hypothesis on gender stereotypes

ORIGINAL RESEARCH article

Comparing prescriptive and descriptive gender stereotypes about children, adults, and the elderly.

• Department of Psychological Sciences, University of San Diego, San Diego, CA, United States

Gender stereotypes have descriptive components, or beliefs about how males and females typically act, as well as prescriptive components, or beliefs about how males and females should act. For example, women are supposed to be nurturing and avoid dominance, and men are supposed to be agentic and avoid weakness. However, it is not clear whether people hold prescriptive gender stereotypes about children of different age groups. In addition, research has not addressed prescriptive gender stereotypes for the elderly. The current research measured prescriptive gender stereotypes for children, adults, and elderly men and women in 3 studies to (a) compare how prescriptive gender stereotypes change across age groups and (b) address whether stereotypes of males are more restrictive than stereotypes of females. Students (Studies 1 and 2) and community members (Study 3), which were all U.S. and majority White samples, rated how desirable it was for different target groups to possess a list of characteristics from 1 (very undesirable ) to 9 ( very desirable ). The target age groups included toddlers, elementary-aged, adolescent, young adult, adult, and elderly males and females. The list of 21 characteristics was created to encompass traits and behaviors relevant across a wide age range. In a meta-analysis across studies, prescriptive stereotypes were defined as characteristics displaying a sex difference of d > 0.40 and an average rating as desirable for positive prescriptive stereotypes (PPS) or undesirable for negative proscriptive stereotypes (NPS) for male or females of each age group. Results replicated previous research on prescriptive stereotypes for adults: Women should be communal and avoid being dominant. Men should be agentic, independent, masculine in appearance, and interested in science and technology, but avoid being weak, emotional, shy, and feminine in appearance. Stereotypes of boys and girls from elementary-aged to young adults still included these components, but stereotypes of toddlers involved mainly physical appearance and play behaviors. Prescriptive stereotypes of elderly men and women were weaker. Overall, boys and men had more restrictive prescriptive stereotypes than girls and women in terms of strength and number. These findings demonstrate the applicability of prescriptive stereotypes to different age groups.

Introduction

Gender stereotypes are both descriptive and prescriptive in nature. That is gender stereotypes have descriptive components, which are beliefs about what men and women typically do. They also contain strong prescriptive components, or beliefs about what men and women should do ( Fiske and Stevens, 1993 ; Cialdini and Trost, 1998 ). This prescriptive nature is assumed to stem from the high level of contact and interdependence between men and women (e.g., Fiske and Stevens, 1993 ), which not only allows perceivers to create estimates of how men and women actually act but also creates expectations for how they should act.

Prescriptive stereotypes can have positive and negative components: (a) positive prescriptive stereotypes (PPS) designate desirable behaviors that one sex is encouraged to display more than the other and (b) negative proscriptive stereotypes (NPS) designate undesirable behaviors that one sex should avoid more than the other. These proscriptive stereotypes often involve characteristics that are undesirable in either sex, but are permitted in one sex, while being proscribed for the other. For example, according to past research ( Prentice and Carranza, 2002 ; Rudman et al., 2012b ), women are supposed to be communal (warm, sensitive, cooperative; PPS for women) and avoid dominance (e.g., aggressive, intimidating, arrogant; NPS for women), and men are supposed to be agentic (assertive, competitive, independent; PPS for men) and avoid weakness (e.g., weak, insecure, emotional; NPS for men). Yet dominance and weakness, which are undesirable, negative traits, are tolerated in men or women, respectively.

The current research measures both prescriptive and descriptive gender stereotypes to answer several questions about their content and magnitude. One first basic question is whether gender stereotypes have prescriptive components not only for adult men and women, but for males and females across different age groups, from toddlers to the elderly. Assuming prescriptive stereotypes exist across these age groups, the current research addresses how both the content and magnitude of prescriptive gender stereotypes changes across age groups. In addition, the current research compares the magnitude of PPS and NPS for males and females within each age group.

Adult Prescriptive Stereotypes

The fact that gender stereotypes are prescriptive is important to our perceptions of men and women because prescriptive stereotypes indicate approved (or disapproved) behavior. Violations of these prescriptions create strong reactions in perceivers. Whereas violations of descriptive stereotypes often cause surprise, given the person is not acting how the perceiver thought most men or women act, violations of prescriptive stereotypes create reactions of anger and moral outrage, because the person is not acting as they are supposed to act ( Rudman and Glick, 2010 ).

Thus, descriptive gender stereotypes can lead to prejudice and discrimination based on a perceived incongruency between gender stereotypes and role requirements, and prescriptive stereotypes can also produce prejudice if individuals violate gender norms (e.g., Burgess and Borgida, 1999 ; Heilman, 2001 ; Eagly and Karau, 2002 ). Specifically, the angry, moral outrage created by the violation of prescriptive stereotypes can lead to backlash, or social or economic penalties for the stereotype violator (e.g., dislike or not being hired for a position). Rudman et al. (2012a , b) posit that backlash against both female and male targets works to maintain the status hierarchy and keep men in high status positions, but limits agentic women's access to these same positions. For example, women who violate prescriptive stereotypes by acting dominant are disliked and therefore less likely to be hired even though they are seen as competent ( Rudman et al., 2012a ). Men can also be the recipients of backlash when they violate prescriptive stereotypes by lacking agency and showing weakness ( Moss-Racusin et al., 2010 ; see summary by Rudman et al., 2012a ).

Because of this backlash effect, prescriptive stereotypes can predict prejudice, even when descriptive stereotypes do not. For example, when male and female targets had equivalent resumes participants' descriptive stereotypes did not predict evaluations of the targets, but prescriptive stereotypes did predict prejudice toward women pursuing masculine roles ( Gill, 2004 ). Prescriptive stereotypes also create pressures on women and men to act in certain ways, and thus men and women avoid violating stereotypes or hide their non-conforming behavior to avoid penalties, which increases the rate of stereotypical behavior and perpetuates perceivers' stereotypes ( Prentice and Carranza, 2004 ; Rudman and Glick, 2010 ; Rudman et al., 2012a ). Thus, prescriptive stereotypes have important ramifications for behavior.

Whether these prescriptive stereotypes are more restrictive for adult men or women is unclear. Much research has investigated backlash toward women, perhaps because women are often held back from high status positions, which is seen as an important discriminatory outcome in society. However, there are several forms of evidence that suggest men's behaviors may be more restricted than women's in adulthood. For example, although they did not have a direct measure of prescriptive stereotypes, Hort et al. (1990) demonstrated that men were described in more stereotypical terms than women. Other evidence for a restrictive male stereotype stems from looking at the outcomes of stereotype violation. According to the status incongruity hypothesis, there are two prescriptive stereotypes that could create backlash for men (lacking agency and displaying weakness) and only one for women (displaying dominance; Rudman et al., 2012a ). This argument suggests that men are viewed more negatively than women for violating gender norms because men loose status (while women gain status) with the violation ( Feinman, 1984 ; Sirin et al., 2004 ), and status is seen as a positive, desirable outcome. In addition, theories about precarious manhood also suggest that men have to publically and repeatedly prove their strength to be called men because manhood is an uncertain, tenuous social status ( Vandello and Bosson, 2013 ). Even a single feminine or unmanly act could discount a man's status as a man, resulting in avoidance of feminine behaviors. According to this logic, these pressures may create strong prescriptive stereotypes for men to act agentically and avoid weakness to be considered a man—a pressure that is not as strong for women. Lastly, a sexual orientation perspective also indicates that men would be judged more harshly for feminine behavior than women are for masculine behavior because (a) men who display feminine behaviors are more likely to be perceived as gay than women who display masculine behavior (e.g., Deaux and Lewis, 1984 ; Herek, 1984 ; McCreary, 1994 ; Sirin et al., 2004 ), and (b) gay men are perceived more negatively than lesbians (e.g., Kite and Whitley, 1996 ). Given all of these ideas, prescriptive stereotypes may be stronger for men as a way to avoid these negative outcomes of a loss of status, manhood, and perceptions of homosexuality. The current research quantifies prescriptive stereotypes for males and females to assess their content and magnitude and attempts to make comparisons across the stereotypes for males and females.

Prescriptive Stereotypes About Children

Penalties for stereotype violations also occur for children who act in counterstereotypical ways. Several studies show that reactions from both child (e.g., Smetana, 1986 ; Levy et al., 1995 ) and adult (e.g., Feinman, 1981 ; Martin, 1990 ; Sandnabba and Ahlberg, 1999 ) respondents demonstrate more negative consequences (e.g., approval, evaluations) of counterstereotypical behavior from boys than girls ranging from ages 3 to 8 years old. This negative reaction toward boys is often stronger in men than women (e.g., Martin, 1990 ). Parents give little latitude for boys' behaviors but encourage both feminine behavior as well as masculine occupations and interests for girls, even complaining that their daughters can be “too girly” with pink, princess paraphilia ( Kane, 2012 ). Boys who are “sissies” are especially negatively perceived, whereas girls who are “tomboys” have both feminine and masculine interests and traits and therefore do not violate gender stereotypes as strongly ( Martin, 1990 , 1995 ; Martin and Dinella, 2012 ). Boys also elicit negative reactions for shy behavior, presumably because this behavior violates the male gender role ( Doey et al., 2014 ). As with adults, boys' behavior may be more restricted because of links between feminine behavior and homosexuality (e.g., Sandnabba and Ahlberg, 1999 ; Sirin et al., 2004 ). Thus, the consequences for violating stereotypes appear to be especially harsh for boys, and boys tend to be bounded by stricter rules of gender conformity and are subject to stronger “gender policing” than girls. These penalties, similar to backlash in the adult literature, suggest that violations of prescriptive stereotypes are at play. However, the research on children's norm violations does not frame the negative outcomes for counterstereotypical behavior in terms of violations of prescriptive stereotypes. In fact, it is not clear whether people even hold strong prescriptive gender stereotypes about children.

In one study that did address prescriptive stereotypes in children, Martin (1995) measured both descriptive and prescriptive gender stereotypes by asking adults how typical (measuring descriptive stereotypes) and how desirable (measuring prescriptive stereotypes) a list of 25 traits were for 4–7 year old boys or girls. As Martin (1995) predicted, the typicality ratings differed more often than the desirability ratings: The descriptive stereotypes indicated that boys and girls differed on 24 of 25 of the traits, which were selected to contain some masculine, feminine, and neutral items. Yet only 16 of the 25 traits showed sex differences in desirability: Martin (1995) found that boys should enjoy mechanical objects, be dominant, be independent, be competitive, like rough play, and be aggressive but avoid crying/getting upset or being frustrated (compared to girls). Girls should be gentle, neat/clean, sympathetic, eager to soothe hurt feelings, well-mannered, helpful around the house, and soft-spoken and avoid being noisy. Although there were fewer prescriptive than descriptive stereotypes about children in this research, these findings also show that prescriptive gender stereotypes exist for children of elementary-school age in ways that are consistent with adult prescriptive stereotypes.

Although prescriptive stereotypes may exist for younger ages, one could argue that younger people may not be held to as high of a standard for their behavior because they are considered to be more malleable than older targets (see Neel and Lassetter, 2015 ). To the extent that children are seen as still learning their gender roles and associated appropriate behaviors, people may be more lenient and prescriptive stereotypes might be weaker. On the other hand, adults' descriptive gender stereotypes of children were stronger than their descriptive stereotypes of adults ( Powlishta, 2000 ), and the same effect may apply to prescriptive stereotypes resulting in stronger stereotypes of children. Thus, the magnitude of prescriptive gender stereotypes for children of different ages and how they compare to adult prescriptive gender stereotypes is unclear.

Prescriptive Stereotypes About Other Age Groups

Once males and females are old enough to understand their gender roles, perceivers may be less lax about what is desirable behavior. Not only may older teens be seen as more in charge of their own behavior, but adolescence and young adulthood highlights differences between males and females in ways that were not relevant to children given the advent of puberty and the initiation of dating scripts. Thus, stereotypical self-perceptions and peer pressure for conformity to gender roles may intensify during adolescence for both males and females ( Massad, 1981 ; Hill and Lynch, 1983 ; Galambos et al., 1990 ). This “gender intensification hypothesis” states that there is an acceleration of gender-differential socialization and increased pressure to conform during adolescence. However, it is unclear if these self-beliefs would transfer to adults' stereotypes of male and female teens. Based on these ideas, one could predict that prescriptive stereotypes adults hold are stronger for adolescents. Whether males' behaviors would still be more restricted is unclear. Some researchers argue that gender role pressures intensify at this age mostly for boys ( Massad, 1981 ; Galambos et al., 1990 ), which is in line with ideas about precarious manhood, where boys have to continue to strive to become men through their public behavior whereas girls become women through the natural process of menstruation and other biological changes that occur in adolescence ( Vandello and Bosson, 2013 ). However, other researchers suggest a confluence of factors increase pressures on girls' behavior in adolescence compared to childhood, with the leniency given to girls to be tomboys replaced with stricter gender norms and a pressure to exhibit feminine behaviors and interests within a heterosexual dating environment ( Hill and Lynch, 1983 ). Thus, it is unclear whether boys would still be more restricted in their behavior than girls and generally how prescriptive stereotypes may change or emerge for adolescents and young adults.

On the other side of the age range, research has not focused on prescriptive gender stereotypes in the elderly. There is some evidence that descriptive gender stereotypes become more similar for elderly targets, in part because men's attributes become less masculine ( Kite et al., 1991 ; DeArmond et al., 2006 ; Thompson, 2006 ). Conversely, other evidence shows that when compared to old women, older men are still seen as more competent, higher in autonomy, and less dependent ( Canetto et al., 1995 ), demonstrating the continued existence of gender stereotypes. However, most of the research on aging stereotypes measures the negativity of the stereotypes (e.g., Hummert et al., 1995 ; Laditka et al., 2004 ) and not whether they are gendered. Thus, researchers have not addressed prescriptive stereotypes in the elderly or compared these to stereotypes of young adult or middle-aged men and women. Perhaps elderly men have less pressure to demonstrate their manhood and provide for a family, and thus their restrictions lessen, making violations of gender roles less severe than for younger individuals.

Current Research

In 3 studies, the current research measured prescriptive and descriptive gender stereotypes for various age groups, including children, adults, and the elderly. In all studies, participants rated how desirable and typical it was for different target groups to possess a list of characteristics. The list of characteristics included a variety of traits and behaviors, many of which have not been used in past research on adult stereotypes, to cover the types of behaviors that may be more relevant to childhood. For example, research on the parental treatment of boys vs. girls demonstrated higher levels of pressure for gendered interests and activities rather than traits (e.g., Lytton and Romney, 1991 ).

Through this method, the current research attempts to measure prescriptive gender stereotypes of toddlers, elementary-aged children, adolescents, young adults, adults, and the elderly to compare the content and strength of these stereotypes and answer several questions. In particular, assuming that gender stereotypes toward children and the elderly are also prescriptive in nature, current research addresses how both the content and magnitude of prescriptive gender stereotypes changes across age groups. Specifically, based on the emphasis on policing boys' behavior in childhood, one might expect that prescriptive stereotypes would be stronger for boys than adult men. Alternatively, these stereotypes may remain strong across age groups. Conversely, however, prescriptive feminine stereotypes may start weaker for girls and increase with age. Because descriptive stereotypes were also measured, prescriptive stereotypes can be compared to the typicality of each characteristics in males and females. Secondly, the research compares the number and magnitude of PPS and NPS for males and females within each age group to answer the question of whether males are more restricted than females in their behavior. Participants also answered a direct question comparing the desirability of stereotype violating behavior in males vs. females. Research suggests greater restrictions for males are likely for children, but the difference in strength and magnitude of prescriptive gender stereotypes has not been directly tested for specific age groups of children or for adult or elderly stereotypes.

Participants

Student participants in Studies 1 and 2 took part in a laboratory setting for course credit. In Study 1 ( n = 137), participants were 64.2% women; the mean age was 18.73 years ( SD = 1.07); 72.3% were White/Caucasian, 16.8% Hispanic/Latino, 11.7% Asian, 5.1% Black/African American, and 6.6% other or unreported (in all studies participants could select as many racial groups as apply). In Study 2 ( n = 91), participants were 65.9% women; the mean age was 19.10 years ( SD = 1.97); 76.9% were White/Caucasian, 15.4% Asian, 12.1% Hispanic/Latino, 2.2% African American, and 8.8% other or unreported.

In Study 3 ( n = 120), participants recruited through Amazon's Mechanical Turk (MTurk; see Buhrmester et al., 2011 ; Mason and Suri, 2012 ) participated for $0.30 for a 15-min survey. Participants were 59.3% women; the mean age was 38.17 years ( SD = 13.67); 70.8% were White/Caucasian, 7.5% Hispanic/Latino, 6.7% Black/African American, 5.0% Asian, and 4.1% other or unreported.

Procedure and Designs

All procedures were approved by the USD Institutional Review Board and all materials are available upon request. Participants in Studies 1 and 2 gave written informed consent, but participants in Study 3 indicated their informed consent online as a waiver of written consent was obtained from the IRB. Participants in all three studies rated the prescriptive and/or descriptive stereotypes of 3–6 groups of boys/men and/or girls/women. In Study 1, each participant rated 3 target groups of either males or females of different ages in a 3 (target age: elementary school, adults, elderly) × 2 (target sex: male, female) × 2 (stereotype rating: prescriptive, descriptive) mixed-model design, with target age and stereotype rating as within-subjects. In Study 2, targets were expanded to more age groups and participants rated 2 target groups of males and females of the same age in a 5 (target age: toddlers, elementary-aged, adolescent, young adult, adult) × 2 (target sex: male, female) × 2 (stereotype rating: prescriptive, descriptive) mixed-model design, with target sex and stereotype rating as within-subjects. In Study 3, the sample was broadened to community participants, who rated 6 groups of males or females of various ages in a 6 (target age: toddlers, elementary-aged, adolescent, young adult, adult, elderly) × 2 (target sex: male, female) × 2 (stereotype rating: prescriptive, descriptive) mixed-model design, with target age as within-subjects. In all studies, the levels of the within-subject variable were presented in a random order. Target age was designated with a label and a corresponding age group: toddlers (~2–5 years old), elementary-aged children (~5–12 years old), adolescents (~12–18 years old), young adults (~18–30 years old), adults (~30–50 years old), the elderly (over ~65 years old). See Table 1 for a comparison of study designs.

Table 1. Comparison of the three Studies' methods.

The instructions stated that the survey asked about the desirability of characteristics for males and females of different age groups. In Studies 1 and 2, prescriptive stereotype ratings were presented first, then the comparison of prescriptive stereotypes, and finally the descriptive ratings. To circumvent social desirability pressures, the instructions pointed out that the researchers were not interested in personal opinions but judgments of how society evaluates these characteristics for males and females of different age groups. Participants were then thanked for their time and debriefed about the purpose of the study.

A sensitivity analysis in G*Power ( Faul et al., 2007 ) demonstrated that this research was able to detect with 80% power a between-subjects target sex effect of d = 0.37 in Study 1, a within-subjects target sex effect of d between 0.53 and 0.50 (with n between 17 and 19 per target age condition) in Study 2, and a between-subjects target sex effect of d = 0.55 for prescriptive stereotypes and d = 0.56 for prescriptive stereotypes in Study 3. Thus, with a cut-off of d = 0.40 to define a prescriptive stereotype, these studies had acceptable power to detect effects of larger magnitudes, although results from near the cutoff should be taken with caution.

Prescriptive Stereotypes

In Studies 1 and 2 participants rated the characteristics of target groups in response to the question, “How DESIRABLE it is in American society for [elementary school boys (~5–12 years old)] to possess the following characteristics? That is, we want to know how [boys] SHOULD act” [emphasis in original]. In Study 3 the second sentence read, “That is, regardless of how boys actually act, we want to know how society thinks [elementary school boys] SHOULD act.” The scale ranged from 1 ( very undesirable ) to 9 ( very desirable ). This question is similar to the prescriptive stereotype question and response options from Prentice and Carranza (2002) , who also used a bi-polar scale.

Descriptive Stereotypes

In Studies 1 and 2 participants also rated the characteristics of target groups in response to the question, “Indicate how COMMON or TYPICAL each of the following characteristics is in [elementary school boys (~5–12 years old)] in American society. That is, we want to know how adult females USUALLY act” [emphasis in original]. In Study 3, the question asking about descriptive stereotypes read “How COMMON or TYPICAL is it in American society for [elementary school boys (~5–12 years old)] to possess the following characteristics? That is, we want to know how society thinks [boys] USUALLY act.” In all studies the scale ranged from 1 ( very atypical ) to 9 ( very typical ).

Characteristics

Both types of stereotypes were rated on 19–21 characteristics, created by grouping the traits from previous research ( Martin, 1995 ; Prentice and Carranza, 2002 ; Rudman et al., 2012b ) based on similarity, and adding some additional characteristics to cover a larger variety of traits and behaviors and include characteristics more applicable to children (e.g., shy, noisy, interests, play, and dress style). The full list of characteristics is given in Table 2 .

Table 2. Characteristics rated for prescriptive and descriptive stereotypes.

To make it easier for participants to rate groups of characteristics (instead of individual traits), participants were instructed to note that not all traits would apply equally across age groups, but within each list of characteristics some may apply more to some age groups than others. Participants were asked to think about the meaning of the overall list as they rated each group, instead of focusing only on 1 or 2 traits in the list. One benefit of grouping traits this way is that it allowed the characteristics to be more applicable across age groups. Participants may have focused on slightly different traits, but all of the traits on a list represented the overall concept being measured, allowing for a comparison of that concept across ages even thought it might manifest as different behaviors in different age groups. Thus, participants could apply that concept to a certain age group, instead of attempting to rate an individual trait that may or may not seem relevant to each age group.

Prescriptive Comparisons

In Studies 1 and 2, participants were also asked to compare the desirability of behavior of males and females who are likely violating their prescriptive stereotypes. Specifically, in two questions, participants compared (a) males (of a certain age) acting communal to females (of the same age) acting agentic (PPS of the other sex) and (b) males (of a certain age) acting weak to females (of the same age) acting dominant (NPS for that sex). Communion, agency, weakness, and dominance were defined using the same lists of characteristic given in Table 2 . The scale ranged from 1 ( considerably less desirable for males to act nurturing/weak ) to 7 ( considerably less desirable for females to act assertive/dominant ).

Results and Discussion

The raw data supporting the conclusions of this article can be requested from the author. Effect sizes for both prescriptive and descriptive stereotypes are the standardized difference between the relevant conditions, or Cohen's d . I corrected the small-sample bias in estimates of d using the conversion to Hedges' g , but refer to the effect sizes as d . In Study 1 and 3, effect sizes were calculated by dividing the difference in ratings for male and female targets at each of the different age groups by the pooled standard deviation. In Study 2, where target sex was within-subjects, effect sizes were calculated by dividing the difference in ratings by the average standard deviation, in order to facilitate the meta-analysis across studies (see Lakens, 2013 ). These effect sizes were then meta-analyzed using fixed-effects across the three studies, when the same age group was rated. A fixed-effects rather than random-effects meta-analysis was more appropriate because the studies had nearly identical measures and the sample of studies was too small to yield a reliable estimate of the between-study variability needed in random-effects computations (see Borenstein et al., 2009 ).

Table 3 provides the effect sizes in the meta-analysis of prescriptive stereotypes (see the Supplementary Tables for effects for each study separately). As defined by Rudman et al. (2012b) , prescriptive stereotypes were defined as traits displaying a sex difference of d > 0.40 and an average rating as desirable (>6 for PPS) or undesirable (<4 for NPS) for males or females. These two criteria mean that a large difference between the desirability of the characteristic between males and females does not necessarily classify as a stereotype if it is not also highly desirable or undesirable for one sex. Based on these criteria, PPS and NPS for males and females are designated in Table 3 . To facilitate comparisons across age groups, the bottom rows of Table 3 report the number of characteristics that meet the criteria to be considered as PPS and NPS and the average effect size for these PPS and NPS.

Table 3. Meta-analyzed prescriptive stereotypes ( d ) by target age.

It is clear from these data that prescriptive gender stereotypes exist across age groups, satisfying the assumption that prescriptive stereotypes are relevant for each age group. Thus, the data are described in relation to two questions: (a) comparing the content and magnitude of prescriptive gender stereotypes across age groups and (b) comparing the magnitude of PPS and NPS for males and females within each age group.

Comparisons Across Target Age

Toddlers had very few prescriptive stereotypes, and with the exception of being communal for girls, their stereotypes were not about traits but physical appearance and toys. Toddler boys had both strong PPS to have a masculine appearance and play with masculine toys and NPS to avoid having a feminine appearance or playing with feminine toys. Girls had strong PPS to have a feminine appearance and play with feminine toys as well as a weaker PPS to be communal. Although these prescriptive stereotypes were strong, other trait-based stereotypes were much weaker, suggesting that people do not have gendered expectations of toddlers' traits—perhaps because their personalities are perceived as not yet formed and more malleable (e.g., Neel and Lassetter, 2015 ). People do, however, have strong prescriptions about how toddlers should look and what they should play with, contradicting Campenni's (1999) research showing that gender-appropriateness of toys for toddlers were less stereotypical than ratings for older children.

As early as elementary school, prescriptive gender stereotypes similar to those for adults emerged. The strongest stereotypes for school-aged children were again for physical appearance and behavior, with the same pattern as for toddlers. At this age, sex-typed interests also appeared as prescriptive stereotypes, where it was seen as desirable for boys to be interested in math and science and girls to be interested in language and arts—but it is important to note that opposite sex-typed interests did not meet the criteria for proscriptive stereotypes. Trait stereotypes also met the criterion for elementary school-aged children: It was desirable for boys to be agentic and active and avoid being shy, weak, or emotional. Girls, on the other hand, should be communal as well as wholesome and avoid being dominant or noisy. These prescriptive stereotypes are very similar to those found by Martin (1995) for 4–7 year old children, including agency, interest in mechanical objects, rough play and avoiding weakness for boys and communal traits and avoiding noise for girls. The proscription of shyness for boys of this age group is also consistent with Doey et al's. (2014) analysis of the social (in)acceptability of shyness for school-aged boys. Martin (1995) did label independence as a desirable trait for boys (which did not meet the criteria for a prescriptive stereotype until adolescents in these data) and being neat, well-mannered, and helpful around the house for girls, which were not directly measured in the current data.

Stronger prescriptive gender stereotypes may emerge in elementary school-aged children, compared to toddlers, because by this age people believe that counterstereotypical behavior is predictive of adult counterstereotypical behaviors ( Sandnabba and Ahlberg, 1999 ), and so prescriptive stereotypes become relevant in order to pressure normative behavior. Thus, people appear to believe that elementary-aged children are no longer considered as malleable in their personality as toddlers. Conversely, there was no evidence for the idea that stereotypes for children would be stronger than stereotypes of adults—if anything, they were slightly weaker, although not by much.

Trait prescriptive stereotypes of male and female adolescents were intensified slightly compared to younger children, but not to a high degree and the average prescriptive stereotypes were not different in magnitude from younger children. These stereotypes were also not much different than adult stereotypes. Thus, there is not a lot of support for the idea that adolescence highlights gender differences and intensifies prescriptions based on the magnitude of the stereotypes.

There were some changes in the content of the stereotypes in adolescence and young adulthood, however. Starting in adolescence, PPS for toy/play behavior fell away for both males and females, although NPS to avoid opposite sex-typed toys remained with females picking up the admonition to avoid masculine toys. Stereotypes for physical appearance also remained, at about the same magnitude as for children. PPS for males to be agentic and independent as well as be interested in math and science increased from adolescence into adulthood, but the stereotype for males to be active peaked in adolescence. These PPS are now similar in magnitude to NPS for males to avoid being shy, weak, or emotional. Young adulthood brings a new PPS for males to be intelligent, which remains with age.

For females, adolescence bought a PPS to be likeable and a NPS to be sexually active and young adulthood a NPS for rebelliousness, but none of these stereotypes met the criteria for a stereotype in any other age group. PPS for girls and women to be communal grew with age and peaked in young adulthood, and NPS to avoid dominance grew into adulthood as well. The strongest prescriptive stereotypes for adolescent girls through adult women were to have a feminine appearance and be communal and avoid dominance and masculine toys.

These results replicated previous research on prescriptive stereotypes for adults ( Prentice and Carranza, 2002 ; Rudman et al., 2012b ), showing that women should be communal and avoid being dominant and men should be agentic and independent but avoid being weak and emotional. Adult prescriptive stereotypes were expanded in the current study by including more characteristics: Women should also have a feminine appearance and be interested in languages/arts, and avoid having a masculine appearance and being sexually active or noisy. Men should also have a masculine appearance, be interested in science/math/technology/mechanical objects, and be sexually active, but avoid being shy and appearing feminine. Adult men were also supposed to be sexually active, compared to women.

Stereotypes for the elderly were weaker for both men and women. Men were still supposed to have masculine interests, be agentic, and be intelligent as well as avoid feminine toys, appearing feminine, and weakness, but these stereotypes were weaker than those for adults from 30 to 50 years old. For elderly women, all stereotypes fell away except for a PPS to be communal, which was also weaker than for other age groups (excepting toddlers). These results are consistent with the findings that descriptive gender stereotypes weaken for elderly targets (e.g., DeArmond et al., 2006 ; Thompson, 2006 ). These stereotypes were also inconsistent across studies (see Supplementary Tables), suggesting that prescriptive gender stereotypes may be less relevant to older age groups.

Overall, these results demonstrated that the content and magnitude of prescriptive stereotypes do change for different age groups, focusing on activities and appearance at the youngest ages studied here, with trait stereotypes increasing for elementary-aged children and continuing through adulthood. There was not much evidence for an intensification of prescriptive gender stereotypes for adolescents, as these stereotypes were similar to both the elementary and young adult age groups. Stereotypes then waned for elderly targets, supporting the notion that prescriptive gender stereotypes also weaken with age.

Comparison of Male vs. Female Stereotypes

One test of the question of whether males' behavior is more restricted than females' behavior depends on the number and magnitude of the PPS and NPS in each age group. Based on the data counting and averaging prescriptive stereotypes of males and females of each age group presented in Table 3 , the stereotypes were more restrictive for males than females at nearly every age group. Although toddlers had few prescriptive stereotypes, the ones that did exist demonstrated that toddler boys had both strong PPS and NPS, whereas girls had only strong PPS but no strong NPS to avoid masculine things. From elementary-aged through adults, females gained weak NPS and the magnitude of male PPS and NPS decreased slightly, but overall the same pattern held. Even though stereotypes for the elderly are weaker for both males and females, the prescriptive stereotypes were still more numerous and stronger for men than women.

In nearly every age group (except the elderly), the average NPS were larger than PPS for males, suggesting that males are directed more based on what they should not do rather than what they should do. Conversely, female PPS stereotypes were stronger than female NPS and male PPS, thus females are directed more based on what they should do rather than what they should not do. Thus, the stronger pressure on males to conform to gender stereotypes focuses on telling boys and men behaviors to avoid. This idea is interesting in relation to precarious manhood, which suggests that men's status as a man is easily lost—especially if they display feminine behaviors ( Vandello and Bosson, 2013 ) that in this research made up NPS for males.

A second test of this question of greater restrictions for males involves the prescriptive comparison bi-polar questions that directly asked participants whether it was less desirable for males or females to violate stereotypes. These questions were identical in Studies 1 and 2 (but omitted in Study 3), and the means are presented in Table 4 . It is worth noting that in the current study agency did not meet the criterion for a NPS for females and communion did not meet the criterion for a NPS for males. However these characteristics were PPS for the other sex, and this question is labeled as positive violations because it describes males and females acting in ways prescribed to the other sex. Weakness and dominance were proscribed behaviors for males and females, respectively, and thus these are labeled negative violations because for males to act weak and females to act dominant violates NPS.

Table 4. Means and standard deviations for comparisons for desirability of violating prescriptive stereotypes by target age.

Most of the means were different from the midpoint of the scale (4), except for positive violations for adults and negative violations for elementary-aged, elderly (in Study 1), and toddlers (in Study 2). Repeated measures analysis of variance (ANOVA) on the positive and negative violations demonstrated that ratings varied by target age for positive violations in Study 1, F (2, 256) = 21.34, p < 0.001, partial η 2 = 0.14, and Study 2, F (4, 360) = 14.09, p < 0.001, partial η 2 = 0.14, and for negative violations in Study 1, F (2, 258) = 36.73, p < 0.001, partial η 2 = 0.22, and Study 2, F (4, 360) = 22.09, p < 0.001, partial η 2 = 0.20. Contrasts showed that for positive violations, it was less desirable for males to be communal than females to be agentic for adolescents, elementary-aged, and young adults but less desirable for females to be agentic than males to be communal in toddlers and the elderly. For negative violations, it was less desirable for males to be weak than females to be dominant for adolescents, young adults, and adults, and in no cases was it less desirable for females to be dominant than for males to be weak.

These results support the notion that males' behavior is more restricted than females even when asking people directly to compare the behaviors of males and females. Although toddlers and the elderly were exempt from these restrictions, there was greater concern, compared to females being agentic or dominant, that (a) elementary-aged boys should not be communal, (b) adolescent boys and young adult men should be not be communal or weak, and (c) adult men should not be weak. A greater emphasis on males' than females' prescriptive violations in these questions was strongest for adolescents, supporting the idea that these concerns more strongly emerge at puberty, even though the overall magnitude of prescriptive stereotypes were not strongest for adolescents. Interestingly, concerns for the positive violations of the elderly reverse, such that it was more concerning if females behave agentically than if males behave communally, consistent with the idea that male stereotypes evolve to include more communal elements in the elderly. Thus, these data that required participants to directly compare the violation of stereotypes for males and females supported the conclusion that males are more restricted in their behavior from elementary school to adulthood.

Prescriptive Stereotype Summary

In sum, these findings demonstrated the applicability of prescriptive stereotypes to different age groups, but also their variation depending on the age of the target group. The largest stereotypes for toddlers and elementary-aged youth were for girls to have and for boys to avoid a feminine appearance and playing with feminine toys. Prescriptive stereotypes for very young boys and girls were focused on appearance and play behaviors, and were especially proscriptive for boys—telling them more what not to do than what to do. Trait stereotypes appeared for elementary school-aged children, and the prescriptions for the usual suspects of communion, agency, dominance, and weakness remained into adulthood. Stereotypes for the elderly were then again minimized, demonstrating that people hold elderly men and women to few standards of gendered behavior, although elderly men still had more prescriptive stereotypes than elderly women. Overall, it does appear that males received more pressure in the form of prescriptive stereotypes, especially NPS about what not to do, across all age groups and especially for toddlers.

Table 5 displays the average effect size across the three studies in the meta-analysis of descriptive stereotypes. The Supplementary Tables show the effect sizes for each study separately. Similar to Martin (1995) , the effect sizes were often larger for descriptive than prescriptive stereotypes not only for children but for most age groups. Using criterion of d > 0.40 (similar to the prescriptive stereotype criterion) to qualify as a descriptive stereotype, 98 out of 126 (77.8%) effects over all age groups qualify as descriptive stereotypes. Thus, males and females were often rated as typically different even when the behavior was not prescribed for one sex over the other. However, descriptive stereotypes were highly correlated with prescriptive stereotypes for toddlers, r (19) = 0.95, p < 0.001, elementary-aged, r (19) = 0.97, p < 0.001, adolescents, r (19) = 0.94, p < 0.001, young adults, r (19) = 0.94, p < 0.001, adults, r (19) = 0.95, p < 0.001, and the elderly, r (19) = 0.77, p < 0.001. Thus, prescriptive and descriptive stereotypes aligned, although these high correlations may be an outcome of having the same participants rate both desirable and typical behaviors in Studies 1 and 2.

Table 5. Meta-analyzed descriptive stereotypes ( d ) by target age.

Limitations and Future Research

It is important to note that this research was conducted with majority White samples from the United States. The predominately White samples likely used White targets as their reference group, since target race was not specified. Thus, caution should be used when extrapolating the results to participants or targets of other racial groups. Previous research has demonstrated that that descriptive stereotypes of men and women are more similar to stereotypes of White men and White women than to gender stereotypes of other racial groups ( Ghavami and Peplau, 2013 ) and Blacks are seen as more masculine and Asians as more feminine than Whites ( Galinsky et al., 2013 ). There is also reason to suspect that prescriptive gender stereotypes may vary by race, as Black female leaders do not experience backlash for being dominant ( Livingston et al., 2012 ). Thus, it is important to acknowledge the current results describe stereotypes of Whites for Whites, but more research will be needed to know if other racial groups show similar prescriptive gender stereotypes for different age groups and if men of other racial groups are more restricted in their behavior than women.

In addition, the current ratings were all perceptions of adults (college students or older) of various age groups, from toddlers to the elderly. Missing are ratings of each age group of its own stereotypes (e.g., toddlers of toddlers; adolescents of adolescents; the elderly of the elderly). Suggesting similarity in prescriptive stereotypes across participant age groups, previous research demonstrated that children's reactions to norm violators (e.g., Smetana, 1986 ; Levy et al., 1995 ) show the same pattern of greater disapproval of counterstereotypical behavior from boys than girls that adults demonstrate in other studies. In addition, Powlishta (2000) found that children's and adults' descriptive stereotypes of child and adult targets were quite similar, although the difference between ratings of males and females on femininity was weaker for child than adult participants. Descriptive stereotypes of the elderly were also weaker for elderly respondents than middle-age or young respondents ( Hummert et al., 1995 ). It is unknown whether similar effects of participant age would occur for prescriptive stereotypes, which might be conceptually more difficult for children to understand as they designate desirable behavior rather than actual behavior. Stereotypes of one's own age group would be interesting to study, but with the current data I was interested in whether adults view different age groups differently. The stereotypes adults hold about children impact how children behave through gender role socialization, modeling, and direct tutelage ( Witt, 1997 ; Bussey and Bandura, 2004 ). Adults' beliefs about adolescents can also be important, as parents' stereotypical beliefs about adolescents' focus on peers and social concerns impacted parents' perceptions and their child's behavior ( Jacobs et al., 2005 ). Thus, parental beliefs about gender stereotypes can influence their children's gender role behavior, so understanding adults' views of children is important. Future research could assess whether parental status matters to these views, to see if greater familiarity with children or adolescents changes adults' views of prescriptive gender stereotypes.

The current research also did not assess possible reasons for the differences in prescriptive stereotypes across age groups. For example, the research did not attempt to measure the impact of stereotype violations on status, manhood, or perceived sexual orientation, which are all possible mechanisms for the policing of boys and men in terms of what they are not supposed to do. It may be the case that these mechanisms vary across age groups. The smaller prescriptive stereotypes in toddlers may be due greater perceived malleability in personality and trait characteristics, and behaviors of younger children may not speak as directly to sexual orientation (see McCreary, 1994 ). In addition, if these concerns are reduced or removed for the elderly, this may help to explain the reduced size of prescriptive gender stereotypes in this age group. Future research should continue to address these issues across a wide variety of age groups.

The meta-analytic results presented here average across three studies with different research designs. However, it is important to note that Study 2 had larger effect sizes (see Supplementary Tables), most likely because target sex was within-subjects, encouraging participants to draw sharper distinctions between the male and female groups. These target contrast effects have occurred in other research. For example, Thompson (2006) found that old men were rated as more masculine and less feminine when compared to old women than when compared to young men. Participants in the current research rated the targets in a random order by age, minimizing any one specific age comparison when averaging across participants, but stereotypes may also differ depending on the presentation order of age groups. Thus, the size of the stereotypes may depend on the research design used to capture them.

Implications

Because prescriptive stereotypes exist across age groups, the mechanism causing the negative reactions and backlash to counterstereotypical behavior may be the same for both children and adults—a violation of prescriptive stereotypes. However, different types of behavior would violate prescriptive stereotypes in adults and children, based on the specific content and magnitude of these stereotypes. For example, negative reactions to children might focus more on violations of physical appearance or play behaviors, rather than traits, whereas reactions to adolescents and adults could result from violations of both trait and appearance prescriptive stereotypes. Future research should address prescriptive stereotypes as a mechanism for negative reactions to children, adults, and the elderly who display counterstereotypical behaviors. Backlash could also vary with perceiver's ideology—non-traditional participants might see stereotype violations as a positive rather than a negative event (see Gaunt, 2013 ).

Conclusions

The current findings demonstrated the applicability of prescriptive stereotypes to different age groups, from toddlers to the elderly, and presented their content and magnitude. All age groups had prescriptive stereotypes, although the content and magnitude of those stereotypes varied across age groups. Prescriptive stereotypes for toddlers contained elements of play and appearance, whereas trait stereotypes appeared starting for elementary-aged children. Prescriptive stereotypes for the elderly were minimized, suggesting less pressure to conform to expectations. Prescriptions for males focused on NPS that admonish what not to do, whereas females' stronger PPS focused on what girls and women are supposed to do. Thus, overall, males' behavior was more restrictive based on these stereotypes. The current research describes the current state of prescriptive gender stereotypes for a variety of age groups, and the consequences of these stereotypes for socialization and backlash as well as how the stereotypes might differ across racial groups deserve further study.

Ethics Statement

These studies were carried out in accordance with the recommendations of ethical standards of the American Psychological Association. The protocol were approved by the University of San Diego's Institutional Review Board (IRB). Participants in Studies 1 and 2 gave written informed consent in accordance with the Declaration of Helsinki, but in Study 3 participants did not because a waiver of written consent was granted by the IRB. Instead, participants consented online before participating in the study.

Author Contributions

AK conceived, planned, and carried out the experiments, analyzed the data, interpreted the results, and wrote the manuscript.

Conflict of Interest Statement

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

Acknowledgments

I would like to thank my research assistants Rita Taylor and Brooke Miller for their help with data collection. Publication is made possible by a grant from the College of Arts and Sciences, University of San Diego.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2018.01086/full#supplementary-material

Borenstein, M., Hedges, L. V., Higgins, J. P. T., and Rothstein, H. R. (2009). Introduction to Meta-Analysis . Chichester: Wiley.

Google Scholar

Buhrmester, M., Kwang, T., and Gosling, S. D. (2011). Amazon's Mechanical Turk: a new source of inexpensive, yet high-quality, data? Perspect. Psychol. Sci. 6, 3–5. doi: 10.1177/1745691610393980

PubMed Abstract | CrossRef Full Text | Google Scholar

Burgess, D., and Borgida, E. (1999). Who women are, who women should be: descriptive and prescriptive gender stereotyping in sex discrimination. Psychol. Publ. Policy Law 5, 665–692. doi: 10.1037/1076-8971.5.3.665

CrossRef Full Text | Google Scholar

Bussey, K., and Bandura, A. (2004). “Social cognitive theory of gender development and functioning,” in The Psychology of Gender 2nd Edn , eds A. H. Eagly, A. E. Deall, and R. J. Sternberg (New York, NY: The Guilford Press), 92–119.

Campenni, C. E. (1999). Gender stereotyping of children's toys: a comparison of parents and nonparents. Sex Roles 40, 121–138.

Canetto, S. S., Kaminski, P. L., and Felicio, D. M. (1995). Typical and optimal aging in women and men: is there a double standard? Int. J. Aging Hum. Dev. 40, 187–207. doi: 10.2190/RX0U-T56B-1G0F-266U

Cialdini, R. B., and Trost, M. R. (1998). “Social influence: social norms, conformity, and compliance,” in The Handbook of Social Psychology 4th Edn, Vol. 2 , eds D. T. Gilbert, S. T. Fiske, and G. Lindzey (Boston, MA: McGraw-Hill), 151–192.

DeArmond, S., Tye, M., Chen, P. Y., Krauss, A., Rogers, D. A., and Sintek, E. (2006). Age and gender stereotypes: new challenges in a changing workplace and workforce. J. Appl. Soc. Psychol. 36, 2184–2214. doi: 10.1111/j.0021-9029.2006.00100.x

Deaux, K., and Lewis, L. L. (1984). Structure of gender stereotypes: interrelationships among components and gender label. J. Pers. Soc. Psychol. 46, 991–1004. doi: 10.1037/0022-3514.46.5.991

Doey, L., Coplan, R. J., and Kingsbury, M. (2014). Bashful boys and coy girls: a review of gender differences in childhood shyness. Sex Roles 70, 255–266. doi: 10.1007/s11199-013-0317-9

Eagly, A. H., and Karau, S. J. (2002). Role congruity theory of prejudice toward female leaders. Psychol. Rev. 109, 573–598. doi: 10.1037/0033-295X.109.3.573

Faul, F., Erdfelder, E., Lang, A. G., and Buchner, A. (2007). G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191. doi: 10.3758/BF03193146

Feinman, A. (1984). A status theory of the evaluation of sex-role and age-role behavior. Sex Roles 10, 445–456. doi: 10.1007/BF00287561

Feinman, S. (1981). Why is cross-sex-role behavior more approved for girls than for boys? A status characteristics approach. Sex Roles 7, 289–300. doi: 10.1007/BF00287543

Fiske, S. T., and Stevens, L. E. (1993). “What's so special about sex?: gender stereotyping and discrimination,” in Gender Issues in Contemporary Society , eds S. Oskamp and M. Costanzo (Newbury Park, CA: Sage), 173–196.

Galambos, N. L., Almeida, D. M., and Petersen, A. C. (1990). Masculinity, femininity, and sex role attitudes in early adolescence: exploring gender intensification. Child Dev. 61, 1905–1914. doi: 10.2307/1130846

Galinsky, A. D., Hall, E. V., and Cuddy, A. J. C. (2013). Gendered races: implications for interracial marriage, leadership selection, and athletic participation. Psychol. Sci. 24, 498–506. doi: 10.1177/0956797612457783

Gaunt, R. (2013). Breadwinning moms, caregiving dads: double standard in social judgments of gender norm violators. J. Fam. Issues 34, 3–24. doi: 10.1177/0192513X12438686

Ghavami, N., and Peplau, L. A. (2013). An intersectional analysis of gender and ethnic stereotypes: testing three hypotheses. Psychol. Women Q. 37, 113–127. doi: 10.1177/0361684312464203

Gill, M. J. (2004). When information does not deter stereotyping: prescriptive stereotyping can foster bias under conditions that deter descriptive stereotyping. J. Exp. Soc. Psychol. 40, 619–632. doi: 10.1016/j.jesp.2003.12.001

Heilman, M. E. (2001). Description and prescription: How gender stereotypes prevent women's ascent up the organizational ladder. J. Soc. Issues 57, 657–674. doi: 10.1111/0022-4537.00234

Herek, G. M. (1984). Beyond “homophobia”: a social psychological perspective on attitudes toward lesbians and gay men. J. Homosex. 10, 1–21.

PubMed Abstract | Google Scholar

Hill, J. P., and Lynch, M. E. (1983). “The intensification of gender-related role expectations during early adolescence,” in Girls at Puberty: Biological and Psychosocial Perspectives , eds J. Brooks-Gunn and A. C. Petersen (New York, NY: Plenum Press), 201–228.

Hort, B. E., Fagot, B. I., and Leinbach, M. D. (1990). Are people's notions of maleness more stereotypically framed than their notions of femaleness? Sex Roles 23, 197–212.

Hummert, M. L., Garstka, T. A., Shaner, J. L., and Strahm, S. (1995). Res. Aging 17, 168–189. doi: 10.1177/0164027595172004

CrossRef Full Text

Jacobs, J. E., Chhin, C. S., and Shaver, K. (2005). Longitudinal links between perceptions of adolescence and the social beliefs of adolescents: are parents' stereotypes related to beliefs held about and by their children? J. Youth Adolesc. 34, 61–72. doi: 10.1007/s10964-005-3206-x

Kane, E. W. (2012). The Gender Trap: Parents and the Pitfalls of Raising Boys and Girls. New York, NY: New York University Press.

Kite, M. E., Deaux, K., and Miele, M. (1991). Stereotypes of young and old: does age outweigh gender. Psychol. Aging 6, 19–27. doi: 10.1037/0882-7974.6.1.19

Kite, M. E., and Whitley, B. E. Jr. (1996). Sex differences in attitudes toward homosexual persons, behaviors, and civil rights: a meta-analysis. Person. Soc. Psychol. Bull. 22, 336–353. doi: 10.1177/0146167296224002

Laditka, S. B., Fischer, M., Laditka, J. N., and Segal, D. R. (2004). Attitudes about aging and gender among young, middle age, and older college-based students. Educ. Gerontol. 30, 403–421. doi: 10.1080/03601270490433602

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t -tests and ANOVAs. Front. Psychol. 26:863. doi: 10.3389/fpsyg.2013.00863

Levy, G. D., Taylor, M. G., and Gelman, S. A. (1995). Traditional and evaluative aspects of flexibility in gender roles, social conventions, moral rules, and physical laws. Child Dev. 66, 515–531. doi: 10.2307/1131594

Livingston, R. W., Rosette, A. A., and Washington, E. F. (2012). Can an agentic Black women get ahead? The impact of race and interpersonal dominance on perceptions of female leaders. Psychol. Sci. 23, 354–358. doi: 10.1177/0956797611428079

Lytton, H., and Romney, D. M. (1991). Parents' differential socialization of boys and girls: a meta-analysis. Psychol. Bull. 109, 267–296.

Martin, C. (1990). Attitudes and expectations about children with nontraditional and traditional gender roles. Sex Roles 22, 151–165. doi: 10.1007/BF00288188

Martin, C. (1995). Stereotypes about children with traditional and nontraditional gender roles. Sex Roles 33, 727–751. doi: 10.1007/BF01544776

Martin, C. L., and Dinella, L. M. (2012). Congruence between gender stereotypes and activity preference in self-identified tomboys and non-tomboys. Arch. Sex. Behav. 41, 599–610. doi: 10.1007/s10508-011-9786-5

Mason, W., and Suri, S. (2012). Conducting behavioral research on Amazon's Mechanical Turk. Behav. Res. Methods 44, 1–23. doi: 10.3758/s13428-011-0124-6

Massad, C. M. (1981). Sex role identity and adjustment during adolescence. Child Dev. 52, 1290–1298. doi: 10.2307/1129518

McCreary, D. R. (1994). The male role and avoiding femininity. Sex Roles 31, 517–531. doi: 10.1007/BF01544277

Moss-Racusin, C. A., Phelan, J. E., and Rudman, L. A. (2010). When men break the gender rules: status incongruity and backlash toward modest men. Psychol. Men Mascul. 11, 140–151. doi: 10.1037/a0018093

Neel, R., and Lassetter, B. (2015). Growing fixed with age: lay theories of malleability are target age-specific. Person. Soc. Psychol. Bull. 41, 1505–1522. doi: 10.1177/0146167215600529

Powlishta, K. K. (2000). The effect of target age on the activation of gender stereotypes. Sex Roles 42, 271–282. doi: 10.1023/A:1007095307977

Prentice, D. A., and Carranza, E. (2002). What women and men should be, shouldn't be, are allowed to be, and don't have to be: the contents of prescriptive gender stereotypes. Psychol. Women Q. 26, 269–281. doi: 10.1111/1471-6402.t01-1-00066

Prentice, D. A., and Carranza, E. (2004). “Sustaining cultural beliefs in the face of their violation: the case of gender stereotypes,” in The Psychological Foundations of Culture , eds M. Schaller and C. S. Crandall (Mahwah, NJ: Lawrence Erlbaum Associates), 259–280.

Rudman, L. A., and Glick, P. (2010). The Social Psychology of Gender: How Power and Intimacy Shape Gender Relations . New York, NY: Guilford Press.

Rudman, L. A., Moss-Racusin, C. A., Glick, P., and Phelan, J. E. (2012a). “Reactions to vanguards: advances in backlash theory,” in Advances in Experimental Social Psychology Vol. 45 , eds P. Devine and A. Plant (London: Elsevier), 167–227.

Rudman, L. A., Moss-Racusin, C. A., Phelan, J. E., and Nauts, S. (2012b). Status incongruity and backlash effects: defending the gender hierarchy motivates prejudice against female leaders. J. Exp. Soc. Psychol. 48, 165–179. doi: 10.1016/j.jesp.2011.10.008

Sandnabba, N. K., and Ahlberg, C. (1999). Parents' attitudes and expectations about children's cross-gender behavior. Sex Roles 40, 249–263.

Sirin, S. R., McCreary, D. R., and Mahalik, J. R. (2004). Differential reactions to men and women's gender role transgressions: perceptions of social status, sexual orientation, and value dissimilarity. J. Men Stud. 12, 119–132. doi: 10.3149/jms.1202.119

Smetana, J. G. (1986). Preschool children's conceptions of sex-role transgressions. Child Dev. 57, 862–871.

Thompson, E. H. (2006). Images of old men's masculinity: still a man? Sex Roles 55, 633–648. doi: 10.1007/s11199-006-9119-7

Vandello, J. A., and Bosson, J. K. (2013). Hard won and easily lost: a review of synthesis of theory and research on precarious manhood. Psychol. Men Mascul. 14, 101–113. doi: 10.1037/a0029826

Witt, S. D. (1997). Parental influence on children's socialization to gender roles. Adolescence 32, 253–259.

Keywords: gender, stereotypes, prescriptions, children, adults, elderly, age

Citation: Koenig AM (2018) Comparing Prescriptive and Descriptive Gender Stereotypes About Children, Adults, and the Elderly. Front. Psychol . 9:1086. doi: 10.3389/fpsyg.2018.01086

Received: 01 April 2018; Accepted: 07 June 2018; Published: 26 June 2018.

Reviewed by:

Copyright © 2018 Koenig. 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 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: Anne M. Koenig, [email protected]

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.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Establishing the content of gender stereotypes across development

Jessica sullivan.

1 Department of Psychology, Skidmore College, Saratoga Springs, New York, United States of America

Angela Ciociolo

2 Angela Ciociolo Marketing and Design, Grafton, Massachusetts, United States of America

Corinne A. Moss-Racusin

Associated data.

Data area available on our OSF page: https://osf.io/6r4ce/?view_only=d9c59e1237e045c2be571abd94b711b6 .

Gender stereotypes shape individuals’ behaviors, expectations, and perceptions of others. However, little is known about the content of gender stereotypes about people of different ages (e.g., do gender stereotypes about 1-year-olds differ from those about older individuals?). In our pre-registered study, 4,598 adults rated either the typicality of characteristics (to assess descriptive stereotypes), or the desirability of characteristics (to assess prescriptive and proscriptive stereotypes) for targets who differed in gender and age. Between-subjects, we manipulated target gender (boy/man vs. girl/woman) and target age (1, 4, 7, 10, 13, 16, or 35). From this, we generated a normed list of descriptive, prescriptive, and proscriptive gender-stereotyped characteristics about people across the early developmental timespan. We make this archive, as well as our raw data, available to other researchers. We also present preliminary findings, demonstrating that some characteristics are consistently ungendered (e.g., challenges authority), others are gender-stereotypic across the early developmental timespan (e.g., males from age 1 to 35 tend to be dirty), and still others change over development (e.g., girls should be submissive, but only around age 10). Implications for gender stereotyping theory—as well as targets of gender stereotyping, across the lifespan—are discussed.

Introduction

Gender plays an important role in daily life. While beliefs about gender differ within and across cultures [ 1 ]. Within particular cultures, there are often gender stereotypes (e.g., behaviors, characteristics, or attributes) that are deemed to be more normative and/or desirable for one gender than another [ 1 , 2 ]. Adults in the United States who violate gender stereotypes often experience social and/or economic penalties, commonly referred to as backlash [ 3 – 12 ]. For example, women who violate stereotypes by self-promoting on a job interview are less likely to be hired than identical men, while men who violate stereotypes by being self-effacing were less likely to be hired than identical women [ 8 ].

While the vast majority of existing work explores backlash against adults, recent research provided the first evidence that even gender-deviant children (in this case, preschoolers) experience backlash [ 13 ]: adults report liking gender non-conforming 3-year-olds less than their gender-conforming peers. This suggests that gender stereotypes have real-world consequences for adults and children alike. And yet, virtually nothing is known about how gender stereotypes change over development. Are stereotypic beliefs and expectations about young children the same as those about older children, adolescents, and adults? Are some traits consistently gendered across the lifespan, while others fluctuate? If they fluctuate, in what patterns? The present study investigates these questions, providing a novel assessment of how gender stereotypes change across the early developmental timespan.

Prior research demonstrates that, on average, adults in the United States believe that women should be communal (e.g., warm, supportive) and should not be dominant (e.g., aggressive, self-promoting); in contrast, men should be agentic (e.g., ambitious, independent) and should not be weak (e.g., passive, emotional) [ 12 ]. Violations of these gender stereotypes lead to backlash for adults in the U.S. (see [ 12 ] for a discussion). While the vast majority of empirical research on backlash has been conducted with participants in the United States, the limited data collected with participants in other countries including Australia [ 14 ], France [ 15 ], India [ 16 ], and China [ 17 ] reveal largely similar patterns (although more cross-cultural work is badly needed; see [ 18 ] for review).

Given the current data, it is unclear whether children in the United States, e.g., 1-year-old boys and girls, are held by adults to the same standards. Are they? If not, when in a child’s development do adults begin to apply gender stereotypes? Are there previously undocumented gender-stereotypes that apply only in childhood? More broadly, do gender stereotypes remain stable as children age, or do they fluctuate across development? In the present study, we asked adults to make judgments about the typicality and desirability of a large number of characteristics for targets of a wide variety of ages. This allowed us to characterize not only the presence or absence of a particular gender stereotype but also the developmental trajectories of these stereotypes.

In general, there are three types of gender stereotypes, each of which we measured in our study. Descriptive stereotypes involve characteristics that are thought to be typical of a particular gender [ 19 ]. For example, women in the United States are typically viewed as more self-aware and more anxious than men, while men are typically viewed as more extroverted and forgetful than women [ 2 ]. While individuals who violate descriptive stereotypes may surprise others, these individuals generally do not encounter backlash [ 19 ]. However, the same is not true for individuals who violate prescriptive and proscriptive stereotypes. Prescriptive stereotypes describe how members of a particular gender should behave. For example, women should be communal (e.g., cheerful, patient, and interested in children), while men should be agentic (e.g., athletic, ambitious, and assertive; [ 2 , 12 ]). Proscriptive stereotypes are those that describe how members of a particular gender should not behave. For example, women should not be dominant (e.g., stubborn or rebellious), while men should not be weak (e.g., emotional or yielding; [ 2 , 12 ]). Of importance, people who violate prescriptive and proscriptive stereotypes typically encounter social and economic penalties (i.e. backlash; [ 12 , 20 ]).

A large body of work has sought to characterize the content of descriptive, prescriptive, and proscriptive stereotypes about adult men and women. For example, Social Role Theory [ 21 , 22 ] posits that stereotypes about men and women stem directly from the sex-differentiated social roles traditionally occupied by men relative to women. Due to biological predispositions in early evolutionary history, men were more likely to be hunters, and women gatherers. Over time, people ascribed role-consistent traits to men and women, and these stereotypes took on prescriptive as well as descriptive components. However, because children are not yet capable of occupying these adult roles, it is unclear how, when, and why gender stereotypes should be applied to them. Relatedly, the stereotype content model proposes that stereotypes about adults cluster around two core dimensions: competence and warmth [ 18 , 23 , 24 ]. People who are viewed as high in warmth and low in competence (e.g., elderly people, housewives) elicit paternalistic stereotypes because they are perceived as low-status and non-competitive [ 24 ]. In contrast, targets who are viewed as low in warmth and high in competence (e.g., feminists, wealthy people) are met with stereotypes characterized by envy. Indeed, according to the stereotype content model, one reason that stereotype-violating women encounter backlash is that they are perceived as both high-status and competitive and therefore threaten the existing power structure. A potential challenge for this model—and one motivation for creating the current database of gender stereotypes about targets across the lifespan—is that children, by virtue of their relative lack of power, are economically, physically and socially powerless (relative to adults) and therefore are rarely thought of as threatening to the existing social order. Why, then should they experience backlash? More generally, it’s not obvious that competence and warmth are the appropriate domains for characterizing children. In short, neither theory nor empirical data suggest that gender stereotypes about children and adults are necessarily identical or even similar.

In fact, the recent work that has attempted to characterize adults’ gender stereotypes about children [ 13 , 25 ] has found that gender stereotypes about children appear to differ—at least in some ways—from those about adults. One recent study found that gender stereotypes about appearance, toy preference, and communality may be present for toddlers but that many other types of stereotypes that apply to adults may not apply to young children [ 25 ]. For example, although adult men and elderly men are described as more intelligent than women, toddler girls and elementary-aged girls are described as more intelligent than boys [ 25 ]. This study provides an exciting and promising window into understanding how gender stereotypes differ across the lifespan; however, because it elicited ratings only for developmental categories (e.g., “adolescents (ages 12–18)”), it does not allow us to draw conclusions about developmental trajectories. Another recent study found that the gender stereotypes that apply to 3-year-old children are often meaningfully different from those that apply to adults: unlike for adults, traits that were rated as most typical for boys were rated as undesirable, and stereotypes about children were more likely to center around appearance than is typical for adults [ 13 ]. However, it is not possible to know whether some of these apparent developmental differences can be attributed to changes in gender stereotypes across the developmental timecourse, or whether they can instead be attributed to methodological differences across studies (e.g., in the stereotypes tested; in sample size; c.f. [ 25 ] which addresses some of these issues). More generally, these studies provide a promising starting point but do not provide a large dataset for future researchers to utilize and do not allow us to characterize the developmental trajectory of gender stereotypes.

Our study—which will catalogue gender stereotypes across development (e.g., ages 1–35)—is consequential for at least four reasons. First, in order for our general theories of gender stereotyping (e.g., Social Role Theory (e.g., [ 21 , 22 ]) and the Stereotype Content Model [ 23 , 24 ]) to be useful for understanding and predicting children’s learning about gender stereotypes, they must fit the data not only for adults but also for children. Second, in order to effectively study and predict gender backlash [ 13 , 26 ], it is critical that we first understand the stereotypes that underlie backlash. Third, most theoretical approaches assume that gender stereotypes are learned; this implies that stereotypes could and should change over development, although there is very little data to speak to this. Fourth, from a practical perspective, adults interact with others (including children) throughout the early developmental timespan; parents, educators, and policy-makers would do well to understand the nature of the gender stereotypes that might be guiding their interactions. While evidence does suggest that people appear to encounter backlash for violating gender stereotypes across adulthood and childhood alike (e.g., [ 13 ]), for most ages research has done little to identify what these childhood gender stereotypes—and thus, their violations—even are . While we know that backlash exists, the nature of childhood gender stereotypes remain unclear, so we cannot yet predict the circumstances under which it is likely to occur across the lifespan.

In the present study, we measured adults’ gender stereotypes about infants (age one), children (ages 4, 7 and 10), adolescents (age 16), and adults (age 35). To do this, we measured gendered stereotypes about targets that ranged in age from 1 to 35. We present a database of normed gender stereotypes along with pre-registered findings generated by this study. This database fills a critical gap in the literature and will provide a set of developmental norms for researchers interested in gender development, gender backlash, and gender stereotypes.

All materials, methods, and analyses were approved via Skidmore College’s IRB, and pre-registered ( https://osf.io/7ahks ).

Participants

Our target sample size pre-exclusions was 4,900, which we requested via TurkPrime [ 27 ]; this number was selected in order to ensure that we had approximately 100 participants per cell of our design. Participants were native English speakers who were aged 18+, who had at least a 97% approval rating for prior Mechanical Turk HITs, and who had between 100–10,000 HITs. In total, 5,260 participants consented. We did not collect demographic data from our participants and therefore cannot assess the extent to which they are representative of the general population of the United States. Consistent with our pre-registration, we excluded participants who failed to complete at least 80% of the questions ( n = 308), and who failed any attention check ( n = 354). This resulted in a final N of 4,598.

The current study utilized a 2 [target gender: male, female] x 2 [rating type: pre/proscriptive, descriptive] x 7 [target age: 1, 4, 7, 10, 13, 16, 35] between-subjects design. This is in contrast to previous work that has manipulated these factors within-subjects [ 25 ]. In addition, our use of particular ages (e.g., “seven-year-old”) contrasts with that in previous work, which has elicited clusters of ratings (e.g., “elementary-school boys”; [ 25 ]).

We randomly assigned participants to conditions and to items within conditions. There were 175 characteristics and five attention checks, and participants were randomly assigned to see approximately 60% of the items. For each condition (e.g., for ratings of the desirability of characteristics for one-year-old boys), we obtained an average of 160 usable participants (min = 137, max = 187). For each characteristic (e.g., “pretty”), we obtained an average of 102 ratings per cell.

Materials and procedure

Our bank of characteristics consisted of 175 unique items from previous work [ 2 , 12 , 13 , 28 ]. These included behaviors (e.g., wrestles), traits (e.g., pretty), preferences (e.g., loves pink), and appearance-related items (e.g., wears tutus).

Participants first viewed instructions as follows: “Today you will be answering questions about how [common or typical / desirable] you think particular traits are among [age] [boys/girls/men/women]”. For example, participants in one condition saw: “Today you will be answering questions about how [common or typical/desirable] you think particular traits are among 1-year-old boys”.

Participants then rated each characteristic on a 1–9 Likert-Style scale with 1 indicating “not at all X” (where X was either “desirable” or “common/typical”) and 9 indicating “very X”; 5 was labeled as “Neutral.” They were reminded of the instructions: “Indicate how typical/desirable it is in American society for [age, gender] to possess each of the following characteristics. [Scale = 1–9; 1 = Not at all Typical/Desirable; 9 = Very Typical/Desirable]”. Characteristics were randomly ordered and randomly sampled from the body of possible characteristics described above. After rating each characteristic, we collected participants’ ages and genders.

As pre-registered (and as noted above), we excluded participants who were unlikely to be attending to the task by not answering enough questions or by incorrectly completing comprehension checks. We also excluded from analyses two items that were mistakenly included in our battery: “doesn’t wait his turn” (since it accidentally included a gendered pronoun) and “is clean” (since we also had the item “clean”).

Two primary goals of the current work were to develop a database of gender stereotypes across the developmental early timespan and to provide these data to other researchers. To this end, our data are available on our OSF page (https://osf.io/9pgkd/).

We had three additional specific goals, each of which was pre-registered: (1) to identify items that were and weren’t consistently gendered across the early developmental timespan (e.g., to find a list of gender stereotypes that characterize girls and women throughout development); (2) to identify items for which stereotypes changed over the early developmental timespan (e.g., items that are stereotypical at young ages but not at older ages); and (3) to identify items for which there were stereotypes at particular ages (e.g., to find a list of all gender stereotypes for 1-year-olds).

As pre-registered, we constructed linear models that predicted ratings for each characteristic from target gender (boy/girl), age (continuous), and their interaction. This allowed us to identify items where gender interacted with age (indicating that the presence and/or nature of the gender stereotype changed over development; these data are discussed later) and also items that were consistently gendered (i.e. there was no interaction with age, but rather a simple effect of gender).

Items that were not gender-stereotyped

We first report items for which we found no effect of target gender and no interaction of gender and age. In other words, these were the items for which—when considering the entirety of our dataset—we had no evidence, at any age, of gender stereotyping. For ratings of typicality, 29/175 (16.6%) characteristics that showed no effect of gender are depicted in Table 1 . For ratings of desirability, 59/175 characteristics (33.71%) showed no effect of gender as depicted in Table 2 . In other words, there is no evidence that these 29 traits constitute descriptive gender stereotypes ( Table 1 ), or that these 59 traits constitute prescriptive or proscriptive gender stereotypes ( Table 2 ).

a Despite the lack of overall effects of gender, characteristic showed a pairwise difference at least one age.

Stereotypes that were consistent across development

Ratings of typicality (i.e., descriptive stereotypes).

We first considered items for which we found a simple effect of target gender (For brevity, we do not report main effects of age in the main paper, although readers may access our data in our repository (https://osf.io/9pgkd/); items described here with a main effect of gender may also have shown main effects of age; only those items for which (a) there was an age*gender interaction (discussed later) or (b) no main effect of gender are excluded from the reporting below.). Our preliminary analysis revealed 93 items for which there was a simple effect of target gender (and no interaction with age) on ratings of typicality. In other words, ratings of typicality for these items differed depending on whether the target was a boy or a girl, and this gender difference did not depend on target age. As pre-registered, we identified the items for which the Cohen’s d effect size of the comparison of ratings for boys vs. girls was larger than 0.4; this is in keeping with past work [ 2 , 13 ]. This yielded 25 stereotypes about typicality that persisted across the developmental timeline; 15 of these met our pre-registered criteria for being descriptive stereotypes (effect size larger than 0.4 and a mean typicality above 6), while 10 did not (these met our effect size criteria but not the mean rating criteria and therefore were simply relatively more common for one gender than the other; we thus refer to them as “more typical” rather than “descriptive”). These are displayed in Tables ​ Tables3 3 and ​ and4 4 .

Note . All traits included here are ones for which the Cohen’s d effect size comparing ratings for boys/men vs. girls/women was larger than 0.4. Traits classified as descriptive demonstrated a mean typicality rating for boys/men above 6, while those classified as more typical demonstrated a mean below 6.

a Characteristic is also a prescription for boys (see Table 5 ).

b Characteristic is also a proscription for girls (see Table 5 ).

Note . All traits included here are ones for which the Cohen’s d effect size comparing ratings for girls/women vs. boys/men was larger than 0.4. Traits classified as descriptive demonstrated a mean typicality rating for girls/women above 6, while those classified as more typical demonstrated a mean below 6.

a Characteristic is also a prescription for girls (see Table 5 ).

b Characteristic is also a proscription for boys (see Table 5 ).

Ratings of desirability (i.e., Prescriptive and proscriptive stereotypes)

Our preliminary analysis also revealed 85 items that showed a simple effect of gender for ratings of desirability. In other words, these items were rated as consistently more desirable for one gender than the other, and this gender difference did not depend on target age. Of these, 12 met our threshold for effect size ( Table 5 ). We found 4 items that were prescriptive for boys/men and 2 for girls/women; these were the characteristics for which there was an effect size of at least 0.4 and a desirability rating above 6. We also found 2 items that were more desirable for boys/men and 1 for girls/women ( n = 1); these characteristics met our threshold for effect size but were neither rated as especially desirable (rating above 6) or undesirable (rating below 4); we refer to these as “more desirable” rather than “prescriptive.” Finally, we found traits that were proscriptive for girls/women ( n = 2), and traits that were proscriptive for boys/men ( n = 1); these characteristics met our threshold for effect size and had a mean desirability rating below 4.

Note . All traits included here are ones for which the Cohen’s d effect size comparing ratings for girls/women vs. boys/men was larger than 0.4. Traits classified as prescriptive demonstrated a mean desirability rating above 6, while those classified as proscriptive demonstrated a mean below 4. Items that met our effect size criteria but that displayed means above 4 and below 6 are described as “more desirable” for a particular gender.

a Characteristics that were also rated as descriptive/more typical for boys (see Table 3 ).

b Characteristics that were also rated as descriptive/more typical for girls (see Table 4 ).

In our previous work, we demonstrated that characteristics descriptive of 3-year-old boys also tended to be rated as undesirable (i.e., below the midpoint of our 9-point desirability scale), while the opposite was true for 3-year-olds girls [ 13 ]. We extend this finding in the present dataset; the mean desirability rating for the desirable characteristics in Table 5 for boys was 4.29 (i.e., undesirable), while it was 6.08 (i.e. desirable) for girls; these values differed significantly ( p = .003). These data suggest that the characteristics that describe boys/men are rated as less desirable than those that describe girls/women (and, in fact, are rated as undesirable) across the lifespan. Further, we highlight that there were noticeably fewer traits viewed as consistently typical for boys/men (8) than for girls/women (17).

Stereotypes that change over development

We next consider the characteristics for which we found a significant gender by age interaction. These were the items for which the magnitude of the gender gap changed across the developmental timeline—in other words, traits that are gender stereotypic, but as a function of target age. We found 43 characteristics where gender differences in ratings of typicality interacted with age ( Table 6 ) and 22 characteristics where gender differences in ratings of desirability interacted with age ( Table 7 ).

Note . Asterisks indicate that characteristic also had a pre/proscriptive interaction (see Table 7 ).

Note . Asterisks indicate that characteristic also had a descriptive interaction (see Table 6 ).

Our next goal was to understand the nature of these interactions. To do this, as pre-registered, we visualized each interaction. We then exploratorily qualitatively clustered characteristics based on shared developmental patterns. To do this, the lead author clustered the visualizations (see Fig 1 ) based on visual similarity, and the other two authors checked the clustering. Due to the subjective and qualitative nature of this classification process, the resulting clusters should be interpreted as useful ways of digesting our otherwise exceptionally dense dataset, and as helpful jumping-off points for future research. Fig 1 defines and depicts each cluster. Every characteristic and its cluster are depicted in Tables ​ Tables6 6 (for ratings of typicality) and 7 (ratings of desirability).

Stereotypes at each age

As pre-registered, for each age, we classified each item according to whether it met the criteria for being a descriptive (mean rating of at least 6 for one gender, effect size of at least .4 for the difference in ratings for boys/men vs. girls/women), prescriptive (mean rating of at least 6 for one gender, effect size of at least .4 for the difference in ratings for boys/men vs. girls/women), or proscriptive stereotype (mean rating of less than 4 for one gender, effect size of at least .4 for the difference in ratings for boys/men vs. girls/women). We did this for every item whether or not there was a significant interaction of age and gender in the analyses above; this was because we decided, a priori , that it was important to identify individual stereotypes at each age. Of course, as with all situations with such a large number of comparisons, even though our threshold was not statistical significance, we encourage readers to be cautious in interpreting each particular effect as it is likely that some of these effects emerged by chance alone.

This process of classification had three main outcomes. First, we assessed how many characteristics were classified as gender stereotypes at each age (see Fig 2 ). Interestingly, stereotypes were most frequent between the ages of 7 and 16, peaking at age 10. While the rate of prescriptive stereotypes appeared approximately the same across the developmental timespan, proscriptive and descriptive stereotypes were most frequent during childhood in our dataset. In other words, for female targets, there were more stereotypes applied to 7-year-olds than to adults, to 10-year-olds than to adults, to 13-year-olds than to adults, and to 16-year-olds than to adults. This is particularly striking because much of the existing research has focused on understanding gender stereotypes only about adults.

Notes . Y-axis is a count of stereotypes that met our pre-registered stereotype threshold. Black indicates prescriptive stereotypes, dark gray indicates proscriptions, and light gray indicates descriptive stereotypes. Note that some characteristics are double-counted (e.g., if an item was a descriptive and prescriptive stereotype at a particular age, it contributes to both the descriptive count and the prescriptive count). These data suggest that gender stereotypes are prevalent across the developmental timeline, and that children—not adults—may be subject to the most gender stereotypes.

Next, we explored whether there were more de-, pre-, or pro-scriptions for boys/men relative to girls/women and whether the frequencies of these stereotypes changed over the early developmental timespan ( Table 9 ). We found that a larger proportion of descriptive stereotypes were about girls/women (65.4%) than about boys/men (34.6%; p < .0001). A larger proportion of proscriptive stereotypes were about boys/men (72.3%; p < .0001) than about girls/women (27.7%); there were no effects of age on the distribution of stereotypes in either of these cases (all p >.10). Interestingly, this gendered asymmetry did not emerge for prescriptive stereotypes, which were equally frequent for boys/men (49.6%) and girls/women (50.4%, p = .93). Again, there were no effects of age on the gendered distribution of these stereotypes. These data suggest that while girls/women may consistently be subject to a relatively higher proportion of descriptive stereotypes, boys/men are subject to a higher proportion of proscriptive stereotypes throughout the lifespan.

Note . “Other ages” column indicates the other target ages (4, 7, 10, 13, 16, 35) for which this characteristic was also descriptive.

*Characteristic was also a description for that age and gender (see Table 8 ).

The final outcome is a table of all items that meet the criteria for being de-, pre-, or pro-scriptive at each age range. These are available in our repository. To illustrate these findings for one age group, in Tables ​ Tables8 8 and ​ and9, 9 , we pull out all descriptive ( Table 8 ) and prescriptive ( Table 9 ) stereotypes for one-year-olds.

a Characteristic was also a prescription for boys (see Table 9 ).

b Characteristic was also a prescription for girls (see Table 9 ).

c Characteristic was also a proscription for boys (see Table 9 ).

d Characteristic was also a proscription for girls (see Table 9 ).

In the present study, we measured adults’ stereotypes about male and female targets across the early developmental timespan (i.e. from infancy through early adulthood). To do this, we presented over 4,000 adults with a list of characteristics and asked them to rate either the desirability or typicality of those characteristics. Critically, participants rated the characteristics for targets that were either male or female and that were either 1, 4, 7, 10, 13, 16, or 35-years-old. This allowed us to develop the largest known normed database of gender stereotypes and to shed light on several questions about how descriptive, prescriptive, and proscriptive gender stereotypes change across the developmental timeline.

Rather than demonstrating stable stereotypic expectations for boys, girls, men, and women throughout the lifespan, our data revealed numerous developmental trends in the nature of gender stereotypes. First, items that were consistently gendered (main effects) were very rare; less than 10% of our items were consistently rated as being descriptive or pre/pro-scriptive stereotypes. Further, 29 characteristics were never descriptive of either gender, and 59 were never pre/pro-scriptive of either gender. These data suggest that theories of gender stereotypes need to take into account the fact that stereotypes are applied differently to targets of different ages—an idea that has not received significant attention in the literature thus far (to our knowledge).

The existence of a sizable subset of ungendered characteristics suggests that demand characteristics were unlikely to be responsible for our findings. In interpreting these results, it is important to note that we selected each of our 175 target characteristics from the existing literature [ 2 , 12 , 13 , 28 ]—these were items for which we had strong reason to believe that stereotypes might emerge. Indeed, for some items that are considered relatively central to defining particular gender stereotypes, we found no effects whatsoever of gender (e.g., there were no gendered effects on ratings of desirability for items like helping , wholesome , is a leader , bossy , challenges authority , controlling , moody , friendly , good listener , competent or polite , all of which are items that previous work has suggested may be gendered). These data highlight the importance of empirically testing the presence or absence of gendered stereotypes one-at-a-time (in contrast to some previous work, which has clustered traits; see [ 25 ]).

While there was a sizeable subset of traits for which there was no evidence of gender stereotyping, the majority of traits did show some evidence of gendering (it is important to note that many of these effects, while statistically significant, did not reach our pre-registered effect size criteria and therefore are not reported in our main paper; they are available in our repository). Together, our data strongly suggest that most characteristics were stereotyped and that gender stereotypes change over development. Even given our stringent criteria, 43 characteristics showed significant age by gender interactions for ratings of typicality, and 22 characteristics showed significant age by gender interactions for ratings of desirability (We wish to note, of course, that “desirability” is a complex construct and certainly not a construct that is likely to be fully addressed via a single likert-scale question. We use this terminology in order to most closely approach the ways in which previous work (e.g., Prentice & Carranza, 2002) has discussed prescriptive and proscriptive gender stereotypes.). These data highlight the importance of taking a developmental approach to studying gender stereotypes. After all, our theories of the development of gender stereotypes will necessarily differ depending on whether a particular stereotype persists throughout the lifespan, emerges only in adulthood, peaks at the onset of puberty, or displays some other pattern.

Below, we discuss some of the more important developmental changes that we identified. Future research should further explore the nature of and mechanisms underscoring these changes. Additionally, we hope that other researchers will find our database immediately useful in informing the development of new research materials. For example, researchers interested in backlash targeting young adolescents will likely wish to manipulate the gender typicality of targets’ traits, but could not previously be certain about which traits are actually viewed as gender stereotypic for this particular age group. Thus, our database now enables the development of evidence-based stimulus materials conveying the gender (counter)stereotypicality of targets across the developmental timespan.

As an important first step, we qualitatively and exploratorily classified the several developmental patterns that emerged in our data. These classifications were driven by the shape of the data and not by any theoretical expectations about which stereotypes might fall into each category. Because these clusters were generated exploratorily and subjectively, we encourage future researchers to use these primarily to motivate future confirmatory research and to generate testable theories. Further, a visual inspection of our data suggests the possibility that some of the developmental changes in gender stereotyping may be non-linear. The present study was not designed to differentiate possible developmental trajectories and did not sample ages densely enough to effectively do so (e.g., step-functions vs. logarithmic vs. quadratic vs. linear; see [ 29 ] for review). Thus, we encourage researchers to explore our dataset, and to conduct further research specifically aimed at detecting differences in the shape of change of gender stereotyping across the lifespan. In addition, we describe some of the qualitative patterns that emerged in our dataset below.

First, we note the presence of the Gender Differences Emerge Late category of stereotypes; these can be found in Fig 1 and towards the bottom of Tables ​ Tables6 6 and ​ and7. 7 . These are items for which we identified gender stereotypes in adulthood but found that these stereotypes were minimal or absent for younger targets (e.g., ambitious, submissive). These items are important because they shed light on the existing adult gender literature in that some of these items are critical to existing theories of gender development.

The fact that there are any characteristics for which the direction of a significant perceived gender gap switches throughout the lifespan (i.e., the Gender Difference Switches cluster) is a particularly novel and surprising revelation. Additionally, it is noteworthy that some gender stereotypes appear to be strongest in adolescence (i.e., the Adolescent Gender Differences—Boost and Adolescent Gender Differences—Reduction clusters) or in childhood (the Childhood Gender Differences cluster), or in only one age group (e.g., Gender Differences Fluctuate) . We have no theoretical account for these particular clusters at this moment and note that none of the clusters cleave neatly along existing theoretical lines.

Notably, we found 20 stereotypes that apply even to 1-year-old children. Of importance, from a purely developmental perspective, some of these characteristics could be difficult for children in this age group to display. For example, our data revealed that 1-year-old boys should be “athletic” and “love sports” and cannot “wear tutus” or “love pink.” It is unclear how a 1-year-old boy (who may not yet be walking and is unlikely to be talking) could adequately convey their athleticism and enthusiasm for sports, or their disdain for a color category they likely have no cognitive appreciation for and an article of clothing they are unlikely to have selected themselves. Similarly, 1-year-old girls should be “graceful” and “like princesses,” and should not be “dirty” or “challenge authority.” One might reasonably expect that infants and toddlers are too young to be constrained by these sorts of expectations, and that instead, adults would simply focus on whether very young children are healthy and meeting appropriate developmental milestones. Indeed, from this perspective, it is noteworthy that any characteristics emerged as prescriptive and proscriptive for 1-year-olds . Certainly, the current work makes the novel contribution of demonstrating that even infants appear to experience the effects of gender stereotyping.

Our results also clearly suggest that the stereotypes that individuals are faced with change over development. Of importance, existing theories of gender stereotypes were not created and thus, are unlikely to be able to account for these changes. For example, both backlash theory [ 30 ] and the Stereotype Content Model [ 23 , 24 ] emphasize that adult men are expected to exhibit agentic, competence-related traits while women are expected to display communal, warmth-related traits. However, when examining the pre- and proscriptions, we uncovered that for young children it is not apparent that warmth and competence are the primary stereotypic dimensions relevant for classifying children. Instead, consistent with our prior work [ 13 ], young children’s pre- and proscriptions appear to be much more linked to appearance (e.g., clothing choices) and overt, developmentally-relevant behaviors (e.g., play preferences). This suggests that it will be important to expand the existing literature to think more broadly about the nature, fluctuation, and impact of gender stereotypes throughout the lifespan. We hope that future researchers will utilize our developmental data to hone their theories of and predictions about the origins and time course of gender stereotypes.

While much of the adult literature has focused on stereotypes about women, our data show several ways in which boys and men may experience negative gender stereotyping. First, we found that the stereotypes that were considered typical of boys and men were also more often rated as unfavorable (see also [ 13 ]); this was true across the developmental timespan studied. Second, we found that across the timespan studied, there were more proscriptive stereotypes for boys/men than for girls/women. These results build upon a growing body of work demonstrating that gender stereotypes can have profound consequences for men as well as women (for a discussion, see [ 4 ]). For example, men appear to encounter backlash when they violate gender stereotypes by expressing interest in female gender-typed careers [ 5 ], behave modestly on a job interview [ 6 ], or disclose their emotions [ 31 ]. Further, recent work has shown that adults’ reactions to 3-year-old boys who violate gender stereotypes may be particularly harsh relative to same-aged girls who violate gender stereotypes [ 13 ]. Taken together, these findings suggest that future work should continue to consider the ways in which gender stereotyping impacts perceptions of targets across the gender spectrum.

While the analyses reported here were pre-registered, we nevertheless consider them to be exploratory: we didn’t have strong predictions about which stereotypes would persists across development (e.g., we found that boys/men are more rowdy and competitive, girls/women are more flatterable and caring), which peak in adolescence (e.g., girls cry more than boys; boys have a bigger appetite than girls), which would show stereotype vacillations across the timespan (e.g., boys/men are only sometimes more stingy than girls/women), and which would show no substantial gender stereotypes at all (e.g., neither gender is more bratty, stubborn, materialistic, rational, weak, or independent). While it may be tempting to believe that some of the developmental patterns that we demonstrate are the result of noise in the data, we believe it is unlikely that the patterns we see are false positives. First, our sample sizes are large: each datapoint (e.g., the mean rating of typicality for intelligent for 3-year-olds boys) consists of 100 ratings. While it is possible for noise to be (erroneously) treated as signal, we believe that our high-powered design has likely revealed many provocative patterns of the development of gender stereotypes that were undetected in prior studies. Second, we pre-registered our data collection techniques and analyses and relied on measuring effect sizes (in addition to null hypothesis significance testing), reducing the likelihood that the patterns in our data emerged due to questionable research practices or because our design was overpowered. For these reasons, we hope future researchers will take seriously both the predicted and surprising developmental findings reported in our dataset.

We wish to note two major limitations of this study. First, we treated gender as a binary, when we know that gender is actually a continuum (see [ 32 ] for review). Tellingingly, none of our participants noted any concerns about our binarization of gender. While we do not believe that a binarized view of gender is the right one, we do believe that the average adult in the United States assumes it to be, and our participants were familiar with and able to discuss gender in a binary way. To this point, we also note that we only sampled adults in the United States. We have no reason to believe that these stereotypes generalize to other cultural contexts, and indeed, this calls for additional cross-cultural research that can provide culturally-specific information about the content of descriptive, prescriptive, and proscriptive gender stereotypes across the lifespan.

In sum, we provide a novel and rich resource for future researchers cataloging the content of gender stereotypes across the early developmental timespan. The current results highlight the importance of expanding current theories of gender stereotyping to include developmental perspectives. Simply put, current theories of gender stereotyping may be specific to one point in development (i.e., adulthood). While this is useful for informing our understanding of the ways in which gender stereotypes about adults impact perceptions of adults, additional work is needed to shed light on the ways in which gender stereotypes shape and constrain social perceptions and experiences across the lifespan. Our analyses suggest several fruitful specific directions for new programs of research (e.g., focusing on the impacts of stereotyping on infants and their caregivers; emphasizing research on boys; using developmental trajectories to inform conceptual accounts of stereotyping), and we hope that researchers will use our dataset as a resource to inform both their theoretical and empirical future work.

Funding Statement

The authors received no specific funding for this work.

Data Availability

• PLoS One. 2022; 17(7): e0263217.

Decision Letter 0

21 Jan 2021

PONE-D-20-25142

Establishing the Content of Gender Stereotypes Across the Lifespan

Dear Dr. Sullivan,

thank you for submitting your manuscript to PLOS ONE. After careful consideration by myself and two experts in the field, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

In your revision, please pay particular attention to the technical and statistical aspects of your work as mentioned in the Reviewwers' comments.  Also, statements of study primacy (such as "this is the first study..") have to be well-grounded.  So, please make sure to justify the original rationale of your work and discuss your work and findings in the context of existing literature such as Koenig (2018), making clear the novelty of your research. 

Please submit your revised manuscript not later than six months from this date as otherwise, any revision has to be considered a new submission. 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 include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see:  http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Alexander N. 'Sasha' Sokolov, Ph.D.

Academic Editor

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1.) Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: I Don't Know

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: PONE-D-20-25142: Establishing the Content of Gender Stereotypes Across the Lifespan

I found this paper, which described gender stereotypes across childhood and adolescence, interesting and well-written. I would like to see the method and results of this work more directly compared to those of past studies that cataloged prescriptive and descriptive stereotypes – including Koenig (2018, who also assessed prescriptive and descriptive stereotypes across age categories, very similar to the current study) as well as Prentice (in terms of the results for adults). I believe this study adds information above these past studies, but it would be helpful if the method and results were compared to highlight how this study extends and builds on this past research. For example, I found it odd that the focus in the intro is on the stereotype content model for adults (pg. 11), which is about descriptive stereotypes, rather than this past work on prescriptive and descriptive stereotypes.

I applaud the researchers for preregistering the study, but I was unclear what exactly was preregistered. The first time preregistration is mentioned on page 13 says “preregistered yet exploratory findings” but it is unclear at this point in the paper what these exploratory analyses would be. It would be useful here to outline the three specific goals currently listed later on page 16, which would help guide the reader and understand what was preregistered.

How was the list of traits created? Some of the traits seems to apply better to children or to adults. What were participants to do when rating whether adults wear tutus (unlikely) or “gets pushed around by other kids” (did this say “other adults” when rating adults?), for example?

It might not be possible for space reasons, but it would be quite helpful if the graphs of the different “visualization pattern” of stereotypes over time were incorporated into the table listing the stereotypes with different patterns, as it is difficult to remember the various patterns. Perhaps one prototypical stereotype could be directly graphed as an example, rather than (what I assume are currently) hypothetical patterns. It was unclear how it was decided which characteristics fit each pattern – was this a visual inspection by the researchers (and if so, was this done individually and then the labels compared, as is done for qualitative codings?) or was there a quantitative analysis of similarity (or whether the effect was linear or quadratic, etc.)? Also, the characteristic “childlike” is listed twice in Table 6 under two different patterns.

I would also have liked more discussion of the findings in terms of, for example, the types/groups of traits that showed different patterns or results. Are there common themes (e.g., communion, agency, negative communion, negative agency, competence, physical characteristics, personality traits vs. behaviors) in the traits that show different results? This would make it a bit easier to digest the findings, as well as having the individual traits listed out for the specifics.

Reviewer #2: This manuscript reports a study of gender stereotypes across a variety of ages. This study appears to have been well-conducted overall. I have a few comments for the authors to consider as they move forward.

In several places, the manuscript refers to effects over "the lifespan" or "the developmental timespan," but 6 of the seven ages are children/adolescents, and the other one, 35, is used as a stand in for all adults. Truly examining this over the lifespan would sample more widely across adulthood and include older adult ages (e.g., 65, 85). Please reframe the description of these effects to more accurately reflect the represented ages.

Relatedly, for Figure 2, the caption says that the data suggest that children may be subjected to the most gender stereotypes, but again you have six ages for children and only one for all of adulthood! I think this is quite a stretch to say given the data.

It's unclear to me what "desirable" attributes are, as participants respond to these items. Are they saying they personally feel these attributes are desirable, or that they are seen in general as desirable? Do the authors have any validity evidence from this or other work using this wording that can tell us how best to understand how participants likely interpret "desirable" in this context? Literature on cultural vs. personal stereotypes would also be relevant to reference here.

The manuscript referred to preregistrations, which is great, but when I looked on OSF to see them I couldn't find them. This may be due to OSF's admittedly clunky navigation system (or my clunky inability to navigate it) but please do ensure that the preregistrations are made available.

The description of the analytical approach comes a little late - in the "Ratings of Typicality" section, but it looks like it was already employed to evaluate the "items that were not stereotyped" section. Please introduce the approach earlier to give context for how to interpret all reported results.

The Mturk sample is very large, which is great, but Mturk skews very White and likely other ways as well (e.g., politically liberal). Given that the stated aim is in part to create normed ratings of stereotypes, this should be explicitly acknowledged and discussed in the Discussion section.

There is some ambiguity about what analysis was performed, exactly (e.g., for "stereotypes that change over the developmental timespan," did the authors use regression to identify interactions, or only the descriptive method described in this paragraph?). Explicitly and clearly describing all analyses would be helpful. For this reason I chose "I don't know" about whether the analyses have been performed appropriately and rigorously, but with just a little more explanation, I would answer "Yes."

I particularly appreciate the focus on effect sizes. Could the authors explain their thinking behind choosing these particular cutoffs (e.g., why does a rating of 6 or higher count).

Social roles theory of gender stereotypes seems relevant here, especially with respect to its focus on agency and communion, which are close to warmth and competence. I would encourage the authors to reference this work if/as relevant.

The approach taken here (using established traits from literature on gender stereotypes) makes sense. For their future work, I would encourage the authors to consider whether a bottom-up approach could complement this work, as that could allow them to see if there are unique gender stereotypes that arise at different ages that are not well-captured by existing gender stereotypes (which could reflect only certain ages).

6. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at  gro.solp@serugif . Please note that Supporting Information files do not need this step.

Author response to Decision Letter 0

12 May 2021

Detailed responses to reviewers are included in our cover letter.

Submitted filename: Response to Reviewers.docx

Decision Letter 1

13 Jul 2021

PONE-D-20-25142R1

Establishing the Content of Gender Stereotypes Across Development

thank you for submitting your revised manuscript to PLOS ONE.  After careful consideration, we feel that it has merit but as it currently stands, still has to be improved to fully meet PLOS ONE’s publication criteria.  I appreciate very much the efforts of two experts in the filed who have provided their detailed and valuable feedback on your revision, and both been positive about your work.  Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process, paying particular attention to the following points below: 

1) As you will see from the comments, both Reviewwers have had difficulty in accessing documents mentioned as pre-registered.  Please provide a correct link to the files.

2) Please make sure to address the technical and statistical issues raised by both Reviewers such as multiplicity corrections and statistical trend analyses.  In addition, please (a) state if data sets were tested for normality and which (parametric, nonparametric) statistical inference was used for respective sets, (b) which statistic (and directional or not) was used for each p-value, and (c) add variability measures where appropriate.

Also please respond thoroughly to the other concerns expressed by the Reviewers. 

Please submit your revised manuscript within six moinths from this date as after that any revision has to be considered a new submission.  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.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see:  http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at  https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

Thank you for choosing PLOS ONE for communicating your research.

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

2. Is the manuscript technically sound, and do the data support the conclusions?

3. Has the statistical analysis been performed appropriately and rigorously?

4. Have the authors made all data underlying the findings in their manuscript fully available?

5. Is the manuscript presented in an intelligible fashion and written in standard English?

6. Review Comments to the Author

Reviewer #1: The authors have revised the manuscript to address the reviewer’s concerns, making the paper even stronger and clearer. The data set is large, but the authors do a good job of aligning the analysis of the data with their goals and distilling the main points of the findings. I appreciate the thoroughness of their response to the previous reviews. I have only a few very minor suggestions for the authors to take into consideration as they move forward with editing, to make the paper as clear as possible.

Pg. 5-6: “because it elicited ratings only for developmental categories (e.g., “adolescents (ages 12-18)”),” This sentence is not complete.

Pg. 6: “in order for our general theories of gender stereotyping (e.g., Social Role Theory (e.g., Eagly & Wood, 2012; 2016) and the Stereotype Content Model; Fiske et al., 2002; Fiske et al., 1999) to be useful, they must fit the data not only for adults but also for children.” I’m not sure the authors want to imply these theories are not useful, as they were designed for stereotypes of adults and have created meaningful bodies of research. Certainly, it's possible they may not apply well to stereotypes of children (or it could be that the social roles of children do impact their gender stereotypes, as would be suggested by social role theory, which doesn't mean the stereotypes have to mirror adult stereotypes). So this sentence could be reworded to suggest these theories may need to be adapted or new theories created to address gender stereotypes across development, without throwing out these theories entirely. Otherwise, this seems like a strong point – to say these theories would not be useful if gender stereotypes differ across ages – that would need more elaboration and discussion that this one sentence.

Pg. 12: The list of traits that are not prescriptive for gender includes three items that reference "kids" – gets pushed around by other kids, pulls other kids’ hair, thinks it’s funny when other kids are crying. It is unclear whether these items were used verbatim for all groups (even adolescents and adults) or whether the item changed to reflect the age group that was being rated (e.g., “gets pushed around by other adults”).

Given the multiple tests of comparison, it would be useful to include a statement at the beginning of the results that indicates the criterion for labeling something as a significant main effect or interaction (was it p < .05?). This may be stated in the preregistration, but the links provided for the preregistration took me only to the data and I did not see a preregistration document there (although perhaps I did not know where to look - but I suggest checking the link to make sure it leads to the preregistration documents). I know it is the effect size that qualifies a characteristics as a stereotype or not, but it is discussed how many traits showed significant effects, so knowing this cutoff is still relevant.

Pg. 17: The authors reference “In our previous work, we demonstrated that..” but do not give a citation (likely for blinding for review). I would suggest now adding in a citation here so that readers know what work is being referenced.

Table 6: Perhaps in the “direction switches” category it could be indicated which gender was higher at the youngest ages and then which was high later (the “switch”), instead of indicating “N/A.”

Table 8 has blanks when the stereotype was not relevant to that age group, but Table 9 uses dashes (and one blank). Do these dashes also represent ages where the stereotype was not relevant? I would suggest using blanks in both tables as that seems the most intuitive (and I believe APA style suggests dashes when the data are not collected/irrelevant, which is not the case here).

Reviewer #2: I was a reviewer on the prior submission. I really like the aim of this project – to descriptively catalogue gender stereotypes at a range of ages. The current manuscript is stronger and has overall addressed my comments well. I have a few comments that I hope will help to strengthen the paper further.

In response to my original comment about the pre-registration (#17), I appreciate the clarification, but I still can’t find the files. When I go to the OSF page, at first the only options available to me are “Files” “Wiki” and “Analytics”. Then when clicking “Analytics”, “Registrations” appears. But then when I click on “Registrations”, I get a message “there was an error loading this list”. So, I still have not been able to review the registrations.

I really appreciate the authors moving away from phrases like “lifespan” and being clearer in identifying the ages examined in this work. However, the phrase “across the developmental timespan” (e.g., as used in the abstract and the introduction) still implies that the developmental timespan encompasses only childhood / up to middle adulthood, when in fact development continues throughout the lifespan. Perhaps a phrase like “early lifespan” or “early developmental timespan” or “from childhood through early adulthood” would be more accurate?

In the method: “For example, participants in one condition saw: “Today you will be

answering questions about how [common or typical/desirable] you think particular traits are among 1-year-old boys”.”

Should either “common or typical” or “desirable” be listed, as it’s an example, or did participants within this condition actually rate on both commonness and desirability (which is what this wording could suggest)?

I really like the visualization for the interactions. But, only a linear term of age is used, which could potentially omit some characteristics that would have quadratic/cubic effects for one gender but not another. If there are nonlinear patterns of interaction e.g., if girls tend to increase and then decrease on a trait, whereas for boys it remains flat, that won’t necessarily appear in the interactions as specified if the overall (linear) change for girls is steady. It looks from the visualizations that for many of those characteristics that showed a difference in the linear slope (i.e., a significant interaction), there was likely some difference in quadratic/cubic change, too – e.g., childhood gender differences, adolescent gender differences – boost, and fluctuating gender differences. These traits just happen to have been identified by the linear interactions because there is also an overall difference in slope between genders, and not because the models run could identify these kinds of patterns. This suggests that there may be characteristics for which males and females differ in their quadratic/cubic patterns but not in their overall linear term. Could the authors re-rerun the regressions and see if including a quadratic or cubic term for age would suggest additional characteristics to explore? This can be done by adding both age(centered)^2 as a linear term and as interacting with gender, and age(centered)^3 as a linear term and as interacting with gender. This could potentially identify additional characteristics that fit the current set of visualization patterns, and/or new ones.

Regarding: “Critically, these findings are unlikely to be due to biases in the characteristics tested in our study: if they were, we would not expect the symmetrical rate of descriptive stereotypes.” (p. 28).

I don’t quite follow this, can the authors provide a little more explanation of the logic here?

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Author response to Decision Letter 1

23 Sep 2021

Reviewer #1

Reviewer #1: The authors have revised the manuscript to address the reviewer’s concerns, making the paper even stronger and clearer. The data set is large, but the authors do a good job of aligning the analysis of the data with their goals and distilling the main points of the findings. I appreciate the thoroughness of their response to the previous reviews. I have only a few very minor suggestions for the authors to take into consideration as they move forward with editing, to make the paper as clear as possible.

We thank the reviewer for their kind words, and appreciate the remaining suggestions, which we have responded to below.

The sentence now reads “This study provides an exciting and promising window into understanding how gender stereotypes differ across the lifespan; however, because it elicited ratings only for developmental categories (e.g., “adolescents (ages 12-18)”), it does not allow us to draw conclusions about developmental trajectories.”

Thank you for pointing out this unfortunate wording. We do not wish to imply that the SCM isn’t useful. We now say: “First, in order for our general theories of gender stereotyping (e.g., Social Role Theory (e.g., Eagly & Wood, 2012; 2016) and the Stereotype Content Model; Fiske et al., 2002; Fiske et al., 1999) to be useful for understanding and predicting children’s learning about gender stereotypes, they must fit the data not only for adults but also for children”

Thank you for pointing out this ambiguity. These items were used verbatim.

We apologize again for the error in the preregistration link. Our alpha was .05, and all tests were two-tailed. Our effect-size cutoffs were also pre-registered.

Thank you. We have now added our citation.

When we attempted to implement this suggestion, we found that it looked quite clunky (e.g., “higher for girls at ages 1 and 4, and higher for boys for age 7+”). In addition, the analyses required to determine the precise nature of the “switch” are different from the analyses used in the rest of the table (the former involves pairwise comparisons within age, while the latter involves regression models over the entire dataset); we were concerned that reporting particular age information in this table might confuse readers.

However, we agree with the reviewer that “N/A” is not the ideal notation for this table. We now say “direction switches” in this column. Readers who are interested in understanding the precise nature of the switch can access our data and supplemental materials.

We have now made our formatting consistent, by changing all dashes to blanks.

Reviewer #2: I was a reviewer on the prior submission. I really like the aim of this project – to descriptively catalogue gender stereotypes at a range of ages. The current manuscript is stronger and has overall addressed my comments well. I have a few comments that I hope will help to strengthen the paper further.

Thank you! We really appreciate the time you’ve put into improving the manuscript.

We are really sorry -- we (accidentally!) had two OSF pages for the same project with very similar titles; one had the data and the other had the preregistration. The correct preregistration link is now throughout the article, and we have moved all datasets etc… from the other OSF page to the correct one. As noted above, the correct link to the pre-registration is: https://osf.io/7ahks and the correct link the OSF page containing that pre-registration is: https://osf.io/9pgkd/

Thank you. We have changed it to “early developmental timespan” in most cases, and to the “developmental timepsan studied” when we are referring to the ages tested in our study.

answering questions about how [common or typical/desirable] you think particular traits are among 1-year-old boys”.”Should either “common or typical” or “desirable” be listed, as it’s an example, or did participants within this condition actually rate on both commonness and desirability (which is what this wording could suggest)?

It now says “...questions about how [“common or typical”][“desirable”] you think particular traits are among…”

We agree that this is a fascinating possibility, and one that is ripe for examination in future research. However, comparing models in these suggested ways would add a substantial amount of complexity and information to the paper, which is already dense. Adding to the complexity is that these analyses would not only be exploratory, but also atheoretical: while we acknowledge that there are some visible non-linearities in our data, we know of no theories that predict a particular cubic or quadratic effect of age -- or, critically, an interaction of target gender with a cubic or quadratic age function. This makes it challenging to explore this question in a targeted way -- when conducting exploratory analyses that deviate from the plan, it is best practice to do so when theoretically motivated.

More generally, our ability to make fine-grained determinations about the shape of developmental change hinges on the density of our age sampling; while our sampling of different ages across childhood allows us to make general statements about the overall trajectory, without fine-grained sampling of ages (i.e., at least of every year), any descriptions of the shape of developmental change will be impacted substantially by our sampling frequency (for an excellent description and demonstration of this in the domain of motor development, see Adolph et al., 2008).

Our goal is to provide some preliminary observations about trends in these data, and then make the dataset available for other researchers to examine in numerous different ways. As such, we have added the sentence to the General Discussion on pp. 31-32. :

“Further, a visual inspection of our data suggests the possibility that some of the developmental changes in gender stereotyping may be non-linear. The present study was not designed to differentiate possible developmental trajectories and did not sample ages densely enough to effectively do so (e.g., step-functions vs. logarithmic vs. quadratic vs. linear; see Adolph, 2008 for review). Thus, we encourage researchers to explore our dataset, and to conduct further research specifically aimed at detecting differences in the shape of change of gender stereotyping across the lifespan. In addition, we describe some of the qualitative patterns that emerged in our dataset below”

Regarding: “Critically, these findings are unlikely to be due to biases in the characteristics tested in our study: if they were, we would not expect the symmetrical rate of descriptive stereotypes.” (p. 28). I don’t quite follow this, can the authors provide a little more explanation of the logic here?

Upon revisiting that paragraph, we also are unsure what we intended with this sentence -- we have removed it from the manuscript.

Submitted filename: DevTraj_Reponse_Sept.docx

Decision Letter 2

17 Jan 2022

PONE-D-20-25142R2

thank you for your revised manuscript above.  We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/ , click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at gro.solp@gnillibrohtua .

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact gro.solp@sserpeno .

Thank you for submitting your research to PLOS ONE.

Kind regards and stay safe and healthy in 2022,

Additional Editor Comments (optional):

Reviewer #2: All comments have been addressed

Reviewer #1: (No Response)

Reviewer #2:  Yes:  Rebecca Neel

Acceptance letter

15 Feb 2022

Dear Dr. Sullivan:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact gro.solp@sserpeno .

If we can help with anything else, please email us at gro.solp@enosolp .

Thank you for submitting your work to PLOS ONE and supporting open access.

PLOS ONE Editorial Office Staff

on behalf of

Dr. Alexander N. Sokolov

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Establishing the content of gender stereotypes across development

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Psychology, Skidmore College, Saratoga Springs, New York, United States of America

Roles Data curation, Software, Visualization, Writing – review & editing

Affiliation Angela Ciociolo Marketing and Design, Grafton, Massachusetts, United States of America

Roles Conceptualization, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing

• Jessica Sullivan, 
• Angela Ciociolo, 
• Corinne A. Moss-Racusin

• Published: July 12, 2022
• https://doi.org/10.1371/journal.pone.0263217
• Peer Review
• Reader Comments

Gender stereotypes shape individuals’ behaviors, expectations, and perceptions of others. However, little is known about the content of gender stereotypes about people of different ages (e.g., do gender stereotypes about 1-year-olds differ from those about older individuals?). In our pre-registered study, 4,598 adults rated either the typicality of characteristics (to assess descriptive stereotypes), or the desirability of characteristics (to assess prescriptive and proscriptive stereotypes) for targets who differed in gender and age. Between-subjects, we manipulated target gender (boy/man vs. girl/woman) and target age (1, 4, 7, 10, 13, 16, or 35). From this, we generated a normed list of descriptive, prescriptive, and proscriptive gender-stereotyped characteristics about people across the early developmental timespan. We make this archive, as well as our raw data, available to other researchers. We also present preliminary findings, demonstrating that some characteristics are consistently ungendered (e.g., challenges authority), others are gender-stereotypic across the early developmental timespan (e.g., males from age 1 to 35 tend to be dirty), and still others change over development (e.g., girls should be submissive, but only around age 10). Implications for gender stereotyping theory—as well as targets of gender stereotyping, across the lifespan—are discussed.

Citation: Sullivan J, Ciociolo A, Moss-Racusin CA (2022) Establishing the content of gender stereotypes across development. PLoS ONE 17(7): e0263217. https://doi.org/10.1371/journal.pone.0263217

Editor: Alexander N. Sokolov, Eberhard-Karls-Universitat Tubingen Medizinische Fakultat, GERMANY

Received: August 11, 2020; Accepted: January 14, 2022; Published: July 12, 2022

Copyright: © 2022 Sullivan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data area available on our OSF page: https://osf.io/6r4ce/?view_only=d9c59e1237e045c2be571abd94b711b6 .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Gender plays an important role in daily life. While beliefs about gender differ within and across cultures [ 1 ]. Within particular cultures, there are often gender stereotypes (e.g., behaviors, characteristics, or attributes) that are deemed to be more normative and/or desirable for one gender than another [ 1 , 2 ]. Adults in the United States who violate gender stereotypes often experience social and/or economic penalties, commonly referred to as backlash [ 3 – 12 ]. For example, women who violate stereotypes by self-promoting on a job interview are less likely to be hired than identical men, while men who violate stereotypes by being self-effacing were less likely to be hired than identical women [ 8 ].

While the vast majority of existing work explores backlash against adults, recent research provided the first evidence that even gender-deviant children (in this case, preschoolers) experience backlash [ 13 ]: adults report liking gender non-conforming 3-year-olds less than their gender-conforming peers. This suggests that gender stereotypes have real-world consequences for adults and children alike. And yet, virtually nothing is known about how gender stereotypes change over development. Are stereotypic beliefs and expectations about young children the same as those about older children, adolescents, and adults? Are some traits consistently gendered across the lifespan, while others fluctuate? If they fluctuate, in what patterns? The present study investigates these questions, providing a novel assessment of how gender stereotypes change across the early developmental timespan.

Prior research demonstrates that, on average, adults in the United States believe that women should be communal (e.g., warm, supportive) and should not be dominant (e.g., aggressive, self-promoting); in contrast, men should be agentic (e.g., ambitious, independent) and should not be weak (e.g., passive, emotional) [ 12 ]. Violations of these gender stereotypes lead to backlash for adults in the U.S. (see [ 12 ] for a discussion). While the vast majority of empirical research on backlash has been conducted with participants in the United States, the limited data collected with participants in other countries including Australia [ 14 ], France [ 15 ], India [ 16 ], and China [ 17 ] reveal largely similar patterns (although more cross-cultural work is badly needed; see [ 18 ] for review).

Given the current data, it is unclear whether children in the United States, e.g., 1-year-old boys and girls, are held by adults to the same standards. Are they? If not, when in a child’s development do adults begin to apply gender stereotypes? Are there previously undocumented gender-stereotypes that apply only in childhood? More broadly, do gender stereotypes remain stable as children age, or do they fluctuate across development? In the present study, we asked adults to make judgments about the typicality and desirability of a large number of characteristics for targets of a wide variety of ages. This allowed us to characterize not only the presence or absence of a particular gender stereotype but also the developmental trajectories of these stereotypes.

In general, there are three types of gender stereotypes, each of which we measured in our study. Descriptive stereotypes involve characteristics that are thought to be typical of a particular gender [ 19 ]. For example, women in the United States are typically viewed as more self-aware and more anxious than men, while men are typically viewed as more extroverted and forgetful than women [ 2 ]. While individuals who violate descriptive stereotypes may surprise others, these individuals generally do not encounter backlash [ 19 ]. However, the same is not true for individuals who violate prescriptive and proscriptive stereotypes. Prescriptive stereotypes describe how members of a particular gender should behave. For example, women should be communal (e.g., cheerful, patient, and interested in children), while men should be agentic (e.g., athletic, ambitious, and assertive; [ 2 , 12 ]). Proscriptive stereotypes are those that describe how members of a particular gender should not behave. For example, women should not be dominant (e.g., stubborn or rebellious), while men should not be weak (e.g., emotional or yielding; [ 2 , 12 ]). Of importance, people who violate prescriptive and proscriptive stereotypes typically encounter social and economic penalties (i.e. backlash; [ 12 , 20 ]).

A large body of work has sought to characterize the content of descriptive, prescriptive, and proscriptive stereotypes about adult men and women. For example, Social Role Theory [ 21 , 22 ] posits that stereotypes about men and women stem directly from the sex-differentiated social roles traditionally occupied by men relative to women. Due to biological predispositions in early evolutionary history, men were more likely to be hunters, and women gatherers. Over time, people ascribed role-consistent traits to men and women, and these stereotypes took on prescriptive as well as descriptive components. However, because children are not yet capable of occupying these adult roles, it is unclear how, when, and why gender stereotypes should be applied to them. Relatedly, the stereotype content model proposes that stereotypes about adults cluster around two core dimensions: competence and warmth [ 18 , 23 , 24 ]. People who are viewed as high in warmth and low in competence (e.g., elderly people, housewives) elicit paternalistic stereotypes because they are perceived as low-status and non-competitive [ 24 ]. In contrast, targets who are viewed as low in warmth and high in competence (e.g., feminists, wealthy people) are met with stereotypes characterized by envy. Indeed, according to the stereotype content model, one reason that stereotype-violating women encounter backlash is that they are perceived as both high-status and competitive and therefore threaten the existing power structure. A potential challenge for this model—and one motivation for creating the current database of gender stereotypes about targets across the lifespan—is that children, by virtue of their relative lack of power, are economically, physically and socially powerless (relative to adults) and therefore are rarely thought of as threatening to the existing social order. Why, then should they experience backlash? More generally, it’s not obvious that competence and warmth are the appropriate domains for characterizing children. In short, neither theory nor empirical data suggest that gender stereotypes about children and adults are necessarily identical or even similar.

In fact, the recent work that has attempted to characterize adults’ gender stereotypes about children [ 13 , 25 ] has found that gender stereotypes about children appear to differ—at least in some ways—from those about adults. One recent study found that gender stereotypes about appearance, toy preference, and communality may be present for toddlers but that many other types of stereotypes that apply to adults may not apply to young children [ 25 ]. For example, although adult men and elderly men are described as more intelligent than women, toddler girls and elementary-aged girls are described as more intelligent than boys [ 25 ]. This study provides an exciting and promising window into understanding how gender stereotypes differ across the lifespan; however, because it elicited ratings only for developmental categories (e.g., “adolescents (ages 12–18)”), it does not allow us to draw conclusions about developmental trajectories. Another recent study found that the gender stereotypes that apply to 3-year-old children are often meaningfully different from those that apply to adults: unlike for adults, traits that were rated as most typical for boys were rated as undesirable, and stereotypes about children were more likely to center around appearance than is typical for adults [ 13 ]. However, it is not possible to know whether some of these apparent developmental differences can be attributed to changes in gender stereotypes across the developmental timecourse, or whether they can instead be attributed to methodological differences across studies (e.g., in the stereotypes tested; in sample size; c.f. [ 25 ] which addresses some of these issues). More generally, these studies provide a promising starting point but do not provide a large dataset for future researchers to utilize and do not allow us to characterize the developmental trajectory of gender stereotypes.

Our study—which will catalogue gender stereotypes across development (e.g., ages 1–35)—is consequential for at least four reasons. First, in order for our general theories of gender stereotyping (e.g., Social Role Theory (e.g., [ 21 , 22 ]) and the Stereotype Content Model [ 23 , 24 ]) to be useful for understanding and predicting children’s learning about gender stereotypes, they must fit the data not only for adults but also for children. Second, in order to effectively study and predict gender backlash [ 13 , 26 ], it is critical that we first understand the stereotypes that underlie backlash. Third, most theoretical approaches assume that gender stereotypes are learned; this implies that stereotypes could and should change over development, although there is very little data to speak to this. Fourth, from a practical perspective, adults interact with others (including children) throughout the early developmental timespan; parents, educators, and policy-makers would do well to understand the nature of the gender stereotypes that might be guiding their interactions. While evidence does suggest that people appear to encounter backlash for violating gender stereotypes across adulthood and childhood alike (e.g., [ 13 ]), for most ages research has done little to identify what these childhood gender stereotypes—and thus, their violations—even are . While we know that backlash exists, the nature of childhood gender stereotypes remain unclear, so we cannot yet predict the circumstances under which it is likely to occur across the lifespan.

In the present study, we measured adults’ gender stereotypes about infants (age one), children (ages 4, 7 and 10), adolescents (age 16), and adults (age 35). To do this, we measured gendered stereotypes about targets that ranged in age from 1 to 35. We present a database of normed gender stereotypes along with pre-registered findings generated by this study. This database fills a critical gap in the literature and will provide a set of developmental norms for researchers interested in gender development, gender backlash, and gender stereotypes.

All materials, methods, and analyses were approved via Skidmore College’s IRB, and pre-registered ( https://osf.io/7ahks ).

Participants

Our target sample size pre-exclusions was 4,900, which we requested via TurkPrime [ 27 ]; this number was selected in order to ensure that we had approximately 100 participants per cell of our design. Participants were native English speakers who were aged 18+, who had at least a 97% approval rating for prior Mechanical Turk HITs, and who had between 100–10,000 HITs. In total, 5,260 participants consented. We did not collect demographic data from our participants and therefore cannot assess the extent to which they are representative of the general population of the United States. Consistent with our pre-registration, we excluded participants who failed to complete at least 80% of the questions ( n = 308), and who failed any attention check ( n = 354). This resulted in a final N of 4,598.

The current study utilized a 2 [target gender: male, female] x 2 [rating type: pre/proscriptive, descriptive] x 7 [target age: 1, 4, 7, 10, 13, 16, 35] between-subjects design. This is in contrast to previous work that has manipulated these factors within-subjects [ 25 ]. In addition, our use of particular ages (e.g., “seven-year-old”) contrasts with that in previous work, which has elicited clusters of ratings (e.g., “elementary-school boys”; [ 25 ]).

We randomly assigned participants to conditions and to items within conditions. There were 175 characteristics and five attention checks, and participants were randomly assigned to see approximately 60% of the items. For each condition (e.g., for ratings of the desirability of characteristics for one-year-old boys), we obtained an average of 160 usable participants (min = 137, max = 187). For each characteristic (e.g., “pretty”), we obtained an average of 102 ratings per cell.

Materials and procedure

Our bank of characteristics consisted of 175 unique items from previous work [ 2 , 12 , 13 , 28 ]. These included behaviors (e.g., wrestles), traits (e.g., pretty), preferences (e.g., loves pink), and appearance-related items (e.g., wears tutus).

Participants first viewed instructions as follows: “Today you will be answering questions about how [common or typical / desirable] you think particular traits are among [age] [boys/girls/men/women]”. For example, participants in one condition saw: “Today you will be answering questions about how [common or typical/desirable] you think particular traits are among 1-year-old boys”.

Participants then rated each characteristic on a 1–9 Likert-Style scale with 1 indicating “not at all X” (where X was either “desirable” or “common/typical”) and 9 indicating “very X”; 5 was labeled as “Neutral.” They were reminded of the instructions: “Indicate how typical/desirable it is in American society for [age, gender] to possess each of the following characteristics. [Scale = 1–9; 1 = Not at all Typical/Desirable; 9 = Very Typical/Desirable]”. Characteristics were randomly ordered and randomly sampled from the body of possible characteristics described above. After rating each characteristic, we collected participants’ ages and genders.

As pre-registered (and as noted above), we excluded participants who were unlikely to be attending to the task by not answering enough questions or by incorrectly completing comprehension checks. We also excluded from analyses two items that were mistakenly included in our battery: “doesn’t wait his turn” (since it accidentally included a gendered pronoun) and “is clean” (since we also had the item “clean”).

Two primary goals of the current work were to develop a database of gender stereotypes across the developmental early timespan and to provide these data to other researchers. To this end, our data are available on our OSF page (https://osf.io/9pgkd/).

We had three additional specific goals, each of which was pre-registered: (1) to identify items that were and weren’t consistently gendered across the early developmental timespan (e.g., to find a list of gender stereotypes that characterize girls and women throughout development); (2) to identify items for which stereotypes changed over the early developmental timespan (e.g., items that are stereotypical at young ages but not at older ages); and (3) to identify items for which there were stereotypes at particular ages (e.g., to find a list of all gender stereotypes for 1-year-olds).

As pre-registered, we constructed linear models that predicted ratings for each characteristic from target gender (boy/girl), age (continuous), and their interaction. This allowed us to identify items where gender interacted with age (indicating that the presence and/or nature of the gender stereotype changed over development; these data are discussed later) and also items that were consistently gendered (i.e. there was no interaction with age, but rather a simple effect of gender).

Items that were not gender-stereotyped

We first report items for which we found no effect of target gender and no interaction of gender and age. In other words, these were the items for which—when considering the entirety of our dataset—we had no evidence, at any age, of gender stereotyping. For ratings of typicality, 29/175 (16.6%) characteristics that showed no effect of gender are depicted in Table 1 . For ratings of desirability, 59/175 characteristics (33.71%) showed no effect of gender as depicted in Table 2 . In other words, there is no evidence that these 29 traits constitute descriptive gender stereotypes ( Table 1 ), or that these 59 traits constitute prescriptive or proscriptive gender stereotypes ( Table 2 ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0263217.t001

https://doi.org/10.1371/journal.pone.0263217.t002

Stereotypes that were consistent across development

Ratings of typicality (i.e., descriptive stereotypes)..

We first considered items for which we found a simple effect of target gender (For brevity, we do not report main effects of age in the main paper, although readers may access our data in our repository (https://osf.io/9pgkd/); items described here with a main effect of gender may also have shown main effects of age; only those items for which (a) there was an age*gender interaction (discussed later) or (b) no main effect of gender are excluded from the reporting below.). Our preliminary analysis revealed 93 items for which there was a simple effect of target gender (and no interaction with age) on ratings of typicality. In other words, ratings of typicality for these items differed depending on whether the target was a boy or a girl, and this gender difference did not depend on target age. As pre-registered, we identified the items for which the Cohen’s d effect size of the comparison of ratings for boys vs. girls was larger than 0.4; this is in keeping with past work [ 2 , 13 ]. This yielded 25 stereotypes about typicality that persisted across the developmental timeline; 15 of these met our pre-registered criteria for being descriptive stereotypes (effect size larger than 0.4 and a mean typicality above 6), while 10 did not (these met our effect size criteria but not the mean rating criteria and therefore were simply relatively more common for one gender than the other; we thus refer to them as “more typical” rather than “descriptive”). These are displayed in Tables 3 and 4 .

https://doi.org/10.1371/journal.pone.0263217.t003

https://doi.org/10.1371/journal.pone.0263217.t004

https://doi.org/10.1371/journal.pone.0263217.t005

Ratings of desirability (i.e., Prescriptive and proscriptive stereotypes)

Our preliminary analysis also revealed 85 items that showed a simple effect of gender for ratings of desirability. In other words, these items were rated as consistently more desirable for one gender than the other, and this gender difference did not depend on target age. Of these, 12 met our threshold for effect size ( Table 5 ). We found 4 items that were prescriptive for boys/men and 2 for girls/women; these were the characteristics for which there was an effect size of at least 0.4 and a desirability rating above 6. We also found 2 items that were more desirable for boys/men and 1 for girls/women ( n = 1); these characteristics met our threshold for effect size but were neither rated as especially desirable (rating above 6) or undesirable (rating below 4); we refer to these as “more desirable” rather than “prescriptive.” Finally, we found traits that were proscriptive for girls/women ( n = 2), and traits that were proscriptive for boys/men ( n = 1); these characteristics met our threshold for effect size and had a mean desirability rating below 4.

In our previous work, we demonstrated that characteristics descriptive of 3-year-old boys also tended to be rated as undesirable (i.e., below the midpoint of our 9-point desirability scale), while the opposite was true for 3-year-olds girls [ 13 ]. We extend this finding in the present dataset; the mean desirability rating for the desirable characteristics in Table 5 for boys was 4.29 (i.e., undesirable), while it was 6.08 (i.e. desirable) for girls; these values differed significantly ( p = .003). These data suggest that the characteristics that describe boys/men are rated as less desirable than those that describe girls/women (and, in fact, are rated as undesirable) across the lifespan. Further, we highlight that there were noticeably fewer traits viewed as consistently typical for boys/men (8) than for girls/women (17).

Stereotypes that change over development

We next consider the characteristics for which we found a significant gender by age interaction. These were the items for which the magnitude of the gender gap changed across the developmental timeline—in other words, traits that are gender stereotypic, but as a function of target age. We found 43 characteristics where gender differences in ratings of typicality interacted with age ( Table 6 ) and 22 characteristics where gender differences in ratings of desirability interacted with age ( Table 7 ).

https://doi.org/10.1371/journal.pone.0263217.t006

https://doi.org/10.1371/journal.pone.0263217.t007

Our next goal was to understand the nature of these interactions. To do this, as pre-registered, we visualized each interaction. We then exploratorily qualitatively clustered characteristics based on shared developmental patterns. To do this, the lead author clustered the visualizations (see Fig 1 ) based on visual similarity, and the other two authors checked the clustering. Due to the subjective and qualitative nature of this classification process, the resulting clusters should be interpreted as useful ways of digesting our otherwise exceptionally dense dataset, and as helpful jumping-off points for future research. Fig 1 defines and depicts each cluster. Every characteristic and its cluster are depicted in Tables 6 (for ratings of typicality) and 7 (ratings of desirability).

https://doi.org/10.1371/journal.pone.0263217.g001

Stereotypes at each age

As pre-registered, for each age, we classified each item according to whether it met the criteria for being a descriptive (mean rating of at least 6 for one gender, effect size of at least .4 for the difference in ratings for boys/men vs. girls/women), prescriptive (mean rating of at least 6 for one gender, effect size of at least .4 for the difference in ratings for boys/men vs. girls/women), or proscriptive stereotype (mean rating of less than 4 for one gender, effect size of at least .4 for the difference in ratings for boys/men vs. girls/women). We did this for every item whether or not there was a significant interaction of age and gender in the analyses above; this was because we decided, a priori , that it was important to identify individual stereotypes at each age. Of course, as with all situations with such a large number of comparisons, even though our threshold was not statistical significance, we encourage readers to be cautious in interpreting each particular effect as it is likely that some of these effects emerged by chance alone.

This process of classification had three main outcomes. First, we assessed how many characteristics were classified as gender stereotypes at each age (see Fig 2 ). Interestingly, stereotypes were most frequent between the ages of 7 and 16, peaking at age 10. While the rate of prescriptive stereotypes appeared approximately the same across the developmental timespan, proscriptive and descriptive stereotypes were most frequent during childhood in our dataset. In other words, for female targets, there were more stereotypes applied to 7-year-olds than to adults, to 10-year-olds than to adults, to 13-year-olds than to adults, and to 16-year-olds than to adults. This is particularly striking because much of the existing research has focused on understanding gender stereotypes only about adults.

Notes . Y-axis is a count of stereotypes that met our pre-registered stereotype threshold. Black indicates prescriptive stereotypes, dark gray indicates proscriptions, and light gray indicates descriptive stereotypes. Note that some characteristics are double-counted (e.g., if an item was a descriptive and prescriptive stereotype at a particular age, it contributes to both the descriptive count and the prescriptive count). These data suggest that gender stereotypes are prevalent across the developmental timeline, and that children—not adults—may be subject to the most gender stereotypes.

https://doi.org/10.1371/journal.pone.0263217.g002

Next, we explored whether there were more de-, pre-, or pro-scriptions for boys/men relative to girls/women and whether the frequencies of these stereotypes changed over the early developmental timespan ( Table 9 ). We found that a larger proportion of descriptive stereotypes were about girls/women (65.4%) than about boys/men (34.6%; p < .0001). A larger proportion of proscriptive stereotypes were about boys/men (72.3%; p < .0001) than about girls/women (27.7%); there were no effects of age on the distribution of stereotypes in either of these cases (all p >.10). Interestingly, this gendered asymmetry did not emerge for prescriptive stereotypes, which were equally frequent for boys/men (49.6%) and girls/women (50.4%, p = .93). Again, there were no effects of age on the gendered distribution of these stereotypes. These data suggest that while girls/women may consistently be subject to a relatively higher proportion of descriptive stereotypes, boys/men are subject to a higher proportion of proscriptive stereotypes throughout the lifespan.

The final outcome is a table of all items that meet the criteria for being de-, pre-, or pro-scriptive at each age range. These are available in our repository. To illustrate these findings for one age group, in Tables 8 and 9 , we pull out all descriptive ( Table 8 ) and prescriptive ( Table 9 ) stereotypes for one-year-olds.

https://doi.org/10.1371/journal.pone.0263217.t008

https://doi.org/10.1371/journal.pone.0263217.t009

In the present study, we measured adults’ stereotypes about male and female targets across the early developmental timespan (i.e. from infancy through early adulthood). To do this, we presented over 4,000 adults with a list of characteristics and asked them to rate either the desirability or typicality of those characteristics. Critically, participants rated the characteristics for targets that were either male or female and that were either 1, 4, 7, 10, 13, 16, or 35-years-old. This allowed us to develop the largest known normed database of gender stereotypes and to shed light on several questions about how descriptive, prescriptive, and proscriptive gender stereotypes change across the developmental timeline.

Rather than demonstrating stable stereotypic expectations for boys, girls, men, and women throughout the lifespan, our data revealed numerous developmental trends in the nature of gender stereotypes. First, items that were consistently gendered (main effects) were very rare; less than 10% of our items were consistently rated as being descriptive or pre/pro-scriptive stereotypes. Further, 29 characteristics were never descriptive of either gender, and 59 were never pre/pro-scriptive of either gender. These data suggest that theories of gender stereotypes need to take into account the fact that stereotypes are applied differently to targets of different ages—an idea that has not received significant attention in the literature thus far (to our knowledge).

The existence of a sizable subset of ungendered characteristics suggests that demand characteristics were unlikely to be responsible for our findings. In interpreting these results, it is important to note that we selected each of our 175 target characteristics from the existing literature [ 2 , 12 , 13 , 28 ]—these were items for which we had strong reason to believe that stereotypes might emerge. Indeed, for some items that are considered relatively central to defining particular gender stereotypes, we found no effects whatsoever of gender (e.g., there were no gendered effects on ratings of desirability for items like helping , wholesome , is a leader , bossy , challenges authority , controlling , moody , friendly , good listener , competent or polite , all of which are items that previous work has suggested may be gendered). These data highlight the importance of empirically testing the presence or absence of gendered stereotypes one-at-a-time (in contrast to some previous work, which has clustered traits; see [ 25 ]).

While there was a sizeable subset of traits for which there was no evidence of gender stereotyping, the majority of traits did show some evidence of gendering (it is important to note that many of these effects, while statistically significant, did not reach our pre-registered effect size criteria and therefore are not reported in our main paper; they are available in our repository). Together, our data strongly suggest that most characteristics were stereotyped and that gender stereotypes change over development. Even given our stringent criteria, 43 characteristics showed significant age by gender interactions for ratings of typicality, and 22 characteristics showed significant age by gender interactions for ratings of desirability (We wish to note, of course, that “desirability” is a complex construct and certainly not a construct that is likely to be fully addressed via a single likert-scale question. We use this terminology in order to most closely approach the ways in which previous work (e.g., Prentice & Carranza, 2002) has discussed prescriptive and proscriptive gender stereotypes.). These data highlight the importance of taking a developmental approach to studying gender stereotypes. After all, our theories of the development of gender stereotypes will necessarily differ depending on whether a particular stereotype persists throughout the lifespan, emerges only in adulthood, peaks at the onset of puberty, or displays some other pattern.

Below, we discuss some of the more important developmental changes that we identified. Future research should further explore the nature of and mechanisms underscoring these changes. Additionally, we hope that other researchers will find our database immediately useful in informing the development of new research materials. For example, researchers interested in backlash targeting young adolescents will likely wish to manipulate the gender typicality of targets’ traits, but could not previously be certain about which traits are actually viewed as gender stereotypic for this particular age group. Thus, our database now enables the development of evidence-based stimulus materials conveying the gender (counter)stereotypicality of targets across the developmental timespan.

As an important first step, we qualitatively and exploratorily classified the several developmental patterns that emerged in our data. These classifications were driven by the shape of the data and not by any theoretical expectations about which stereotypes might fall into each category. Because these clusters were generated exploratorily and subjectively, we encourage future researchers to use these primarily to motivate future confirmatory research and to generate testable theories. Further, a visual inspection of our data suggests the possibility that some of the developmental changes in gender stereotyping may be non-linear. The present study was not designed to differentiate possible developmental trajectories and did not sample ages densely enough to effectively do so (e.g., step-functions vs. logarithmic vs. quadratic vs. linear; see [ 29 ] for review). Thus, we encourage researchers to explore our dataset, and to conduct further research specifically aimed at detecting differences in the shape of change of gender stereotyping across the lifespan. In addition, we describe some of the qualitative patterns that emerged in our dataset below.

First, we note the presence of the Gender Differences Emerge Late category of stereotypes; these can be found in Fig 1 and towards the bottom of Tables 6 and 7 . These are items for which we identified gender stereotypes in adulthood but found that these stereotypes were minimal or absent for younger targets (e.g., ambitious, submissive). These items are important because they shed light on the existing adult gender literature in that some of these items are critical to existing theories of gender development.

The fact that there are any characteristics for which the direction of a significant perceived gender gap switches throughout the lifespan (i.e., the Gender Difference Switches cluster) is a particularly novel and surprising revelation. Additionally, it is noteworthy that some gender stereotypes appear to be strongest in adolescence (i.e., the Adolescent Gender Differences—Boost and Adolescent Gender Differences—Reduction clusters) or in childhood (the Childhood Gender Differences cluster), or in only one age group (e.g., Gender Differences Fluctuate) . We have no theoretical account for these particular clusters at this moment and note that none of the clusters cleave neatly along existing theoretical lines.

Notably, we found 20 stereotypes that apply even to 1-year-old children. Of importance, from a purely developmental perspective, some of these characteristics could be difficult for children in this age group to display. For example, our data revealed that 1-year-old boys should be “athletic” and “love sports” and cannot “wear tutus” or “love pink.” It is unclear how a 1-year-old boy (who may not yet be walking and is unlikely to be talking) could adequately convey their athleticism and enthusiasm for sports, or their disdain for a color category they likely have no cognitive appreciation for and an article of clothing they are unlikely to have selected themselves. Similarly, 1-year-old girls should be “graceful” and “like princesses,” and should not be “dirty” or “challenge authority.” One might reasonably expect that infants and toddlers are too young to be constrained by these sorts of expectations, and that instead, adults would simply focus on whether very young children are healthy and meeting appropriate developmental milestones. Indeed, from this perspective, it is noteworthy that any characteristics emerged as prescriptive and proscriptive for 1-year-olds . Certainly, the current work makes the novel contribution of demonstrating that even infants appear to experience the effects of gender stereotyping.

Our results also clearly suggest that the stereotypes that individuals are faced with change over development. Of importance, existing theories of gender stereotypes were not created and thus, are unlikely to be able to account for these changes. For example, both backlash theory [ 30 ] and the Stereotype Content Model [ 23 , 24 ] emphasize that adult men are expected to exhibit agentic, competence-related traits while women are expected to display communal, warmth-related traits. However, when examining the pre- and proscriptions, we uncovered that for young children it is not apparent that warmth and competence are the primary stereotypic dimensions relevant for classifying children. Instead, consistent with our prior work [ 13 ], young children’s pre- and proscriptions appear to be much more linked to appearance (e.g., clothing choices) and overt, developmentally-relevant behaviors (e.g., play preferences). This suggests that it will be important to expand the existing literature to think more broadly about the nature, fluctuation, and impact of gender stereotypes throughout the lifespan. We hope that future researchers will utilize our developmental data to hone their theories of and predictions about the origins and time course of gender stereotypes.

While much of the adult literature has focused on stereotypes about women, our data show several ways in which boys and men may experience negative gender stereotyping. First, we found that the stereotypes that were considered typical of boys and men were also more often rated as unfavorable (see also [ 13 ]); this was true across the developmental timespan studied. Second, we found that across the timespan studied, there were more proscriptive stereotypes for boys/men than for girls/women. These results build upon a growing body of work demonstrating that gender stereotypes can have profound consequences for men as well as women (for a discussion, see [ 4 ]). For example, men appear to encounter backlash when they violate gender stereotypes by expressing interest in female gender-typed careers [ 5 ], behave modestly on a job interview [ 6 ], or disclose their emotions [ 31 ]. Further, recent work has shown that adults’ reactions to 3-year-old boys who violate gender stereotypes may be particularly harsh relative to same-aged girls who violate gender stereotypes [ 13 ]. Taken together, these findings suggest that future work should continue to consider the ways in which gender stereotyping impacts perceptions of targets across the gender spectrum.

While the analyses reported here were pre-registered, we nevertheless consider them to be exploratory: we didn’t have strong predictions about which stereotypes would persists across development (e.g., we found that boys/men are more rowdy and competitive, girls/women are more flatterable and caring), which peak in adolescence (e.g., girls cry more than boys; boys have a bigger appetite than girls), which would show stereotype vacillations across the timespan (e.g., boys/men are only sometimes more stingy than girls/women), and which would show no substantial gender stereotypes at all (e.g., neither gender is more bratty, stubborn, materialistic, rational, weak, or independent). While it may be tempting to believe that some of the developmental patterns that we demonstrate are the result of noise in the data, we believe it is unlikely that the patterns we see are false positives. First, our sample sizes are large: each datapoint (e.g., the mean rating of typicality for intelligent for 3-year-olds boys) consists of 100 ratings. While it is possible for noise to be (erroneously) treated as signal, we believe that our high-powered design has likely revealed many provocative patterns of the development of gender stereotypes that were undetected in prior studies. Second, we pre-registered our data collection techniques and analyses and relied on measuring effect sizes (in addition to null hypothesis significance testing), reducing the likelihood that the patterns in our data emerged due to questionable research practices or because our design was overpowered. For these reasons, we hope future researchers will take seriously both the predicted and surprising developmental findings reported in our dataset.

We wish to note two major limitations of this study. First, we treated gender as a binary, when we know that gender is actually a continuum (see [ 32 ] for review). Tellingingly, none of our participants noted any concerns about our binarization of gender. While we do not believe that a binarized view of gender is the right one, we do believe that the average adult in the United States assumes it to be, and our participants were familiar with and able to discuss gender in a binary way. To this point, we also note that we only sampled adults in the United States. We have no reason to believe that these stereotypes generalize to other cultural contexts, and indeed, this calls for additional cross-cultural research that can provide culturally-specific information about the content of descriptive, prescriptive, and proscriptive gender stereotypes across the lifespan.

In sum, we provide a novel and rich resource for future researchers cataloging the content of gender stereotypes across the early developmental timespan. The current results highlight the importance of expanding current theories of gender stereotyping to include developmental perspectives. Simply put, current theories of gender stereotyping may be specific to one point in development (i.e., adulthood). While this is useful for informing our understanding of the ways in which gender stereotypes about adults impact perceptions of adults, additional work is needed to shed light on the ways in which gender stereotypes shape and constrain social perceptions and experiences across the lifespan. Our analyses suggest several fruitful specific directions for new programs of research (e.g., focusing on the impacts of stereotyping on infants and their caregivers; emphasizing research on boys; using developmental trajectories to inform conceptual accounts of stereotyping), and we hope that researchers will use our dataset as a resource to inform both their theoretical and empirical future work.

• View Article
• PubMed/NCBI
• Google Scholar
• 32. Fausto-Sterling A. (2002). Sexing the Body : Gender politics and the construction of sexuality . Basic Books: New York.

• Search Menu
• Sign in through your institution
• Author Guidelines
• Submission Site
• Self-Archiving Policy
• Why Submit?
• About Journal of Computer-Mediated Communication
• About International Communication Association
• Editorial Board
• Advertising & Corporate Services
• Journals Career Network
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

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

• < Previous

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.

• Article contents
• Figures & tables
• Supplementary Data

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

• Permissions Icon Permissions

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.

Baker S. A. , Walsh M. J. ( 2023 ). ‘A mother’s intuition: it’s real and we have to believe in it’: How the maternal is used to promote vaccine refusal on Instagram . Information, Communication & Society , 26 ( 8 ), 1675 – 1692 . https://doi.org/10.1080/1369118X.2021.2021269

Google Scholar

Bamman D. , Eisenstein J. , Schnoebelen T. ( 2014 ). Gender identity and lexical variation in social media . Journal of Sociolinguistics , 18 ( 2 ), 135 – 160 . https://doi.org/10.1111/josl.12080

Bobel C. ( 2010 ).  The Paradox of natural mothering.  Temple University Press.

Brooks M. E. , Kristensen K. , Van Benthem K. J. , Magnusson A. , Berg C. W. , Nielsen A. ... & Bolker B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R journal , 9 (2), 378-400. 378 – 400 . https://doi.org/10.3929/ethz-b-000240890

Brossard D. , Scheufele D. A. ( 2013 ). Science, new media and the public . Science , 339 ( 6115 ), 40 – 41 . https://doi.org/10.1126/science.1232329

Brouwer J. , Kawakami K. , Rojahn K. , Postmes T. T. ( 1997 ). De effecten van de (on) zichtbaarheid van sekse als categorie op zelfstereotypering [The effects of gender (in)visibility on self-stereotyping]. In De Dreu C. K. W. , de Vries N. K. , Van Knippenberg D. , Rutte C. (Eds.), Fundamentele sociale psychologie (Vol. 11 , pp. 150 – 157 ). Tilburg University Press .

Google Preview

Butkowski C. P. , Dixon T. L. , Weeks K. R. , Smith M. A. ( 2020 ). Quantifying the feminine self (ie): Gender display and social media feedback in young women’s Instagram selfies . New Media & Society , 22 ( 5 ), 817 – 837 . https://doi.org/10.1177/1461444819871669

Canfield K. N. , Menezes S. , Matsuda S. B. , Moore A. , Mosley Austin A. N. , Dewsbury B. M. , Feliú-Mójer M. , McDuffie K. W. B. , Moore K. , Reich C. A. , Smith H. M. , Taylor C. ( 2020 ). Science communication demands a critical approach that centers inclusion, equity, and intersectionality . Frontiers in Communication , 5 ( 2 ), 1 – 8 . https://doi.org/10.3389/fcomm.2020.00002

Carli L. L. , Alawa L. , Lee Y. , Zhao B. , Kim E. ( 2016 ). Stereotypes about gender and science: Women≠scientists . Psychology of Women Quarterly , 40 ( 2 ), 244 – 260 . https://doi.org/10.1177/0361684315622645

Casas A. , Webb Williams N. ( 2022 ). Introduction to the special issue on images as data . Computational Communication Research , 4 ( 1 ), 1 – 10 . https://doi.org/10.5117/CCR2022.1.000.CASA

Center for an Informed Public, Digital Forensic Research Lab, Graphika, & Stanford Internet Observatory ( 2021 ). The long fuse: Misinformation and the 2020 election. Stanford Digital Repository: Election Integrity Partnership. v1.3.0. Retrieved Dec 18, 2023, from https://purl.stanford.edu/tr171zs0069

Chen K. , Jeon J. , Zhou Y. ( 2021 ). A critical appraisal of diversity in digital knowledge production: Segregated inclusion on YouTube . New Media & Society , 25 ( 11 ), 2855-2876. https://doi.org/10.1177/14614448211034846

Chen A. , Chen K. , Zhang J. , Meng J. , Shen C. ( 2023 ). When national identity meets conspiracies: The contagion of national identity language in public engagement and discourse about COVID-19 conspiracy theories . Journal of Computer-Mediated Communication , 28 ( 1 ), 1–12. https://doi.org/10.1093/jcmc/zmac034

Chinn S. , Hiaeshutter-Rice D. , Chen K. ( 2023 ). How digital science influencers polarize supportive and skeptical communities around politicized science: A cross-platform and over-time comparison . Political Communication . https://doi.org/10.1080/10584609.2023.2201174

Duan Z. , Shao A. , Hu Y. , Lee H. , Liao X. , Suh Y. , Kim J. , Yang K. C. , Chen K. , Yang S. ( 2023 ). Constructing Vec-tionaries to Extract Latent Message Features from Texts: A Cast Study of Moral Appeals. arXiv preprint arXiv:2312.05990 .

Data Reportal ( 2023 , April 10). Essential TikTok statistics and trends for 2023 . https://datareportal.com/essential-tiktok-stats

Ellemers N. ( 2018 ). Gender stereotypes . Annual Review of Psychology , 69 , 275 – 298 . https://doi.org/10.1146/annurev-psych-122216-011719

Evans S. K. , Pearce K. E. , Vitak J. , Treem J. W. ( 2017 ). Explicating affordances: A conceptual framework for understanding affordances in communication research . Journal of Computer-Mediated Communication , 22 ( 1 ), 35 - 52 . https://doi.org/10.1111/jcc4.12180

Ford M. R. , Lowery C. R. ( 1986 ). Gender differences in moral reasoning: A comparison of the use of justice and care orientations . Journal of Personality and Social Psychology , 50 ( 4 ), 777 . https://doi.org/10.1037/0022-3514.50.4.777

Geise S. , Baden C. ( 2015 ). Putting the image back into the frame: Modeling the linkage between visual communication and frame-processing theory . Communication Theory , 25 ( 1 ), 46 – 69 . https://doi.org/10.1111/comt.12048

Global Media Insight ( 2023 , April 10). YouTube users statistics 2023 . https://www.globalmediainsight.com/blog/youtube-users-statistics/#demographics

Hogg M. A. , Hains S. C. ( 1996 ). Intergroup relations and group solidarity: Effects of group identification and social beliefs on depersonalized attraction . Journal of Personality and Social Psychology , 70 ( 2 ), 295 . https://doi.org/10.1037/0022-3514.70.2.295

Hopp F. R. , Fisher J. T. , Cornell D. , Huskey R. , Weber R. ( 2021 ). The extended Moral Foundations Dictionary (eMFD): Development and applications of a crowd-sourced approach to extracting moral intuitions from text . Behavior Research methods , 53 , 232 – 246 . https://doi.org/10.3758/s13428-020-01433-0

Huddy L. , Terkildsen N. ( 1993 ). Gender stereotypes and the perception of male and female candidates . American Journal of Political Science , 37 ( 1 ), 119 – 147 . https://doi.org/10.2307/2111526

Kennedy E. H. , Dzialo L. ( 2015 ). Locating gender in environmental sociology . Sociology Compass , 9 ( 10 ), 920 – 929 . https://doi.org/10.1111/soc4.12303

Kim S. J. , Chen K. ( 2022 ). The use of emotions in conspiracy and debunking videos to engage publics on YouTube . New Media & Society , 0 ( 00 ), 1 – 22 . https://doi.org/10.1177/14614448221105877

Kim S. J. , Villanueva I. I. , Chen K. ( 2023 ). Going beyond affective polarization: How emotions and identities are used in anti-vaccination TikTok videos . Political Communication , 1 – 20 . https://doi.org/10.1080/10584609.2023.2243852

Li J. , Su M. H. ( 2020 ). Real talk about fake news: Identity language and disconnected networks of the US public’s “fake news” discourse on Twitter . Social Media+ Society , 6 ( 2 ), 1–14. https://doi.org/10.1177/2056305120916841

Lu Y. , Shen C. ( 2023 ). Unpacking multimodal fact-checking: Features and engagement of fact-checking videos on Chinese TikTok (Douyin) . Social Media+ Society , 9 ( 1 ), 1–16. https://doi.org/10.1177/20563051221150406

McKinnon M. , O’Connell C. ( 2020 ). Perceptions of stereotypes applied to women who publicly communicate their STEM work . Humanities and Social Sciences Communications , 7 ( 1 ), 1 – 8 . https://doi.org/10.1057/s41599-020-00654-0

Meco D. L. , Wilfore K. ( 2021 , March 8). Gendered disinformation is a national security problem. Brookings . https://www.brookings.edu/articles/gendered-disinformation-is-a-national-security-problem/

Miller David I. , Eagly Alice H. , Linn Marcia C. ( 2015 ). Women’s representation in science predicts national gender-science stereotypes: Evidence from 66 nations. Journal of Educational Psychology . 107 ( 3 ) 631 – 644 . https://doi.org/10.1037/edu0000005

Niazi F. , Inam A. , Akhtar Z. ( 2020 ). Accuracy of consensual stereotypes in moral foundations: A gender analysis .  PloS One ,  15 ( 3 ), e0229926 . https://doi.org/10.1371/journal.pone.0229926

Operario D. , Fiske S. T. ( 2004 ). Stereotypes: Content, structures, processes, and context. In Brewer M. B. , Hewstone M. (Eds.), Social cognition (pp. 120 – 141 ). Blackwell Publishing .

Papadimoulis D. ( 2018 , April 10). GENDERNEUTRAL LANGUAGE in the European Parliament. European Parliament . https://www.europarl.europa.eu/cmsdata/151780/GNL_Guidelines_EN.pdf

Parker K. , Horowitz J. M. , Stapler R. ( 2017 , December 5). Americans see different expectations for men and women . Pew Research Center. https://www.pewresearch.org/social-trends/2017/12/05/americans-see-different-expectations-for-men-and-women/

Proust V. , Saldaña M. ( 2022 ). “What a Nasty Girl!” Incivility and gendered symbolic violence in news discussions . Feminist Media Studies , 1 – 18 . https://doi.org/10.1080/14680777.2022.2142640

Reicher S. D. , Spears R. , Postmes T. ( 1995 ). A social identity model of deindividuation phenomena . European Review of Social Psychology , 6 ( 1 ), 161 – 198 . https://doi.org/10.1080/14792779443000049

Roberts M. E. , Stewart B. M. , Tingley D. ( 2019 ). Stm: An R package for structural topic models . Journal of Statistical Software , 91 ( 1 ), 1 – 40 . https://doi.org/10.18637/jss.v091.i02

Schwartz H. A. , Eichstaedt J. C. , Kern M. L. , Dziurzynski L. , Ramones S. M. , Agrawal M. , Ungar L. H. ( 2013 ). Personality, gender, and age in the language of social media: The open-vocabulary approach . PloS one , 8 ( 9 ), e73791 . https://doi:10.1371/journal.pone.0073791

Statista ( 2018 ). Average YouTube video length as of December 2018, by category . https://www.statista.com/statistics/1026923/youtube-video-category-average-length/

Tajfel H. , Turner J. C. ( 2004 ). The social identity theory of intergroup behavior. In Jost, J. T. & Sidanius, J. (Eds.), Political psychology (pp. 276 – 293 ). Psychology Press . https://doi.org/10.4324/9780203505984-16

Tavits M. , Pérez E. O. ( 2019 ). Language influences mass opinion toward gender and LGBT equality . Proceedings of the National Academy of Sciences , 116 ( 34 ), 16781 – 16786 . https://doi.org/10.1073/pnas.1908156116

Tracey G. , Olivia B. , Jean M. , Megan H. , Sagri S. ( 2022 ). Why does gender matter for immunization? Vaccine . https://doi.org/10.1016/j.vaccine.2022.11.071

Turner J. C. , Reynolds K. J. ( 2012 ). Self-categorization theory. In: Van Lange P. A. M. , Kruglanski A. W. , Higgins E. T. (Eds.), Handbook of Theories of Social Psychology (p. 399 – 417 ). Sage Publications Ltd . https://doi.org/10.4135/9781446249222.n46 .

Vowles K. , Hultman M. ( 2021 ). Dead white men vs. Greta Thunberg: Nationalism, misogyny, and climate change denial in Swedish far-right digital Media . Australian Feminist Studies , 36 ( 110 ), 414 – 431 . https://doi.org/10.1080/08164649.2022.2062669

Walther J. ( 2011 ). Theories of computer-mediated communication and interpersonal relations. In Knapp M. , Daly J. (Eds), The Sage Handbook of Interpersonal Communication (4th ed.). Sage Publications, Inc .

Westerman D. , Van Der Heide B. , Klein K. A. , Walther J. B. ( 2008 ). How do people really seek information about others?: Information seeking across Internet and traditional communication channels . Journal of Computer-Mediated Communication , 13 ( 3 ), 751 – 767 . https://doi.org/10.1111/j.1083-6101.2008.00418.x

Yin L. , Brown M. ( 2018 ). SMAPPNYU/youtube-data-api (Initial Release). Zenodo . https://doi.org/10.5281/zenodo.1414418

Zhang H. , Peng Y. ( 2021 ). Image clustering: An unsupervised approach to categorize visual data in social science research . Sociological Methods & Research, 0 (0), 1–54. https://doi.org/10.1177/00491241221082603

Zhang Y. , Chen F. , Suk J. , Yue Z. ( 2023 ). WordPPR: A researcher-driven computational keyword selection method for text data retrieval from digital media . Communication Methods and Measures . Online First. https://doi.org/10.1080/19312458.2023.2278177

Author notes

Supplementary data

Email alerts, citing articles via.

• Recommend to Your Librarian
• Advertising and Corporate Services

Affiliations

• Online ISSN 1083-6101
• Copyright © 2024 International Communication Association
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.