what conclusions can be drawn from research on gender stereotypes

• A-Z Publications

Annual Review of Psychology

Volume 69, 2018, review article, gender stereotypes.

• Naomi Ellemers 1
• View Affiliations Hide Affiliations Affiliations: Faculty of Social Sciences, Utrecht University, 3508 TC Utrecht, Netherlands; email: [email protected]
• Vol. 69:275-298 (Volume publication date January 2018) https://doi.org/10.1146/annurev-psych-122216-011719
• First published as a Review in Advance on September 27, 2017
• © Annual Reviews

There are many differences between men and women. To some extent, these are captured in the stereotypical images of these groups. Stereotypes about the way men and women think and behave are widely shared, suggesting a kernel of truth. However, stereotypical expectations not only reflect existing differences, but also impact the way men and women define themselves and are treated by others. This article reviews evidence on the nature and content of gender stereotypes and considers how these relate to gender differences in important life outcomes. Empirical studies show that gender stereotypes affect the way people attend to, interpret, and remember information about themselves and others. Considering the cognitive and motivational functions of gender stereotypes helps us understand their impact on implicit beliefs and communications about men and women. Knowledge of the literature on this subject can benefit the fair judgment of individuals in situations where gender stereotypes are likely to play a role.

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Article contents

Gender in a social psychology context.

• Thekla Morgenroth Thekla Morgenroth Department of Psychology, University of Exeter
•  and  Michelle K. Ryan Michelle K. Ryan Dean of Postgraduate Research and Director of the Doctoral College, University of Exeter
• https://doi.org/10.1093/acrefore/9780190236557.013.309
• Published online: 28 March 2018

Understanding gender and gender differences is a prevalent aim in many psychological subdisciplines. Social psychology has tended to employ a binary understanding of gender and has focused on understanding key gender stereotypes and their impact. While women are seen as warm and communal, men are seen as agentic and competent. These stereotypes are shaped by, and respond to, social contexts, and are both descriptive and prescriptive in nature. The most influential theories argue that these stereotypes develop in response to societal structures, including the roles women and men occupy in society, and status differences between the sexes. Importantly, research clearly demonstrates that these stereotypes have a myriad of effects on individuals’ cognitions, attitudes, and behaviors and contribute to sexism and gender inequality in a range of domains, from the workplace to romantic relationships.

• gender stereotypes
• gender norms
• social psychology
• social role theory
• stereotype content model
• ambivalent sexism
• stereotype threat

Introduction

Gender is omnipresent—it is one of the first categories children learn, and the categorization of people into men and women 1 affects almost every aspect of our lives. Gender is a key determinant of our self-concept and our perceptions of others. It shapes our mental health, our career paths, and our most intimate relationships. It is therefore unsurprising that psychologists invest a great deal of time in understanding gender as a concept, with social psychologists being no exception. However, this has not always been the case. This article begins with “A Brief History of Gender in Psychology,” which gives an overview about gender within psychology more broadly. The remaining sections discuss how gender is examined within social psychology more specifically, with particular attention to how gender stereotypes form and how they affect our sense of self and our evaluations of others.

A Brief History of Gender in Psychology

During the early years of psychology in general, and social psychology in particular, the topic gender was largely absent from psychology, as indeed were women. Male researchers made claims about human nature based on findings that were restricted to a small portion of the population, namely, white, young, able-bodied, middle-class, heterosexual men [see Etaugh, 2016 ; a phenomenon that has been termed androcentrism (Hegarty, & Buechel, 2006 )]. If women and girls were mentioned at all, they were usually seen as inferior to men and boys (e.g., Hall, 1904 ).

This invisibility of women within psychology changed with a rise of the second wave of feminism in the 1960s. Here, more women entered psychology, demanded to be seen, and pushed back against the narrative of women as inferior. They argued that psychology’s androcentrism, and the sexist views of psychologists, had not only biased psychological theory and research, but also contributed to and reinforced gender inequality in society. For example, Weisstein ( 1968 ) argued that most claims about women made by prominent psychologists, such as Freud and Erikson, lacked an evidential grounding and were instead based on these men’s fantasies of what women were like rather than empirical data. A few years later, Maccoby and Jacklin ( 1974 ) published their seminal work, The Psychology of Sex Differences , which synthesized the literature on sex differences and concluded that there were few (but some) sex differences. This led to a growth of interest in the social origins of sex differences, with a shift away from a psychology of sex (i.e., biologically determined male vs. female) and toward a psychology of gender (i.e., socially constructed masculine vs. feminine).

Since then, the psychology of gender has become a respected and widely represented subdiscipline within psychology. In a fascinating analysis of the history of feminism and psychology, Eagly, Eaton, Rose, Riger, and McHugh ( 2012 ) examined publications on sex differences, gender, and women from 1960 to 2009 . In those 50 years, the number of annual publications rose from close to zero to over 6,500. As a proportion of all psychology articles, one can also see a marked rise in popularity in gender articles from 1960 to 2009 , with peak years of interest in the late 1970s and 1990s. In line with the aforementioned shift from sex differences to gender differences, the largest proportion of these articles fall into the topic of “social processes and social issues,” which includes research on gender roles, masculinity, and femininity.

However, as interest in the area has grown, the ways in which gender is studied, and the political views of those studying it, have become more diverse. Eagly and colleagues note:

we believe that this research gained from feminist ideology but has escaped its boundaries. In this garden, many flowers have bloomed, including some flowers not widely admired by some feminist psychologists. (p. 225)

Here, they allude to the fact that some research has shifted away from societal explanations, which feminist psychologists have generally favored, to more complex views of gender difference. Some of these acknowledge the fact that nature and nurture are deeply intertwined, with both biological and social variables being used to understand gender and gender differences (e.g., Wood & Eagly, 2002 ). Others, such as evolutionary approaches (e.g., Baumeister, 2013 ; Buss, 2016 ) and neuroscientific approaches (see Fine, 2010 ), focus more heavily on the biological bases of gender differences, often causing chagrin among feminists. Nevertheless, much of the research in social psychology has, unsurprisingly, focused on social factors and, in particular, on gender stereotypes. Where do they come from and what are their effects?

Origins and Effects of Gender Stereotypes

A stereotype can be defined as a “widely shared and simplified evaluative image of a social group and its members” (Vaughan & Hogg, 2011 , p. 51) and has both descriptive and prescriptive aspects. In other words, gender stereotypes tell us what women and men are like, but also what they should be like (Heilman, 2001 ). Gender stereotypes are not only widely shared, but they are also stubbornly resistant to change (Haines, Deaux, & Lofaro, 2016 ). Both the origin and the consequences of these stereotypes have received much attention in social psychology. So how do stereotypes form? The most widely cited theories on stereotype formation—social role theory (SRT; Eagly, 1987 ; Eagly, Wood, & Diekman, 2000 ) and the stereotype content model (SCM; Fiske, Cuddy, Glick, & Xu, J., 2002 )—answer this question. Both of these models focus on gender as a binary concept (i.e., men and women), as does most psychological research on gender, although they could potentially also be applied to other gender groups. Both theories are considered in turn.

Social Role Theory: Gender Stereotypes Are Determined by Roles

SRT argues that gender stereotypes stem from the distribution of men and women into distinct roles within a given society (Eagly, 1987 ; Eagly et al., 2000 ). The authors note the stability of gender stereotypes across cultures and describe two core dimensions: agency , including traits such as independence, aggression, and assertiveness, and communion , including traits such as caring, altruism, and politeness. While men are generally seen to be high in agency and low in communion, women are generally perceived to be high in communion but low in agency.

According to SRT, these gender stereotypes stem from the fact that women and men are over- and underrepresented in different roles in society. In most societies, even those with higher levels of gender equality, men perform less domestic work compared to women, including childcare, and spend more time in paid employment. Additionally, men disproportionately occupy leadership roles in the workforce (e.g., in politics and management) and are underrepresented in caretaking roles within the workforce (e.g., in elementary education and nursing; see Eagly et al., 2000 ). Eagly and colleagues argue that this gendered division of labor leads to the formation of gender roles and associated stereotypes. More specifically, they propose that different behaviors are seen as necessary to fulfil these social roles, and different skills, abilities, and traits are seen as necessary to execute these behaviors. For example, elementary school teachers are seen to need to care for and interact with children, which is seen to require social skills, empathy, and a caring nature. In contrast, such communal attributes might be seen to be less important—or even detrimental—for a military leader.

To the extent that women and men are differentially represented and visible in certain roles—such as elementary school teachers or military leaders—the behaviors and traits necessary for these roles become part of each respective gender role. In other words, the behaviors and attributes associated with people in caretaking roles, communion, become part of the female gender role, while the behaviors and attributes associated with people in leadership roles, agency, become part of the male gender role.

Building on SRT, Wood and Eagly ( 2002 ) developed a biosocial model of the origins of sex differences which explains the stability of gendered social roles across cultures. The authors argue that, in the past, physical differences between men and women meant that they were better able to perform certain tasks, contributing to the formation of gender roles. More specifically, women had to bear children and nurse them, while men were generally taller and had more upper body strength. In turn, tasks that required upper body strength and long stretches of uninterrupted time (e.g., hunting) were more often carried out by men, while tasks that could be interrupted more easily and be carried out while pregnant or looking after children (e.g., foraging) were more often carried out by women.

Eagly and colleagues further propose that the exact tasks more easily carried out by each sex depended on social and ecological conditions as well as technological and cultural advances. For example, it was only in more advanced, complex societies that the greater size and strength of men led to a division of labor in which men were preferred for activities such as warfare, which also came with higher status and access to resources. Similarly, the development of plough technology led to shifts from hunter–gatherer societies to agricultural societies. This change was often accompanied by a new division of labor in which men owned, farmed, and inherited land while women carried out more domestic tasks. The social structures that arose from these processes in specific contexts in turn affected more proximal causes of gender differences, including gender stereotypes.

It is important to note that this theory focuses on physical differences between the genders, not psychological ones. In other words, the authors do not argue that women and men are inherently different when it comes to their minds, nor that men evolved to be more agentic while women evolved to be more communal.

Stereotype Content Model: Gender Stereotypes Are Determined by Group Relations

The SCM, formulated by Fiske and colleagues ( 2002 ), was not developed specifically for gender, but as an explanation of how stereotypes form more generally. Similar to SRT, the SCM argues that gender stereotypes arise from societal structures. More specifically, the authors suggest that status differences and cooperation versus competition determine group stereotypes—among them, gender stereotypes. This model also suggests two main dimensions to stereotypes, namely, warmth and competence. The concept of warmth is similar to that of communion, previously described, in that it refers to being kind, nice, and caring. Competence refers to attributes such as being intelligent, efficient, and skillful and is thus different from the agency dimension of SRT.

The SCM argues that the dimensions of warmth and competence originate from two fundamental dimensions—status and competition—which characterize the relationships between groups in every culture and society. The degree to which another group is perceived to be warm is determined by whether the group is in cooperation or in competition with one’s own group, which is in turn associated with perceived intentions to help or to harm one’s own group, respectively. While members of cooperating groups are stereotyped as warm, members of competing groups are stereotyped as cold. Evidence suggests that these two dimensions are indeed universal and can be found in many cultures, including collectivist cultures (Cuddy et al., 2009 ). Perceptions of competence, however, are affected by the status and power of the group, which go hand-in-hand with the group’s ability to harm one’s own group. Those groups with high status and power are stereotyped as competent, while those that lack status and power are stereotyped as incompetent.

Groups can thus fall into one of four quadrants of this model. Members of high status groups who cooperate with one’s own group are seen as unequivocally positive—as warm and competent—while those of low status who compete with one’s own group are seen as unequivocally negative—cold and incompetent. More interesting are the two groups that fall into the more ambivalent quadrants—those who are perceived as either warm but incompetent or competent but cold. Applied to gender, this model suggests—and research shows—that typical men are stereotyped as competent but cold, the envious stereotype, while typical women are stereotyped as warm but incompetent, the paternalistic stereotype.

However, these stereotypes do not apply equally to all women and men. Rather, subgroups of men and women come with their own stereotypes. Research demonstrates, for example, that the paternalistic stereotype most strongly applies to traditional women such as housewives, while less traditional women such as feminists and career women are stereotyped as high in competence and low in warmth. For men, there are similar levels of variation—the envious stereotype applies most strongly to men in traditional roles such as managers and career men, while other men are perceived as warm but incompetent (e.g., senior citizens), as cold and incompetent (e.g., punks), or as warm and competent (e.g., professors; Eckes, 2002 ). The section “Gender Stereotypes Affect Emotions, Behavior, and Sexism” discusses the consequences of these stereotypes in more detail.

The Effects of Gender Stereotypes

SRT and the SCM explain how gender stereotypes form. A large body of work in social psychology has focused on the consequences of these stereotypes. These include effects on the gendered perceptions and evaluations of others, as well as effects on the self and one’s own self-image, behavior, and goals.

Gendered Perceptions and Evaluations of Others

Our group-based stereotypes affect how we see members of these groups and how we judge those who do or do not conform to these stereotypes. Gender differs from many other group memberships in several ways (see Fiske & Stevens, 1993 ), which in turn affects consequences of these stereotypes. First, argue Fiske and Stevens, gender stereotypes tend to be more prescriptive than other stereotypes. For example, men may often be told to “man up,” to be tough and dominant, while women may be told to smile, to be nice, and to be sexy (but not too sexy). While stereotypes of other groups also have prescriptive elements, it is probably less common to hear Asians be told to be better at math or African Americans to be told to be more musical. The consequences of these gendered prescriptions are discussed in the section “Gender Stereotypes Affect the Evaluation of Women and Men.” Second, relationships between women and men are characterized by an unusual combination of power differences and close and frequent contact as well as mutual dependence for reproduction and close relationships. The section “Gender Stereotypes Affect Emotions, Behavior, and Sexism” discusses the effects of these factors.

Gender Stereotypes Affect the Evaluation of Women and Men

The evaluation of women and men is affected by both descriptive and prescriptive gender stereotypes. Research on these effects has predominantly focused on those who occupy counterstereotypical roles such as women in leadership or stay-at-home fathers.

Descriptive stereotypes affect the perception and evaluation of women and men in several ways. First, descriptive stereotypes create biased perceptions through expectancy confirming processes (see Fiske, 2000 ) such that individuals, particularly those holding strong stereotypes, seek out information that confirms their stereotypes. This is evident in their tendency to neglect or dismiss ambiguous information and to ask stereotype-confirming questions (Leyens, Yzerbyt, & Schadron, 1994 ; Macrae, Milne, & Bodenhausen, 1994 ). Moreover, people are more likely to recall stereotypical information compared to counterstereotypical information (Rojahn & Pettigrew, 1992 ) Second, descriptive gender stereotypes also bias the extent to which men and women are seen as suitable for different roles, as described in Heilman’s lack of fit model ( 1983 , 1995 ) and Eagly and Karau’s role congruity theory ( 2002 ). These approaches both suggest that the degree of fit between a person’s attributes and the attributes associated with a specific role is positively related to expectations about how successful a person will be in said role. For example, the traits associated with successful managers are generally more similar to those associated with men than those associated with women (Schein, 1973 ; see also Ryan, Haslam, Hersby, & Bongiorno, 2011 ). Thus, all else being equal, a man will be seen as a better fit for a managerial position and in turn as more likely to be a successful manager. These biased evaluations in turn lead to biased decisions, such as in hiring and promotion (see Heilman, 2001 ).

Prescriptive gender stereotypes also affect evaluations, albeit in different ways. They prescribe how women and men should behave, and also how they should not behave. The “shoulds” generally mirror descriptive stereotypes, while the “should nots” often include behaviors associated with the opposite gender. Thus, what is seen as positive and desirable for one gender is often seen as undesirable for the other and can lead to backlash in the form of social and economic penalties (Rudman, 1998 ). For example, women who are seen as agentic are punished with social sanctions because they violate the prescriptive stereotype that women should be nice, even in the absence of information indicating that they are not nice (Rudman & Glick, 2001 ). These processes are particularly problematic in combination with the effects of descriptive stereotypes, as individuals may face a double bind—if women behave in line with gender stereotypes, they lack fit with leadership positions that require agency, but if they behave agentically, they violate gender norms and face backlash in the form of dislike and discrimination (Rudman & Glick, 2001 ). Similar effects have been found for men who violate prescriptive masculine stereotypes, for example, by being modest (Moss-Racusin, Phelan, & Rudman, 2010 ) or by requesting family leave (Rudman & Mescher, 2013 ). Interestingly, however, being communal by itself does not lead to backlash for men (Moss-Racusin et al., 2010 ). In other words, while men can be perceived as highly agentic and highly communal, this is not true for women, who are perceived as lacking communion when being perceived as agentic and as lacking agency when being perceived as communal.

Gender Stereotypes Affect Emotions, Behavior, and Sexism

Stereotypes not only affect how individuals evaluate others, but also their feelings and behaviors toward them. The Behavior from Intergroup Affect and Stereotypes (BIAS) map (Cuddy, Fiske, & Glick, 2007 ), which extends the SCM, describes the relationship between perceptions of warmth and competence of certain groups, emotions directed toward these groups, and behaviors toward them. Cuddy and colleagues argue that bias is comprised of three elements: cognitions (i.e., stereotypes), affect (i.e., emotional prejudice), and behavior (i.e., discrimination), and these are closely linked. Groups perceived as warm and competent elicit admiration while groups perceived as cold and incompetent elicit contempt. Of particular interest to understanding gender are the two ambivalent combinations of warmth and competence: Those perceived as warm, but incompetent—such as typical women—elicit pity, while those perceived as competent, but cold—such as typical men—elicit envy.

Similarly, perceptions of warmth and competence are associated with behavior. Cuddy and colleagues ( 2007 ) argue that the warmth dimension affects behavioral reactions more strongly than competence because it stems from perceptions that a group will help or harm the ingroup. This leads to active facilitation (e.g., helping) when a group is perceived as warm, or active harm (e.g., harassing) when a group is perceived as cold. Competence, however, leads to passive facilitation (e.g., cooperation when it benefits oneself or one’s own group) when the group is perceived as competent, and passive harm (e.g., neglecting to help) when the group is perceived as incompetent.

How these emotional and behavioral reactions affect women and men has received much attention in the literature on ambivalent sexism and ambivalent attitudes toward men (Glick & Fiske, 1996 , 1999 , 2001 ). According to ambivalent sexism theory (AST), sexism is not a uniform, negative attitude toward women or men. Rather, it is comprised of hostile and benevolent elements, which arises from status differences between, and intimate interdependence of, the two genders. While men possess more economic, political, and social power, they depend on women as their mothers and (for heterosexual men) as romantic partners. Thus, while they are likely to be motivated to keep their power, they also need to find ways to foster positive relations with women.

Hostile sexism combines the beliefs that (a) women are inferior to men, (b) men should have more power in society, and (c) women’s sexuality poses a threat to men’s status and power. This form of sexism is mostly directed toward nontraditional women who directly threaten men’s status (e.g., feminists or career women), and women who threaten the heterosexual interdependence of men and women (e.g., lesbians)—in other words, toward women perceived to be competent but cold.

Benevolent sexism is a subtler form of sexism and refers to (a) complementary gender differentiation , the belief that (traditional) women are ultimately the better gender, (b) protective paternalism , where men need to cherish, protect, and provide for women, and (c) heterosexual intimacy , the belief that men and women complement each other such that no man is truly complete without a woman. This form of sexism is directed mainly toward traditional women.

While benevolent sexism may seem less harmful than its hostile counterpart, it ultimately provides an alternative mechanism for the persistence of gender inequality by “keeping women in their place” and discouraging them from seeking out nontraditional roles (see Glick & Fiske, 2001 ). Exposure to benevolent sexism is associated with women’s increased self-stereotyping (Barreto, Ellemers, Piebinga, & Moya, 2010 ), decreased cognitive performance (Dardenne, Dumont, & Bollier, 2007 ), and reduced willingness to take collective action (Becker & Wright, 2011 ), thus reinforcing the status quo.

With perceptions of men, Glick and Fiske ( 1999 ) argue that attitudes are equally ambivalent. Hostile attitudes toward men include (a) resentment of paternalism , stemming from perceptions of unfairness of the disproportionate amounts of power men hold, (b) compensatory gender differentiation , which refers to the application of negative stereotypes to men (e.g., arrogant, unrefined) so that women can positively distinguish themselves from them, and (c) heterosexual hostility , stemming from male sexual aggressiveness and interpersonal dominance. Benevolent attitudes toward men include maternalism , that is, the belief that men are helpless and need to be taken care of at home. Interestingly, while such attitudes portray women as competent in some ways, it still reinforces gender inequality by legitimizing women’s disproportionate amount of domestic work. Benevolent attitudes toward men also include complementary gender differentiation , the belief that men are indeed more competent, and heterosexual attraction , the belief that a woman can only be truly happy when in a romantic relationship with a man.

Cross-cultural research (Glick et al., 2000 , 2004 ) suggests that ambivalent sexism and ambivalent attitudes toward men are similar in many ways and can be found in most cultures. For both constructs, the benevolent and hostile aspects are distinct but positively related, illustrating that attitudes toward women and men are indeed ambivalent, as the mixed nature of stereotypes would suggest. Moreover, ambivalence toward women and men are correlated and national averages of both aspects of sexism and ambivalence toward men are associated with lower gender equality across nations, lending support to the idea that they reinforce the status quo.

Gender Stereotypes Affect the Self

Gender stereotypes not only affect individuals’ reactions toward others, they also play an important part in self-construal, motivation, achievement, and behavior, often without explicit endorsement of the stereotype. This section discusses how gender stereotypes affect observable gender differences and then describes the subtle and insidious effects gender stereotypes can have on performance and achievement through the inducement of stereotype threat (Steele & Aronson, 1995 ).

Gender Stereotypes Affect Gender Differences

Gender stereotypes are a powerful influence on the self-concept, goals, and behaviors. Eagly and colleagues ( 2000 ) argue that girls and boys observe the roles that women and men occupy in society and accommodate accordingly, seeking out different activities and acquiring different skills. They propose two main mechanisms by which gender differences form. First, women and men adjust their behavior to confirm others’ gender-stereotypical expectations. Others communicate their gendered expectations in many, often nonverbal and subtle ways and react positively when expectations are confirmed and negatively when they are not. This subtle communication of expectations reinforces gender-stereotypical behavior as people generally try to elicit positive, and avoid negative, reactions from others. Importantly, the interacting partners need not be aware of these expectations for them to take effect.

The second process by which gender stereotypes translate into gender differences is the self-regulation of behavior based on identity processes and the internalization of stereotypes (e.g., Bem, 1981 ; Markus, 1977 ). Most people form their gender identity based on self-categorization as male or female and subsequently incorporate attributes associated with the respective category into their self-concept (Guimond, Chatard, Martinot, Crisp, & Redersdorff, 2006 ). These gendered differences in the self-concepts of women and men then translate into gender-stereotypical behaviors. The extent to which the self-concept is affected by gender stereotypes—and in turn the extent to which gendered patterns of behavior are displayed—depends on the strength and the salience of this social identity (Hogg & Turner, 1987 ; Onorato & Turner, 2004 ). For example, individuals may be more likely to display gender-stereotypical behavior when they identify more strongly with their gender (e.g., Lorenzi‐Cioldi, 1991 ) or when their gender is more likely to be salient, which is more likely to be the case for women (Cadinu & Galdi, 2012 ).

However, many different subcategories of women exist—housewives, feminists, lesbians—and thus what it means to identify as a woman, and behave like a woman, is likely to be complex and multifaceted (e.g., Fiske et al., 2002 ; van Breen, Spears, Kuppens, & de Lemus, 2017 ). Moreover, research demonstrates that the salience of gender in any given context also determined the degree to which an individual displays gender-stereotypical behavior (e.g., Ryan & David, 2003 ; Ryan, David, & Reynolds, 2004 ). For example, Ryan and colleagues demonstrate that while women and men act in line with gender stereotypes when gender and gender difference are salient, these differences in attitudes and behavior disappear when alternative identities, such as those based on being a student or being an individual, are made salient.

Gender Stereotypes Affect Performance and Achievement

The consequences of stereotypes go beyond the self-concept and behavior. Research in stereotype threat describes the detrimental effects that negative stereotypes can have on performance and achievement. Stereotype threat refers to the phenomenon whereby the awareness of the negative stereotyping of one’s group in a certain domain, and the fear of confirming such stereotypes, can have negative effects on performance, even when the stereotype is not endorsed. The phenomenon was first described by Steele and Aronson ( 1995 ) in the context of African Americans’ intellectual test performance, but has since been found to affect women’s performance and motivation in counterstereotypical domains such as math (Nguyen & Ryan, 2008 ) and leadership (Davies, Spencer, & Steele, 2005 ). This affect holds true even when minority group members’ prior performance and interest in the domain are the same as those of majority group members (Spencer, Steele, & Quinn, 1999 ). Moreover, the effect is particularly pronounced when the minority member’s desire to belong is strong and identity-based devaluation is likely (Steele, Spencer, & Aronson, 2002 ).

Different mechanisms for the effect of stereotype threat have been proposed. Schmader, Johns, and Forbes ( 2008 ) suggest that the inconsistency between one’s self-image as competent and the cultural stereotype about one’s group’s lack of competence leads to a physiological stress response that directly impairs working memory. For example, when made aware of the widely held stereotype that women are bad at math, a female math student is likely to experience an inconsistency. This inconsistency, the authors argue, is not only distressing in itself, but induces uncertainty: Am I actually good at math or am I bad at math as the stereotype would lead me to believe? In an effort to resolve this uncertainty, she is likely to monitor her performance more than others—and more than in a situation in which stereotype threat is absent. This monitoring leads to more conscious, less efficient processing of information—for example, when performing calculations that she would otherwise do more or less automatically—and a stronger focus on detecting potential failure, taking cognitive resources away from the actual task. Moreover, individuals under stereotype threat are more likely to experience negative thoughts and emotions such as fear of failure. In order to avoid the interference of these thoughts, they actively try to suppress them. This suppression, however, takes effort. All of these mechanisms, the authors argue, take working memory space away from the task in question, thereby impairing performance.

The aim of this article is to give an overview of gender research in social psychology, which has focused predominantly on gender stereotypes, their origins, and their consequences, and these are all connected and reinforce each other. Social psychology has produced many fascinating findings regarding gender, and this article has only just touched on these findings. While research into gender has seen a great growth in the past 50 years and has provided us with an unprecedented understanding of women and men and the differences (and similarities) between them, there is still much work to be done.

There are a number of issues that remain largely absent from mainstream social psychological research on gender. First, an interest and acknowledgment of intersectional identities has emerged, such as how gender intersects with race or sexuality. It is thus important to note that many of the theories discussed in this article cannot necessarily be applied directly across intersecting identities (e.g., to women of color or to lesbian women), and indeed the attitudes and behaviors of such women continue to be largely ignored within the field.

Second, almost all social psychological research into gender is conducted using an overly simplistic binary definition of gender in terms of women and men. Social psychological theories and explanations are, for the most part, not taking more complex or more fluid definitions of gender into account and thus are unable to explain gendered attitudes and behavior outside of the gender binary.

Finally, individual perceptions and cognitions are influenced by gendered stereotypes and expectations, and social psychologists are not immune to this influence. How we, as psychologists, ask research questions and how we interpret empirical findings are influenced by gender stereotypes (e.g., Hegarty & Buechel, 2006 ), and we must remain vigilant that we do not inadvertently seek to reinforce our own gendered expectations and reify the gender status quo.

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1. Psychology largely conceptualizes gender as binary. While this is problematic in a number of ways, which we touch upon in the Conclusion section, we largely follow these binary conventions throughout this article, as it is representative of the social psychological literature as a whole.

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Article Contents

Lay summary, literature review, data and methods, supplementary material, data availability, acknowledgments.

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Uncovering gender stereotypes in controversial science discourse: evidence from computational text and visual analyses across digital platforms

Kaiping Chen, Zening Duan and Sang Jung Kim contributed equally to this article.

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Kaiping Chen, Zening Duan, Sang Jung Kim, Uncovering gender stereotypes in controversial science discourse: evidence from computational text and visual analyses across digital platforms, Journal of Computer-Mediated Communication , Volume 29, Issue 1, January 2024, zmad052, https://doi.org/10.1093/jcmc/zmad052

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This study examines how gender stereotypes are reflected in discourses around controversial science issues across two platforms, YouTube and TikTok. Utilizing the Social Identity Model of Deindividuation Effects, we developed hypotheses and research questions about how content creators might use gender-related stereotypes to engage audiences. Our analyses of climate change and vaccination videos, considering various modalities such as captions and thumbnails, revealed that themes related to children and health often appeared in videos mentioning women, while science misinformation was more common in videos mentioning men. We observed cross-platform differences in portraying gender stereotypes. YouTube’s video descriptions often highlighted women-associated moral language, whereas TikTok emphasized men-associated moral language. YouTube’s thumbnails frequently featured climate activists or women with nature, while TikTok’s thumbnails showed women in Vlog-style selfies and with feminine gestures. These findings advance understanding about gender and science through a cross-platform, multi-modal approach and offer potential intervention strategies.

This study explores the use of gender cues and stereotypes in digital science communication on two platforms, YouTube and TikTok, focusing on climate change and vaccination. We observed that on both platforms, women are often associated with themes related to children and health, while men are often mentioned with themes related to conspiracy and misinformation discourses. Notably, gender stereotypes differ across platforms. YouTube’s video descriptions tend to emphasize moral language associated with women, such as care, fairness, and loyalties, whereas TikTok’s video descriptions highlight moral language associated with men, such as authority. Visual differences were also found across platforms. YouTube thumbnails often depict women in nature sceneries or as women scientists and activists. TikTok thumbnails tend to feature women in Vlog-style selfies and with feminine gestures. Our findings offer insights and implications for developing strategies to alleviate gender stereotypes in digital science discussions.

Stereotypes are cognitive structures in our minds that assign defining characteristics to social groups ( Operario & Fiske, 2004 ). A recent Pew Research study ( Parker et al., 2017 ) surveyed participants in the United States to name traits that society values most in men and women. The responses revealed substantial differences in values assigned to genders—while honesty, ambition, and strength were the most valued traits in men, physical attractiveness, empathy, nurturing, and kindness were the expected traits for women.

Gender stereotypes have been extensively documented in social psychology ( Ellemers, 2018 ). Research in computer-mediated communication (CMC) has found that gender stereotypes can be exacerbated when group identification becomes activated and in relatively anonymous environments compared to interpersonal communication ( Brouwer et al., 1997 ). Recent studies on social media have examined gendered language use, highlighting how women received more violent language compared to their male counterparts ( Bamman et al., 2014 ).

This previous research provides a valuable foundation for understanding gender stereotypes. However, significant gaps remain, particularly concerning how these stereotypes are represented on social media platforms. The first gap is a limited understanding of how gender stereotypes manifest in discussions about science on social media. Social media have become major sources for people to seek and learn information about science ( Brossard & Scheufele, 2013 ). It is essential to examine gendered portrayals on digital platforms because they can either reinforce or challenge deeply ingrained biases about different genders in science. For instance, studies in science communication show that gender stereotypes distorted public perception of women in STEM professions (McKinnon & O’ Connell, 2020). Gendered narratives also exaggerated women’s parental and moral responsibilities in scientific issues, such as environmental protection ( Bobel, 2010 ) and children’s health ( Tracey et al., 2022 ). While these findings reveal public misperceptions of women in scientific issues, little is known about how public discourse on social media might amplify or mitigate these existing gender biases. If social media reinforces these gender stereotypes, it can have detrimental consequences for achieving gender equity in science, perpetuating deeply-seated gender biases among audiences and making them vulnerable to accepting misinformation by leveraging such biases. To theorize how content creators and audiences on social media might deploy and engage with gender stereotypes, we draw from the Social Identity Model of Deindividuation Effects (SIDE) theory ( Hogg & Hains, 1996 ; Reicher et al., 1995 ; Walther, 2011 ), which explicates how the activation of group cues might stimulate individuals to take actions to categorize themselves more along with group identities. In our study context, we expect that stereotypes held about science and gender in offline social settings might also replicate on digital platforms.

Second, discourses on social media encompass both messages promoting science and misinformation on scientific issues. Social media users encounter both types of information, shaping their opinions about gender and science ( Kim & Chen, 2022 ). For example, some misleading narratives may exploit gender stereotypes, targeting groups like mothers with incorrect information about vaccines. On the other hand, credible sources like the Centers for Disease Control and Prevention (CDC) could also use gender cues to counteract the spread of such misleading narratives. Hence, our study examines gender stereotypes in both scientific discourse and misinformation on social media.

The second gap is the lack of cross-platform research to examine gender stereotypes, which has limited our understanding about how gender patterns might resemble or differ across different platforms. Each social media platform has its own media logics and affordances ( Evans et al., 2017 ; Westerman et al., 2008 ). Consequently, the nature of discourse and audience engagement can differ significantly. Exploring these differences will provide a holistic view of how content creators and audiences discuss gender in the context of science across multiple platforms. For example, one platform might reinforce certain gender stereotypes, while another might challenge them. Without cross-platform insights, our understanding of gender stereotypes in digital science communication remains incomplete and potentially limited. Our study aims to fill this gap to inform strategies for science communication professionals addressing gender stereotypes on different platforms.

The third gap is the shortfall of multi-modal research studying gender stereotypes on social media. Multimodality refers to the convergence of various modalities, as seen in video messages incorporating text, images, and audio ( Kim et al., 2023 ). While most studies have traditionally focused on examining either the text (i.e., the language people use to discuss women and men) or visuals (e.g., feminine/masculine gestures), the emergence of video-based platforms such as YouTube and TikTok necessitates a more comprehensive understanding of gender stereotypes. This involves examining textual elements (e.g., video captions), visual components (e.g., thumbnails, series of image frames), and audio aspects (e.g., speech segments) in a multi-modal fashion. Audiences interact with different formats to form their opinions, and a multi-modal approach provides a more comprehensive understanding of how gender stereotypes are perpetuated or challenged.

Our literature review is structured as follows: Section “Gender stereotypes in science communication” explains gender stereotypes and how scholars in science communication have studied gender stereotypes, including gendered moral stereotypes. Section “Gender stereotypes in science communication” ends by highlighting a gap in the literature concerning how gender stereotypes might manifest on social media platforms. Section “Looking at gender bias on social media through the lens of SIDE” draws upon the SIDE theory to explicate how content creators and audiences might use and engage with gender stereotypes when they communicate science, and how these patterns might vary across platforms.

Gender stereotypes in science communication

Gender stereotypes is a core topic examined across social psychology, sociology, and science communication and often reflect the primary importance people place on judging men and women ( Ellemers, 2018 ). For example, men are typically evaluated based on task performance, such as the successful completion of tasks, whereas women are more often judged by their social relationships, showing warmth and care for others ( Ellemers, 2018 ). In a similar vein, psychological studies found that a typical man is perceived as tough, aggressive, and assertive, whereas a typical woman is viewed as warm, gentle, kind, and passive ( Huddy & Terkildsen, 1993 ).

Understanding gender stereotypes is crucial for science communication, as these stereotypes can influence (1) public perceptions of professionals who produce scientific knowledge and (2) how the public communicates about science issues. McKinnon and O’Connell (2020) observed that women in STEM professions are often stereotyped as “bitchy” and “emotional.” Carli et al. (2016) found that while men and scientists are perceived as “agentic” by the public, women are seen as “communal,” and their traits are less associated with scientists. These biases underscore the impact of gender stereotypes on perceptions of scientists and science.

Gender stereotypes also manifest in public discussions about science issues. Among the various types of gender stereotypes individuals hold, “gendered moral stereotypes” refer to associations between specific gender groups and particular ethical values ( Niazi et al., 2020 ). As Parker et al. (2017) noted, while people expect honesty in men, empathy and kindness are expected in women. In short, people hold different moral stereotypes toward genders; women are often given more weight to care, while men are given more weight to authority.

In science and health contexts, these stereotypes also attribute moral roles to genders. For instance, ecofeminism theories suggest that women are often stereotyped as caregivers, more responsible for environmental risks than men ( Kennedy & Dzialo, 2015 ). This instrumentalization of women’s moral role in the environment is also documented in health issues. For instance, the theory of motherhood notes disproportionate burdens on women in immunization choices for children ( Bobel,2010 ; Tracey et al., 2022 ). Although studies have indicated differences in morality between women and men ( Ford & Lowery, 1986 ), not many have explored the differences of how people perceive and express morality between them. Therefore, there is a gap in the literature on how gendered moral stereotypes are portrayed when the public talks about issues on science ( Niazi et al., 2020 ).

Gender biases are increasingly evident in public discussions on climate change and vaccination, though this evidence is largely anecdotal ( Baker & Walsh, 2023 ). Analyzing gender biases in controversial science topics is urgent, as misinformation spreaders can exploit these biases to advance their agenda. For example, Baker and Walsh (2023) demonstrated how Instagram influencers deliberately targeted their anti-vaccination messages at mothers, invoking an idealized maternal self-image to take care of their children. Vowles and Hultman (2021) also showed how misogyny is positively associated with climate change denial. These instances highlight the need to investigate gender stereotypes in science and misinformation discourse. Our study addresses this by examining (1) how content creators discuss controversial science topics like climate change and vaccination in relation to different genders and (2) how audiences engage with these gendered narratives. Thus, our article raises a broader research question: How are gender stereotypes portrayed in controversial science communication across social media?

In the next section, we theorize the patterns of gender stereotypes on digital platforms through the lens of SIDE. We also take one step further to discuss how SIDE can be contextualized with platform features (e.g., multimodality) to explore gender patterns across platforms.

Looking at gender bias on social media through the lens of SIDE

Social identity model of deindividuation effects and gender stereotyping.

Social media has become a primary source for accessing and learning about science information and misinformation ( Brossard & Scheufele, 2013 ; Kim & Chen, 2022 ). The Brookings Report ( Meco & Wilfore, 2021 ) highlighted how gendered disinformation campaigns, featuring fake stories and sexually charged images, have spread online, distorting public understanding of women’s rights in science. These gendered narratives on social media are harmful to audiences seeking to educate themselves about scientific issues. To unpack how content creators and audiences on social media utilize and engage with gendered narratives, the SIDE theory offers a valuable framework.

The SIDE theory was proposed to study the effects of cues that indicate common social categories of group members in CMC. In digital discussions of public issues, individuals tend to prioritize social identities over their individual identities ( Chen et al., 2023 ; Reicher et al., 1995 ). Social identity can be invoked through various mechanisms, such as social categorization of the self, in-group prototyping, or depersonalization to embody group identity ( Hogg & Hains, 1996 ). Central to the SIDE theory are two elements: “visual anonymity” and “group identification” ( Walther, 2011 ). These elements influence how people perceive gender. For instance, Brouwer et al. (1997) found that participants exhibited more gender-typical behaviors when their gender identity was emphasized, compared to when it was obscured. Participants in the anonymous environment also reported increased gender-typical behaviors.

Anonymity in CMC is not a universal characteristic. For example, on platforms such as YouTube, video creators may be visible to their audiences, who can access these creators’ profiles. Conversely, the second element of the SIDE theory, “group identification,” consistently applies across various social media contexts. Research indicates that people use various social identity cues, such as gender or partisanship, to communicate about science issues across platforms ( Kim et al., 2023 ). Group identities become salient in some situations and muted in others on social media. This variability allows us to investigate when group identities are made salient by content creators, what narratives they often attach to these social groups, and how audiences engage with content that utilizes group cues vs. without cues. In the following two parts, we review existing studies on gender stereotypes on social media, highlight the gaps in these studies, and propose our research question and hypothesis.

Gender Stereotypes in Content Creation

Scholars have noted the presence of gender stereotypes in various CMC settings. Proust and Saldaña (2022) analyzed comments on a Chilean news website and found that comments about women often criticized their personality and work performance, labeling them as overly emotional, unintelligent, or unprofessional. In contrast, comments about men typically criticized specific actions rather than their professionalism. Bamman et al. (2014) identified lexical markers of gender on Twitter, such as pronouns, emotional terms, and kinship terms, and showed that users with more gendered language styles tend to have gendered social networks. Analyzing Facebook messages from 75,000 volunteers, Schwartz et al. (2013) found that women users favored emotional and socially oriented language, while men used more swear words and object-referencing terms.

RQ1: When content creators communicate science issues on social media, how do they employ gender-associated stereotypes?

While some stereotypes may be linked to moral dimensions, our goal is also to explore other potential stereotypes through unsupervised machine learning methods. This approach aims to uncover gender stereotypes beyond the morality dimensions.

Audience Engagement with Gender Stereotypes

For audiences who engage with content on social media, such as videos, the “group identification” aspect of the SIDE theory posits that when group cues are made salient, individuals are motivated to adhere to group norms and express their group identities. This is grounded in the psychological concepts of self-categorization and social identification ( Tajfel & Turner, 2004 ). These mechanisms suggest that individuals seek group affiliations to affirm and defend their views, often seeking in-group solidarity and defending their ingroups against perceived threats to their identities ( Turner & Reynolds, 2012 ). For instance, when social media content highlights a specific social category—like video thumbnails featuring women—both women and men audiences are more likely to engage with the content. Their engagement, whether through viewing, commenting, or sharing, may be motivated by a desire to signal one’s in-group solidarity and (or) to counter perceived out-groups. As Walther (2011 , p. 450) notes, “if a CMC user experiences a social identification, the user will relate to other CMC users on the basis of in-group (or out-group) dynamics.”

H1: Science messages on social media that employ gender cues are likely to receive more views and likes compared to those without gender cues.

RQ2 : How do audiences react to science messages on social media that use gender cues versus those that use gender-neutral language?

Applying SIDE across platforms: platform affordance and gender language in science discourse

While the SIDE theory provides a theoretical foundation for explaining the amplification of gender stereotypes on social media and user interactions, current scholarship falls short in comprehending the potential disparities or resemblances in gender patterns across different platforms. Investigating these cross-platform differences or similarities in gender markers holds both theoretical and practical significance. Theoretically, this exploration contributes to a more nuanced understanding of the digital interaction landscape, recognizing that each online platform offers a unique environment characterized by its specific user demographics. Different platforms mediate information through varying affordances ( Westerman et al., 2008 ), influencing how content creators and audiences use these features to achieve their communicative goals ( Evans et al., 2017 ). Therefore, communication dynamics may vary across platforms due to differences in technological features, audience characteristics, and the interplay between them, all of which affect content creation and consumption. For instance, some research shows that text-intensive platforms like Reddit and Twitter utilize gender markers to signal identities and stereotypes ( Bamman et al., 2014 ), while visually oriented platforms like Instagram often employ gender cues through feminine gestures to shape impression and identity performance ( Butkowski et al., 2020 ).

Practically, understanding how gender stereotypes are portrayed in science discourse across various platforms is crucial for developing targeted strategies to counter these stereotypes and promote gender equity in science communication. Insights from cross-platform analyses can guide communication practitioners and users in navigating social media discussions more responsibly. As Canfield et al. (2020) stressed, a critical examination of equity and inclusion in science communication is imperative for addressing discriminatory practices that marginalize underrepresented voices in STEM conversations. Our research offers empirical evidence on how the usage of gender markers in digital science discourse might resemble or differ across two popular video platforms. This evidence suggests actionable implications for educators and communication professionals to effectively challenge these stereotypes.

Although an increasing number of studies have started to explore gender markers on various platforms, most research remains limited to single-platform analyses. Cross-platform comparisons, particularly concerning gender marker usage, are scarce, despite evidence of their distinct usage patterns. Our research takes a step further by investigating how content creators deploy gender stereotype language across YouTube and TikTok, two platforms gaining popularity and people using these platforms to access science information ( Brossard & Scheufele, 2013 ). While both platforms focus on video content, differences in features such as video length could motivate content creators to adopt different communication strategies. For instance, in terms of video length, YouTube tends to provide longer videos with an average of 11 minutes ( Statista, 2018 ), while TikTok videos are in shorter format, limiting a video to 3 minutes before 2022. This variation in video length might shape how content creators structure their content to attract attention. In shorter videos, communicating key messages quickly is vital. Limited time restricts content creators to illustrate details of issues, and the use of heuristic cues is essential to draw attention. Thus, content creators on shorter-form videos might reply more on social identity cues compared to videos in a longer form. However, it could also be the opposite, that content creators might use more social cues on longer-form videos to sustain audiences’ attention.

RQ3: How do TikTok and YouTube differ in portraying gender stereotypes in science messages?

RQ4: How do content creators integrate texts and visuals to express gender stereotypes in science videos on social media platforms?

Data description

We collected datasets from two social media platforms, YouTube and TikTok, to understand gender stereotypes on two controversial science issues: climate change and vaccination. To collect the dataset, we drew from keywords used in existing literature (e.g., Chen et al., 2021 ; Kim et al., 2023 ) on the two science issues such as “climate change,” “climate engineering,” “global warming,” “chemtrails,” and “vaccination” and “anti-vaccination” (see Supplementary Section A ; for the latest method on keyword selection for data collection, see Zhang et al. 2023 ). We used the YouTube Application Programming Interface (API) and the open-source Python package developed by Yin and Brown (2018) which allow researchers to retrieve videos based on the number of views. We randomly sampled hundreds of highly viewed videos between January 2015 and December 2022 for each keyword under the two issues, resulting in a total of 7,452 non-duplicated YouTube videos. For TikTok, we used a third-party platform, Junkipedia, a research tool from the Algorithmic Transparency Institute ( Center for an Informed Public et al., 2021 ), and retrieved 6,293 TikTok videos. Both tools allow researchers to collect video descriptions and video thumbnails, user engagement metrics such as the number of likes, views, and comments each video has received, and channel-level information such as a channel’s name.

Scope condition

For YouTube videos, our sampling is based on the number of views in the YouTube API ranking parameter during the data collection process, collecting videos that are relatively more viewed. For instance, many videos in our dataset are viewed by tens of thousands of people. However, there is still a wide distribution of views as some videos are still only viewed by a hundred people (see Supplementary Section B ). This means that when we interpret the gender stereotype patterns on YouTube, we were examining those videos that are relatively more influential rather than a random sample of the YouTube universe. However, these more viewed videos are important to study as they had more reach and could thus shape more audiences’ engagement and perspectives on gender and science.

Measurements

Identify gender cues in texts.

To develop the list of lexica related to gender cues, we first developed a series of personal pronoun words that consist of gender pronouns (e.g., she , her , hers , he , him , his ) and gender-related relational words (e.g., mom , sister , girlfriend , grandma , wife , husband , father , son , niece , uncle , paternal ). Then, we refined our initial list by using the gender pronouns words by (1) sub-setting video descriptions with gender cues in the initial list and (2) finding new words with gender cues by conducting an n-gram analysis using the R quanteda package that allows researchers to find words and phrases often associated with gender cues. Through this iterative process, we selected 65 words to capture women-related cues mentioned in the video description, and 60 words to capture men-related cues mentioned in the video description (see Supplementary Section C ). Across our 13,745 video descriptions, 3.90% mentioned women-related cues only, 8.88% men-related cues only, 1.73% mentioned both gender-related cues, and 85.49% mentioned neither cue.

Identify gender stereotypes and gendered moral stereotypes in texts

We utilized Moral Foundations Vec-tionary, a word embeddings-based computational model ( Duan et al., 2023 ), to identify moral cues in video descriptions. Vec-tionary incorporates and expands the information learned from a validated moral dictionary (eMFD, Hopp et al., 2021 ) to a high-dimensional semantic space using a nonlinear optimization model (see Supplementary Section D ). The eMFD, used as the foundation of Vec-tionary, is derived from text annotations by crowdsourcing coders, assigning varying weights to various moral foundations for approximately 3,270 English words. We validated Vec-tionary used in the context of this study through a human annotation process (see Supplementary Section E ).

While the Vec-tionary allows us to computationally study gendered morality language, we also wanted to identify other gender stereotypes. Thus, we also utilized an unsupervised machine learning method, the Structural Topic Model (STM) ( Roberts et al., 2019 ), to identify the differences in the themes between video descriptions that mention cues related to men and women. In the STM, we included covariates such as whether a video description mentions women or men cues, whether a video comes from TikTok or YouTube, and whether a video is about climate change or vaccination. To select the optimal number of topics for human-in-the-loop labeling, we conducted model diagnostics (e.g., held-out likelihood, semantic coherence, and residual) and examined the meaningfulness of the keywords generated by each model and we set the number of topics at 20 ( Supplementary Section F ). We used the R package “stm” and its “estimateEffect” function to compare what themes are more likely to be associated with video description that use men vs. women-related cues.

Identify gender stereotype patterns in visuals

To measure gender stereotypes that appear in science communication messages using multi-modal approach, we also identified gender and moral stereotypes in visuals. First, we downloaded thumbnails of YouTube and TikTok videos that use cues related to men ( n  =   1,394) or cues related to women ( n  =   778) in the video description. To classify images by themes, we applied a transfer learning-based unsupervised machine learning approach ( Zhang & Peng, 2021 ). Zhang and Peng (2021) found that the transfer learning-based unsupervised machine learning approach significantly outperforms other computer vision methods when exploring themes, such as bag-of-visual models or self-supervised learning models. To understand how different themes arise when thumbnails are associated with pronouns indicating (1) men or (2) women in video descriptions, we used k-means clustering to categorize photos into 10 clusters for each male and female dataset. We manually labeled clusters by looking at associated sample images. When images were similar across different clusters, we grouped them in the same category. For the video descriptions that use men cues, we labeled five themes (conspiracy, men politicians and scientists, husband getting the vaccine, religious items, and not found). For video descriptions that use women cues, we labeled nine themes (nature, Greta Thunberg, vlog selfie, women leaders, research, chemtrail and greenhouse, pop culture-style cartoon, baby, and not found). The procedure of determining the optimal number K and the detailed validation process of the unsupervised machine learning approach for image analysis can be found in Supplementary Section G .

Analysis methods

To test RQ1 and RQ3, we conducted Welch’s t -tests to examine the frequency of using moral languages between video descriptions that mention cues related to women vs. those that mention cues related to men, for YouTube and TikTok, respectively. To examine H1 and RQ2, we used negative binomial regressions to predict the number of views and the number of likes a video received, respectively. The independent variables in H2 are whether a video description mentions cues related to women or not (binary) and whether a video description mentions cues related to men or not (binary). The independent variable in RQ2 is whether a video description mentions gender cues or gender-neutral cues. For both H1 and RQ2, we controlled author-level information, including unique clusters of channel IDs, the platform, and the issues. We implemented the negative binomial model using the glmmTMB R package, which provides functions for analyzing linear and generalized linear mixed models, including zero-inflation ( Brooks et al., 2017 ).

The use of gender cues in science video descriptions

We found that overall, there was a much higher percentage of reference to cues related to men in science videos on both YouTube and TikTok platforms. For the vaccine issue, 6.99% of videos referred to words related to women, while 9.30% of videos referred to words related to men. Similarly, for the climate change issue, only 4.91% of videos were associated with words related to women while 11.30% of videos referred to words related to men. This pattern shows that in digital public spheres, when it comes to science discourse, men-related words more often occur compared to women-related words, echoing existing patterns we observe in science knowledge production in our society where scientists are often seen as a male profession ( Miller et al., 2015 ).

The user of gender stereotypes in video descriptions (RQ1)

Figure 1 shows the effect of using cues related to men and women in video descriptions on topic prevalence for each of the 18 (out of 20) labeled topics from our STM analysis. The point estimate is the mean effect of gender cues on topic prevalence, and the lines are 95% confidence intervals. Estimates above zero are topics that are more likely to be mentioned in video descriptions that use cues related to women. Estimates below zero are topics that are more likely to be mentioned in video descriptions that use cues related to men. We found that topics related to Vaccination for Children and Women (Topic 15; b  =   0.044, p < .001), Children Vaccination/Baby Product (Topic 1; b  =   0.036, p < .001), and Side Effects and Vaccination Safety for Children and Pregnant Women (Topic 16; b  =   0.036, p < .001) appeared more frequently in videos that mentioned cues related to women. In contrast, topics related to Misinformation and Climate Change Denial (Topic 2; b  =   −0.040, p < .001), Conspiracy Theory such as how the government uses climate change as an excuse for building climate weapons (Topic 6; b  =   −0.019, p < .01), Meteorology Science (Topic 9; b  =   −0.018, p < .05), and Jimmy Kimmel’s Claims on Climate Change (Topic 10; b  =   −0.017, p < .05) were discussed much more frequently in videos that mentioned men. Our observations from the themes in the STM suggest that topics related to discussions about women’s care of nature and children’s health are more likely to appear in video descriptions that use cues associated with women, in line with ecofeminism and the theory of motherhood. Further details on keywords highly associated with each topic, example video descriptions, and topic prevalence can be found in Supplementary Section H .

Effect of using women vs. men-related cues in the video descriptions on topic prevalence, with mean and 95% confidence intervals.

Examining the use of moral languages in video descriptions that use cues related to women vs. men ( Table 1 ), we found that for the YouTube science video descriptions, those that use cues related to women exhibited higher levels of Care ( b  =   2.536, d  =   0.147, p < .05), Loyalty ( b  =   3.534, d  =   0.202, p < .001), Fairness ( b  =   3.776, d  =   0.210, p < .001), and Sanctity ( b  =   2.240, d  =   0.128, p < .05) moral words compared to those video descriptions that use cues related to men. However, this gendered moral stereotype pattern is not observed on TikTok (RQ3). On the other hand, the Authority moral cue ( b  =   −2.141, d  =   −0.239, p < .05) was more frequently used in video descriptions that use men-related cues on TikTok. Examples of video descriptions that use these moral cues can be found in Supplementary Section I .

Two-sample t -test (Welch’s t -test) for moral valence comparison between video descriptions that mentioned women-related cues only vs. mentioned men-related cues only on TikTok and YouTube

^ p  < .1,

p < .05,

p < .01,

p < .001.

The use of gender stereotypes in thumbnails (RQ4)

To understand how gender patterns are reflected in visuals, Figure 2 presents our findings from the image analysis on thumbnails. We found that for the 532 video descriptions that mentioned women-related cues only, 25% of the thumbnails from these videos featured Greta Thunberg, the youth activist for the global climate movement that often uses moral appeals to call upon people to protect the environment (see Figure 2C ). Seventeen percent of the thumbnails featured nature-related scenes such as oceans and planets. Twenty-two percent of the thumbnails featured Vlog-style women selfies showing women doing daily life activities and posing feminine gestures while talking about science (see Figure 2B ). Although we found the use of gendered moral language in video descriptions, we did not observe morality stereotype themes, such as care, used in thumbnails.

Thumbnail analysis comparing video descriptions that use women vs. men cues.

For the 1,216 video descriptions that mentioned cues related to men only, we observed different visual patterns. First, nearly 48.77% of images featured pictures related to conspiracy theories about these science issues, such as a big poster on chemtrail, either propagating or debunking the conspiracies. This echoes the findings from the STM, where we also observed that themes related to conspiracy and misinformation discussions appeared much more in video descriptions that use cues mentioning men. Second, the categories of images across the men-mentioned videos are less diverse compared to the videos mentioning women. The dominant image categories feature conspiracy discussions and men politicians or scientists.

Examining the patterns of image categories between videos mentioning women vs. men across two platforms (RQ3), we found that for the 386 YouTube video descriptions that mentioned cues related to women, about 34% of their thumbnails featured Greta Thunberg, and 22% featured images related to nature. For the 146 TikTok videos descriptions that mentioned women cues, only 2% of their thumbnails featured Greta Thunberg, and about 82% featured Vlog women selfies. This cross-platform difference in thumbnails suggest an interesting pattern that TikTok videos with cues related to women have a higher percentage of visuals that emphasize feminine traits compared to YouTube.

Gender cues, morality, and user engagement (H1)

We found a positive association between the use of gender cues in video descriptions and audience engagement (H1 supported). As Table 2 shows, using cues related to women in video descriptions resulted in 1.461 times (e 0.379 ) higher views ( b  =   0.379, p < .001) and 1.606 times (e 0.474 ) more likes ( b  =   0.474, p < .001). Similarly, using cues related to men in video descriptions was associated with 1.420 times (e 0.351 ) more views ( b  =   0.351, p < .001) and 1.374 times (e 0.318 ) more likes ( b  =   0.318, p < .001). These findings suggest regardless of whether cues related to women or men are mentioned, using gender cues are associated with higher number of likes and views for science videos on social media.

Negative binomial regression models predicting the engagement size of science videos on two platforms (H1)

Note. Reference group: platform = TikTok, issue = Climate Change. SE, standard error. The beta coefficients can be interpreted as the log of the ratio of expected counts. For the full model, please refer to Supplementary Section J .

p < .001;

We further compared how audiences react to video descriptions that use gender cues vs. use gender-neutral cues (e.g., we, they) (RQ2). The use of gender-specific words in video descriptions was associated with a slight increase of 1.139 times (e 0.130 ) in views ( b  =   0.130, p = .205) and 1.171 times (e 0.158 ) in likes ( b  =   0.158, p = .113); however, none of these differences was statistically significant ( Table 3 ). This suggests that future interventions could potentially use gender-neutral cues in digital video messages, as our study did not find evidence that such language would result in less engagement.

Negative binomial regression models predicting the engagement size of science videos on two platforms (RQ2)

Gender stereotypes have become a central topic across the fields of social psychology, social media studies, and science communication. While discourses around scientific issues have increasingly occurred on social media, it is still little understood to what extent social media discourse on science issues reinforces existing social biases, such as gender stereotypes attached to men and women in addressing environmental and health challenges. Drawing from the SIDE theory, we investigated the use of gender cues and stereotypes by content creators in digital science discourse across platforms (YouTube, TikTok) and how audiences engage with these gendered narratives. Below, we discuss the contributions of our findings.

Gender stereotypes in digital science communication

Our analysis reveals that social media discourse on controversial science issues, such as climate change and vaccination, often features cues that are more associated with men than women in video descriptions. Our STM analysis further showed a broad pattern of gender stereotypes in video texts (RQ1). When video descriptions mentioned cues related to women, they were more likely to focus on themes related to care, children, and health. When video descriptions mentioned cues related to men, however, they were more likely to discuss science misinformation and conspiracies. These findings align with ecofeminist and motherhood theories, emphasizing that women often bear the burden of caregiving responsibilities ( Bobel, 2010 ; Tracey et al., 2022 ).

In terms of gendered morality stereotypes, we observed a higher proportion of care-related, loyalty-related, and fairness-related words in YouTube video descriptions with cues associated with women, compared to those with men (RQ1). Similarly, there was a higher proportion of using authority-related words in TikTok’s video descriptions with cues related to men, compared to those with women (RQ1). These findings suggest distinct moral dimensions associated with women and men in science discourse on social media.

Cross-platform patterns on using gender stereotypes in science communication

While many studies have focused on gender stereotypes on a single social media platform, our article takes a step further by exploring how these patterns might be similar or different across platforms (RQ3). By extending SIDE theory to different platform contexts, we revealed whether different video platforms—YouTube and TikTok would have differences in portraying gender stereotypes. Our findings uncovered several interesting patterns.

First, regarding the use of moral stereotypes in video descriptions, we found that while YouTube video descriptions used more care-, loyalty-, and fairness-related words when they mentioned women, these patterns did not apply to TikTok. For TikTok, its video descriptions used more authority-related words when mentioning men. However, this moral stereotype associated with men was not observed on YouTube. This divergence presents mixed evidence for RQ3 and suggests that cross-platform differences in gender stereotypes may depend on the specific gender being examined. Stereotypes associated with one gender might be more pronounced on one platform, while another platform may highlight stereotypes of a different gender.

Besides cross-platform differences on gendered morality stereotypes in the video descriptions, our study also identified cross-platform differences in visuals. On TikTok, V-log style selfies were more frequently used among videos that mentioned cues related to women in their video descriptions compared to on YouTube. Differently, on YouTube, among videos that mentioned women-related cues, the thumbnails featured more about women scientists and activists. As shorter format video platforms may need to grab audience attention immediately, content creators are more likely to bomb viewers with visual social cues to generate clicks to survive in a competitive attention economy. These findings underscore the importance of examining communication patterns across diverse social media contexts, as the audience base, the platform features, and thus the mediation between platform features and audiences vary. Our research contributes to the growing scholarship that examines how people use social identity cues across platforms. For instance, Chinn et al. (2023) found that in-group and out-group identity words are used by content creators in science discourse differently on Instagram vs. Facebook, the former building solidarity among in-groups and the latter creating contention toward out-groups.

Gender cues, morality, and user engagement

Our findings also contribute to literature on studying gender stereotypes on social media by revealing that not only the content creators but also the consumers tend to view and click more likes toward videos that use gender cues ( H1 ). This poses challenges for science discourse as we observe that the patterns of gender stereotypes in offline science communication have been reflected on social media platforms, and the audience further reinforce these gender patterns through engaging with the gendered science discourse more.

Our findings offer implications for intervention strategies to mitigate gender stereotypes in (digital) science discourse. The first suggestion for intervention strategies is to dissociate certain moral stereotypes from specific genders. Given women are portrayed with more care and responsibility laden words across topics on climate and health ( RQ1 ), there is a pressing need to reconstruct our digital science discourse by emphasizing shared responsibilities and actions across genders in addressing environment and health challenges. Future research can design experiments to test the impact of framing science issues as shared responsibilities across different genders vs. focusing on a single gender, assessing the effects on public engagement with science issues.

In the context of user engagement, even though using gender cues related to women and men was associated with more views and likes ( H1 ), there was no statistically significant difference in user engagement between videos using gender cues versus those employing gender-neutral words such as “we” and “they” ( RQ2 ). This finding shows that gender stereotypes could be effectively mitigated by using collective terms without compromising the level of user engagement. The benefits of using gender-neutral words extend beyond maintaining user engagement; they also contribute to enhancing public understanding of gender roles, as scholars noted that using gender-neutral terms to describe people can reduce how people associate genders with existing stereotypes ( Tavits & Pérez, 2019 ).

Studying gender stereotypes through multimodality

Our article also demonstrates the importance of examining gender patterns in a multimodal format to triangulate our understanding of how content creators integrate visuals and texts to convey gender stereotypes in science videos ( RQ4 ). As stressed in CMC research, messages on social media are often expressed in varying models (e.g., audio, images, texts) ( Geise & Baden, 2015 ). This multifaceted nature of CMC requires researchers to understand how content creators leverage these diverse modes to represent social cues and stereotypes. We found that gender stereotypes are conveyed differently across various delivery modes. While care-related themes were often expressed in text (e.g., video descriptions) when science videos mentioned women, thumbnails of these videos did not use care-related visuals such as mothers taking care of their babies. Instead, the visuals portrayed themes related to women politicians and activists and selfies of ordinary citizens displaying feminine gestures. This use of V-log style selfies (e.g., headshots, feminine gestures) was also found in other scholarship where researchers have noted that gender stereotypes related to identity performance are frequently used in the cosmetics industry (e.g., online videos about skincare, body image building) to attract audiences ( Butkowski et al., 2020 ). Our findings showed that this type of identity performance also prevails in science discourse on social media.

Besides providing new knowledge to advance our understanding about how gender patterns look differently in different modalities, our article also demonstrates how researchers could integrate text and visual analyses to measure and document gender stereotypes in a multi-modal format. We integrated and validated a variety of computational tools, such as using unsupervised topic modeling and moral word embedding to discover various dimensions of gender stereotypes. Joining the recent effort to explore image-as-data in communication research ( Casas & Webb Williams, 2022 ), our article introduces how researchers could use an unsupervised image clustering method to discover gender stereotypes from visuals. This multi-modal research presents new avenues for studying CMC on video-based platforms.

Limitation and future directions

We acknowledge several limitations. First, the YouTube videos we analyzed were sampled using YouTube’s ranking parameter. Given our limits of sample size for each keyword and date, the videos collected were often top performers for those dates. Consequently, the gender stereotype patterns observed might not represent all YouTube videos. Future studies could explore the differences in gender stereotypes between highly and lesser-viewed videos to comprehensively understand gender stereotypes in digital science communication. We also encourage future research to enrich the lexicon of science misinformation, as the issue evolves, and to investigate how gender stereotypes may vary between scientific vs. misinformation discourses. Second, our study serves as a starting point to investigate how content creators might employ varied gender strategies across platforms and remains exploratory. While our findings shed light on the potential interventions for different platforms, this study does not delve into the specific features of each platform that could influence gender stereotypes and audience engagement. Future research could interview content creators and audiences to understand which platform features are considered during content creation. This would guide analytical research on the relationship between platform features (e.g., length, audience basis) and gendered patterns. Finally, we use the term “content creators” to mean those producing TikTok and YouTube videos. However, we recognize that viewers can also be content creators, especially those who leave comments. It is valuable for future research to analyze video comments to see if videos with more gender stereotypes elicit more gendered comments.

Our study contributes a valuable step toward understanding gender stereotypes in science communication on social media. This area, though less explored, is vital for public perception of science and scientists. Our findings also advance CMC research in three key ways: (1) revealing how computer-mediated environments might amplify existing societal biases about gender and science, (2) offering insights into how gendered patterns might vary across platforms, and (3) highlighting how a multimodal approach to measuring gender stereotypes can uncover important similarities and differences of gender biases in social media texts and visuals. Lastly, by showing that videos using gender-neutral collective terms like “we” or “they” can achieve similar engagement levels as gendered videos, this study provides insights for potential interventions to counteract gender stereotypes in the digital communication of science.

Supplementary material is available at Journal of Computer-Mediated Communication online.

The dataset and the scripts to replicate the findings are deposited at Harvard Dataverse: https://doi.org/10.7910/DVN/FISS8S .

We would like to thank Luyu Xu for data visualization assistance.

Funding support for this article was provided by the WARF Accelerator Big Data Challenge Grant, the Robert F. and Jean E. Holtz Center, and the Graduate Student Research Fund at the School of Journalism and Mass Communication, University of Wisconsin-Madison.

Conflict of Interest : The authors declare that they have no conflict of interest.

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Gender stereotypes change outcomes: a systematic literature review

• December 2021
• Journal of Humanities and Applied Social Sciences ahead-of-print(ahead-of-print)
• ahead-of-print(ahead-of-print)

• University of Kelaniya

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ORIGINAL RESEARCH article

Mapping gender stereotypes: a network analysis approach.

• 1 Department of Social Psychology and Anthropology, University of Salamanca, Salamanca, Spain
• 2 Department of Social and Organizational Psychology, National University of Distance Education, Madrid, Spain
• 3 Department of Social Psychology, University of Granada, Granada, Spain

Introduction: Stereotypes have traditionally been considered as “mental pictures” of a particular social group. The current research aims to draw the structure of gender stereotypes and metastereotype schemes as complex systems of stereotypical features. Therefore, we analyze gender stereotypes as networks of interconnected characteristics.

Method: Through an online survey ( N = 750), participants listed the common female and male features to build the structure of the gender stereotypes. Participants also listed the common features of how members of one gender think they are viewed by people of the other gender to build the structure of gender metastereotypes.

Results: Our results suggest that female stereotypes are characterized by a single community of features consistently associated such as intelligent, strong , and hardworkers . Female metastereotype, however, combines the previous community with another characterized by weak and sensitive . On the contrary, the male stereotype projected by women is characterized by a community of features associated such as intelligent, strong , and hardworker , but male in-group stereotypes and metastereotypes projected by men are a combination of this community with another one characterized by features associated such as strong, chauvinist , and aggressive .

Discussion: A network approach to studying stereotypes provided insights into the meaning of certain traits when considered in combination with different traits. (e.g., strong-intelligent vs. strong-aggressive). Thus, focusing on central nodes can be critical to understanding and changing the structure of gender stereotypes.

1. Introduction

The research about gender stereotypes has been a longstanding storyline in social science and has inspired much research in gender theories ( Eagly, 1987 ; Wood and Eagly, 2015 ). Social psychologists have focused on studying gender stereotypes because they are a key construct in explaining the differences between women and men regarding their behavior and attitudes, as well as ideologies that perpetuate gender inequality ( Eagly, 1987 ; Ellemers, 2018 ). Similarly, the stereotypes that people of one gender have about the way in which they are viewed by people of another gender—i.e., gender metastereotypes—are key to determine intergroup relations ( Gómez, 2002 ; Babbitt et al., 2018 ). Therefore, understanding current gender stereotypes and metastereotypes influences how people evaluate others and themselves, which condition gender intra- and intergroup relationships ( Ellemers, 2018 ).

Stereotypes have been traditionally considered “mental pictures” of a specific social group ( Lippmann, 1922 ). Although initially the research was focused on finding the features attributed to gender, it was found that those features could be organized in dimensions ( Broverman et al., 1972 ; Fiske et al., 2002 ). From that moment, the research on gender stereotypes has grown on the rationale that the content of stereotypes consists of a few dimensions that can summarize a wide range of stereotypical features. Although this approach has profoundly enriched the understanding of stereotypes, it loses the information provided by single features and their interactions, which, as a whole, determine a more complex structure of gender stereotypes. In the current research, we aim to deepen the structure of current gender stereotypes by depicting them as complex systems, that is, as an ensemble of many elements which are interacting in a particular way, resulting in a robust organization ( Ladyman and Wiesner, 2020 ). To address them as complex systems, we take a network approach, considering stereotypical features as the basic elements, to explore how they interact with each other and draw the structure of gender stereotypes. We proposed a bottom-up strategy to build the structure of gender stereotypes from the free responses of the participants, given that this strategy allows us to reflect the spontaneous current social representation that individuals embrace ( Moscovici, 1984 ).

1.1. Gender stereotypes

Stereotypes have been defined as overgeneralized, rigid, and exaggerated beliefs about the characteristics, attributes, and behaviors of members of certain groups ( Cardwell, 1996 ). People stereotype others in an attempt to understand their social world ( Ellemers, 2018 ). Gender stereotypes have been considered as people's shared beliefs about the traits of women and men ( Sczesny et al., 2019 ). A common task in social psychology is identifying the content of gender stereotypes, given that traditional gender stereotypes maintain and reinforce gender inequalities ( Ellemers, 2018 ). Following the literature, men are traditionally stereotyped as being more agentic than women, whereas women are stereotyped as being more communal than men ( Fiske et al., 2002 ; Hentschel et al., 2019 ). Agency and communion are the fundamental dimensions of human orientation and social judgment ( Abele and Wojciszke, 2014 ). Agency refers to goal achievement and task functioning, emphasizing two facets: assertiveness and competence. Assertiveness reflects the motivational and purposive component of agency, whereas competence reflects the ability component. On the contrary, communion refers to the maintenance of relationships and the desire for affiliation, emphasizing the warmth and morality facets. Warmth refers to affective motives, and morality refers to benevolence, ethics, and social values ( Abele and Wojciszke, 2014 ; Wojciszke and Abele, 2019 ). Similar differences in gender stereotypes have been proven across different cultures ( Williams et al., 1999 ). Although it is common an emphasis on personality traits, there is also evidence of a multi-component construction of gender stereotypes. Deaux and Lewis (1983) identified components of gender stereotypes in traits, role behaviors, occupations, and physical appearance. Interestingly for the current research, Deaux and Lewis (1984) shown, in subsequent research, that those components dynamically implicate each other.

Stereotypes about social groups can be held by the in-group, which is defined as in-group stereotype. Whereas stereotyping out-groups is usually related to discrimination toward their members ( Wilder, 1984 ), the in-group stereotype has implications for self-concept and self-categorization ( Hogg and Turner, 1987 ). Therefore, stereotypes do not only influence how we evaluate others but also ourselves. Similarly, metastereotypes are conceptually distinct from in-group stereotypes because metastereotypes refer to individual group members' beliefs about how others view their group, whereas in-group stereotypes refer to individuals' beliefs about their own group ( Gómez, 2002 ; Babbitt et al., 2018 ). A metastereotype is associated with feelings toward intergroup interaction and attitudes and assessments of the out-group members ( Owuamalam et al., 2013 ).

Understanding the content of gender stereotypes requires recognizing the relevance of social roles ( Eagly and Karau, 2002 ). According to the social role theory, gender stereotypes are built on social roles ( Eagly, 1987 ). Traditionally, women and men have been segregated into different atmospheres: women in domestic work or care-related jobs, and men in leadership or skill-related jobs ( Lippa et al., 2014 ). This unequal segregation enacts gender differences in the sense that people form their impressions of others through observing their behavior ( Gilbert and Malone, 1995 ). Therefore, people create different images of women and men. In other words, everyday observations of the different roles of women and men underlie gender stereotypes ( Eagly, 1987 ; Sczesny et al., 2019 ).

Because gender stereotypes are a result of people's beliefs and expectations of women and men in a given cultural context, they may change as the context changes ( Diekman and Eagly, 2000 ; Sczesny et al., 2019 ). Given the advances toward gender equality and the decrease of the unequal distribution of women and men in different roles ( Lippa et al., 2014 ; World Economic Forum, 2020 ), gender stereotypes are expected to reflect this change ( Eagly and Karau, 2002 ). Some findings of stereotypical ascriptions for women and men have found that, indeed, there have been changes, but others that some stereotypes persist. For instance, Haines et al. (2016) tested whether gender stereotypes had changed over 30 years (1983–2014). Their findings suggested that people continued to rate women as more communal than men and rated men as more agentic than women, just as people did 30 years ago. Recent research in the Spanish context showed no evidence of stereotype changes in agentic and communal personality traits between 1985 and 2018 ( Moya and Moya-Garófano, 2021 ). Similarly, Hentschel et al. (2019) found that gender stereotypes persisted in terms of communality features. However, they did not find differences by sex in independent and leadership roles, suggesting there have been changes in these components of the agency dimension. In this line of thinking, other results suggested an increase in perceived competence of women across time ( Duehr and Bono, 2006 ; Eagly et al., 2020 ). The difference between assertiveness and competence within the agency dimension seems to be relevant nowadays because contemporary female stereotypes may reflect lower assertiveness than those of men but not competence ( Sczesny et al., 2019 ). Similarly, women attributed to themselves less traditional feminine features in 2012 than in 1993; however, there was no significant difference in the features men attributed to themselves in the same period ( Donnelly and Twenge, 2017 ).

Therefore, although some literature suggests that there have been changes in gender stereotypes, others suggest that they have remained stable for a long time. Despite changes in social roles that might work as a force for changing gender stereotypes, the nature of stereotypes also provides them with forces for remaining unchanged throughout time (e.g., through memory and attributional processes; Hilton and von Hippel, 1996 ). We should highlight that the evidence that gender stereotypes are changing, although slowly, suggests that they are dynamic systems. Here, we propose that in addition to dynamic systems, gender stereotypes can be understood as complex systems, which in turn can shed light on how they change ( van Geert, 2019 ). Complex systems can be seen as an ensemble of elements that are interacting in a particular way, resulting in a robust organization ( Ladyman and Wiesner, 2020 ). To understand the gender stereotype as complex systems, we use a network approach.

1.2. Stereotypes as a network of features

To understand how to work a complex system, we must understand not only how to work their elements but also how they act together as a whole ( Bar-Yam, 1997 ). In the current research, we inspected the complex interaction of single features and how they draw the mental gender stereotype pictures in descriptive terms—i.e., descriptive gender stereotype. To do so, we drew the current research on the literature of the network approach. In psychology, the network approach posits indicators are not only passive items that reflect latent constructs but also they might form dynamic and complex systems because of the interactions among themselves ( Cramer et al., 2010 ; Robinaugh et al., 2020 ). Therefore, from a network perspective, features that characterized gender stereotypes would not be interchangeable indicators that could be averaged. Instead, features of gender stereotypes take particular meanings depending on their position in the overall network and their interaction with other features ( Cramer et al., 2010 ). Accordingly, we could shed light on the structure of the gender stereotypes and metastereotypes building the associative networks of features that reflect the gender schemes widespread in the society. We should note that this approach would be different from the construction of associative networks of the features in which are not assumed any underlying structure.

Gender stereotypes from a network approach would be built from a set of interacting features. It is likely that the network approach would reflect more accurately how stereotypes are represented in the mind as “mental pictures” ( Sayans-Jiménez et al., 2019 ). Some initial research has already used network analyses to map stereotypes, but they are still based on their analysis of the general dimensions of the stereotypes' content ( Grigoryev et al., 2019 ; Sayans-Jiménez et al., 2019 ), which limit the information provided by predefined categories. Therefore, in the current research, we modeled gender stereotypes using features as nodes instead of dimensions and based on people's free responses without constraining them to previous categories. We raised that understanding gender stereotypes as complex systems and addressing them from a network approach using features as their basic elements might shed light on several issues in the study of stereotypes.

First, gender stereotypes can be seen as complex systems ( van Geert, 2019 ), which help us to understand their changes ( Diekman and Eagly, 2000 ; Sczesny et al., 2019 ). For instance, in times of rapid social changes, as in current times, changes in gender stereotypes are unlikely to occur in all the people in the same way or at the same speed. Consequently, nowadays, traditional and modern forms of gender stereotypes might be coexisting in society. To address this point, we should explore the structure of gender stereotypes to test to what extent gender stereotypes are homogeneous or heterogeneous nowadays. Because the dimensional approach used to address this issue has limitations in accounting for the complexity of a wide variety of gender stereotypes, we can take a network approach to account for the coexistence of attributes linked to gender stereotypes. By considering gender stereotypes as the co-occurrence of several features people identify, we can explicitly model a network of attributes that depict people's gender perceptions. Similarly, network analyses provide tools to detect the network's underlying substructure, which is crucial in identifying clusters of attributes that are more likely to appear together—i.e., to check whether everyone raises a similar gender stereotype, which would suggest that they are homogeneous, or whether different groups in the population raise different gender stereotype, which would point out that gender stereotypes are heterogeneous.

Second, once we have built the structure of gender stereotypes, we can compare qualitatively the similarities and differences between those projected by women and men. For instance, female stereotypes can be projected by women—i.e., in-group stereotype—, by men, and by the beliefs that women have about the female stereotype projected by men—i.e., metastereotype. This distinction is important because the three forms of stereotypes might be related to different outcomes. Following the previous literature on stereotypes, the female in-group stereotype should be related to women's self-concept and self-categorization ( Hogg and Turner, 1987 ); the female stereotypes held by men condition the relationship of men toward women ( Wilder, 1984 ); and metastereotypes could influence women's attitudes toward men ( Owuamalam et al., 2013 ). These three forms of stereotypes are likely to reinforce each other. What is unknown is to what extent they share a similar structure. We focus on the differences between gender stereotypes and metastereotypes held by male and female participants.

Third, a network perspective might help to qualify the meanings to attribute to each stereotypical feature according to co-occurring features. We know from Asch's (1946) classic studies that the impression formed toward someone when we defined him/her as intelligent, skillful, industrious, determined, practical, and cautious is quite different if we add warm or cold to the list. Indeed, the perception of a person would vary if people think she is intelligent and warm or intelligent and cold. This result suggests that features attributed to a person interact with each other to build their meanings rather than they are interpreted separately. Thus, the general impression that we build of others emerges from the interaction of the features that we attribute to them. Although this process happens from features to the interpretation (bottom-up), a parallel process could be happening from interpretation to attributed characteristics (top-down), which would be guided by schemes (e.g., gender schemes, Bem, 1981 ). We suggest that this process applies also to the groups when individuals attribute them stereotypical features, particularly to gender stereotypes. However, given that the mainstream approach did not take into account the interactions between features because considering them interchangeable indicators that could be averaged on general indices, we unknown how stereotypical gender features interact with each other and how representation is built. The network approach allows us to fill this gap using a co-occurrence matrix of features the participants mentioned to conduct the network analyses. This strategy allows us to account for the association of all the features simultaneously. In this way, we can check the interactions between features for a novel and more detailed understanding of gender stereotypes.

2. The present research

The current research aims to explore the structure of the current gender stereotypes from a network approach. Given that we are interested in the widespread schemes related to gender stereotypes and metastereotypes, we adopted a strategy that reflects the spontaneous current social representation that individuals embrace ( Moscovici, 1984 ). Therefore, we used a free association technique to collect our data ( Tsoukalas, 2006 ). Afterward, we conducted a bottom-up strategy to analyze our data applying network analyses to identify the pattern of associations among attributes, which describe the structure of gender stereotypes. Finally, we extended these analyses to gender metastereotypes to look at their structure and similarities with the in-group stereotype.

2.1. Methods

2.1.1. participants.

We conducted an online study by sending an email to the university community of a city in southern Spain. There were 750 respondents to our study (512 women, 190 men, 5 others, and 43 missing gender information), who ranged from 18 to 64 years old ( M = 24.85, SD = 7.78). There were 585 participants that indicated that Spanish was their native language (30 were not and 135 missing language information). There were no exclusions, so we include all the participants in the analyses. Participants' sociodemographic features are available in Supplementary material (Section S1).

2.1.2. Procedure and measurement

All the procedures performed in this study were in accordance with the ethical standards of the institutional research committee. First, we asked participants about gender stereotypes of both men and women. We counterbalanced the order of presenting the gender stereotypes to avoid order effects. Given that we were interested in the descriptive gender stereotype, participants read the following instruction before providing their answers: “Think about all the women (men) you know—i.e., relatives, friends, work or university colleagues, women (men) who appear on television, on social networks, in books…—What are the features that all those women (man) have in common?” Participants were asked to write ten open-ended answers. We suggested answering for at least five features.

Afterward, we asked them about their gender, and according to their answer, we asked them about the metastereotype, 1 that is, we were interested in how members from one gender think they are viewed by people from the other gender. Therefore, if they indicated they were women (men), we asked them to think about all the men (women) they knew. Then, participants were asked to write what those men (women) thought about women (men). They were to list these features in 10 open-ended responses. We encouraged them to answer for at least five features.

2.1.2.1. Data processing

Given that participants provided open-ended answers, we obtained a high diversity of words referring to similar features. Thus, three researchers manually performed the lemmatization of the data corpus, which consisted in keeping the base form of each word (lemma) by removing the inflectional ending or word derivations participants used (e.g., friends, friendly, friendship, and friendliness indicate the same lemma, friend). This strategy is commonly used in linguistic processing to reduce the variability of words that capture the same meaning. Although this process might lead us to lose sight of some nuances, this is a practical procedure to simplify large amounts of information that can be redundant, which allows us to reach a more substantive interpretation. Furthermore, this process was performed manually, allowing researchers to detect potential ambiguities and select the most appropriate lemma according to the context.

Because our original data corpus was mostly composed of single words in Spanish, after the lemmatization, we automatically translated each word into English. Then, two researchers verified the translation's accuracy by reviewing whether those words fit the original meaning properly. This stage allowed a new check on the quality of lemmatization and appropriate use of words. The raw and coded material is available in the online Supplementary material ( https://osf.io/cmf6a/ ).

We used the co-occurrence matrix of features the participants mentioned to conduct the network analyses. The network analysis strategy allowed us to account for the relationship of all the features simultaneously to reveal patterns of association. Networks are made up of two components: nodes and edges. Nodes correspond to participants' responses (i.e., features), and edges reflect the associations between nodes (i.e., features mentioned by the same person are connected). Thus, we had as many nodes as named features regardless of how many times these features were mentioned, and edges reflect the co-occurrence of every pair of features the same participant mentioned. To illustrate the procedure, imagine that a participant A answered three features of female stereotypes: intelligent, resilience, and hardworking ( Figure 1 , upper left). Therefore, we would have three nodes and three edges: intelligent-resilience, intelligent-hardworking, and resilience-hardworking. Another participant B answered three more features: intelligent, resilience, and sensitive ( Figure 1 , upper right). Therefore, the combination of those two personal networks provides an outcome graph composed of four nodes and five edges ( Figure 1 , bottom left). Finally, to visualize this network, the nodes' size increases according to the number of times they were mentioned, and edges are collapsed ( Figure 1 , bottom right). Note that the links between intelligent and resilience collapse, becoming one single edge with a weight equaling 2, and therefore, suggesting that this relationship is stronger than the others. Note also that the nodes' intelligent and resilient increase their size, suggesting that they were more times named.

Figure 1 . Example of co-occurrence network. Each circle represents one feature (node), and in parenthesis is the number of times that such attribute is mentioned (centrality degree); lines represent the edges, and the number next to the line represents the weight of each association. The upper figures indicate individual networks for two participants, and the bottom figure indicates the aggregated network per group.

This procedure was extended to the whole data set, allowing us to identify the network constituent elements (i.e., stereotypical features) and the pattern of association between them (i.e., stereotypes structure). The network analysis was supported by Gephi 0.9.2 software ( Bastian et al., 2009 ).

2.2. Results and discussion

The results for descriptive text analyses about gender stereotypes and metastereotypes features are depicted in the Supplementary material (Section S2). We conducted network analyses for each gender stereotype to identify how the features mentioned above were interconnected to form the structure of gender stereotypes. We calculated their communities—i.e., (sub)set of nodes whose connections are stronger than their connection with the rest of the nodes in the network ( Blondel et al., 2008 )—and visualized them by colors. Moreover, we visualize the networks according to the features of the nodes: the bigger nodes—i.e., those named more times together with other features—are in the central part of the network, and the more related nodes—i.e., those named more times together—are placed closer to each other.

2.2.1. Female stereotype

The female stereotype that men showed was composed of a network that included 853 nodes, with 4,398 co-occurrences collapsed in 3,474 edges. Communality analyses showed three large clusters of features. The strongest co-occurrences were intelligent - strong (22), loving–intelligent (18), intelligent–hardworking (17), fighter–hardworking (17), and fighter-intelligent (15). As notably seen in Figure 2 , the largest community (colored by light purple) represents 26% of the network, and their nodes with the highest centrality degree—i.e., the number of relationships that each node has with other nodes—are intelligent (215 2 ), hardworking (149), and fighters (139). These results suggest that these features had more relevance in this community, given they were the most connected ( Brandes et al., 2005 ). Nevertheless, there are additional communities, although with lower representation. The second community (colored by green) represents 7.5% of the network, and their nodes with the highest degree are brave (67), patience (35), and proud (34). Finally, the third community (colored by blue) represents 5.39% of the network, and their nodes with the highest degree are chatty (84), long hair (71), and hips (38).

Figure 2 . Network for female stereotypes mentioned by men.

The female stereotype that women provided (in-group stereotype) was composed of a network that included 853 nodes, with 13,600 co-occurrences collapsed in 7,711 edges. The strongest co-occurrences were intelligent - strong (92), hardworking–intelligent (86), fighter–strong (66), strong–hardworking (66), and fighter-hardworking (64). As seen in Figure 3 , the most extensive community (colored by light purple) represents 49.36% of the network and their nodes with the highest degree are similar to those that men provided: intelligent (352), strong (312), and hardworking (299). Similarly to the female stereotype projected by men, there are additional communities but with lower representation. The second community (colored by green) represents 10.79% of the network, and their nodes with the highest degree are long hair (67), thin (35), and bosom (34). Finally, the third community (colored by blue) represents 8.68% of the network and their nodes with the highest degree are unsafe (84), feminist (71), and dependent (38).

Figure 3 . Network for female stereotypes mentioned by women.

In short, the female in-group stereotype was similar to the stereotypes that men have about women. These results suggest that the female stereotype (established by both women and men) is composed mainly by one community characterized by positive valued features linked among them such as intelligent, strong, hardworking , and fighter . The exception was that one of the main links in the women stereotypes projected by men was intelligent - loving , suggesting an important nuance in the meaning of the term intelligent when men attributed it to women. The weight this community has in the network suggests that the female stereotype is mainly, although not wholly, homogeneous. We should note that although most central features in this community might be considered agentic features, there are also other features more related to communion (e.g., sensitive, emphatic, and loving). The results also showed that there were additional small communities that characterized the female stereotype. Both women and men conveyed their female stereotype with physical features—i.e., long hair, thin, bosom , and hips .

Moreover, the female in-group stereotype also included a small community characterized by traditional features that reflect some type of vulnerability (e.g., unsafe and dependent ). It is noteworthy that in this community, the feature of feminist also appeared. However, this feature should be interpreted into their network context, not only by their direct meaning. The label feminist has been traditionally stigmatized and used by some people as a negative feature to describe women ( Anastosopoulos and Desmarais, 2015 ). Our results suggest that this is the connotation in which some (very few) women used this label. Indeed, what our network analyses suggest is that when women used feminist to describe the female stereotype, they also tended to use unsafe, dependent, submissive , or pent . Therefore, the meaning that they seemed to attribute to the feminist label could be linked to the stigmatized sense of the word. Yet, it is also likely that feminist is linked to undermining features as a way of compensating such vulnerability.

2.2.2. Male stereotype

The male stereotype provided by women included 995 nodes with 11,794 co-occurrences collapsed in 8,378 edges. The strongest co-occurrences were intelligent - hardworking (44), strong–intelligent (40), strong–hardworking (36), nice–intelligent (32), and nice-hardworking (29). As seen in Figure 4 , the largest community (colored by light purple) represents 46.53% of the network, and their nodes with the highest degree are similar to those that women answered: strong (320), intelligent (305), and hardworking (259). There are additional communities but with lower representation. The second community (colored by green) represents 13.67% of the network, and their nodes with the highest degree are high (102), beard (58), and brunettes (45). Finally, the third community (colored by blue) represents 10.95% of the network, and their nodes with the highest degree are simple (96), tough (75), and rough (54).

Figure 4 . Network for male stereotypes mentioned by women.

The male in-group stereotype included 995 nodes, with 4,145 co-occurrences collapsed in 3,582 edges. The strongest co-occurrences were intelligent - hardworking (13), strong–intelligent (13), likable–intelligent (9), nice–intelligent (9), and strong-hardworking (9). As seen in Figure 5 , the largest community (colored by light purple) represents 19.6% of the network, and their nodes with the highest degree are similar to those women answered about male stereotype: intelligent (141), hardworking (131), and nice (120). Interestingly, there is a second extensive community. This second community (colored by green) represents 13.67% of the network, and their nodes with the highest degree are strong (149), aggressive (89), and hardheaded (64), showing a relatively strong interconnection between those features: strong-aggressive (4), strong-hardheaded (3), and strong-hardheaded (2). Finally, the third community (colored by blue) represents 7.14% of the network, and their nodes with the highest degree are cheerful (56), chatty (45), and beard (44).

Figure 5 . Network for male stereotypes mentioned by men.

These results suggest that male stereotypes that both women and men answered were different. First, the male stereotype women referred to has a structure with a big cluster, whereas male in-group stereotype has a structure with two big clusters. Second, unlike female stereotypes in which we could distinguish between traditional and modern female stereotypes, the structure of male stereotype seems to reflect larger communities that could be considered as a traditional stereotype. Indeed, the male stereotype that women projected was represented mainly by one single community characterized by features such as strong, intelligent , and hardworking , which we could define as a traditional male stereotype ( Prentice and Carranza, 2002 ). This community was similar to one of the two large communities identified in the male in-group stereotype (colored by light purple in Figure 5 ). The other larger community identity for the male in-group stereotype (colored by green in Figure 5 ) was characterized by the features of strong, aggressive , and hardheaded , which are also defined as traditional ones. Following previous research that evaluated the valence of male traditional stereotypes in the Spanish context ( Martínez-Marín and Martínez, 2019 ), we can interpret that the main difference between both communities is their valence: Men characterized as intelligent and hardworking are positive features of traditional men, whereas men characterized as aggressive and hardheaded are negative features of traditional men. Especially the latter community suggests a widespread stereotype of hegemonic masculinity ( Messerschmidt, 2019 ) characterized by toughness, among others ( Levant et al., 2013 ).

It is worth noting the ambiguity of the valence of the feature strong . Indeed, one of the strengths of the network perspective is that we should interpret the meaning of a feature by taking into account its interactions with other features. When we evaluated the valence of strong in the sentiments analyses, it was considered a positive feature, in line with previous research ( Martínez-Marín and Martínez, 2019 ; see Section S2 in Supplementary material ). In this regard, women project a homogeneous male stereotype characterized by strong, together with intelligent and hardworking, which suggests it can be interpreted as a positive feature. Indeed, we considered this part of the stereotype as a positive traditional male stereotype. However, the male in-group stereotype used more frequently the word strong together with aggressive and hardheaded, which could be considered negative features. Certainly, the image of a strong man is different whether he is also described as intelligent and as hardworking or as aggressive and hardheaded.

Finally, a similarity between the male stereotype projected by both women and men, and shared with the female stereotype, is that a small part of the network is based on physical features (e.g., high, beard, brunettes, beautiful , and athletes ).

2.2.3. Female metastereotypes

The female metastereotype included 632 nodes, with 9,742 co-occurrences collapsed in 6,851 edges. The strongest co-occurrences were intelligent - pretty (29), weak–sensitive (28), intelligent–hardworking (27), friendly–intelligent (25), and sensitive-intelligent (24). As seen in Figure 6 , the two largest communities (colored by light purple and green) are quite similar in size representing 45.41% and 44.94% of the network, respectively. The nodes with the highest degree of the first community are sensitive (238), weak (226), and motherly (172). The nodes with the highest degree of the second community are intelligent (288), hardworking (222), and pretty (200). Finally, the third community (colored by blue) represents 5.22% of the network, and their nodes with the highest degree are made-up (27), long hair (26), and friendship (22).

Figure 6 . Network for female metastereotype.

These results suggest that the structure is heterogeneous because it is represented mainly by two communities. One community seems to reflect the traditional female stereotype which is characterized by the features of sensitive, weak , and motherly . By contrast, the other community is characterized by the features of intelligent and hardworking , which seem to reflect the modern female stereotype. However, there is a crucial nuance in this last community given the role of pretty , which qualify importantly the meaning of intelligent and hardworking . It must be emphasized that these two communities are similar to women's in-group stereotypes except for their relative size. Whereas, women's in-group stereotype is mainly representative of modern female stereotypes, considering the traditional one is only a small part of the network; when women think of how men stereotype them, both traditional and modern communities take on a similar relevance. These results suggest that most women tend to in-group stereotype as modern women (i.e., intelligent, hardworking , and strong ), although a few of them keep the traditional stereotype (i.e., dependent and submissive) . However, when looking at their beliefs about how men see them, women show more disagreement in the features mentioned. There is a similar number of women who think men see them as modern and traditional. Nevertheless, this does not appear to be the case. In other words, the female stereotype (both projected by men and women) is more homogeneous than the female metastereotype. Both women and men think about the female stereotype mainly as a modern stereotype. We did not find evidence that the female stereotypes projected by men reflected traditional features. Moreover, a part of the female metastereotype is represented by physical features—i.e., long hair, thin, bosom, hips —similar to the female stereotype.

2.2.4. Male metastereotypes

The male metastereotype included 412 nodes, with 3,115 co-occurrences collapsed in 2,756 edges. The strongest co-occurrences were chauvinist - strong (8), strong–intelligent (8), strong–brave (8), strong–aggressive (6), and protective-hardworking (5). As seen in Figure 7 , the largest communities (colored by light purple) represent 32.04% of the network, and their nodes with the highest degree are hardworking (94), intelligent (78), and friendly (70). The second community (colored by green) represents 23.54% of the network, and their nodes with the highest degree are strong (135), chauvinist (120), and insensitive (71). There is a third community (colored by blue) which represents 13.83% of the network, and their nodes with the highest degree are violent (49), heavy (35), and basic (33). Finally, the fourth community (colored by dark gray) represents 9.71% of the network, and their nodes with the highest degree are beautiful (53), athletes (31), and educated (25).

Figure 7 . Network for male metastereotypes.

The male metastereotype's structure was heterogeneous, given that it was mainly built from two main communities. These communities were quite similar to those found for the male in-group stereotype: traditional positive male stereotype (i.e., intelligent and hardworking ) and traditional negative male stereotype (i.e., strong, aggressive , and hardheaded ). Therefore, the male metastereotype was quite similar to the male in-group stereotype but different from the male stereotype women project, which was homogeneous with a main community of the traditional positive male stereotype (i.e., intelligent and hardworking ). The main network analysis results are summarized in Supplementary material (Section S4). To see a summary of all the network communities, see Table 1 .

Table 1 . Summary of the female and male stereotypes that compose the network communities.

3. General discussion

The aim of this research was to map the structure of gender stereotypes and metastereotypes as complex systems via a network approach. Our results suggest that the female stereotype women project is similar to that of how men project it. Both women and men have a female stereotype that is mainly homogeneous. The main community that characterizes women as intelligent, hardworking, strong, and fighters can be interpreted as a modern female stereotype. However, the male stereotype women project is different from how men project it. Women characterize men mainly homogeneously, with the main community using features such as intelligent, hardworking, strong, and fighters, which could be interpreted as a traditional positive male stereotype. However, men characterize themselves heterogeneously. There is a community similar to that of what women project (i.e., intelligent, hardworking, and fighter), and another community characterized by features such as strong, aggressive, and hardheaded, which could be interpreted as a traditional negative male stereotype.

Additionally, according to our results, women and men show a heterogeneous metastereotype. Two central communities characterize both female and male metastereotypes. The female metastereotype is characterized by what could be defined as a modern female stereotype as well as a traditional female stereotype. The male metastereotype, on the contrary, is characterized by what could be defined as a traditional positive male stereotype as well as a traditional negative male stereotype. We highlight that both women and men project a metastereotype differently than the gender stereotype that the opposite gender projects. Therefore, it seems that both women and men project a distorted metastereotype in comparison with the opposite gender views, but for a different reason. Women think that men's beliefs about women are different from the beliefs that women have about themselves, which seems not to be the case because both women and men seem to share a similar female stereotype. However, men think that women's beliefs about men are the same that men have about themselves, which seems not to be the case because women and men showed different male stereotypes. It is worth highlighting this point because of the mismatch between metastereotype and how out-group stereotype in-group might condition the intergroup relationships ( Scherer et al., 2015 ).

As we mentioned above, the network approach provides us with the full interaction between nodes ( Cramer et al., 2010 )—i.e., features—which means that each feature attributed to each gender stereotype should be interpreted according to their interactions with other features. We already discussed above the feminist label applies to the female in-group stereotype and the strong valence applies to the male stereotype. In this regard, we must stress how the feature strongly interacted with other features in both female and male stereotypes. Strong is a feature that appeared in the male and female stereotypes with a high frequency and acted as an important node within the networks. However, in the female stereotype, strong had a relatively high co-occurrence with features such as empathic, loving, sensitive , and likable . By contrast, in men's in-group stereotype and metastereotype, strong had a relatively high co-occurrence with features such as aggressive, chauvinist, insensitive , and hardheaded . These results suggest that the notion that participants had when characterizing women as strong was different than when characterizing men. The fact that one feature can be interpreted with different nuances according to other features being presented before it is a well-known phenomenon from Asch's (1946) classic studies. In this line, our results provide evidence that a single feature might have different meanings when it is linked to different features.

3.1. Theoretical implications

The network approach allowed us to operationalize the mental image that our sample had about gender stereotypes recovering the traditional conception of stereotypes ( Lippmann, 1922 ). Our results suggest that these mental images are not wholly homogeneous. Although some gender stereotypes are mainly, but not fully, homogeneous; there are always small proportions of the network that represent alternative views of gender stereotypes. These results might shed light on the literature that addresses whether current gender stereotypes have changed over time. Our results suggest that the female stereotype is mainly homogenous, represented by a mental image that women project with features attributed traditionally to men (e.g., intelligent, hardworking, and strong). This result is in line with research that showed women were seen as more competent than they were several decades ago ( Duehr and Bono, 2006 ; Eagly et al., 2020 ). However, at the same time, a small proportion of women's in-group stereotype is represented by features typically attributed to traditional women (i.e., unsafe, dependent, submissive, or pent). This result is in line with the research that showed that the female stereotype had not changed over time ( Haines et al., 2016 ). According to our results, to some extent, both images live together in society. Although, according to our results, modern female stereotypes seem more prevalent than traditional ones. How both modern and traditional female images coexist is particularly striking in the female metastereotype. Indeed, in the female metastereotype, both communities have similar weights in the network. These results suggest that for some women, there is a mismatch between how they in-group stereotype and how they think that men see them. Given the importance that the metastereotype has in the intergroup relationship ( Gómez, 2002 ; Babbitt et al., 2018 ), it would be worthy for future research to explore how the interaction between the female in-group stereotype and metastereotype might affect the social interaction between group genders.

Moreover, the current study suggests that features attributed to the male stereotype are those traditionally attributed to them. These results are in line with research that has shown how the features attributed to men have not changed in the last decades ( Donnelly and Twenge, 2017 ). Our results suggest that these traditional features might be differentiated by two mental images, which we define as traditional positive and negative male stereotypes. However, one of the advantages of our approach is that participants were able to show freely the features attributed to men, which were then organized according to their co-occurrence. This approach allows us to look closer at the connotations of each community identified. In this regard, it is worth focusing on the community that we have labeled as the traditional positive male stereotype. Although we have considered this community as traditional because the features with the highest degree are traditionally attributed to men, we also need to note the other features with high degrees in the community. Features such as nice, likable, generous, friendly, or loving might qualify the notion that this is a traditional community given that it includes communal features ( Abele and Wojciszke, 2014 ; Wojciszke and Abele, 2019 ). Although including these features in this community seems to be the exception more than the norm, this result might point out that the male stereotype includes communal features in their mental image, which is in line with the literature about new masculinities ( Kaplan et al., 2017 ).

Nonetheless, from a network perspective, the features form dynamic and complex systems because of the interactions among themselves ( Cramer et al., 2010 ; Robinaugh et al., 2020 ). Therefore, we have shown that we should not interpret a single feature as isolated, but in interaction with those that tend to go together—i.e., strong with empathic or aggressive. Then, taking into account the full network of gender stereotypes' implications, it can be assumed that they are not interchangeable indicators that could be averaged on general indices. This is particularly important in some cases where features have contrary nuances. Identical features in different structures may cease to be identical, and they might even become opposed meanings. For instance, sensitive has a relatively high co-occurrence with strong in the female in-group stereotype appearing in the community that reflects modern female stereotypes. Nevertheless, in the female metastereotype, sensitive appeared more times together with weak , which is why it is included in the community of traditional female metastereotype. Therefore, the image projected when someone says that women are sensitive might be quite different if they are thinking sensitive and strong, rather than if they are thinking sensitive and weak. Future research should take these results into consideration, especially those that use close-ended questions to ask about features attributed to gender stereotypes and are averaged in a general index pre-set.

In this line, stereotypical features usually are considered linked with a specific valence—i.e., positive or negative (e.g., Martínez-Marín and Martínez, 2019 ). However, from a network approach, the features might not be perceived as positive or negative themselves. Otherwise, it might depend on what other features interact. For instance, when strong is together with intelligent and hardworking might be considered as more positive than when it is together with aggressive and hardheaded (see male stereotypes). Future research might explore how perceived valence might change according to the position of the features in the whole network.

The current research provided an alternative approach to study gender stereotypes. We propose to understand gender stereotypes as complex systems ( Ladyman and Wiesner, 2020 ), which allow us to know their structure. Knowing their structure provided crucial information to evaluate whether they are homogeneous or heterogeneous, structural similarities between gender stereotypes provided for women and men, or between gender stereotypes and metastereotypes. Moreover, their structure shed light on how stereotypical gender features interact with each other qualifying their meanings. The network approach opens an avenue for future research in the study of stereotypes. Here, we focus on descriptive gender stereotypes, but future research might focus on prescriptive gender stereotypes. Moreover, future research might extend the network approach to the stereotype of other groups (e.g., Black-White; Republican-Democrat, high-low social class).

We conducted a procedure of co-occurrence to build the structure of gender stereotypes and focus on the communities or clusters of this structure. However, network analyses provided other parameters and other procedures to conduct them. For instance, centrality parameters provide information about the inter-connectedness of the features ( Bringmann et al., 2019 ). Then, these parameters help to identify the important aspect of the network as central features which might be potential predictors of outcomes ( Contreras et al., 2019 ). Moreover, it has been developed other procedures to conduct network analyses such as partial correlation network ( Epskamp et al., 2018 ). Partial correlation network estimates the edges as correlations controlling for all other correlations in the network. This procedure allows, for instance, to compare quantitatively two networks. Centrality parameters and the chance of comparing networks might inspire future research questions in the study of stereotypes.

3.2. Practice implications

Identifying the structure of the current gender stereotypes is important for practice. Our results showed that there is a contradictory view of how each gender thinks the other gender sees them. Women are still thinking that men see them in traditional way as weak and sensitive people, and men think women see them as mainly competent but also tough. However, neither case corresponds to how people perceive other persons of the opposite gender. We are therefore dealing with a possible source of conflict between groups. People's misperception of how the other gender sees their gender can negatively impact—directly or indirectly—the behavior toward the out-groups (e.g., O'Brien et al., 2018 ). As such, showing the actual misperception of how we see others and others see us (stereotypes), how we see our gender group (in-group stereotypes), and how we think that others see us (metastereotype) will allow people to understand some of the underpinnings of their behavior and hopefully engage strategies to change it for the better.

Moreover, knowing the structure of gender stereotypes allows us to detect central features within the network stereotypes and to examine how those features are interconnected brings opportunities for social interventions. As posited by Asch (1946) , the features in the stereotype formation can be central or peripheral according to their relevance within the network. Therefore, affecting one central feature is likely to change the overall structure of the whole stereotype. Previous research has shown that political identities are highly influential over the whole person's network of belief systems ( Brandt, 2020 ) and that intervening specific symptoms of some mental diseases can accelerate the therapeutic treatments ( Fried et al., 2020 ). Therefore, finding and altering central nodes of the network stereotypes could have practical implications to change gender stereotypes. In this sense, practitioners can develop social interventions aimed at challenging (boosting) central negative (positive) features that facilitate harmonious relationships between women and men.

Moreover, knowing the structure of gender stereotypes can provide a check about to what extent the policies implemented against gender inequality are proving to be effective and are permeating social representations of society. One of the strategies of these policies is to foster a change in gender roles, which should lead to a change in gender stereotypes ( Eagly et al., 2020 ). The homogeneity of female stereotype and in-group stereotype as a competence-related cluster might be a consequence of the increasing participation of women in the labor force and higher education. Indeed, intelligence and hardworking are features required in the labor and educational field. Although there is still a long way to go in women's participation in gender-incongruent roles, such as STEM field, the structure of the current female stereotype suggests that the social changes in gender roles have permeated in female stereotypes. However, the changes in men's roles have been slower and late, which might explain why they are permeated less in male stereotypes. For instance, paternity leave in Spain has increased from 4 weeks in 2017 to 24 weeks in 2022. Our results point out that some features such as nice, generous, or loving, required to care to the children, are uncommon in the structure of male stereotype. Updating the structure of male stereotype in the close future might work as an indirect proof of the effectiveness of these policies.

3.3. Limitations and future research directions

Finally, there are also some limitations in this research that indicate key directions for further research. First, we used a convenient sample limited to a single country—i.e., Spain. Moreover, the political orientation of our sample was asymmetric toward the left wing, which might be conditioning our results given that previous research has shown that political orientation is related to sexist ideology ( Hodson and MacInnis, 2017 ). Therefore, this procedure might constrain the generalizability of our findings ( Simons et al., 2017 ). Further research with representative samples in Spain and in other countries might help to expand the scope of these results in other contexts for depicting the mental image of gender stereotypes.

Second, participants freely answered the features that they attributed to women and men. Although this procedure has some advantages as discussed above, it also has the drawback that it might trigger a social desirability bias. Moreover, procedures for making quantitative comparisons among networks are still under development, which leads us to ground our analyses on word frequencies and theoretical qualitative interpretations. Future research might use close-ended items to measure the features attributed to gender stereotypes and build psychometric networks that use partial correlations that allow for quantitative comparisons ( Epskamp et al., 2018 ).

Third, text analytics have additional limitations to take into account. For instance, the lemmatization process helps to reduce large amounts of redundant terms and make them handy to manipulate and interpret, but this advantage has a cost since it is possible to lose sight of important nuances underlying similar words. We controlled this limitation by doing the lemmatization process by hand, allowing researchers to retain substantive information. However, this is a time-consuming task that quickly becomes unfeasible when much more information is available. Therefore, a combination of manual and automatic coding would be an optimal solution to overcome this limitation.

Fourth, this research was conducted in the Spanish language and translated into English. There are linguistic terms in the Spanish language with two or more potential translations (e.g., trabajador ) and others without direct translation (e.g., pacientes and chulas ). Therefore, the final terminology might not be a crystal-clear mirror of the original language used. Indeed, even using the same language, the features used for depicting gender stereotypes can be shaped by the cultural context and diverse connotations linked to similar words. Future research could conduct a similar procedure in other languages and cultural contexts to examine to what extent the structure of gender stereotypes is shared across different contexts.

Fifth, reviews of research that followed a network approach have suggested that networks can be unstable and difficult to replicate ( Robinaugh et al., 2020 ), even though some networks have been replicated ( Brandt, 2020 ). Moreover, we should note that our sample size of men was lower than that of women; therefore, the network structure of the men's answers provided may be more unstable because it has fewer components ( Epskamp et al., 2018 ). Therefore, further research would be needed to increase the size of participants and responses to explore whether gender stereotype networks are stable across groups. The replication of network stereotypes, however, should be taken with caution because cultural, political, and historical events can exert substantive influence on how people perceive others. As such, any replication attempt should account for cultural differences that help to explain and understand the emerging pattern of gender stereotypes.

Sixth, there are additional potential explanations for the differences between stereotypes and metastereotypes found. For example, metastereotypes include motivational components (e.g., in-group favoritism) that might drive the differences found in their structures in comparison with stereotypes. Future studies should address how these motivational components could affect or be affected by the structure of metastereotypes and their implications (e.g., valence). Moreover, gender stereotypes and metastereotypes are broad categories that could be divided into subcategories (e.g., by sexual orientation, ethnicity, and social class). Previous research has found that there are differences between subgroups of stereotypes. For instance, DeWall et al. (2005) found that participants perceived six different subgroups of women: professional, feminist, homemaker, female athlete, beauty, and temptress. Therefore, the differences found could be due to that participants were thinking in different subcategories. In this line, there is a large literature about the intersectionality between gender and other stereotypes such as ethnicity, and socioeconomic status, which contain unique features that are not the result of adding gender stereotypes to other stereotypes ( Ghavami and Peplau, 2013 ). For example, recent research has shown that women and men in poverty are perceived differently ( Alcañiz-Colomer et al., 2023 ). More specifically, women are viewed as less personally responsible for being poor than men. This difference in perception of women and men has important implications given that this greater internal attribution of responsibility for being poor, in turn, was linked to less support for social protection policies when the recipients are men. Future research should, therefore, explore differences in the structure of subcategories in gender stereotypes and metastereotypes. Finally, we built our research from a binary perspective (female vs. male) as a general starting point. However, addressing gender as a more fluid construct rather than a categorical one could provide additional insights into the nature of gender stereotypes as recent research highlights the ongoing shift in gender conceptualization away from binary categories to a broader spectrum ( Abed et al., 2019 ; Wickham et al., 2023 ).

In sum, our findings could be extended and refined in several ways. Thus, one of our contributions to this field is to apply text and network analyses to map gender stereotypes from a complex dynamic perspective in such a way that it can be easily followed by other researchers using open-source tools (see Supplementary material for code and files: https://osf.io/cmf6a/ ).

4. Conclusion

Gender stereotypes are key to understand gender inequalities. This is why knowing their structure provides useful information to examine to what extent they are heterogeneous or homogenous, which allows for checking their potential changes. Our results suggest that although the female stereotype is homogenous, the female metastereotype is more heterogeneous and coexists with both modern and traditional female stereotypes. Moreover, the male stereotype women project by participants is homogenous and characterized by a traditional positive male stereotype. By contrast, male in-group stereotypes and metastereotypes are heterogeneous, characterized by two communities: traditional positive male stereotypes and traditional negative male stereotypes. Moreover, stereotypical gender features seem to interact with each other to build gender stereotypes. Addressing gender stereotypes as a complex system from a network approach provides a fertile ground for future research.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Open Science Framework ( https://osf.io/cmf6a/ ).

Ethics statement

The studies involving human participants were reviewed and approved by Ethical Clearance ID: 1696/CEIH/2020. The patients/participants provided their written informed consent to participate in this study.

Author contributions

ÁS-R and EM-B contributed to the conception and design of the study and organized the database. ÁS-R and EG-S performed the analyses of the data. ÁS-R wrote the first draft of the manuscript. EM-B and EG-S wrote sections of the manuscript. All the authors contributed to manuscript revisions, read, and approved the submitted version.

The authors declare that this study received funding from the University of Salamanca. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

Acknowledgments

The authors would like to acknowledge Prof. Miguel Moya for his insightful review, comments, and suggestions on a prepublication draft of this manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1193866/full#supplementary-material

1. ^ Participants who answered “Other” or did not answer the gender question were not included in the analyses of metastereotypes.

2. ^ Values within the parentheses indicate the node degree of centrality, which is interpreted as the number of connections of this feature with other features.

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Keywords: gender stereotypes, gender metastereotypes, in-group stereotypes, networks approach, social perception

Citation: Sánchez-Rodríguez Á, Moreno-Bella E and García-Sánchez E (2023) Mapping gender stereotypes: a network analysis approach. Front. Psychol. 14:1193866. doi: 10.3389/fpsyg.2023.1193866

Received: 25 March 2023; Accepted: 05 June 2023; Published: 18 July 2023.

Reviewed by:

Copyright © 2023 Sánchez-Rodríguez, Moreno-Bella and García-Sánchez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Eva Moreno-Bella, embella@psi.uned.es

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Endorsement of gender stereotypes in gender diverse and cisgender adolescents and their parents

Benjamin deMayo

Department of Psychology, Princeton University, Peretsman Scully Hall, Princeton, New Jersey, United States of America

Shira Kahn-Samuelson

Kristina R. Olson

Associated data.

The data are publicly available on the Open Science Framework: https://osf.io/yxs3r/

Previous work has documented adolescents’ gender stereotype endorsement, or the extent to which one believes men or women should embody distinct traits. However, understanding of gender stereotype endorsement in gender diverse adolescents—those who identify as transgender, nonbinary, and/or gender nonconforming—is limited. Gender diverse adolescents’ experiences with gender raise the question of whether they endorse gender stereotypes with the same frequency as cisgender adolescents. In this study, we investigated three primary research questions: (1) if gender diverse (N = 144) and cisgender (N = 174) adolescents (13–17 years) and their parents (N = 143 parents of gender diverse adolescents, N = 160 parents of cisgender adolescents) endorse gender stereotypes; (2) whether these groups differed from one another in their endorsement of gender stereotypes; and (3) whether parents’ gender stereotyping was related to either their adolescents’ stereotyping and/or their adolescents’ predictions of their parents’ stereotyping. We found (1) that participants showed low amounts of stereotyping; (2) there were no significant differences between gender stereotype endorsement in gender diverse and cisgender adolescents (or between their parents), though parents endorsed stereotypes slightly less than adolescents; and (3) there was a small positive association between adolescents’ stereotyping and their parents’ gender stereotyping. We discuss the limitations of our methods, and the possibility that rates of explicit stereotype endorsement may be changing over time.

Introduction

Gender is one of the most salient social categories, starting early in childhood and continuing into adulthood [ 1 ]. As a result, a large body of psychological research has set out to understand people’s acknowledgment and endorsement of gender stereotypes. Since adolescence is a pivotal time in social, emotional and sexual maturation [ 2 , 3 ] understanding how adolescents, generally defined as young people between puberty and adulthood, conceptualize and endorse gender stereotypes is an especially interesting question [ 4 ].

Almost all research on adolescents’ gender stereotyping has studied cisgender people, or those whose gender identity matches the sex assigned to them at birth. Less is known about gender stereotypes in gender diverse adolescents (including binary transgender, nonbinary and gender nonconforming individuals), despite the growing number of youth identifying with this group [ 5 , 6 ] a. Is the development of gender stereotyping different in this population of young people different than in the cisgender samples that have typically been studied? The question is of both theoretical and practical import. Understanding how gender diverse adolescents conceptualize gender stereotypes could further our understanding of how one’s own experience with gender (non)conformity relates to stereotyping. Further, given the increasing visibility of gender diverse youth in the public sphere, it is critical that their experiences be represented in research documenting the trajectory of gender development across the lifespan.

In the present study, we set out to answer this question by assessing gender stereotyping using a previously validated measure [ 7 ] in a large sample of gender diverse adolescents. Additionally, we collected data from a large sample of cisgender adolescents as a comparison sample in order to assess whether gender diverse adolescents’ gender stereotyping differs from that of cisgender adolescents. Finally, to better understand the relation between parents’ beliefs and adolescents’ beliefs, we asked whether parents’ gender stereotyping is associated with either adolescents’ own stereotyping or adolescents’ perceptions of their parents’ stereotyping.

Gender stereotyping in adolescence

Prior research on gender stereotyping in adolescence has yielded mixed evidence on the extent of adolescents’ gender stereotyping. Some researchers have found adolescence to be a time of life when gender roles intensify markedly (e.g., [ 8 ]), and find, as a consequence, that adolescents tend to rigidly endorse gender stereotypes, even more so than children in late elementary or early middle school [ 9 ]. Making a similar prediction, others posit that repeated reinforcement learning from the social environment regarding gender roles results in continued gender stereotype endorsement well into adolescence [ 10 , 11 ]. Conversely, some studies report an opposite trend of gender flexibility in adolescence [ 12 , 13 ], while others find that substantial individual differences obscure any clear group-level pattern of gender stereotyping in adolescence [ 14 ].

Gender stereotyping in gender diverse youth

Regardless of how one construes prior research on adolescents’ endorsement of gender stereotypes, the findings cannot confidently be applied to gender diverse individuals. In fact, until relatively recently, little empirical work had examined how transgender or other gender diverse youth of any age conceptualized gender stereotypes and whether they would endorse them in a meaningfully different way from cisgender peers. Three recent studies have, however, assessed gender stereotyping in transgender children, their siblings, and matched cisgender participants.

These studies—all of which studied younger children, not adolescents—show mixed evidence, but generally suggest cisgender and gender diverse children do not differ in their level of gender stereotyping. Three- to five-year-old transgender and cisgender children (siblings of transgender children and unrelated cisgender children) did not significantly differ in how much they thought men and women should engage in certain gender-stereotyped activities [ 15 ]. Similarly, 6–11-year-old transgender children, their siblings, and unrelated cisgender children did not differ significantly in their endorsement of prescriptive gender stereotypes; moreover, all groups of children tended to tolerate gender nonconformity [ 16 ].

However, a study focusing on gender stereotyping in 6–8-year-old children found that transgender children and their siblings showed significantly lower levels of gender stereotype endorsement, and more willingness to socially affiliate with gender nonconformers, than the matched cisgender group [ 17 ]. In sum, preschool and elementary aged transgender children appear to endorse gender stereotypes at similar levels as their cisgender peers; when differences do appear, the transgender group appears to show lower levels of stereotype endorsement and greater tolerance of gender nonconformity. Our investigation of adolescents’ gender stereotyping thus adds another data point that can help elaborate any possible between-group differences in stereotype endorsement (or lack thereof).

Parent influence on adolescents’ endorsement of gender stereotyping

In the current work, we were also interested in understanding whether adolescents’ endorsement of gender stereotypes is associated with their parents’ endorsement of gender stereotypes. Previous evidence has shown that parents’ gender-related cognitions are associated with their children’s. Notably, a meta-analysis [ 18 ] examined 43 studies investigating the link between parents’ and children’s gender schemas, and found that parents’ gender-related attitudes about others were modestly associated with their children’s ( r between 0.1 and 0.2). While the measures used, and psychological constructs assessed, in previous work vary considerably (see [ 19 ] for a review on measurement differences), past work generally indicates that parents may influence their children’s thinking about gender [ 18 – 20 ]; we were thus interested in seeing whether we would obtain a similar result when examining gender diverse adolescents’ stereotype endorsements.

Current work

In our current work, we were interested in exploring the extent to which gender diverse adolescents endorse gender stereotypes, and how this may or may not differ from cisgender adolescents’ gender stereotype endorsement. To do so, we recruited both a large sample of gender diverse adolescents as well as a group of cisgender adolescents, both of which completed a common measure of gender stereotyping (adapted from the OAT-AM; [ 7 ]) asking them to indicate how much they believed certain traits should be held by men versus women.

The OAT-AM (along with its equivalent for younger children, the COAT-AM) is a common measure of gender stereotyping in this age group. Studies using this measure have generally found that (presumably, primarily cisgender) adolescents show gender stereotyping (e.g., [ 21 , 22 ]. The OAT-AM carries several advantages which motivated its use in the current work. First, it is a short form that takes little time to complete and can be embedded in a larger study, as was the case here. Second, the scale is high in face validity, in that it probes participants directly on their endorsement of gender stereotypes. It also has good test-retest reliability [ 7 ].

Prior research motivates a variety of predictions with regards to group differences in gender stereotyping between gender diverse and cisgender youth. As discussed above, some prior studies have demonstrated that prepubescent gender diverse and cisgender children show similar levels of gender stereotyping, indicating that the same might be true of the adolescents studied in the current work [ 15 , 16 ]. Conversely, one might expect that gender diverse adolescents would show less rigidity in their attitudes about gender, as has sometimes been observed [ 17 ] (though note that in this work, siblings of transgender youth also showed less gender stereotyping, suggesting that other factors within a household might also contribute to reduced gender stereotyping).

We also asked adolescents’ parents to complete the same gender stereotype endorsement measure as the adolescents and asked adolescents to complete the measure a second time, indicating their predictions of their parents’ responses. The parent measure allowed us to investigate the exploratory question of whether parents of gender diverse adolescents would show different levels of gender stereotype endorsement than parents of cisgender adolescents, as well as to examine the relationship between parents’ responses and their adolescents’. In particular, we were interested in whether parents would show more or less overall stereotyping than their adolescents, and whether parents’ stereotyping would be correlated with their adolescents’ stereotyping.

Prior research that would inform predictions regarding group differences in gender stereotyping among parents is scarce. Some previous studies have speculated that parents of gender diverse children may engage their children in interactions that highlight flexibility in gender roles and communicate that it is acceptable to violate gender norms, as suggested by some findings that transgender children and their siblings tend to show more tolerance of gender nonconformity than cisgender children [ 17 ]. If such speculations are correct, one might expect parents of gender diverse youth to show less gender stereotyping than parents of cisgender youth, and that this apparent flexibility in the parents’ views might be correlated with children and adolescents’ own views on gender stereotypes, perhaps more so than in family units with cisgender children. However, previous research has not directly probed this question. Therefore, the extent to which parents of gender diverse youth might endorse stereotypes differently from parents of cisgender youth—and whether such a difference, if it exists, is related to their children’s own gender stereotype endorsement—remains an open question.

Finally, we asked adolescents to predict their parents’ responses on the measure. With this measure, we were interested in determining if parents’ stereotype endorsement, adolescents’ predictions about parents’ stereotype endorsement, neither, or both were predictive of adolescents’ responses on the same measure. We know of no past work speaking to this question and therefore included it as an additional exploratory research question.

Participants

Participants in this study were either part of the gender diverse group (after exclusions, N = 144 adolescents, N = 143 parents) or the cisgender group (after exclusions, N = 174 adolescents, N = 160 parents). Full parent demographic information can be found in Table 1 ; full adolescent demographic information can be found in Table 2 .

a. More detailed breakdown of participant gender in Supporting Information.

b. χ 2 analysis compares distribution of gender between parents of gender diverse adolescents and parents of cisgender adolescents. For χ 2 analysis on gender, participants were binned into categories of “women” and “other” due to small participant N’s for men and individuals of other genders and the associated constraints for χ 2 analyses.

c. χ 2 analysis compares distribution of ethnicity between parents of gender diverse adolescents and parents of cisgender adolescents. For χ 2 analysis on race, participants were binned into categories of “white” and “non-white” due to small participant N’s in some ethnic/racial categories.

d. t- statistic derived from a 2 independent samples t -test in which each participant’s income value was converted to a 1–5 scale (e.g., < $25,000 ~ 1, $25,001 - $50,000 ~ 2, etc.). The negative value of the t -statistic is interpreted to indicate that parents in the cisgender group reported, on average, higher income levels than those in the gender diverse group.

a. χ 2 analysis compares distribution of race between gender diverse adolescents and cisgender adolescents. For χ 2 analysis on race, participants were binned into categories of “white” and “non-white” due to small participant N’s in some ethnic/racial categories.

b. More detailed breakdown of participant gender in Supporting Information.

c. χ 2 analysis compares distribution of gender between gender diverse adolescents and cisgender adolescents.

d. This participant gave a nonsense answer (“attack helicopter”), but other answers and the recruitment approach used for this participant led us to categorize them as a cisgender participant.

Determining whether adolescents were gender diverse or cisgender

As we describe below, we recruited gender diverse and cisgender adolescents (and their parents) from different channels (which we refer to as the gender diverse recruitment group and the cisgender recruitment group respectively). In the vast majority of cases, adolescents from the gender diverse recruitment group were gender diverse, and adolescents from the cisgender recruitment group were cisgender. However, 4 adolescents from the gender diverse recruitment group identified as cisgender at the time of testing, and 8 adolescents from the cisgender recruitment group identified as transgender, gender nonconforming, or nonbinary, thus qualifying for our purposes as gender diverse at the time of testing. Henceforth, we use adolescents’ own gender identification at the time of the study to determine whether they were counted as part of the gender diverse group or as part the cisgender group .

Gender diverse group: Adolescents (N = 144)

Of the gender diverse adolescents included in this study, 72 are participants the research team had had prior contact with as gender diverse participants in larger longitudinal projects on gender development in U.S. and Canadian transgender or other gender diverse children. These youth were recruited through a variety of different sources including at camps and conferences for gender diverse youth, through medical and mental health providers, via word of mouth and in response to media stories, and through parents’ online searches. These youth have been reported in several past papers about gender development [ 23 – 29 ] and about mental health [ 30 – 34 ]. The current measures were given as part of one wave of data collection. Of these gender diverse adolescents in the larger longitudinal projects, 4 had participated in a study on gender stereotyping that has been previously published [ 16 ]; no other previous publications examining stereotyping include participants in the current work.

On top of these participants with whom the researchers had already included as gender diverse participants in the larger study, there were 72 other adolescent participants in the gender diverse group. In order to expand the sample of gender diverse adolescents for this sample, 64 additional adolescents were recruited through email advertisements to listservs of professional organizations related to transgender health and well-being, parent listservs, and via social media and included as participants in the gender diverse group. Additionally, 3 participants who had previously participated as cisgender children in the aforementioned longitudinal projects identified as gender diverse at the time of the study, as did 5 other participants who had been specifically recruited with the intention of being in the cisgender group in this study (i.e., had not participated in prior studies from this research group). Altogether, the final sample size in the gender diverse group was 144 adolescents.

In addition to the participants above who were included in analyses, we received responses from 16 additional adolescent subject ID’s in the gender diverse group which were excluded from analyses. During data collection, we developed data quality concerns emerging from a small number of the new gender diverse participants recruited from online channels–the only participants with whom the research team had not previously communicated; we therefore reviewed all of these non-longitudinal participants and decided on several exclusion criteria motivated by concerns about false participants (i.e., trolls or bots). All exclusions occurred without looking at the data of interest and were based on implausible inconsistencies in responding. Participants were excluded if (a) multiple consent/assent forms (e.g., the child and their parent) about the same adolescent listed different birth dates (excluded N = 2), (b) a participant reported that the adolescent was assigned male at birth but used only she/her pronouns at birth, or that the adolescent was assigned female at birth but used only he/him pronouns at birth (excluded N = 8), (c) the age of the adolescent did not match the reported birthdate (excluded N = 1), (d) parent and adolescent disagreed entirely on which pronouns were used to refer to the adolescent at equivalent times in their life-span (excluded N = 5; some variation in responses was tolerated, as in cases where a child recalled switching from “he” to “she” a year earlier than parents indicated, but complete deviations were not).

Beyond these adolescents excluded for quality control concerns, 6 additional adolescents recruited into the gender diverse group were excluded for not completing the OAT measures. In all, out of the 166 adolescent subject ID’s we started with in the gender diverse group, we included data from 144.

Gender diverse group: Parents (N = 143)

Parents of gender diverse youth were recruited into the study jointly with their adolescents. We began with survey responses from 198 subject ID’s in the gender diverse parents group. Sixteen of these were the parent surveys associated with the 16 subject ID’s in the gender diverse adolescents group which we excluded for quality control concerns (described above); additionally, another survey response from the gender diverse parents group (which did not have an adolescent response paired with it) was excluded for discrepant consent information. Thus, there were a total of 17 subject ID’s in the gender diverse parents group that were excluded for quality control concerns. On top of these quality control exclusions, 38 parents recruited into the gender diverse group were excluded because they did not have a child who completed a valid administration of the survey (these participants were excluded because this was primarily a study about adolescents’ gender stereotyping); some of these 38 participants also met exclusion criteria for completing the survey too quickly (the full survey included other measures and had a median duration of 24 minutes; 2 parents in the gender diverse group were excluded for completing the survey in less than 5 minutes) or not completing the OAT measure (6 parents in the gender diverse group). In all, out of the 198 parents in the gender diverse group we began with, we had a final N of 143 parents (one parent in the gender diverse group had 2 adolescents participate, hence why the N for parents is one less than the N for adolescents in the gender diverse group).

Cisgender group: Adolescents (N = 174)

Some of the cisgender adolescents in this research are part of the same longitudinal study as the transgender adolescents (N = 71); of these, 67 had previously participated as cisgender comparison participants in prior studies in the larger longitudinal project, and 4 had previously participated as gender diverse participants but identified as cisgender at the time of this study. The 65 adolescents who had previously participated as cisgender comparison participants in the larger longitudinal project were recruited in the past from the Communications Studies Participant Pool at the University of Washington. Of these cisgender adolescents, 4 had participated in a study on gender stereotyping that has been previously published (Rubin et al., 2020).

On top of the 71 adolescents who had previously been part of the larger longitudinal study (either as cisgender or gender diverse participants in the past), we recruited a sample of new cisgender adolescents (N = 103) from the Communications Studies Participant Pool to increase the sample size of the current study.

In addition to the above cisgender adolescents who we included in our analyses, 4 were excluded because they had not completed the OAT measure, and 1 was excluded because of a mismatch between their reported age and their birthdate. Thus, we started with 179 adolescent participants in the cisgender group, and had a final N of 174 adolescent participants in this group after exclusions.

Cisgender group: Parents (N = 160)

Parents of cisgender youth were recruited into the study jointly with their adolescents. We began with 181 parent participants in the cisgender group. Of these, 20 were excluded because they did not have a child who completed a valid administration of the OAT measure (of these 20, 4 had not completed the OAT measure themselves), and 1 other was excluded from the analysis because of a mismatch between their child’s birthdate and their reported age. Thus, we ended up with a sample size of 160 parents in the cisgender group after exclusions.

Of the parents in the cisgender group, 12 had two adolescents participate, and 1 had three adolescents participate. Thus, there were 160 parents in the cisgender group, 14 fewer than the number of adolescents in the cisgender group (N = 174).

Some of the adolescent participants described above were siblings of other adolescents who also participated (N = 27 cisgender participants, N = 2 gender diverse participants). In these cases, parents often filled out the survey two or more times; if they did, we used the survey they completed first and associated it with both siblings, dropping the subsequent submissions.

Parents were sent the study materials via email. After giving consent for themselves and their children to participate, they completed the parent portion of the study. Adolescents could either complete their portion immediately after the parent was finished on the same device, or they could opt to receive a follow-up email with the study materials. In either case, the parent completed their portion first so that they could consent to their own and their child’s participation. The study procedure was approved by IRB protocol #00001527 at the University of Washington.

Participants completed these measures as part of a larger survey that investigated a range of different topics (e.g., mental health, medical transition, etc.). Survey completion took place between April 2019 and April 2020. The present measure was included as a stand-alone measure and therefore its relation to any other measures, beyond the demographics reported in this paper, has not been assessed.

The trait subscale of the OAT-AM asks whether respondents think men, women, or both men and women should have various traits. In our adaptation, participants were shown 25 such traits. Ten traits were designated as stereotypically masculine (e.g., being good at math; being aggressive), ten as stereotypically feminine (e.g., crying a lot, being good at English), and five were gender neutral (e.g., study hard). (Masculine and feminine traits are listed in the Results section below, Table 3 ). Participants rate each trait on a 1 to 5 scale, with 1 indicating that only men should have the trait, 2 indicating that “mostly men, some women” should have the trait, 3 indicating that both women and men should have the trait, 4 indicating that “mostly women, some men” should have the trait, and 5 indicating that only women should have the trait. In the original version of the measure, participants could indicate that “neither men nor women” should have particular traits; however, in this study we excluded that option because it seemed irrelevant to most responses, and we were concerned all negative traits might receive no codable responses as a result. However, we added an option to skip items if participants wanted to do so. For our analysis, masculine traits were reverse-coded, so that for all items, a score of 1 signified gender stereotype endorsement that is incongruent with societal expectations (i.e., men should cry a lot), while a score of 5 signified maximal gender stereotype endorsement congruent with societal expectations (i.e., men should be good at math). Gender neutral items were dropped from analyses for all participants. Skipped items were excluded from the computation of individual participants’ mean scores.

a. The distinction of academic vs. personality “domains” is not present in Liben & Bigler (2002) which first published and validated the trait subscale of the OAT-AM; however, we include it here since it corresponds to an exploratory analysis detailed in the Supporting Information (Section 6).

We obtained 3 final scores on the trait subscale of the OAT-AM per parent-adolescent dyad, each ranging from 1 (strong counter-stereotypical responding) to 5 (strong stereotypical responding): the adolescent self-report measure, the parent self-report measure, and the adolescent prediction about the parent measure. In the latter, the adolescent was asked to indicate how they thought their parent would respond to the trait subscale of the OAT-AM. Cronbach’s α for the parent self-report , adolescent prediction about the parent , and adolescent prediction about the parent measures were 0.64, 0.78, 0.81 respectively; the low inter-item reliability on the parent self-report measure is discussed further in the discussion section.

Primary research questions

We investigated three primary research questions: (1) whether participants showed gender stereotyping; (2) whether there were group differences (both cisgender vs. gender diverse, as well as adolescents vs. parents) in gender stereotype endorsement; and (3) whether adolescents’ gender stereotype endorsement, and their predictions about their parents’ gender stereotype endorsement, were related to parents’ gender stereotype endorsement.

Gender stereotype endorsement

First, we investigated whether adolescents and their parents showed evidence of gender stereotyping. For each participant, we calculated their average self-reported stereotype endorsement score by taking the mean of their responses on the adolescent self-report measure for adolescents and the parent self-report measure for the parents. A one-sample t- test revealed that, averaging across adolescents and parents, participants’ mean gender stereotyping scores ( μ = 3.071, SD = 0.162) were significantly greater than the null value of 3, t (620) = 10.942, p < .001, Cohen’s d = 0.439, indicating that participants endorsed gender stereotypes in a direction that was congruent with societal expectations. However, examination of the actual mean (3.07 on a 5-point scale) indicates that this was a very small tendency overall. Further, as shown in Fig 1 , all groups indicated that “both men and women” should have the stereotypically masculine and feminine traits more than 80% of the time, suggesting that explicit endorsement of stereotypes on the trait subscale of the OAT-AM was relatively rare.

Some parents appeared in multiple dyads.

While overall levels of stereotyping were low, individual items on the OAT-AM varied both in their mean endorsement and in how much response variability they displayed across participants ( Table 3 ). Tables ​ Tables4 4 and ​ and5 5 show means and standard errors of stereotyping scores for items on the trait subscale of the OAT-AM, broken down by the gender of the stereotype (feminine vs. masculine) and domain of the stereotype (academic/extracurricular vs. personality) respectively. Exploratory statistical analyses probing both of these effects are in the Supporting information (Sections 5 and 6).

Group differences in gender stereotype endorsement

Next, we were interested in whether gender diverse adolescents and their parents endorsed gender stereotypes at differing levels from cisgender adolescents and their parents. We fit a linear mixed-effects model predicting participants’ average gender stereotype endorsement scores as a function of the gender identity of the adolescent in the dyad ( gender diverse or cisgender ), whether the respondent was an adolescent or a parent, and the interaction between these two factors. In order to account for the fact that each family has multiple mean scores (one for the parent self-report and at least one other for the adolescent self-report , and occasionally more if a family had multiple adolescents participate), we included “random” intercepts for each family. (In Section 1 of the Supporting Information, we show two alternative methods of analyzing the results in which we treat participants’ responses as a binary variable; we include these analyses to adhere more closely to the recommended scoring procedure recommended by [ 7 ]. The results are similar across either analytic approach.)

Tables ​ Tables6 6 and ​ and7 7 summarize the overall results. We found no significant differences in gender stereotyping on the basis of gender identity; stereotyping in participants from the gender diverse group did not differ from stereotype endorsement from those in the cisgender group , β = -0.01, p = 0.61. However, the mixed-effects regression model does show a significant main effect such that parents’ responses are slightly lower in stereotype endorsement than adolescents’, β = -0.056, p < 0.001, corresponding to a reduction of 0.34 standard deviations in stereotype endorsement.

Reference group is cisgender adolescent self-report.

As an exploratory sub-analysis, we also examined whether any differences in gender stereotyping emerged between parents ( Table 8 ) and adolescents ( Table 9 ) of different genders. An independent-sample t -test comparing gender stereotype endorsement scores of parents who were men and parents who were women revealed that men had higher average gender stereotype endorsement scores than women, t (294) = -2.642, p = 0.009, echoing prior work that has suggested that fathers may hold more explicit gender stereotypes than mothers [ 35 ]. We also performed a one-way ANOVA to examine whether there were any differences in gender stereotype endorsement scores between boys, girls and a group consisting of adolescents who identified as nonbinary or another gender. Adolescents of different genders did not significantly differ from one another ( F (2, 315 = 2.889, p = 0.057); however, exploratory post-hoc Tukey HSD comparisons showed that, while nonbinary or other adolescents did not differ from girls ( p = 0.72) or boys ( p = 0.73), girls may have been slightly less likely to endorse stereotypes than boys ( p = 0.04).

Relationship between parents’ and adolescents’ endorsement of gender stereotypes

Finally, we examined whether parents’ stereotype endorsement ( parent self-report measure) was associated with adolescents’ stereotype endorsement ( adolescent self-report measure) or adolescents’ predictions of their parents’ stereotyping ( adolescent prediction about the parent measure). To examine whether adolescents’ stereotyping was associated with their parents’, we ran a linear regression predicting adolescents’ scores from their parents’ scores. This analysis revealed a very small but significant effect of parent stereotyping score, β = 0.23, t = 2.385, p = 0.018 ( Table 10 ). Similarly, to examine whether the adolescents’ predictions of their parents stereotyping was predictive of their parents’ actual stereotyping, we ran a linear regression predicting adolescents’ predictions about their parents as a function of the parent’s actual stereotyping ( Table 11 ). This analysis did not reveal a significant effect of parent stereotyping score, β = 0.21, t = 1.93, p = 0.05. Both of the aforementioned analyses include some redundancy in the data because some parents had multiple children participate. To account for this nonindependence, we attempted to fit linear mixed-effects models with random intercepts for each family, but since these mixed-effects models obtained singular fits, we instead report the simpler linear models. Estimates of regression coefficients were almost identical between these mixed-effects models and the linear models we report here.

In addition to these analyses, we also conducted exploratory regression analyses to examine whether the relationship between parent self-report and either of the measures completed by the adolescents might be stronger in the gender diverse or cisgender group. Given the overall lack of difference between groups and the exploratory nature of these analyses, we refer readers to the Supporting Information (Section 4) for those results. We also include a correlation table illustrating Pearson’s r correlations between all three of the outcome stereotyping measures ( adolescent self-report , parent self-report , and adolescent prediction about the caregiver ) in the Supporting Information (Section 7).

We used a previously validated measure [ 7 ] that has historically resulted in significant levels of gender stereotyping (e.g, [ 21 ]) to assess gender stereotype endorsement in gender diverse and cisgender adolescents, as well as their parents. Two main findings emerged. First, even though the mean gender stereotype endorsement score across participants was significantly higher than the null value (which would have indicated a complete lack of stereotype endorsement), all groups of adolescents and parents showed remarkably little endorsement of gender stereotypes ( Fig 1 ). On every item of the trait subscale of the OAT-AM, at least 67% of participants who responded to the item endorsed gender stereotype flexibility, indicating that ‘both men and women’ should show a particular trait (e.g., saying both men and women should be good at math); the median rate of choosing ‘both men and women’ across all items was 88%. Parents endorsed stereotypes even less on average than adolescents. Among adolescents, we observed no significant differences between gender diverse participants and cisgender participants. This finding converges with those in [ 15 ] and [ 16 ], in which gender diverse and cisgender children did not show differences in gender stereotyping, though differs from a prior study [ 17 ] which found that 6–8-year-old transgender children showed less gender stereotyping compared to unrelated cisgender children.

Second, we observed a small relationship between adolescents’ stereotype endorsement and their parents’ stereotype endorsement. The size and direction of this effect (small, but positive) is reflective of the more general phenomenon described in [ 18 ] that parents’ thinking about gender is modestly correlated with their children’s.

Apart from these main findings, exploratory analyses suggested that adolescent boys and parents who identified as men showed more gender stereotype endorsement than parents who identified as women and adolescent girls respectively. These results do not bear directly on our original research questions, but the finding regarding parental gender differences shows concordance with prior research [ 35 ].

Several limitations are present in the current work. First, the trait subscale of the OAT-AM, while face valid and used widely the study of gender stereotyping, including in adolescents (e.g., [ 22 ]), showed remarkably little response variability across participants, contributing to a low value of Cronbach’s α on the parent self-report measure; in fact, when indicating their own stereotype endorsement, over half of participants (60% of adolescents, 66% of parents) responded to every item on the scale (excluding skipped items) by saying that “both men and women” should embody the trait in question, meaning that all of the effects observed were driven by fewer than half the participants in the sample. The scale may have been too coarse to show more nuanced group-level differences in endorsement of gender stereotypes, or these may not have been the best example traits for assessing gender stereotyping at this particular moment in history. In future work, it may be more appropriate to use a measure that is less direct than the OAT-AM, since participants may be hesitant to explicitly deem certain traits as “man-like” or “woman-like.” One possible way of avoiding this directness would be to assess participants’ descriptive, rather than prescriptive, stereotypes (in other words, asking people what individuals of different genders do do, not what they should do). Another potentially interesting avenue for future research involves using implicit measures to probe gender-stereotyped attitudes in adolescents in their parents, since participants in the current social climate may not explicitly endorse (or even acknowledge) stereotypes to the same extent as participants in studies several decades ago.

Additionally, our participant sample is also skewed towards white, upper middle- and upper-class people in the United States of America who are politically liberal. As a result, the generalizability of these findings to a more representative sample of the U.S. population, or populations in other cultural or national contexts, is unknown. Participants in the gender diverse and cisgender groups were also not perfectly matched on demographics (Tables ​ (Tables1 1 and ​ and2); 2 ); in particular, parents in the gender diverse group were less affluent and more liberal than parents in the cisgender group. Given that we did not see stark differences in gender stereotype endorsement between groups, we believe it is unlikely that demographic discrepancies compromise the comparability of these samples. However, participants in the cisgender group were primarily drawn from a metropolitan area in the U.S. Pacific Northwest, a region that has a more progressive orientation on social issues than the country as a whole [ 36 ]. As such, it is possible that a more nationally representative sample of cisgender adolescents would have demonstrated a greater propensity to endorse gender stereotypes than the participants in this study.

Despite these limitations, we believe these findings also present a possible summary of how adolescents are in thinking about gender stereotypes today. Perhaps they are endorsing gender stereotypes less than past generations [ 22 ], an intriguing idea for follow-up research.

We found that gender diverse adolescents and cisgender adolescents showed similar levels of endorsement of gender stereotype endorsement, suggesting that the experience of being gender diverse may not exert a strong influence on adolescents’ propensity to endorse gender stereotypes. Adolescents’ parents tended to show less gender stereotype endorsement than adolescents, but all groups’ stereotype endorsement was low. To the extent that adolescents did endorse gender stereotypes, their stereotype endorsement showed a very slight positive association with their parents’ stereotype endorsement. These results contribute to a growing body of empirical work that aims to understand how an increasingly visible cohort of transgender, gender nonconforming and nonbinary youth engage with prevailing societal stereotypes about gender.

Supporting information

Acknowledgments.

We thank Robin Sifre, Natalie Gallagher, and Dominic Gibson for their statistical guidance in preparing this manuscript.

Funding Statement

This work was supported by National Institute of Child Health and Human Development, HD092347 and National Science Foundation, SMA-1837857/SMA-2041463 to K.R.O. K.R.O. also receives funding from the MacArthur Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

• PLoS One. 2022; 17(6): e0269784.

Decision Letter 0

28 Oct 2021

PONE-D-21-20601

Dear Dr. deMayo,

Thank you for submitting your manuscript to PLOS ONE. I was fortunate enough to receive reviews from two experts in the field, and thoroughly read and assessed this paper myself. The reviewers provided very different assessments about the merits of this paper, with Reviewer 1 believing that this could be accepted pending minor revisions and Reviewer 2 expressing hesitation around whether this paper provides sufficiently original research (PLOS ONE publication criterion 1) and a sufficiently appropriate approach to warrant publication in this journal. I agree with the points raised by both of the reviewers. After careful consideration, I believe that this paper has merit and the potential to make a novel contribution to the field, but it does not yet meet PLOS ONE’s publication criteria as it currently stands. I believe that the focus on gender stereotyping among gender diverse and cisgender adolescents and their parents is unique and important; this paper will contribute to a growing literature outlining the experience and beliefs of gender diverse children and their families. Therefore, I would like to invite you to submit a revised version of the manuscript that addresses the points raised during the review process. That said, I must emphasize that I cannot guarantee that a revised manuscript will be accepted in this journal. In addition, I may decide to send this back out for another round of reviews.

I will not reiterate the points raised by the reviewers but will instead highlight a few of my own that should also be addressed in the revision. These include:

• Please be sure to submit the revised manuscript in the appropriate format ( https://journals.plos.org/plosone/s/submission-guidelines ). As one example “Manuscript text should be double-spaced. Do not format text in multiple columns.” You might want to refer to the APA manual (7 th edition) for suggestions as well.

On page two, you indicate that several recent studies have examined gender stereotyping in transgender youth, but then proceed to review papers with children. Please change the wording to reflect this. The more important question, raised also by Reviewer 1, is whether each of these studies were conducted with the same children. That is, are conclusions drawn about the gender stereotyping of gender diverse preschoolers, young children, and older children all using the same (or many of the same) participants as this study that is examining adolescents? If so, this should be clearly explained in the paper. Related to this, please clarify how many gender diverse adolescents were recruited from other sources.

Given that the OAT-AM is almost 20 year old, it seems possible that some of the items are dated. It would be useful to know more about this measure and the results. Was most of the variability driven by some items (if so, which ones) and did some items not vary at all? It would be useful if the items could be detailed on OSF either in the data file (e.g., Parent_OAT_1_Math) and/or in the variable explanations. In addition, it would be ideal to provide not only the final data file, but also a data file that includes all participants (prior to exclusions) on OSF.

In the final sentence of the abstract, you indicate that “these results suggest…” but none of the results reported to that point suggest what follows. This relates to your research questions and hypotheses – it does seem that one of the questions that you address is whether there will be explicit stereotype endorsement and not just a difference between the groups, and you might consider adding this as a research question when revising, as well as revising the abstract to address this inconsistency.

The analyses in the supplement did not make sense to me, as participants’ responses were recoded based on whether they selected 3 (no gender difference) or not. However, the direction of that ‘not’ is important, as it reflects gender stereotyping, or counterstereotypical beliefs. I would recommend re-running these analyses, pitting stereotype-consistent responses against non-stereotype-consistent responses (e.g., 3 or counterstereotypical). Given the restricted variability this might not make any meaningful difference in the results, but this seems a more appropriate analytic approach given your research questions.

My final point is that it would be ideal to expand the scope of this paper if possible. I wonder if there are additional questions that might be addressed, for example if some of these adolescents did provide responses at an earlier time that could be compared. Reviewer 2 provides several other suggestions using the current data (see points 6 & 7) or with additional data (see point 12). Given that the main finding is a lack of difference between groups combined with low variability and a general lack of stereotyping on an older measure, I believe that the findings would be more compelling with additional analyses, or ideally additional data.

I hope that you will decide to revise and resubmit this paper and will look forward to receiving your re-submission. Please submit your revised manuscript by Dec 12 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at  gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Reviewer #2: No

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Reviewer #2: Yes

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Reviewer #1: One concern about dual publication is the use of the same gender diverse adolescents across multiple manuscripts. I know this is a large data set and it is reasonable that it will result in multiple manuscripts, but given how little research has been conducted with gender diverse youth, the repeated publication from one data set may skew the knowledge base. I recommend citing all of the other publications using the same youth in the Participants section.

Otherwise, the analyses are appropriate and the text is clear and complete. The conclusions are appropriate based on the results.

Reviewer #2: This paper investigated explicit gender stereotype endorsement in trans and cis adolescents and their parents. The sample is large and unique, and the topic is timely. The study employed an established measure of gender stereotyping. The study has potential to make a solid contribution to the literature, but I noted several aspects of the study and manuscript that should be addressed prior to further consideration for publication. Please see my comments below.

1. The Introduction could benefit from providing more of a theoretical framework, or at least providing more context to support hypotheses/predictions about why trans, compared with cis, adolescents (and their respective parents) would show more, less, or similar patterns of gender stereotyping. The authors should also give more background on parental gender socialization and associations between parent and adolescent gender stereotyping. Would the authors predict a difference in the strength of the association between trans vs. cis parent-adolescent dyads? If memory serves, Olson and Enright’s 2018 Dev Sci article reported that both trans children and their siblings were similarly accepting of gender nonconformity (compared with other cisgender controls). Could this reflect some differential parental socialization taking place with regard to acceptability of deviations from gender norms? There is also no explanation regarding the value of considering both parents’ actual gender stereotyping vs. adolescents’ perceptions of the extent to which their parents would gender stereotype. In short, the Introduction should be developed further to convey the theoretical and/or practical importance of the kinds of questions being asked here. To the extent that directional predictions (even if competing) can be offered, I encourage that as well.

2. I found that there were several statements of fact that were not backed up by a citation. Examples include claims that the numbers of trans adolescents are increasing and that past work generally finds that parents influence their children’s thinking about gender (regarding this latter one, I wonder whether the authors meant children and adolescents because the latter seems more relevant to concentrate on here). I suggest the authors make sure to back up statements of fact with appropriate citations.

3. What is the benefit of retaining participants who skipped all questions? Seems that if they did not provide data on the dependent variables, they should be dropped because they are leading to a skewed sense of the characteristics of the groups of participants who contributed the key data.

4. The samples do not seem to be matched on demographics. The cisgender sample appears to be more affluent and less white/more multiracial (based on the parent data). Are the authors concerned that this might be an important confound in their analyses? There is some research suggesting racial differences in gender development exist (e.g., work by May Ling Halim). Also, the cisgender controls were recruited from a participant database at the authors’ institution. In the present case, I assume this means that participants were from a relatively urban center in the US Pacific Northwest, which probably has a particular social climate. I wonder whether the gender diverse sample is from a more varied set of backgrounds given they were recruited from across the US and Canada, and whether this is also a relevant confound to consider in weighing the comparability of the participant groups.

5. Can the authors please say more about the recruitment method for online participants as well as for the other samples? I know this is part of a larger project and the authors have maybe shared some of these details elsewhere (especially on the longitudinal sample), but it’s not clear for those who are maybe only reading this paper from this team. Also, I wonder how this sample relates to prior ones that this group reported on with respect to gender stereotyping. There were three relevant studies from this team that were reviewed in the Introduction. Were the participants in this study the same as any of those prior ones? Or is this a completely different cohort?

6. The authors do not note the parent gender. There is literature suggesting mothers and fathers hold different attitudes about gender roles/stereotypes. I suggest reviewing that literature and analyzing by parent gender. Perhaps see work by Joyce Endendijk and colleagues on this topic.

7. The authors do not note the adolescent participants’ gender breakdown. Are there adolescent gender differences among cis samples in gender stereotyping? Any reason to suspect there might be differences between trans boys vs. trans girls vs. nonbinary, and so on? Even a preliminary analysis of this question would be important/interesting. In any case, it is presently unclear how comparable the samples are with respect to the gender composition with regard to cis/trans feminine/masculine individuals.

8. Can the authors please provide reliability analysis data on the OAT-AM for the present sample? Are all the items on this scale contributing to reliability? For example, there is some recent work suggesting that the stereotype that boys are superior at math is not always endorsed in adolescent samples (Morrissey et al 2019 in J Adolescence).

9. The authors state that the OAT-AM used in the current study was adapted lightly. Please explain.

10. It would be helpful to explain how the OAT-AM was scored in the Method section. A statistical analysis subsection would also be helpful to evaluate/understand the analytic approach. As is, the research questions and analyses are all somewhat vague, which makes it difficult to discern whether the optimal approach is being employed.

11. Tables 3: Please provide a more descriptive title.

12. A main finding is that participants, regardless of group or age, were unlikely to endorse prescriptive gender stereotypes. One wonders what might have happened had the authors measured descriptive stereotypes. I also wonder whether we are now in an era where people hold (or at least report) explicit views that run contrary to traditional gender stereotypes. Perhaps an implicit measure would yield some different results. Given this team’s expertise in this area, I would be interested to see some discussion of these possibilities folded into this paper.

13. Page 8, line 255: The authors are making the point that one study found trans children ages 6-8 years gender stereotyped less. But in the Introduction they noted that trans children that study were similar to their cis siblings. So, it seems a little dubious to me to claim in the Discussion that the study of 6-8 year-olds is finding something that suggests a trans vs. cis difference.

14. The figure quality appeared “fuzzy” on my end. Consider revising to higher resolution.

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Reviewer #1: No

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Author response to Decision Letter 0

28 Jan 2022

Please be sure to submit the revised manuscript in the appropriate format ( https://journals.plos.org/plosone/s/submission-guidelines ). As one example “Manuscript text should be double-spaced. Do not format text in multiple columns.” You might want to refer to the APA manual (7th edition) for suggestions as well.

Thank you, we have made sure the paper is appropriately formatted.

Thank you for this suggestion. We have made the language more consistent to reflect the fact that prior research has been conducted with children under the age of 11, but that our study involves adolescents between the ages of 13 and 17. We have also clarified how many participants in the present work are included in previous studies (n=8), and have specified how many gender diverse adolescents were recruited from other sources (n=64).

Thank you for this suggestion. We have included a table in the Supplementary Material that shows the mean score for each item on the scale and its standard error. We have also included reliability analysis for the items on the trait subscale of the OAT-AM in response to a comment from R2. Finally, we have included explanations of each of the items on the trait subscale of the OAT-AM on OSF (wording of items as presented to participants can be found in `oat_items.csv`), as well as versions of the data file with participants prior to exclusions (titled `kid_data_full_osf.csv` for the parents and `parent_data_full_osf.csv`). All of these files can be found in the `resubmission` folder in the OSF page: https://osf.io/yxs3r/files/

Thank you for this suggestion. We have added another research question and a corresponding subsection in the “Results” section that more directly probes whether there was explicit stereotype endorsement at all, not just a difference between the groups. We have also accordingly edited the abstract to reflect this change.

Thank you for this recommendation. We have re-run this analysis in the supplementary material with the gender stereotyping variable recoded as you suggest. We agree with you that knowing the direction matters. In addition, we retained the initial coding (based on whether they selected 3 or not) in the supplement as well, since this is the method of response scoring suggested by the original creators of the OAT-AM, and we want interested readers to be able to compare our results to that work.

Thank you for this suggestion. We agree that additional data would make the findings more compelling, and we looked into the possibility of correlating past data from the same participants with the stereotyping scores reported here; however, only 8 of the adolescents in this study had completed a stereotyping measure in the past . Thus, there was not enough longitudinal data on the present construct to make an interesting addition to the paper.

Further, there are two other issues with adding more data from past administrations with this cohort. First, there is almost no variability in the present results meaning that it would be nearly impossible to observe any meaningful association between these results and anything else. If we move to an even further construct (e.g., peer preferences, self-esteem), we’d be even less likely to find an association. Second, many of the participants in the present work had never participated in a study with us before and therefore we would need to exclude them from such analyses, lowering our sample size, and further reducing the odds of finding significant associations over time.

We did, however, add some additional analyses of the present data in response to points 6 & 7 made by Reviewer 2 (examining gender stereotype endorsement by parents’ gender and adolescents’ gender); we also include an exploratory analysis in the Supplementary Materials that examines whether gender diverse adolescents’ stereotyping was more (or less) predictive of their parents’ stereotyping than cisgender adolescents’ stereotyping.

Reviewer 1:

Reviewer #1: One concern about dual publication is the use of the same gender diverse adolescents across multiple manuscripts. I know this is a large data set and it is reasonable that it will result in multiple manuscripts, but given how little research has been conducted with gender diverse youth, the repeated publication from one data set may skew the knowledge base. I recommend citing all of the other publications using the same youth in the Participants section.

Thank you for this suggestion. We have cited all the other publications using the same youth in the participants section. Importantly, only 8 of the transgender participants completed a past stereotyping measure. Sixty-four of the gender diverse participants and 107 of the cisgender participants have never completed any studies with our team.

Reviewer 2:

Thank you for this suggestion. We have scaffolded the introduction with more relevant discussion of past work that speaks to the inclusion of these measures and research questions. In particular, we have added more background on parental gender socialization as it informs hypotheses about whether parents of transgender or cisgender adolescents might show more or less explicit stereotype endorsement. In addition, we have added text that clarifies why we measured adolescents’ perceptions of the extent to which their parents would endorse gender stereotypes. While we did not have a priori directional predictions, we provide information about why one might speculate different patterns of results given prior literature.

Thank you for this suggestion; we have made sure that all broad statements in the article are supported by prior literature.

We apologize for the error in reporting. Participants who completed no items were not included in analyses. (However, adolescents who completed the self-report items but none of the items predicting the parent’s stereotyping were included in the applicable analyses.)

Thank you for bringing up this point. The samples are not perfectly matched on demographics. We have included chi-squared tests and two-sample t-tests and results in the participant demographics table to illustrate differences between the two groups. Nonetheless, given the general lack of difference on the dependent measures between the groups, we think it is unlikely that demographic differences between the two groups played a large role. However, we have added text to the limitations section highlighting that the two groups are not matched on demographics and that the biases, particularly in the cisgender group, might explain why we observed so little stereotyping.

Thank you for this suggestion. We have included text that clarifies (a) how participants from the Trans Youth Project cohort were recruited (n=79), (b) how many of the gender diverse children in this paper have been profiled in prior studies (n=8), and (c) how gender diverse participants who were not part of the Trans Youth Project cohort were recruited (n = 64).

Thank you for this suggestion. We have included text in the Results section which indicates how many parents of each gender there were in each group, as well as an exploratory analysis examining whether there were differences in stereotyping between parents who identified as men and parents who identified as women. We have also included text citing literature about how mothers and fathers hold different attitudes about gender roles and stereotypes, including work by Endendijk.

Thank you for this suggestion. We have included information about how many adolescents of each gender there were in each group (Table 2) and the means and standard errors on the stereotyping measure for adolescents of different genders (Table 5). Further, we have included an ANOVA that tests for differences in stereotyping by gender.

Thank you for this suggestion. In the results section, we have included Crohnbach’s alpha values in the results for each of the three iterations of the trait subscale of the OAT-AM that we administered. We do not include the full analysis showing how much item contributed to reliability; there were no items that brought down reliability of the scale more than .03. We do note, however, that reliability on the parent self-report measure is lower than the others, and discuss potential reasons for the low variability.

We have clarified that the trait subscale of the OAT-AM was adapted by removing the “Neither men nor women” option and adding a “skip” option for each item.

Thank you for this suggestion. We have clarified in the Methods section how the OAT-AM is scored. Additionally, we have revised the results section around 3 questions, which are hopefully more clear than they were in the first submission.

● The first research question is whether participants showed gender stereotyping at all, without considering group differences. To answer this question, we include a one-sample t-test to test whether the mean value across all participants in the gender-stereotyping measure is different from 3 (the response which corresponds to ‘no gender stereotyping’).

● The second research question is whether there are group differences (both cisgender vs. gender diverse, as well as adolescents vs. parents) in gender stereotyping endorsement. To answer this question, we fit a mixed-effects linear regression predicting an individual’s mean stereotyping score from (1) whether they are an adolescent or a parent and (2) whether they are from a family with a cisgender teen participant or a family with a gender diverse teen participant.

● The third research question is whether adolescents’ gender stereotype endorsement, and their assumptions about their caregivers’ gender stereotype endorsement are predictive of parents’ gender stereotype endorsement. To that effect, we conduct linear regressions that examine the strength of the relationship between adolescents’ responses (of both types) and their parents’ responses.

We have provided more descriptive titles.

Thank you for this suggestion. We have included text in the discussion section that suggests these areas as future topics of interest. We agree that they may be more productive than this focus on prescriptive work moving forward (though we didn’t know that before we found these results.)

Great point. We have amended the language to indicate that trans youth and their siblings differed from unrelated cisgender youth in their stereotyping.

Thank you for this suggestion. We have made sure the figure appears in higher resolution.

Submitted filename: response letter (deMayo, Kahn-Samuelson, & Olson).docx

Decision Letter 1

28 Feb 2022

PONE-D-21-20601R1

Thank you for submitting your manuscript to PLOS ONE. I have now had the opportunity to read your revised manuscript and I believe that you have done a good job of addressing the majority of issues raised in the first round of reviews. As such, I have decided not to send it back out for review, as I feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Instead, I would like to provide you with the opportunity to engage in one final round of revisions and I will hope to make a decision about the manuscript soon after receiving this revision. Please be sure to submit and correspondence directly through the PLOS ONE system to ensure timely responses; my apologies for delays in receiving a decision about this revision as I only received it through the system recently.

• My main concern remains whether this provides sufficiently original research to warrant publication in this journal. I believe that it does, owing largely to the need for additional literature examining the social cognition of transgender youth. This also includes stereotyping by both youth and their parents, as well as youth’s expectations around their parents’ results. This is impressive. One additional question that is not addressed, and that I believe could help to bolster the novelty of this research, would be to also examine male and female stereotypes separately. That is, are there differences for feminine stereotypes or masculine stereotypes? Would you expect differences between these groups on either? This of course would need to be noted as a post-hoc analysis but might add to the substance of the article and possible the findings (e.g., in the case where no differences emerge). Similarly, on page 14 you suggest that more stereotyping occurred for personality traits versus academic domains. Additional analyses could examine this statistically as a post-hoc analysis (although you would need to note that should be interpreted with caution due to the post-hoc nature of this type of analysis). One or both of these might be most appropriately added to the supplement as opposed to the main text and I will leave that to you to decide. In short, I felt that some interesting questions were left unaddressed and addressing them would help to contribute the concerns previously raised by Reviewer 2 regarding the contribution of this article.  

I was a bit confused by some of the information in Table 1. For one, I believe the chi-squared tests are comparing two groups (and show difference between (not among) groups?). For gender, I assume this is between woman and men with other/not reported being excluded from the analysis? For yearly income, it was not clear to me what was being compared. Please clarify.

In the exclusions section, it would seem that after these exclusions were made, the total number of participants listed in the Participants section remained. However, given the placement of this, it was not clear (until the end of the section) whether these were exclusions beyond the N=145. Please start by listing the total number of participants run; that is: We started with a total of X participants; however, “during data collection, we noticed…” to clarify.

Please provide a table of correlations that corresponds to the regression analyses.

Additional considerations:  

Stylistically, I would recommend removing the ‘road map’ on page 4 and instead relying on your headers to guide the reader.

On page 7 you seem to suggest that an advantage of this measure is that it has “often produced clear indications of gender stereotyping in this age group”. I would rephrase as it is not clear from this wording why this is an advantage. I see this instead as a previous finding that is relevant to your research questions and predictions.

I was not clear about the dfs provided for the F-statistic in the first paragraph of page 18. Also, you note that this compares boys, girls, and non-binary/other adolescents, but in the discussion, you note findings regarding boys and girls. If this is an omnibus F with three groups please provide post-hoc direct comparisons.

To decrease possible confusion, I would recommend referring to tables in the supplement as Table S1, etc.

Although you list all ten traits in the supplement, I think that it would also be helpful for readers to see these listed in the manuscript on page 13.

On page 14, you note that more people skipped specific items; you might include those ns in Table S7.

  Overall, I find that you have presented some interesting findings. I think that this has the potential to make an important contribution to our understanding of gender stereotyping in adolescence. I hope that you will decide to revise and resubmit this paper and will look forward to receiving your re-submission. I will hope to make a quick decision after receiving these revisions.

Please submit your revised manuscript by Apr 14 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at  gro.solp@enosolp . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Author response to Decision Letter 1

25 Apr 2022

Dear Dr. Steele,

Thank you so much for allowing us to revise and resubmit our manuscript, “Endorsement of gender stereotypes in gender diverse and cisgender adolescents and their parents”.

The most notable changes to the manuscript include:

We have added analyses probing whether (a) masculine and feminine stereotypes are endorsed to differing extents and (b) whether stereotypes in different domains - academic vs. personality - are endorsed to differing extents. Specifically, as we describe below, we include tables in the manuscript that show means and standard deviations of stereotyping scores broken down on both of these dimensions, and refer readers to the supplementary materials for more detailed statistical inference probing effects related to stereotype gender and domain.

We have added more information about the measure - most notably, a table with all of the items and their means and standard errors - into the manuscript itself.

Below, you will find a point-by-point response to comments from your most recent review of this manuscript.

Thank you again so much for your time in reviewing this work and allowing us to resubmit. We look forward to hearing your decision.

Kristina Olson

My main concern remains whether this provides sufficiently original research to warrant publication in this journal. I believe that it does, owing largely to the need for additional literature examining the social cognition of transgender youth. This also includes stereotyping by both youth and their parents, as well as youth’s expectations around their parents’ results. This is impressive. One additional question that is not addressed, and that I believe could help to bolster the novelty of this research, would be to also examine male and female stereotypes separately. That is, are there differences for feminine stereotypes or masculine stereotypes? Would you expect differences between these groups on either? This of course would need to be noted as a post-hoc analysis but might add to the substance of the article and possible the findings (e.g., in the case where no differences emerge). Similarly, on page 14 you suggest that more stereotyping occurred for personality traits versus academic domains. Additional analyses could examine this statistically as a post-hoc analysis (although you would need to note that should be interpreted with caution due to the post-hoc nature of this type of analysis). One or both of these might be most appropriately added to the supplement as opposed to the main text and I will leave that to you to decide. In short, I felt that some interesting questions were left unaddressed and addressing them would help to contribute the concerns previously raised by Reviewer 2 regarding the contribution of this article.

Thank you for these suggestions. Regarding originality, a few aspects of this work that we believe are an original contribution are: this is the first work on gender stereotyping in transgender adolescents (that we know of!), this is a contemporary test of a “classic” and common measure – that perhaps suggests the measure is getting to be less useful (thereby suggesting researchers might opt for others in the future), and this work is original in its inclusion of teens and their parents to look for within family associations.

In addition, we agree that the suggested analyses further increase the novelty of the work. In the “Gender stereotype endorsement” section of the Results (page 17), we include tables showing the means and standard errors for stereotyping scores, broken down both by the gender of the stereotypes in the items (masculine vs. feminine) and the domain of the stereotype (academic- vs. personality- related). We note that mean scores are higher for personality-related and feminine stereotypes, and point readers to a more detailed analysis examining these effects in the supplement (S5 and S6).

As you noted, we did not preregister a specific prediction for differential endorsement of masculine vs. feminine stereotypes, though some literature suggests that people are generally more comfortable with girls showing masculine stereotypes than the reverse (e.g., Martin, 1990; Coyle, Fulcher, & Trubutschek, 2016), which is consistent with our finding that people endorsed feminine stereotypes as being more for girls (only) than masculine stereotypes were for boys (only).

Thank you for this suggestion; we have clarified what is being compared in each of the tests in Table 1. Specifically, we added notes that specify what is being compared in each of the chi-squared tests in both Table 1 and Table 2. For the chi-square tests comparing race, we are comparing white or non-white groups (because the small cells of specific groups violate assumptions of the chi-square analyses). For the gender comparison, we bin men together with “other/not reported” since participant N’s for men and “other/not reported” are so low. For the yearly income comparison, we converted participants’ reported income categories to a 1-5 scale (1 being the lowest, 5 being the highest) and ran a t-test comparing mean income scores between the gender diverse and cisgender group parents.

Thank you for this suggestion. We have restructured the Participants section slightly so that we include exclusion-related information for each subgroup of participants in our study (gender diverse adolescents and their parents, and cisgender adolescents and their parents). For each subgroup, we describe how many responses we received, how many we excluded for specific reasons, and how many participants were left in the final sample.

We were not exactly sure what table of correlations you are seeking, but we do now report a table of the Pearson’s r correlations between the three main stereotype measures (adolescent self-report, parent self-report, and adolescent prediction about the caregiver) in Supplementary Material 7. Apologies if this is not what you were seeking.

If you were seeking correlations between our categorical predictors (gender diverse vs. cisgender, parent vs. adolescent), the analogous information is captured in the regression summarized in Table 6, which examines group differences in self-reported stereotyping across in both adolescents and parents in the gender diverse and cisgender groups.

Similarly, if you were seeking information about how the relationship between parents’ and adolescents’ stereotyping might be different in the gender diverse vs. cisgender dyads, this information is captured in Tables S5 and S6 in the Supplementary Materials.

If you meant to suggest a different set of correlations, please let us know and we would be happy to add it.

Thank you for this suggestion; we have removed the road map text and instead use conceptual headers throughout.

Thank you for this point. We have removed the text in this paragraph that lists this as an advantage of the measure. Instead, we highlight the results observed in past studies using the trait subscale of the OAT-AM.

Thank you for raising this point. The F statistic refers to a one-way ANOVA comparing mean stereotyping scores among boys, girls, and nonbinary/other adolescents; thus, there were three groups, and 2 degrees of freedom. In addition, as you suggested, we have added post-hoc pairwise tests as well, which match the finding pointed out in the discussion.

Thank you, we have adjusted how we refer to tables in the supplement.

Thank you. We now include a table with all the items (including their means, standard errors, and the number of participants who skipped each) in the “Gender stereotype endorsement” subsection of the Results section (Table 3).

Thank you for this suggestion - we have added participant N’s for those who skipped each item in the table mentioned above (Table 3 in the manuscript).

Submitted filename: response letter.docx

Decision Letter 2

31 May 2022

PONE-D-21-20601R2

I believe that you have appropriately addressed each of my outstanding comments and I am pleased to inform you that your paper is being accepted for publication. I think that this paper will make a nice contribution to the field and want to commend you on this work. I can confirm that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Jenn Steele

Acceptance letter

Dear Dr. deMayo:

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Gender, Voice, and Job Stereotypes

• Research in Progress
• Published: 20 November 2023
• Volume 69 , pages 69–80, ( 2024 )

Cite this article

• Erin Devers   ORCID: orcid.org/0000-0003-1913-5901 1 &
• Carolyn Meeks 1  

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Researchers found men and women are rated differently on various characteristics according to gender stereotypes (Castillo-Mayen and Montes-Bergesin in Anales de Psicologia 30(3):1044–1060, 2014. https://doi.org/10.6018/analesps.30.2.138981 ; Rice and Barth in Gender Issues 33:1–2, 2016. https://doi.org/10.1007/s12147-015-9143-4 ; Smith et al. in Sex Roles 80:159–171, 2019. https://doi.org/10.1007/s11199-018-0923-7 ). These stereotypes impact individuals any time they enter into an interview for a position requiring them to be evaluated on a particular skill set. Another set of research has also found individuals are rated differently according to the pitch of their voice (Fasoli et al. in Arch Sex Behav 46:1261–1277, 2017. https://doi.org/10.1007/s10508-017-0962-0 ; Fasoli and Hegarty in Psychol Women Quart 44(2):234–255, 2019. https://doi.org/10.1177/0361684319891168 ; Klofstad et al. in Proc Roy Soc 279:2698–2704, 2012. https://doi.org/10.1098/rspb.2012.0311 ; O’Connor and Barclay in Evol Hum Behav 38:506–512, 2017. https://doi.org/10.1016/j.evolhumbehav.2017.03.001 ; Oleszkiewicz et al. in Psychon Bull Rev 24:856-862, 2017. https://doi.org/10.3758/s13423-016-1146-y ). This addresses the possibility that pitch of voice and gendered pronouns interact to impact the application of gender stereotypes. Two studies investigated the interaction between gendered pronouns and voice pitch on evaluations in a job interview setting. The first experiment included 68 participants recruited via email at a private Midwestern college. Gendered pronouns (he/she/the candidate) were provided for a job candidate with a gender-neutral voice pitch. There were no statistically significant differences between gendered pronoun conditions in evaluations of job candidates. The second experiment had 180 participants. Both the pitch (high, neutral, low) and gender pronouns (he, she, and the candidate) were manipulated. There were several significant main effects of perceived gender, and interactions were found between perceived gender and pitch of voice. Pitch and gendered pronouns together affected the way job candidates were evaluated. Specifically, the combination of pitch and gendered pronouns increased the application of gender stereotypes specifically related to emotionality.

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Devers, E., Meeks, C. Gender, Voice, and Job Stereotypes. Psychol Stud 69 , 69–80 (2024). https://doi.org/10.1007/s12646-023-00765-z

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