 635–650 | Cite as

Lay Conceptions of Sexual Minority Groups

Original Paper

Abstract

Bisexual people are often implored to “pick a side,” implying that bisexuality is both more controllable and less desirable than heterosexuality or homosexuality. Bisexual people’s status as a social group perceived to fall between a traditionally advantaged group and a traditionally disadvantaged group may have the potential to clarify lay conceptions of sexual orientation. We examined participants’ views of groups varying in sexual orientation by randomly assigning participants (including heterosexual men and women as well as gay men and lesbian women) from four samples to evaluate heterosexual, bisexual, or homosexual targets (N = 1379). Results provided strong evidence for the previously untested theoretical argument that bisexuality is perceived as less stable than heterosexuality or homosexuality. In addition, participants low in Personal Need for Structure rated female (but not male) bisexuality as relatively stable, suggesting that a preference for simple, binary thinking can partially explain a negative conception of an ostensibly “intermediate” identity. Bisexual targets were perceived as falling between heterosexual and homosexual targets in terms of gender nonconformity, and less decisive, less monogamous, and lacking in positive traits that were associated with homosexual targets. In sum, views of bisexual people were both more negative than and qualitatively different from views of gay men and lesbian women. We discuss the results as an illustration of the complex ways that perceivers’ attitudes can differ depending on which target groups they are considering, suggesting that intergroup bias cannot be fully understood without attending to social categories viewed as intermediate.

Keywords

Bisexual Prejudice Sexual orientation Stereotypes 

Introduction

Bisexual people are often implored to “pick a side,” implying that bisexuality is both controllable and undesirable—an unstable status that should be abandoned as soon as possible (see Diamond, 2005; Rust, 1995). This view may stem from the fact that bisexual people violate a persistent assumption about sexual orientation—that it has only two varieties. As a result, being seen as “caught in the middle” may contribute to some of the unique biases they face. Preliminary evidence from other domains of intergroup bias hints that many social groups perceived to fall between a traditionally advantaged group and a traditionally disadvantaged group are disliked (e.g., biracial people: Sanchez & Bonam, 2009; Sanchez, Pauker, & Young, 2014; people with nonbinary gender identities: Tate & Youssef, 2014), perhaps due to a general sense of unease associated with nonbinary ways of thinking about social categories (see Roets & Van Hiel, 2011). This hint of a pattern suggests that bisexual people’s supposed “intermediate” status may have the potential to clarify attitudes about them.

The literature on sexual orientation has at times been constrained by an assumption that attitudes toward gay/lesbian and bisexual people are qualitatively similar (Diamond, 2005, 2008; Rust, 2000b). For example, attention to bisexual people has led researchers to challenge the previously held view that measures of “sexual prejudice” designed to assess homophobia also capture the essence of attitudes toward bisexual people (Carr, 2011; MacDonald, 2000; Savin-Williams, Pardo, Vrangalova, Mitchell, & Cohen, 2010). Bisexual people report being stereotyped and denigrated in ways that differ from the typical treatment of gay/lesbian people (Brewster & Moradi, 2010; Li, Dobinson, Scheim, & Ross, 2013). While bisexual people may fall between heterosexual and gay/lesbian people in some lay conceptualizations of sexual orientation (Carr, 2011; Rust, 2000c), they may not necessarily experience “in between” amounts of stereotyping and prejudice. A more nuanced approach to studying sexual attitudes should examine heterosexual, bisexual, and gay/lesbian people as identifiable social categories, each of which might elicit a unique pattern of attitudes (Worthen, 2011, 2012a, 2012b, 2013).

To examine lay conceptions of sexual orientation groups, we investigated three domains—evaluations, stereotypes, and perceptions of the stability of the relevant sexual orientation. Evaluations reflect explicitly positive or negative feelings about the target group, and thus map onto traditional definitions of explicit prejudice (Allport, 1954). Stereotypes represent attributions of particular traits to “typical” members of a group (Fiske, Cuddy, Glick, & Xu, 2002). Perceptions of stability reflect an often-overlooked dimension of intergroup bias. The belief that bisexual people should “pick a side” rests on the assumption that bisexuality itself is an unstable condition—a temporary or fluid identity, a result of confusion, or an orientation that is not truly “real” (Diamond, 2005; Mohr & Rochlen, 1999; Rust, 2000c). Perceived stability can apply to heterosexual and gay/lesbian identities as well, but it has not yet received much attention in this expanded context.

Lay Conceptions of Bisexual People

Negative attitudes toward bisexual people have been documented in college students, therapists, and more general samples in the United States, South Africa, and Germany (Blumstein & Schwartz, 1974; Butt & Guldner, 1993; De Bruin & Arndt, 2010; Eliason, 1997; Herek, 2002a; Mohr, Israel, & Sedlacek, 2001; Steffens & Wagner, 2004; Welzer-Lang & Tomolillo, 2008; Worthen, 2012a). Such attitudes have been documented in samples of gay men and lesbian women (De Bruin & Arndt, 2010; Mohr & Rochlen, 1999; Mulick & Wright, 2002; Rust, 1995; Worthen, 2011, 2012b), and bisexual people report experiencing prejudice from both heterosexual and gay/lesbian people (Beaber, 2008; Brewster & Moradi, 2010; Li et al., 2013).

We know of only a few studies that randomly assigned participants to evaluate bisexual or gay/lesbian targets, most of which presented therapists with descriptions of clients varying in sexual history (Bowers & Bieschke, 2005; Eubanks-Carter & Goldfried, 2006; Mohr, Weiner, Chopp, & Wong, 2009). For example, Mohr et al. (2009) found that a male client with a current female partner and a prior male partner was viewed as more conflicted and confused than a male client whose partners had all been the same gender. In a nonclinical study, Spalding and Peplau (1997) manipulated the sexual orientation and gender of each member of a fictional couple. Bisexual targets were perceived as more likely than heterosexual targets to cheat on their partners and more likely than heterosexual or gay/lesbian targets to transmit STDs to their partners. Zivony and Lobel (2014) also manipulated the sexual orientation of a hypothetical target in a dating context and found that bisexual men were perceived as more indecisive and less interested in monogamy than heterosexual and gay men.

Other studies have compared evaluations of bisexual and homosexual targets by having each participant evaluate both groups. Herek (2002a) conducted a random digit dialing phone survey that asked heterosexual participants to give “feeling thermometer” ratings for gay men, lesbians, bisexual men, and bisexual women. Herek found that heterosexual Americans evaluated bisexual people, especially bisexual men, more negatively than they evaluated gay and lesbian people. Others have obtained similar results using simple evaluation measures (Pirlott & Neuberg, 2014; Steffens & Wagner, 2004). Eliason (1997) asked undergraduate participants to evaluate the acceptability of bisexual, lesbian, and gay people, and found that bisexual people, especially bisexual men, were evaluated less favorably than gay/lesbian people.

Perceived Stability of Sexual Orientations

Perhaps the most distinctive perception of bisexuality is the idea that it is not a stable sexual orientation but rather “just a phase” or the result of confusion (Israel & Mohr, 2004; Mohr & Rochlen, 1999; Rust, 2000c, 2002; Worthen, 2013). For example, media sources have portrayed female bisexuality as an add-on to conventional heterosexuality (Diamond, 2005), and some gay men and lesbian women believe that bisexual people are actually gay or lesbian but mislabel themselves as bisexual because they are afraid of homophobia, because they are indecisive, or because they lack self-awareness (Blumstein & Schwartz, 1974; Rust, 1995). A number of theorists have argued that perceived instability might be one of the most important dimensions distinguishing conceptions of bisexual people from conceptions of gay/lesbian people in the U.S. (Diamond, 2005; Israel & Mohr, 2004; Rust, 2000c; Worthen, 2013).

Despite the theoretical importance of perceived stability, there is little direct evidence that people perceive bisexual orientations as less stable than heterosexual or gay/lesbian orientations. Rust (1995) reported that lesbian participants were more likely to believe that bisexual women would eventually identify as lesbian rather than that lesbian women would eventually identify as bisexual. In addition, at least two studies (Mohr, Chopp, & Wong, 2013; Zivony & Lobel, 2014) have found that open-ended characterizations of bisexual men (compared to heterosexual and gay/lesbian men) were more likely to describe them as confused and conflicted. Mohr and Rochlen (1999) developed a measure of perceptions of the stability of bisexuality, finding that low stability ratings were consistently associated with negative evaluations of bisexual people. The latter result has been replicated (Mohr et al., 2001; Worthen, 2011), but no studies have directly compared the perceived stability of bisexuality to the perceived stability of heterosexuality or homosexuality.

Extending Past Work on Conceptions of Sexual Minorities

We conducted a set of experiments designed to permit comparisons between assessments of heterosexual, bisexual, and gay/lesbian targets on the dimensions of evaluations, perceived stability, and trait stereotypes. Since past studies (e.g., Eliason, 2001; Herek, 2000, 2002a, 2002b) have found that men evaluate lesbian and bisexual women more positively than they evaluate gay and bisexual men, we also structured the experiments to permit comparisons between male and female target groups. We asked each participant to assess one of six target groups—heterosexual men, heterosexual women, bisexual men, bisexual women, homosexual men, and homosexual women. We used the word “homosexual” rather than “gay” and “lesbian” in order to keep the terminology as consistent as possible in each condition.

Predictions About Evaluations, Perceived Stability, and Trait Stereotypes

Given the limitations of the available information about attitudes toward bisexual people in particular, it would be premature to propose an overarching theory explaining differences among sexual orientation targets. Instead, we present several predictions based on the information available. Our goal was to provide a broad assessment of differences between target groups to reinforce the importance of including multiple categories in research.

In line with past work, we expected heterosexual participants to express more positive attitudes toward heterosexual targets than toward homosexual targets (Herek, 2000, 2002b), and more positive attitudes toward homosexual targets than toward bisexual targets (Herek, 2002a). Since members of marginalized groups frequently express relatively favorable explicit evaluations of their ingroup relative to the dominant group on average (Rudman, Feinberg, & Fairchild, 2002) despite moderation by internalized stigma (Herek, Gillis, & Cogan, 2009), we also predicted that gay men and lesbian women would rate homosexual targets more positively than heterosexual and bisexual targets.

Although prior empirical work has not compared the perceived stability of heterosexual, bisexual, and homosexual orientations using the same measures, theoretical accounts stress that doubts about stability appear to be particularly relevant to bisexual targets (e.g., Mohr & Rochlen, 1999). Therefore, we predicted that stability ratings would be lower for bisexual targets than for heterosexual and homosexual targets.

We developed four predictions about trait stereotypes that might differ between target sexual orientation groups. The first pertained to femininity and masculinity. Gay men are viewed as more feminine than heterosexual men and lesbian women are viewed as more masculine than heterosexual women (Blashill & Powlishta, 2009a, 2009b; Schope & Eliason, 2004; Taylor, 1983). Although bisexual people do not fit the typical heterosexual model of gender relations, they are not depicted as gender incongruent to the same extent as gay/lesbian people (Diamond, 2005; Mohr et al., 2013). Therefore, we predicted that bisexual targets would be differentiated by gender—bisexual women would be viewed as feminine and bisexual men would be viewed as masculine—more than homosexual targets but less than heterosexual targets.

Second, we anticipated that participants would rate bisexual targets as less decisive than heterosexual or homosexual targets as predicted by Rust (1995): if everyone is expected to experience attraction toward only men or toward only women, then those who claim to experience both must simply be of two minds generally, even outside the context of romance. Third, we predicted that bisexual targets would be perceived as especially likely to prefer open relationships, to care more about sex than emotional commitment, to cheat on their partners, and to have and transmit sexually transmitted diseases (Beaber, 2008; Blumstein & Schwartz, 1974; Israel & Mohr, 2004; Klesse, 2005; Ochs, 1996; Rust, 1995, 2000c; Spalding & Peplau, 1997).

Fourth, positive stereotypes about gay/lesbian people may not be broadly applied to all sexual minorities. As blatant prejudice against gay men and lesbian women has become less socially acceptable, some people have become reluctant to describe gay/lesbian targets as possessing negative traits, yet still hold “positive” stereotypes about these groups (Morrison & Bearden, 2007; Taylor, 1983). However, norms against describing gay/lesbian targets negatively do not necessarily proscribe describing bisexual targets negatively. Furthermore, there is little reason to expect specific positive stereotypes to generalize beyond the most visible sexual minority groups (gay men and lesbian women) to include all sexual minorities, especially ones perceived as less stable and thus perhaps less informative as social categories (see Hamilton, 2007; Jost & Hamilton, 2005). Therefore, we expected positive stereotypes of homosexual targets not to apply to bisexual targets, resulting in more positive ratings for homosexual than bisexual targets on trait adjectives (see Fiske et al., 2002).

Individual Differences in Evaluations and Stability Assessments of Sexual Minorities

We expected several individual differences to predict conceptions of sexual minorities in general and bisexual targets in particular. The most important of these was Personal Need for Structure (PNS; Neuberg & Newsom, 1993; Thompson, Naccarato, & Parker, 1989), which measures the motivation to think about the world in simple, manageable terms. PNS correlates with stereotypes of outgroups (Newheiser & Dovidio, 2012). It has also been linked specifically to negative views of gay/lesbian (Smith & Gordon, 1998) and bisexual people (Mohr & Rochlen, 1999). Some theorists have argued that negative views of bisexual people may stem from the perception that bisexuality introduces unwanted ambiguity into an otherwise straightforward model of sexual relationships (Ochs, 1996; Rust, 1995, 2000c; Worthen, 2013). Consequently, we expected PNS to predict the perception of bisexual orientations as unstable relative to heterosexual and homosexual orientations (Israel & Mohr, 2004; Rust, 2000c).

We also considered several ideological variables related to conceptions of sexual minorities, namely sexual permissiveness (Hendrick, Hendrick, & Reich, 2006), traditional gender ideologies, political orientation, and religiosity. Some have predicted that at least some of these individual differences would be more predictive of attitudes toward bisexual people than of attitudes toward gay/lesbian people (e.g., because they pertain to stereotypes of bisexual people) (Diamond, 2005; Israel & Mohr, 2004; Klesse, 2005; Rust, 2000b), whereas others have predicted that they would be uniformly predictive of evaluations of both bisexual and gay/lesbian targets (e.g., because both bisexual and gay/lesbian targets violate traditional norms) (Herek, 2002a; Whitley, 2009; Worthen, 2012a, 2013; Wright, Mulick, & Kincaid, 2007).

Method

Participants

Participants included 103 lesbian women, 147 gay men, 3 homosexual participants who did not specify gender, 3 homosexual participants who specified nonbinary gender identities, 664 heterosexual women, 454 heterosexual men, and 5 heterosexual participants who did not specify gender (N = 1379). The 11 participants with nonbinary or unknown genders were excluded from analysis procedures involving participant gender, but not from other analysis procedures.

This project applied the same experimental method to four samples, which we refer to as samples A, B, C, and D. The framing of the study in recruitment materials and consent forms was consistent across samples. For example, our recruitment materials (e.g., fliers) described the study as being about “issues related to sexuality and sexual minorities” and asked participants to share their views for the sake of “the diversity of the sample” (see Yost & Thomas, 2012). Only adults living in the United States at the time of participation were considered eligible.

We addressed the possibility of inattentive online responding by employing two checks. First, participants were asked explicitly whether any of their responses were intended as jokes. Those who said “yes” or declined to answer this question (N = 17) were excluded from analysis. Second, participants were twice asked to report their birth year—once at the beginning of the study to ensure that all participants were adults, and again at the end with the main demographic measures. Those who gave different responses to the two instances of this item (N = 14) were excluded on the assumption that they were either highly inattentive or minors who lied about their age in order to participate. Less than 2.2 % of participants were excluded on these bases.

Sample A (n = 935; 772 heterosexual, 163 gay/lesbian) consisted of volunteers who completed the study online without compensation. These participants were recruited via websites hosting classified advertisements, blogs, and paid advertisements on listservs and social networking platforms (e.g., Craigslist, Facebook). Sample B (n = 180; 168 heterosexual, 12 gay/lesbian) consisted of paid workers from Amazon.com’s Mechanical Turk (MTurk) service, which is designed to facilitate rapid completion of online tasks requiring human input. Sample C (n = 121; 63 heterosexual, 58 gay/lesbian) consisted of workers from SocialSci.com’s participant pool, who earned points that could be redeemed for gift cards. Based on pre-existing demographic information, we made our study available to sexual minorities without alerting them to the fact that sexual orientation played a role in their eligibility. Sample D (n = 143; 120 heterosexual, 23 gay/lesbian) consisted of participants who completed paper copies of the survey in a university setting. These participants were recruited via local e-mail lists and fliers posted in businesses around campus and the city. Each paper-and-pencil participant was paid $10 to complete an extended version of the survey, which contained additional measures presented after the main survey so as not to alter the procedure described below.

Across samples, participants ranged in age from 18 to 77 years (M = 33.91, SD = 12.68). Most participants were White (N = 1055); 85 were Asian, 70 were Black, 68 were Latino/a, 56 were multiracial, 26 gave other racial or ethnic categories, and 19 declined to specify. When asked to identify as either “more conservative” or “more liberal,” the majority (N = 981) picked liberal, while 374 picked conservative and 24 declined to specify. In line with our intentionally diverse sampling procedures (Meyer & Wilson, 2009; Uncles & Kwok, 2013), the four samples differed significantly on each major demographic variable, so the demographic distribution of each sample is reported in Table 1.
Table 1

Demographic characteristics of all four samples

 

Sample A

Sample B

Sample C

Sample D

Age (years)

M = 35.88

SD = 12.93

M = 35.54

SD = 11.61

M = 28.65

SD = 9.55

M = 23.44

SD = 7.07

Race/ethnicity

    

 Asian

N = 21

N = 13

N = 10

N = 41

 Black

N = 37

N = 11

N = 3

N = 19

 Latino/a

N = 48

N = 3

N = 7

N = 10

 White

N = 749

N = 151

N = 94

N = 61

 Other

N = 23

N = 0

N = 2

N = 1

 Multiple

N = 39

N = 2

N = 5

N = 10

 Not reported

N = 18

N = 0

N = 0

N = 1

Religion

    

 Christian/Catholic

N = 361

N = 78

N = 23

N = 59

 Other

N = 110

N = 20

N = 18

N = 19

 Multiple

N = 25

N = 3

N = 5

N = 8

 Agnostic/Atheist

N = 121

N = 36

N = 38

N = 27

 None

N = 223

N = 42

N = 30

N = 22

 Not reported

N = 95

N = 1

N = 7

N = 8

 Religiosity (9-point scale)

M = 3.73

SD = 2.40

M = 3.32

SD = 2.60

M = 2.58

SD = 2.21

M = 3.87

SD = 2.58

Political orientation

    

 “More liberal”

N = 651

N = 120

N = 98

N = 112

 “More conservative”

N = 267

N = 57

N = 22

N = 28

 Not reported

N = 17

N = 3

N = 1

N = 3

Formal education

    

 High school

N = 103

N = 14

N = 15

N = 9

 Some college

N = 332

N = 54

N = 39

N = 91

 Associate’s degree

N = 125

N = 17

N = 7

N = 5

 Bachelor’s degree

N = 199

N = 63

N = 32

N = 16

 Graduate school

N = 175

N = 32

N = 27

N = 22

 Not reported

N = 1

N = 0

N = 1

N = 0

Region of US

    

 Midwest

N = 306

N = 49

N = 32

 

 Northeast

N = 238

N = 29

N = 34

N = 143a

 South

N = 250

N = 59

N = 29

 

 West

N = 133

N = 43

N = 26

 

 Not reported

N = 8

N = 0

N = 0

 

For information about participant gender and sexual orientation in each sample, see the Participants section of the main text

aAll participants from Sample D completed the study in person in the Northeast

Procedure

After giving informed consent, participants completed the PNS scale, described below (Neuberg & Newsom, 1993). Next, all participants were asked to rate 11 possible definitions of “bisexual man” and “bisexual woman” and then select their preferred definition. They also rated a set of statements about the political status of sexual orientation which are not examined here.

Participants were then randomly assigned to rate one of six target groups—heterosexual men, heterosexual women, bisexual men, bisexual women, homosexual men, or homosexual women. This language was chosen to maintain consistency of phrasing between conditions. Participants completed measures of their evaluations of the target group, perceptions of the stability of the target group’s sexual orientation, and trait stereotypes about the target group, all of which are described in the Measures section below. Next, participants completed the measures of sexual permissiveness and gender ideology, as described below, in a counterbalanced order.

Participants then specified their gender, race and/or ethnicity, religious identification(s), and sexual orientation, all with open-ended response fields to enable participants to respond flexibly and to avoid presenting some response options as particularly desirable (see Beaber, 2008; Rust, 2000a). We grouped participants into categories using algorithms designed to account for misspellings, synonyms, and abbreviations. The algorithm for sexual orientation categorized participants as heterosexual, gay/lesbian, bisexual, or other, via a process that was independently shown to line up with forced-choice self-categorizations.1 Finally, participants completed the measures of political orientation and religiosity described below.

Measures

Items for all multi-item scales were theoretically derived. Except where specified, each item had a six-point response scale ranging from “Strongly disagree” (1) to “Strongly agree” (6), and scale scores were computed using unweighted means of the scale items.

Evaluation

Participants responded to a series of items designed to measure their evaluations of their randomly assigned target group. The first item, “general evaluation,” asked “In general, how do you feel about [target group]?” For this item only, participants responded on a 101-point sliding scale from “Very negatively” (0) to “Very positively” (100).

The next six items measured “liking” for the target group on six-point scales. Because prior measures of liking for sexual orientation groups have used different items to refer to bisexual and gay/lesbian targets, we combined items from social distance measures (Crandall, 1991) and measures of tolerance for sexual minorities (Herek, 1984; Mohr & Rochlen, 1999). Sample items, using bisexual women as an example target group, included “I would like to be friends with a bisexual woman,” “I sometimes try to avoid bisexual women,” and “Female bisexuality is immoral.” This measure of liking had satisfactory estimated reliability in the combined sample (α = .87), in each sample individually (α > .77), and for each sexual minority target group individually (α > .86). Few people agreed with items like “Male heterosexuality is immoral,” so the computed reliability of the measure was more moderate for heterosexual male (α = .74) and female (α = .69) targets.

The liking and general evaluation measures were strongly correlated with each other, r(1169) = .71, p < .0001. However, we analyzed them separately because they were measured on different response scales and because 208 participants who responded to the liking items did not respond to the general evaluation measure, so they were measured in slightly different samples.

Perceived Stability of Sexual Orientation

To measure the perceived stability of each sexual orientation, we adapted Mohr and Rochlen’s (1999) measure of “stability,” rewording the items so that they would apply to each randomly assigned target group. Sample items, using bisexual women as an example target group, included “Most women who identify as bisexual have not yet discovered their true sexual orientation” and “Female bisexuality is a stable sexual orientation.” These items were presented along with the evaluation items in a random order.

This measure of stability had satisfactory estimated reliability in the combined sample (5 items, α = .85), in each sample individually (α > .72), and for each sexual minority target group individually (α > .81). Few people agreed with items like “Most men who identify as heterosexual have not yet discovered their true sexual orientation,” so the computed reliability of the measure was more moderate for heterosexual male (α = .69) and female (α = .66) targets.

Trait Stereotypes

We selected trait stereotypes to measure based on past work on conceptions of sexual minority groups. We presented participants with 28 statements about their randomly assigned target group, in a random order. Each statement made an assertion about what the group was like “on average.” For example, “On average, bisexual women are emotional.” The full list of 28 trait stereotypes is presented in Table 2.
Table 2

Overall mean agreement with 28 trait stereotypes of six target groups (standard deviations are in parentheses)

 

Heterosexual women

Bisexual women

Homosexual women

Heterosexual men

Bisexual men

Homosexual men

Effect sizesm

Sexual orientation

S. O. × Gender

Feminine

4.44 (1.00)a

3.74 (1.09)b

3.31 (1.09)c

2.12 (0.94)e

2.86 (1.29)d

3.46 (1.28)bc

.001

.175*

Masculine

2.03 (0.94)e

2.68 (1.15)d

3.05 (1.25)c

4.33 (1.10)a

3.46 (1.16)b

3.25 (1.13)bc

.002

.134*

Decisive

3.55 (1.18)bcd

3.48 (1.12)cd

3.87 (1.15)a

3.79 (1.15)abc

3.43 (1.14)d

3.84 (1.18)ab

.022*

.003

Prefer one partnerf

4.48 (1.21)a

3.89 (1.21)b

4.37 (1.39)a

3.77 (1.29)b

3.61 (1.31)b

3.90 (1.39)b

.019*

.004

Open relationshipsg

2.00 (1.12)c

2.70 (1.34)b

2.06 (1.12)c

2.82 (1.37)ab

3.06 (1.42)a

2.65 (1.33)b

.033*

.006

Care more about sexh

2.11 (0.95)c

2.55 (1.36)b

2.00 (1.16)c

3.55 (1.46)a

2.80 (1.43)b

2.64 (1.28)b

.028*

.037*

Likely to cheati

2.37 (1.09)c

2.52 (1.35)bc

1.98 (1.04)d

3.10 (1.29)a

2.84 (1.43)ab

2.53 (1.22)bc

.029*

.005

Would never cheatj

2.80 (1.30)bc

3.05 (1.24)ab

3.17 (1.32)a

2.46 (1.17)c

2.89 (1.25)ab

2.95 (1.36)ab

.021*

.001

Have STDk

2.15 (1.04)b

2.16 (1.20)b

1.64 (0.92)c

2.52 (1.18)a

2.58 (1.46)a

2.58 (1.36)a

.010

.013*

Given someone STDl

2.27 (1.15)bc

2.03 (1.11)c

1.64 (0.97)d

2.78 (1.26)a

2.54 (1.44)ab

2.47 (1.44)ab

.023*

.004

Promiscuous

2.61 (1.11)c

2.87 (1.46)bc

2.27 (1.20)d

3.41 (1.32)a

3.08 (1.44)ab

3.10 (1.34)ab

.012

.013

Sexually experienced

3.58 (1.15)ab

3.81 (1.26)a

3.41 (1.29)b

3.71 (1.13)ab

3.60 (1.24)ab

3.67 (1.20)ab

.003

.008

Sexually skilled

3.58 (1.14)ab

3.77 (1.18)a

3.57 (1.23)ab

3.46 (1.23)ab

3.44 (1.26)b

3.65 (1.06)ab

.001

.006

Sincere

3.99 (1.12)b

3.94 (1.09)b

4.35 (1.04)a

3.72 (1.06)b

3.95 (1.10)b

4.35 (1.06)a

.041*

.003

Dependable

4.14 (0.98)bc

4.03 (1.10)bc

4.43 (1.04)a

3.92 (1.07)c

3.97 (1.13)c

4.31 (1.10)ab

.026*

.001

Loyal to their friends

4.10 (1.15)b

4.20 (1.07)b

4.61 (1.03)a

4.24 (1.06)b

4.27 (1.09)b

4.62 (1.10)a

.034*

.001

Competent

4.24 (1.13)b

4.24 (1.15)b

4.62 (1.11)a

4.23 (1.04)b

4.27 (1.11)b

4.63 (1.13)a

.027*

.000

Intelligent

4.17 (1.14)b

4.16 (1.05)b

4.51 (1.10)a

3.97 (1.08)b

4.07 (1.04)b

4.51 (0.99)a

.035*

.001

Warm

4.07 (1.06)ab

3.91 (1.03)bc

4.10 (1.11)ab

3.67 (1.07)c

3.88 (1.10)bc

4.35 (1.08)a

.021*

.013*

Untrustworthy

2.31 (1.15)ab

2.13 (1.15)bc

1.97 (1.12)c

2.61 (1.17)a

2.33 (1.24)ab

2.10 (1.21)bc

.022*

.001

Deceptive

2.60 (1.30)ab

2.23 (1.15)cd

1.94 (1.05)d

2.81 (1.13)a

2.53 (1.29)abc

2.28 (1.23)bc

.040*

.001

Lazy

2.16 (1.00)b

1.97 (0.96)bc

1.82 (0.96)c

2.66 (1.18)a

2.03 (1.03)bc

1.84 (0.96)c

.052*

.011

Unfriendly

2.21 (1.01)ab

2.06 (0.98)ab

1.97 (1.10)b

2.26 (1.03)a

1.96 (0.92)b

1.96 (1.04)b

.013*

.001

Physically healthy

3.90 (1.13)bc

4.01 (1.05)ab

4.09 (1.20)ab

3.67 (0.99)c

3.93 (1.04)bc

4.22 (1.07)a

.020*

.004

Mentally healthy

4.14 (1.17)ab

4.26 (1.24)ab

4.35 (1.26)a

4.00 (1.14)b

4.07 (1.27)b

4.33 (1.22)ab

.010

.000

Emotional

4.23 (1.24)a

3.70 (1.18)b

3.67 (1.20)b

3.03 (1.14)c

3.71 (1.10)b

4.15 (1.12)a

.009

.090*

Gentle

3.94 (1.03)ab

3.77 (1.01)b

3.73 (1.16)b

3.36 (1.07)c

3.73 (0.97)b

4.11 (1.09)a

.011

.032*

Aggressive

2.75 (1.10)bc

2.92 (1.21)b

2.88 (1.30)b

3.61 (1.24)a

2.46 (1.07)cd

2.41 (1.07)d

.041*

.066*

Superscript letters a, b, c, d, and e are used to indicate the results of individual Tukey tests comparing the six groups in each row. Cells in the same row with different letters differed significantly with p < .05. See the effect size columns for the results of statistical tests at a more conservative threshold

Most items were phrased “On average, [target group] are [adjective].” The seven exceptions are listed below

fOn average, [target group] prefer relationships in which they have one romantic and sexual partner at a time

gOn average, [target group] prefer relationships in which it is OK to have sex with people outside of the relationship

hOn average, [target group] care more about sex than emotional commitment

iOn average, [target group] in relationships are likely to cheat on their partners

jOn average, [target group] would never cheat in relationships, even if they had the opportunity

kOn average, [target group] are likely to have a sexually transmitted disease

lOn average, [target group] are likely to have given somebody else a sexually transmitted disease

mThese two columns report partial eta squared for the main effect of target sexual orientation and the interaction between target sexual orientation and target gender, respectively. An asterisk (*) indicates that the effect in question was statistically significant at our adjusted threshold of p < .00022. These estimates stem from a 3 (target sexual orientation) by 2 (target gender) by 2 (participant sexual orientation) by 2 (participant gender) ANOVA model. None of the effects reported in this table was significantly moderated by participant sexual orientation or participant gender at our adjusted threshold (except for the two items pertaining to STDs; see Results)

Some participants reported that they felt uncomfortable responding to items about what a group is like “on average.” Some declined to answer the trait stereotype items or responded to all 28 items in exactly the same way, even ones that were phrased as opposites. These participants (N = 17) were excluded from analysis procedures involving these items.

Although our predictions about the 28 trait items fell into four categories (femininity/masculinity, indecisiveness, sexual behavior, and lack of positive stereotypes), we did not form composite scales for three reasons. First, we wanted to test whether the pattern of differences among target groups on the trait items would match our predictions without applying any a priori structure to them. Second, averaging together groups of items would permit strong effects on some items to mask weaker effects on other items. Third, examining the 28 items using principal component analysis yielded ambiguous results, with potential models ranging from two to five factors and many items that loaded approximately equally onto multiple factors.

Personal Need for Structure

The PNS Scale (Neuberg & Newsom, 1993) measures participants’ motivation to think about the world in simple, manageable terms. The 11-item scale had satisfactory estimated reliability in the combined sample (α = .83), among heterosexual (α = .84) and gay/lesbian participants (α = .83), and in each of the four samples individually (α > .78).

Sexual Permissiveness

To measure individual differences in sexual permissiveness, we combined the permissiveness subscale of Hendrick et al.’s (2006) Brief Sexual Attitudes Scale and Herek’s (2002a) measure of views about sexual behavior. We excluded items about participants’ own sexual behavior (“I would like to have sex with many partners”) and retained items about sexual behavior in general, such as “It is OK to have ongoing sexual relationships with more than one person at a time” (Hendrick et al., 2006). The resulting 9-item scale had satisfactory estimated reliability overall (α = .88), among heterosexual (α = .88) and gay/lesbian participants (α = .86), and in each of the four samples individually (α > .86).

Gender Ideology

We included several items designed to measure three gender-related ideologies. Four items measured gender polarization, the tendency to view men and women as fundamentally different from each other. These items included “Men are fundamentally different from women” and “In most respects, women’s minds are basically the same as men’s minds.” Six items measured complementary stereotypes about men and women, from the Complementary Gender Differences subscales of Glick and Fiske’s (1996, 1999) Ambivalent Sexism Inventory and Ambivalence toward Men Inventory. Sample items include “Many women have a quality of purity that few men possess” and “Men are more willing to take risks than women are.” All gender ideology items were presented together in a random order.

The four-item gender polarization subscale had satisfactory estimated reliability in the combined sample (α = .80), among heterosexual (α = .79) and gay/lesbian participants (α = .81), and in each of the four samples individually (α > .78). The three-item subscale measuring complementary stereotypes of women had satisfactory estimated reliability in the combined sample (α = .79), among heterosexual (α = .78) and gay/lesbian participants (α = .78), and in each of the four samples individually (α > .76). The three-item subscale measuring complementary stereotypes of men also had satisfactory estimated reliability in the combined sample (α = .76), among heterosexual (α = .74) and gay/lesbian participants (α = .81), and in each of the four samples individually (α > .74). The three gender ideology measures were significantly correlated with each other, .32 < r < .48, p < .0001, but these correlations were not large enough to view the constructs as redundant.

Political Orientation and Religiosity

We measured political orientation by asking participants “If you had to categorize yourself as either ‘more liberal’ or ‘more conservative,’ which would you pick?” There is reason to believe that most people show preferences for one side or the other, and these preferences are meaningful indicators of personality and behavior, so we did not permit an intermediate response option for political orientation (Hawkins & Nosek, 2012). We measured religiosity by asking participants “How religious would you say you are?” on a 9-point scale ranging from “Not at all religious” (1) to “Extremely religious” (9). Such self-ratings are strong indicators of the overall construct of religiosity, particularly as it relates to sexual prejudice (Whitley, 2009).

Results

We aggregated across all four samples for our analysis procedures, treating “sample” as an additional predictor variable. The sample variable did not significantly interact with any of the effects we report below, even at a significance threshold unadjusted for multiple tests, p > .05.

Evaluations of Sexual Orientation Groups

For the general evaluation item and the liking scale, we conducted separate 2 (participant gender) × 2 (participant sexual orientation) × 2 (target gender) × 3 (target sexual orientation) analysis of variance (ANOVA) procedures. For both evaluation measures, the largest effect was the predicted interaction between participant sexual orientation and target sexual orientation, F > 21, p < .0001, \( \eta_{p}^{2} \) > .03 (see Table 3 for means and SD). As expected, among heterosexual participants, heterosexual targets were rated more positively than homosexual targets, F > 25, p < .0001, and homosexual targets were rated more positively than bisexual targets, F > 5.5, p < .02. Furthermore, gay and lesbian participants rated homosexual targets more positively than heterosexual targets, F > 20, p < .0001. However, they also rated bisexual targets more positively than heterosexual targets, F > 24, p < .0001, and their evaluations of bisexual and homosexual targets did not differ from each other, F < 1.2, p > .27. Unsurprisingly given our large sample size, other terms in this ANOVA model attained p values <.05, but the other interaction terms all had comparatively small effect sizes, \( \eta_{p}^{2} \) < 0.01. Past studies on sexual prejudice with large sample sizes have similarly focused on effects with \( \eta_{p}^{2} \) > 0.01 rather than examining all effects with p < .05 (Herek, 2002b).
Table 3

Overall mean liking scores, general evaluations, and stability ratings of target sexual orientation groups (standard deviations are in parentheses)

 

Heterosexual targets

Bisexual targets

Homosexual targets

Heterosexual participants

   

 General evaluations

84.37 (19.63)

67.52 (29.25)

74.79 (28.60)

 Liking scores

5.58 (0.62)

5.07 (1.14)

5.26 (1.04)

 Stability ratings

5.10 (0.79)

4.14 (1.23)

4.95 (1.03)

Gay/lesbian participants

   

 General evaluations

68.26 (24.52)

88.08 (15.95)

90.95 (16.37)

 Liking scores

5.28 (0.66)

5.72 (0.41)

5.71 (0.56)

 Stability ratings

5.02 (0.69)

4.61 (1.08)

5.50 (0.62)

Overall, female targets (heterosexual, bisexual, and homosexual women) were evaluated more favorably than male targets (heterosexual, bisexual, and homosexual men) on the general evaluation measure (women: M = 80.46, SD = 24.47; men: M = 73.56, SD = 27.68), F(1, 1136) = 21.76, p < .0001, \( \eta_{p}^{2} \) = .02, and the liking measure (women: M = 5.41, SD = 0.88; men: M = 5.28, SD = 0.97), F(1, 1344) = 6.98, p = .0083, \( \eta_{p}^{2} \) = .01, but not the stability measure (women: M = 4.80, SD = 1.07; men: M = 4.78, SD = 1.10), F(1, 1343) = 0.12, p = .73, \( \eta_{p}^{2} \) = .00

Perceived Stability of Sexual Orientations

Perceived stability was correlated with both general evaluation, r(1169) = .54, p < .0001, and liking, r(1376) = .58, p < .0001, but not as strongly as the two evaluation measures were correlated with each other, r(1169) = .71. We therefore examined stability separately.

We conducted a 2 (participant gender) × 2 (participant sexual orientation) × 2 (target gender) × 3 (target sexual orientation) ANOVA predicting stability ratings. The largest effect was the predicted main effect of target sexual orientation, F(2, 1343) = 108.57, p < .0001, \( \eta_{p}^{2} \) = .14. As expected, bisexual orientations (M = 4.22, SD = 1.21) were viewed as less stable than heterosexual orientations (M = 5.08, SD = 0.77), F(1, 860) = 152.82, p < .0001, or homosexual orientations (M = 5.06, SD = 0.98), F(1, 927) = 137.48, p < .0001. Heterosexual and homosexual orientations were viewed as equally stable, F(1, 899) = 0.29, p = .59.

The effect of target sexual orientation was qualified by a small interaction with participant sexual orientation, F(2, 1343) = 9.28, p < .0001, \( \eta_{p}^{2} \) = .01 (see Table 3 for means and SD). Among heterosexual participants, there was a main effect of target sexual orientation matching the overall main effect described above, F(2, 1105) = 93.67, p < .0001, \( \eta_{p}^{2} \) = .15. Bisexual orientations were viewed as less stable than heterosexual orientations, F(1, 715) = 152.69, p < .0001, or homosexual orientations, F(1, 761) = 102.74, p < .0001. However, heterosexual participants also viewed heterosexual orientations as more stable than homosexual orientations, F(1, 734) = 4.48, p = .035. Among gay and lesbian participants, there was a different main effect of target sexual orientation, F(2, 238) = 24.19, p < .0001, \( \eta_{p}^{2} \) = .17. As in the whole sample, bisexual orientations were viewed as less stable than heterosexual orientations, F(1, 145) = 5.46, p = .021, or homosexual orientations, F(1, 166) = 42.95, p < .0001. However, gay and lesbian participants also viewed heterosexual orientations as significantly less stable than homosexual orientations, F(1, 165) = 20.81, p < .0001. In other words, both heterosexual and gay/lesbian participants viewed bisexual orientations as the least stable and their respective ingroup orientations as the most stable.

Trait Stereotypes

Table 2 includes a list of all 28 trait items and their means and SD for all 6 target groups. In line with our exploratory goals, we conducted 28 four-way ANOVAs (participant gender × participant sexual orientation × target gender × target sexual orientation) predicting each of the 28 trait items individually. Since we were primarily interested in differences between sexual orientation target groups, we examined the 8 terms (in each ANOVA model) that included a main effect or interaction involving target sexual orientation. We employed a Bonferroni correction for these 224 (28 × 8) hypothesis tests, setting the significance threshold for each individual test at .00022 (.05/224). This type of correction is very conservative, making it unlikely that our results were due to chance even though we report a large number of statistical tests (Shaffer, 1995). This analysis procedure resulted in at least one significant effect involving target sexual orientation for all but four trait items (promiscuous, sexually experienced, sexually skilled, and mentally healthy). None of these effects involved interactions with participant sexual orientation or participant gender (except for two items about STDs described below). The effect sizes and hypothesis test outcomes for all main effects of target sexual orientation and all interactions between target sexual orientation and target gender are shown in the last two columns of Table 2. We further explored several key effects using Tukey post hoc tests, the results of which are summarized below. Also, the outcomes of individual Tukey tests comparing all six target groups on each trait item can be found in Table 2.

We found support for our predictions that bisexual targets would be viewed as less gender incongruent, more indecisive, more sexual, and less positively stereotyped than homosexual targets.2 Notably, the traits “feminine” and “masculine” were both predicted by an interaction between target sexual orientation and target gender, such that heterosexual women were perceived as more feminine (and less masculine) than bisexual women, who in turn were perceived as more feminine (and less masculine) than homosexual women. Similarly, heterosexual men were perceived as less feminine (and more masculine) than bisexual men, who in turn were perceived as less feminine (albeit not significantly more masculine) than homosexual men (see Table 2). Regarding decisiveness, there was a main effect of target sexual orientation: homosexual targets were perceived as more decisive than heterosexual targets, p = .033, and heterosexual targets were perceived as more decisive than bisexual targets, p = .018.

Regarding traits related to sexual behavior, bisexual targets were rated as significantly less likely to prefer one partner at a time (and more likely to prefer open relationships) than heterosexual and homosexual targets, p < .0001. Bisexual women were rated as significantly more likely to care more about sex than emotional commitment in relationships than heterosexual and homosexual women. We did not find support for the prediction that bisexual targets would be perceived as any more likely to cheat than heterosexual targets, but both bisexual and heterosexual targets were perceived as more “likely to cheat on their partners” than homosexual targets, p < .0001. We did not find any support for the prediction that bisexual targets would be perceived as especially likely to have and transmit STDs, although we did find strong evidence that homosexual women were perceived as unlikely to have and transmit STDs, in line with a previously documented assumption about lesbian relationships (Dolan, 2005).3

Regarding positive stereotypes of homosexual targets, five positive traits—sincere, dependable, loyal to friends, competent, and intelligent—were predicted by a main effect of target sexual orientation. For all five traits, homosexual targets were perceived more positively than both bisexual and heterosexual targets, p < .0001, and bisexual and heterosexual targets did not differ significantly from each other, p > .49. Similar but more complex effects emerged for five additional positive traits. The trait “warm” was attributed more to homosexual men than to other men, but not differentially attributed among female targets. The trait “physically healthy” showed a main effect of target sexual orientation, such that homosexual targets were viewed as more physically healthy than bisexual targets, p = .015, who were viewed as slightly more physically healthy than heterosexual targets, p = .045. The traits “untrustworthy,” “deceptive,” and “lazy” also showed a main effect of target sexual orientation, such that heterosexual targets were perceived as more untrustworthy, deceptive, and lazy than bisexual targets, p < .017, who were perceived as more untrustworthy, deceptive, and lazy than homosexual targets, p < .036. The trait “unfriendly” showed a main effect of target sexual orientation, such that heterosexual targets were perceived as more unfriendly than both bisexual and homosexual targets, p < .003, but bisexual and homosexual targets did not differ, p = .79. This was the only unambiguously negative trait item on which bisexual and homosexual targets did not differ. In general, homosexual targets were conferred more positive and less negative traits than bisexual targets.

Summary of Bisexual Trait Stereotypes

To supplement the above analysis, we performed one additional procedure to identify traits that were applied differentially to bisexual and other targets, ignoring target gender and participant characteristics. We fit a linear model for each trait item including two contrasts as predictors to compare bisexual targets to heterosexual and homosexual targets, respectively. For this set of 56 tests, we used a separate Bonferroni correction—an individual-level significance threshold of .00089. Compared to heterosexual targets, bisexual targets were viewed as more likely to prefer an open relationship and less likely to prefer one partner at a time, more likely to “never cheat” on their partners, and less likely to be lazy, aggressive, unfriendly, or deceptive. Compared to homosexual targets, bisexual targets were viewed as more likely to prefer an open relationship and less likely to prefer one partner at a time, more likely to cheat in relationships, to care more about sex than emotional commitment, or to have an STD, more likely to be promiscuous or deceptive, and less likely to be warm, sincere, competent, intelligent, decisive, loyal to friends, or dependable.

Individual Differences in Evaluations and Stability

To identify individual differences related to conceptions of sexual orientation groups, we measured PNS and several ideological variables. For each individual difference variable, we fit a separate regression model predicting each dependent measure (general evaluation, liking, or stability) as a function of target sexual orientation, target gender, participant sexual orientation, participant gender, the individual difference variable in question, and all 26 interaction terms involving these five predictor variables. Because we wanted to identify differences by target sexual orientation, we only examined tests of the eight interaction terms that included both the individual difference variable and target sexual orientation. We therefore used a corrected significance threshold of .00625 (.05/8) for these F-tests, which examined the variance explained over and above same-order and lower-order terms in the saturated regression model.

Once a significant effect was identified using the procedure described above, we began with the saturated model predicting the dependent measure in question, and then simplified this model via backward elimination, removing at each step the term with the highest p value that was not a dependency of a higher-order term, but not removing any terms with p < .05, until no further terms could be removed. We then used simple slopes analysis to examine the effect of interest while statistically accounting for the other effects in the model (Aiken & West, 1991). We summarize the results of these simple slopes analysis procedures below. Our trimmed models included some terms that were retained because they were significant at p < .05 but not analyzed in detail because they did not meet our adjusted significance thresholds. The variables involved in these terms were contrast-coded so that the results reported below stem from averages between groups not involved in the interaction (e.g., averaged between men and women).

Personal Need for Structure

In the model predicting the general evaluation measure, no interactions involving PNS were significant, F < 1.9, p > .17, \( \eta_{p}^{2} \) < .004. However, there was a main effect of PNS. In a model including target and participant sexual orientation and gender, PNS was slightly negatively related to general evaluations of all targets, b = −2.47, SE = .90, p = .006, \( \eta_{p}^{2} \) = .01. Similarly, in the model predicting liking, no interactions involving PNS were significant, F < 3.3, p > .04, \( \eta_{p}^{2} \) < .005, but again there was a main effect of PNS. In a model including target and participant sexual orientation and gender, PNS was slightly negatively related to liking of all targets, b = −.11, SE = .03, p < .001, \( \eta_{p}^{2} \) = .01. These results suggest that PNS may relate to negative evaluations of groups in general rather than stigmatized groups in particular (see Newheiser & Dovidio, 2012).

In the model predicting perceived stability, the only significant term involving PNS was the interaction of target sexual orientation, target gender, and PNS, F(2, 1318) = 5.44, p = .004, \( \eta_{p}^{2} \) = .01. We began with a fully saturated model including all five variables and simplified it according to the procedures described above before examining the key three-way interaction. We expected PNS to be negatively related to perceived stability of bisexual orientations. As expected, there was a strong negative relationship between PNS and perceived stability for bisexual female targets, b = −0.35, SE = 0.09, p < .0001, but not for heterosexual female targets, b = 0.07, SE = 0.08, p = .41, or homosexual female targets, b = −0.06, SE = 0.07, p = .43. The slope for bisexual women differed significantly from the slopes for heterosexual and homosexual women, b > 0.28, SE < 0.12, p < .011, which did not differ from each other, b = 0.12, SE = 0.11, p = .26. There was a different pattern among male targets. The relationship between PNS and stability was small but positive for heterosexual targets, b = 0.16, SE = 0.08, p = .050, and nonsignificant for homosexual targets, b = -0.10, SE = 0.08, p = .23, and bisexual targets, b = 0.08, SE = 0.08, p = .28. The slope for heterosexual men differed significantly from the slope for homosexual men, b = 0.26, SE = 0.12, p = .025, but the slope for bisexual men did not differ from either, |b| < 0.18, SE > 0.11, p > .10. See Fig. 1 for a plot of these relationships.
Fig. 1

Personal need for structure was negatively associated with perceived stability of female bisexuality

In sum, PNS uniquely predicted perceived instability of female bisexuality, but not male bisexuality. Bisexual orientations were perceived to be less stable than heterosexual or homosexual orientations overall (at mean PNS, b > 0.80, SE < 0.12, p < .0001), but PNS was only predictive of this difference for female targets.

Ideological Individual Difference Variables

We hypothesized that ideological measures would be equally predictive of conceptions of bisexual and homosexual people. Indeed, the most consistent effect across the six ideological variables and three dependent measures was an interaction between the ideological variable in question and target sexual orientation. In nearly every case, a conservative stance on any ideological variable strongly predicted negative evaluations, less liking, and lower stability ratings of bisexual and homosexual targets, p < .0001, but not heterosexual targets, p > .17. The slopes for bisexual and homosexual targets differed significantly from the slope for heterosexual targets, p < .008, but not from each other, p > .06. In sum, ideological individual differences were broadly related to conceptions of sexual minorities, but did not distinguish between bisexual and homosexual targets. The results were consistent with this pattern for 16 out of the 18 combinations of ideological predictors and dependent measures. The two exceptions occurred for models involving complementary gender stereotypes. First, the interaction between complementary stereotypes about women and target sexual orientation in the model predicting the general evaluation measure did not meet our adjusted significance threshold, but complementary stereotypes about women performed as expected in the models predicting liking and stability. Second, the measure of complementary stereotypes about men was a stronger predictor of low stability ratings for bisexual targets than for homosexual targets, b = 0.16, SE = 0.05, p = .0019.

In two cases, the pattern of ideological conservatism predicting negative attitudes toward sexual minority groups was qualified by an additional relationship: both gender polarization and conservative political ideology were positively related to stability ratings for heterosexual targets, p < .017. In addition, two of the interactions between religiosity and target sexual orientation were qualified by a third variable. First, the effect of religiosity on liking was stronger for female participants than male participants; second, the effect of religiosity on stability was unique to heterosexual participants as compared to gay/lesbian participants. None of these effects substantially altered the overall effect: ideological individual difference variables predicted attitudes toward both sexual minority groups to the same degree.

Comparing Individual Difference Variables

Our main analysis examined each individual difference variable separately as reported above. However, all six ideological measures exhibited similar effects, so we constructed an additional set of three regression models designed to evaluate the unique importance of each individual difference measure in qualifying the relationship between target sexual orientation and attitudes. These models were primarily designed to compare the moderating effects of the six ideological variables, but we also included PNS even though its effects were more complicated. We constructed one regression model for each dependent measure—general evaluation, liking, and stability—using all of the individual difference measures plus target sexual orientation as predictors. In each of these models, we examined the seven two-way interactions between target sexual orientation and each of the individual difference measures. A significant interaction indicates that the given individual difference measure explained additional variance in attitude differences above and beyond the other individual difference measures. Because we were focusing on seven two-way interactions per model, we employed an adjusted significance threshold of .0071 (.05/7).

In all three models (predicting general evaluation, liking, and stability), the interactions between target sexual orientation and the measures of sexual permissiveness, complementary stereotypes about men, and political orientation were significant, F(2, 1114) > 5.55, p < .004, \( \eta_{p}^{2} \) > .009. Consistent with the results from the models including each individual difference measure separately, all three significant individual difference measures predicted attitudes toward both bisexual and homosexual targets, p < .003. None of these individual difference measures predicted attitudes toward heterosexual targets, p > .42, except that politically conservative participants expressed higher stability ratings for heterosexual targets than politically liberal participants did, b = 0.26, SE = 0.11, p = .014. The other four individual difference measures (PNS, gender polarization, complementary stereotypes about women, and religiosity) did not significantly interact with target sexual orientation in any of the three models, F(2, 1314) < 4.45, p > .01, \( \eta_{p}^{2} \) < .007. In short, only one individual difference measure of each type—sexual ideology, gender ideology, and demographic ideology—was necessary to explain the ideological variance in sexual attitudes. These broad categories of ideology cohere with our theoretical goals; determining why complementary stereotypes about men and not women explained unique variance, or why political orientation and not religiosity explained unique variance, is beyond the scope of this paper.

Discussion

Our results suggest that lay conceptions of sexual minorities do not simply reflect the groups’ deviation from heterosexuality. Specifically, views of bisexual people differ substantially and substantively from views of gay/lesbian people. Participants were recruited from different populations and randomly assigned to evaluate heterosexual women, heterosexual men, bisexual women, bisexual men, homosexual women, or homosexual men. This design permitted us to break new ground by drawing comparisons across gender and sexual orientation of perceivers and targets. For example, heterosexual participants evaluated heterosexual targets most positively, followed by homosexual targets, followed lastly by bisexual targets (cf. Herek, 2002a). We did not, however, find that gay men and lesbian women evaluated bisexual targets less positively than homosexual targets. The latter result was consistent with recent work showing that gay/lesbian people, relative to heterosexual people, express more positive attitudes toward bisexual people (e.g., Worthen, 2011, 2012b), but inconsistent with earlier work showing that gay/lesbian people express at least some bias against bisexual people (e.g., Mohr & Rochlen, 1999; Rust, 1995). Perhaps gay/lesbian people’s attitudes toward bisexual people have become more positive. Alternately, perhaps gay/lesbian people primarily express negative attitudes toward bisexual people when making explicit comparisons between bisexual and gay/lesbian groups, as opposed to when they are randomly assigned to evaluate one group in isolation.

Many theoretical and empirical reports have rested on the view that instability perceptions are at the heart of lay conceptions of bisexual people (e.g., Israel & Mohr, 2004; Mohr & Rochlen, 1999), but no prior work has directly demonstrated that bisexuality is viewed as less stable than heterosexuality or homosexuality. We provide strong evidence for this long-held theoretical assertion. The result was highly robust: participants of many varieties (e.g., heterosexual, gay/lesbian, conservative, liberal) viewed bisexual orientations as less stable than other sexual orientations. For example, although both heterosexual and gay/lesbian participants viewed their ingroup orientations as the most stable, they did not view outgroup orientations as unstable to the extent that they deviated from the ingroup. Instead, both heterosexual and gay/lesbian participants agreed that bisexuality was the least stable of all.

We expected participants high in PNS to endorse particularly low ratings of stability for bisexual targets, preferring a binary model of sexual orientation that leaves no room for stable bisexual identities (Neuberg & Newsom, 1993; Rust, 2000c). Results partially supported this prediction—PNS uniquely predicted perceived instability of female bisexuality, but it did not predict perceived instability of male bisexuality. In other words, bisexual orientations were perceived as less stable than heterosexual or homosexual orientations for both male and female targets, but PNS only predicted this difference for female targets. It falls to future research to examine why people lower in PNS do not attribute higher stability to male bisexuality like they do for female bisexuality. One possible reason may be that participants lack clear conceptions of bisexual men. People may view male sexuality in more binary terms than female sexuality due to simplified lay interpretations of sexual plasticity in women (see Diamond, 2007a, 2007b), and thus view bisexual women as more prototypical of bisexual people. Alternately, people may encounter fewer mainstream media representations of bisexual male characters, making their impressions of bisexual men less concrete (Diamond, 2005; Yost & Thomas, 2012). Either way, lacking distinctive images of bisexual men, participants in our studies may have interpreted male bisexuality as unstable regardless of their individual levels of PNS.

Our other findings hinted at a complex pattern of attitudes toward bisexual people. The fact that bisexual targets fell between heterosexual and homosexual targets on the dimensions of masculinity and femininity suggests that they were perceived as an intermediate group in this respect. However, they did not elicit intermediate ratings on other dimensions. For example, bisexual targets were perceived to be less decisive than both heterosexual and homosexual targets, suggesting that some may view an “intermediate” group identity as reflecting an inability to choose between competing alternatives.

We also found that bisexual women were perceived as especially likely to focus on sex over emotional commitment relative to other women. This result, which may reflect media sexualization of bisexual women (Yost & Thomas, 2012), suggests that sexual stereotypes about bisexual targets do not track their perceived masculinity/femininity. Heterosexual men were rated as more focused on sex than heterosexual women, and homosexual men were rated as more focused on sex than homosexual women, but bisexual men and women were not rated differently on this characteristic.

The strongest sexual stereotype of bisexual targets in our sample was the perception that they prefer nonmonogamous relationships. This stereotype may have a “grain of truth,” in that bisexual people may be more open to such relationships than nonbisexual people (in part because they are socially liberal on average), but it seems likely that lay perceptions of bisexual people are at least somewhat exaggerated in this regard (Herek, Norton, Allen, & Sims, 2010; Klesse, 2005; Mark, Rosenkrantz, & Kerner, 2014; Ochs, 1996). Past work suggests that nonmonogamous relationships are widely devalued and viewed as unstable (Conley, Moors, Matsick, & Ziegler, 2012), so this stereotype may discourage bisexual people in monogamous relationships from disclosing their sexual orientation for fear of undermining the perceived legitimacy of their current relationships (Ochs, 1996). Comparable stereotypes and implications might emerge for other groups. For example, people with dual ethnic identities might be viewed as unlikely to adhere to the norms of either culture, and may therefore be reluctant to disclose their complete ethnic identities in some cultural contexts (Sanchez & Bonam, 2009).

Finally, our results documented a number of positive stereotypes of homosexual targets, almost none of which also applied to bisexual targets. Intermediate groups receive relatively little attention in public and scientific discourse, and when positive exemplars of intermediate group members exist, perceivers frequently focus on only the most salient components of their identities (e.g., bisexual people’s same-gender relationships or biracial people’s non-White ancestry). These communication norms may make positive images of “intermediate” group members less accessible, reducing the possibility that people will develop positive stereotypes of these groups. Positive stereotypes do not necessarily benefit the groups they are applied to (see Morrison & Bearden, 2007), but if they are applied disproportionately to traditionally recognized disadvantaged groups over others (e.g., homosexual but not bisexual people), the differential could exacerbate evaluative biases against groups viewed as intermediate.

We identified several individual differences that were related to attitudes toward bisexual people, such as traditional views about gender and religiosity. However, none of the ideological individual differences consistently predicted attitudes toward bisexual people more strongly than they predicted attitudes toward homosexual people. Compared to these ideological constructs, we expect that individual differences in cognitive styles related to preference for binary explanations may represent more promising avenues for understanding the way people think about groups perceived as intermediate.

Intermediate cases are critical in the study of social perception because they allow researchers to identify patterns that would otherwise escape notice. Our results suggest that lay conceptions do not vary uniformly across the spectrum of sexual orientation identities, challenging the assumption that social attitudes closely track models of social identity in general. Not all supposed “in between” groups are alike—important distinctions can be made regarding perceptions of biracial people, partially deaf people, and intermediate age groups, for example—but the present research exemplifies some reasons why examining supposed intermediate cases may be crucial to developing a more complete understanding of intergroup bias.

Limitations and Future Directions

Some strengths of this project carry corresponding limitations. For example, the large sample and expansive set of variables necessitated conservative significance thresholds. These thresholds facilitated our goal of identifying the most robust stereotypes and predictors, but they also could have resulted in a tendency to overlook small but meaningful effects. For example, past work has found that heterosexual men (as compared to heterosexual women) rated bisexual male targets particularly negatively (as compared to bisexual female targets) (Eliason, 1997; Herek, 2002a; Pirlott & Neuberg, 2014). We did not identify such an interaction, but our results should not be construed as suggesting that there is not an important relationship between the gender of the perceiver and the gender of the target, only that this relationship may be smaller than the effects we report in this article. However, some other studies that compared evaluations across target groups have found mostly main effects involving target gender rather than large interactions with target sexual orientation (Spalding & Peplau, 1997). In this light, our results suggest that the prior finding that lesbian and bisexual women are evaluated more positively than gay and bisexual men may partially reflect the tendency to evaluate women in general more positively than men in general (Eagly & Mladinic, 1994; Eagly, Mladinic, & Otto, 1991). Furthermore, gender effects may emerge most strongly in expressions of overt rejection of groups rather than differences in expressed positive evaluations (see Pirlott & Neuberg, 2014). Future work could address this limitation by conducting more narrowly focused studies to address each hypothesized relationship separately.

In order to generate experimental conditions where the structure of each sentence was identical for all target groups, we used the words “heterosexual,” “bisexual,” and “homosexual” in our survey materials. The words “heterosexual” and “bisexual” are in common use, but the words “gay” and “lesbian” are currently favored over “homosexual” in psychological research on sexual minority groups, in part because the word “homosexual” can elicit a greater degree of prejudice (Rios, 2013). This feature of our design may have led us to overestimate the degree of bias against gay/lesbian targets, and to underestimate the degree of bias against bisexual targets relative to gay/lesbian targets.

Some of our trait stereotype categories were measured with relatively few items. Also, we did not measure every important stereotype. Mohr et al. (2013) found that a sample of therapists viewed bisexual men as open-minded, pointing to an exception to our finding that many positive stereotypes did not apply to bisexual targets.

We interpret the effect of PNS on stability ratings for female (but not male) bisexuality as potentially arising from the higher salience of images of bisexual women. This explanation would benefit from empirical examination. For example, people low in PNS who are exposed to images of bisexual men (either in their everyday environments or through an experimental manipulation) might give higher stability ratings for bisexual men than those given by low-PNS individuals in our study. Under such conditions, the relationship between PNS and stability ratings we observed for bisexual women might also apply to bisexual men.

Implications

Knowledge of the nonuniform spectrum of lay conceptions of sexual orientation groups can help researchers to understand and address the social isolation faced by some bisexual people (Balsam, Beadnell, & Molina, 2013). Bisexual people may find it difficult to develop positive, healthy identities in a cultural climate that provides few positive representations (Israel & Mohr, 2004; Li et al., 2013; Ochs, 1996) or focuses on sexually objectifying portrayals (Diamond, 2005). Supporting this idea, Beaber (2008) found that bisexual women who encountered negative attitudes and beliefs were especially likely to experience low social support, low self-esteem, and an elevated risk for depression. However, clear evidence about the details of prejudice and stereotypes, based on explicit comparisons between bisexual and gay/lesbian targets, can facilitate the development of prejudice-reduction programs and more effective efforts to promote inclusive laws and policies (Worthen, 2013).

An important next step in the development of the theoretical perspective we present regarding sexual attitudes will be to assess participants’ views of multiple “in between” groups in a single study. Examining patterns that correspond or differ across such groups would help explain which views arise specifically because such groups are “caught in the middle” and which arise for more domain-specific reasons. Our analysis of bisexual targets has hinted at some potentially meaningful underlying properties of social biases against “intermediate” groups, but only continued study can establish whether these properties apply to biracial targets, people with partial physical impairments, people with multiple religious identities, or others. It is important to study these not quite “in-between” groups, because otherwise it becomes too easy for such groups to fall between the cracks in our understanding of prejudice and discrimination.

Footnotes

  1. 1.

    Only heterosexual and gay/lesbian participants were included in the analytic procedures for this article. Because categorization by sexual orientation was central to the goals of the study, we ran the same algorithm on the open-ended sexual orientation responses of a separate sample of 844 Mechanical Turk workers. After supplying their open-ended responses, these MTurk workers also selected an identity from the following options—“heterosexual or straight,” “homosexual, gay, or lesbian,” “bisexual,” and “None of these terms describe me.” The two measures of sexual orientation only differed in 7 cases (<1 %). The correctly identified cases consisted of 742 heterosexual, 27 gay/lesbian, 63 bisexual, and 5 other sexual orientations. The only cases that would have been included in our present sample based on our algorithm but excluded based on the forced-choice measure were three participants who wrote out “straight” or “heterosexual” but selected “bisexual” or “none of these”.

  2. 2.

    In addition to the effects related to our predictions about target sexual orientation, three effects emerged that illustrated perceptions of heterosexual men. Specifically, heterosexual men were perceived as more aggressive, less emotional, and less gentle than all other target groups. These results were consistent with past work (e.g., Morrison & Bearden, 2007).

  3. 3.

    Two effects involving participant sexual orientation reached our adjusted significance threshold for the STD items. First, target sexual orientation interacted with participant sexual orientation to predict perceived STDs, F(2, 1309) = 9.71, p < .0001, \( \eta_{p}^{2} \) = .02. Among gay and lesbian participants, heterosexual targets were perceived as more likely to have STDs than homosexual targets, p < .0001, but bisexual targets were not significantly different from either of the other target groups, p > .11. Among heterosexual participants, bisexual targets were perceived as more likely to have STDs than homosexual targets, p = .026, but heterosexual targets were not significantly different from either of the other target groups, p > .29. Thus, it seems that heterosexual participants, but not gay and lesbian participants, perceived bisexual targets as having more STDs than their respective ingroups. Second, perceptions of STD transmission were predicted by an interaction between target sexual orientation and participant sexual orientation, F(2, 1301) = 9.23, p = .0001, \( \eta_{p}^{2} \) = .01. Among gay and lesbian participants, heterosexual targets (M = 2.73, SD = 1.28) were perceived as more likely to transmit STDs than bisexual (M = 1.90, SD = 1.14) or homosexual (M = 1.63, SD = 0.83) targets, p < .0007, who did not differ significantly from each other, p = .68. Among heterosexual participants, heterosexual targets (M = 2.48, SD = 1.22) were perceived as more likely to transmit STDs than homosexual targets (M = 2.14, SD = 1.36), p = .0051, but bisexual targets (M = 2.39, SD = 1.34) did not differ significantly from either, p > .06.

Notes

Acknowledgments

This project was supported by a grant from the Fund for Lesbian and Gay Studies at Yale University (FLAGS).

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of PsychologyYale UniversityNew HavenUSA

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