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date: 10 December 2018

Stereotype Measurement in Political Decision Making

Summary and Keywords

Stereotypes are a set of beliefs a person holds about the personal attributes of a group of people. The beliefs are commonly held and understood, which allows people to use them as automatic shortcuts when making evaluations and decisions. Because the beliefs are so broadly understood and easily accessible, they can subconsciously influence opinion formation. In the realm of politics, citizens may use stereotypes to guide evaluations of candidates from stereotyped groups (such as African Americans or women) or as they formulate opinions about a policy that may have a particular group as its perceived beneficiary.

Because many commonly held stereotypes—such as that African Americans are lazy or violent—are not socially desirable, they pose a challenge to researchers attempting to measure them effectively. Thus, as social scientists examine the effects of stereotypes on citizen decision-making, it is important that they carefully consider how to best measure stereotypes. The goal should be to create reliable (consistent) and valid (accurate) measures that minimize social desirability in responses. Measures should reflect clear understanding of the content of the stereotype under examination and incorporate a full range of content to reflect it. One part of understanding the content of a stereotype is considering whether the group under examination is a subtype or subgroup of a larger stereotype category. For example, female politicians as a group constitute a subtype of the larger stereotyped group, women, where female politicians share little overlap in terms of stereotype content with women. Similarly, Black politicians are a subtype of Blacks, sharing little stereotype content. Male politicians, in contrast, form a subgroup of men, where they share many characteristics with the larger group, men. Stereotype content has implications for the link between stereotypes and evaluations of political actors or public policies. The ability to accurately measure stereotypes is a necessary step in understanding when and how people use stereotypes to navigate the political arena.

The intersection of two stereotypes is another important consideration, particularly since the combination of two stereotypes may be more than the sum of its parts. When researchers consider the content of stereotypes, the relationship between one stereotype to others (e.g., subtypes or subgroups), and the intersection of stereotypes, they can create improved measures of stereotypes that optimize reliability and validity.

A variety of explicit and implicit measures exist for researchers to consider, including explicit measures, such as semantic differential scales and open- and closed-ended identification of stereotype content, and implicit measures, such as the Implicit Association Test (IAT) and affective attitude measures. Two-step measures can be used to examine stereotype activation and application. While each of these measures has strengths and weaknesses, all are designed to help researchers better measure stereotypes en route to understanding how stereotypes influence peoples’ attitudes and behaviors. Reliable and valid measures of stereotypes—both implicit and explicit—can help us create more accurate understandings of the political world.

Keywords: gender stereotypes, validity, measurement, social desirability, stereotype content, subtype, subgroup, political decision making, racial stereotypes, reliability, explicit measures, implicit measures, intersectionality

Stereotypes are a set of beliefs a person holds about the personal attributes of a group of people. They are descriptive (what group members are like), prescriptive (what group members should be like), and proscriptive (what group members should not be like; Eagly & Karau, 2002; Prentice & Carranza, 2002; Rosette, Koval, Ma, & Livingston, 2016; Rudman, Moss-Racusin, Phelan, & Nauts, 2012). People access and apply these shared beliefs as a shortcut to make inferences about a complex world. Residents of a particular cultural context typically agree on stereotype content, which can be activated without one’s conscious awareness (Devine, 1989). The application of stereotypes to political decision making—and their potential for causing prejudice against an individual or group—depends on situational context and accessibility as well as individual differences, such as motivation and effort (Fiske & Neuberg, 1990). Given the automaticity with which stereotypes are activated, their use under conscious awareness, and the low social desirability of admitting to stereotype use, measuring both the content and the use of stereotypes has proven to be difficult task for those wishing to quantify their effects.

The Relationship Between Stereotypes and Outcomes

The ability to accurately measure stereotype content is necessary to link stereotypes with evaluations of political actors or public policies. The content of stereotypes differs across social groups and the unique stereotype content should be considered when understanding when and how people use stereotypes to navigate the political arena. For example, one stereotypical trait of African Americans is lazy, a negative trait that relates to White Americans’ evaluations of African Americans as undeserving of government services and that reduces support for redistributive policies (van Doorn, 2015; van Oorschott, 2017). Other studies have found that people who hold negative opinions about the work ethic of African Americans are much less likely to think that (Black) welfare recipients would try to find a job (Peffley & Hurwitz, 1997). There are strong correlations between stereotypes of African Americans as violent or lazy and support for more punitive crime policies, such as longer prison terms and increased support for the death penalty (Peffley, Hurwitz, & Sniderman, 1997). Even when racial considerations are not explicitly activated, stereotypes linking African Americans and violence are so strong that when Whites discuss crime policy, they assume the offender is an African American (Peffley & Hurwitz, 2002).

In addition to policy preferences, stereotypes can exert influence over other aspects of the political process. For example, stereotypes of women include traits like being compassionate and caring; however, although these traits are positive, they do not align with the masculine traits people see as desirable for good leaders, creating a barrier for female candidates in convincing voters to elect them (Bauer, forthcoming; Schneider & Bos, 2014, 2017).

Stereotype content matters in shaping expectations of members of stereotyped groups. Thus, understanding this content can enable researchers in generating strategies for challenging inaccurate or harmful stereotypes. Considering the many real-world political implications of group stereotypes, researchers must accurately understand and measure them.

Measurement Issues

Measurement is extremely important in the social sciences. When constructs are operationalized and measured poorly, tests of theories are compromised. Conversely, rigorous tests include measurements that represent the concepts in theories accurately and consistently. It should be the goal of every study and for every construct under examination to maximize the correspondence between each concept and its operational definition.

Maximizing reliability and validity are critical for stereotype measures. Reliability refers to a measure’s consistency: a measure is considered reliable when it is repeated and a similar result is obtained. In the case of stereotypes, a measure is reliable if a respondent completes the same questions related to endorsing a group stereotype on separate occasions and the results each time indicate the same level of endorsement. The accuracy of a measure is its validity, or the extent to which a measure reflects the concept under examination and relates to other constructs as predicted by theory. The core question researchers must ask to determine validity is whether the measure of stereotype endorsement reflects the individual’s actual endorsement of the stereotype. Content validity is a specific type of validity regarding whether all of the important domains or range of meanings of the stereotype are reflected in the measure. For example, stereotype measures of female politicians would be an accurate reflection of the concept of stereotypes only if they encompass the positive and negative facets of the stereotypes, including beliefs about ideology, traits, and competency to handle particular issues.

Despite the importance of reliable and valid measures to the creation of the best tests of social science theory, intentional measurement often gets short shrift. Indeed, a review of measures of stereotypes with regard to female politicians uncovers great inconsistency. Table 1 (Schneider & Bos, 2014) illustrates how gender-stereotype measures vary across eight different studies. While compassionate appears as a feminine trait measure in many of the studies, none of the other feminine traits is included consistently. With regard to masculine traits, only assertive and aggressive appear in more than one study. Such inconsistency indicates poor content validity, making it hard to test theories of how gender stereotypes influence voters’ choices (Schneider & Bos, 2011, 2014). The variation in measurements results in inconsistent findings.

Table 1. Masculine and Feminine Trait Stereotype Measures as Applied to Politicians

Feminine Traits

Masculine Traits

Rosenwasser & Dean (1989)

Affectionate

Assertive

Cheerful

Defends own beliefs

Compassionate

Forceful

Sensitive to the needs of others

Self-sufficient

Warm

Strong personality

Alexander & Andersen (1993)

Ability to compromise

Ability to handle a crisis

Conservative

Compassionate

Decisiveness

Gets things done

Emotional stability

Handles family responsibilities while serving in office

Tough

Hardworking

Liberal

Moral

Speaks out honestly

Stands up for what they believe

Struggled to get ahead

Works out compromises

Huddy & Terkildsen (1993a)

Cautious

Active

Compassionate

Administrative skills

Emotional

Aggressive

Family-oriented

Ambitious

Gentle

Articulate

People skills

Assertive

Sensitive

Coarse

Talkative

Rational

Trustworthy

Self-confident

Warm

Stern

Tough

Fridkin Kahn (1994)

Integrity

Knowledge

Honesty

Leadership

Compassion

Huddy & Capelos (2002)

Caring

Competent

Honest

Dynamic

Kind

Effective

Moral

Inspiring

Trustworthy

Qualified

Warm

Strong leader

Sanbonmatsu (2002, 2003)

Emotionally suited for politics

Lawless (2004)

Compassionate

Aggressive

Compromising

Assertive

Emotional

Self-confident

Sensitive

Tough

Dolan (2010)

Compassionate

Ambitious

Consensus-building

Assertive

An additional challenge of stereotype measurement is social desirability bias, which occurs when respondents report answers that do not reflect their true preferences or beliefs in order to conform to societal norms of appearing unbiased or sensitive to different groups (Nederhof, 1985; Paulus, 1984, 1986). Research demonstrates that stereotype measurement is particularly susceptible to social desirability bias. Strong social norms discourage endorsement of negative group stereotypes, particularly related to race and ethnicity (Dovidio & Gaertner, 1996). When attempting to measure stereotypes accurately, researchers must design methods that minimize social desirability effects.

Explicit Measures of Stereotypes

Researchers can measure stereotypes using explicit measures, such as semantic differential scales and open- and closed-ended identification of stereotype content. Explicit measures can be designed to minimize social desirability in order to capture affective and cognitive aspects of stereotype content and stereotype endorsement, to differentiate group stereotypes, and to measure intersectional stereotypes. Each type of explicit measure used to understand stereotype endorsement and content comes with its own set of strengths and weaknesses. For example, often measurement is designed to identify the content of only a single group, yet group stereotypes are best understood in relation to stereotypes of other groups. Consideration of the overlap and differentiation between groups can better situate a group’s stereotypical content. Moreover, by measuring only a single group, researchers may overlook whether and how multiple groups intersect to form stereotypes.

Measuring Stereotype Content

Researchers can select from different types of explicit measures that help uncover cognitive and affective aspects of stereotype content and stereotype endorsement. Some examples include versions of semantic differential scales and open-ended lists (Katz & Braly, 1933; McCabe & Brannon, 2004). Semantic differential measures provide respondents with several scales, with antonyms at opposite ends (pleasant—unpleasant; honest—dishonest), and ask them to rate various groups. A similar scaled approach to measuring stereotypes provides respondents several traits, and respondents are then asked to select whether the trait describes almost none, a few, some, many, or almost all members of the group being measured. The frequency with which traits are selected reflects the prevalence of the stereotype. For example, if the majority of respondents select “almost all” to the question of how well the trait ambitious describes female politicians, it can be inferred that this is a trait stereotyped to female politicians.

Measuring stereotypes via open-ended lists is also relatively straightforward; in this classic methodology (Katz & Braly, 1933), respondents are asked to list as many different adjectives or traits that best describe a certain group (women, African Americans, etc.). A similar measure asks respondents to indicate what percentage of a group holds a certain trait or attribute, and the stereotype is then comprised of the traits or attributes ascribed to the highest percentage of group members. Or, respondents may simply be given a list of traits and asked to indicate which traits best describe the group. In this method, it is useful to consider the minimum percentage of respondents who would have to choose a trait for it to be considered stereotypical of the group. Prior studies have used two-thirds of the sample (McCabe & Brannon, 2004; Schneider & Bos, 2011, 2014).

The strength of open-ended lists is that they do not presume stereotypic content in advance. The difficulty, of course, is the volume of data generated that must be coded in a quantifiable way. The lists generated in research using the semantic differential scale, or where respondents are asked what proportion of the group has a particular trait, or where respondents check the traits most characteristic of a group can be problematic if the lists are incomplete. To be as complete as possible, the list should be created from multiple prior studies. Including traits that describe groups that are not the target of interest (e.g., including traits that have been shown to describe African Americans when the target group is women) can help ensure better content validity. To understand stereotypes of politicians, for example, it is crucial to include trait stereotypes, stereotypes about ideology or beliefs, and stereotypes about issues that the politician group may be best at handling; these three categories of stereotypes have been well established as crucial to judgments about politicians (Funk, 1997; Kinder, 1986; Schneider & Bos, 2011, 2014). Because this is time consuming for both researchers and respondents and often not feasible with expensive, nationally representative samples, pilot studies may be of more use to establish stereotype content.

Overcoming Social Desirability Bias

Explicit measures of stereotypes are the measures most likely to be subject to social desirability bias because respondents are aware that it is socially unacceptable to endorse negative group stereotypes (Dovidio & Gaertner, 1996). Political surveys, such as the American National Election Studies, minimize such bias by asking about potentially sensitive topics prefaced with “Some people feel . . . other people feel . . . and some people are in between.” This preface situates both points on the scale as socially acceptable answer options. Another method is to stress the anonymity of respondents’ answers. For instance, participants could drop their answers in a box at the front of the room where the study is taking place, rather than handing them to the researcher. Or, the researcher could leave the room while respondents complete the task. Finally, to reduce social desirability in measures of stereotype content, respondents can be asked what “people in general” think about different groups being measured (Garcia-Marques, Santos, & Mackie, 2006; Schneider & Bos, 2014). Such a question serves to distance the respondent from his or her own views. These techniques do not guarantee that respondents will express their true opinions; however, they do remove a key driver for social desirability bias (wanting to appear to adhere to societal norms) by granting respondents anonymity in their responses.

Subgroups and Subtypes: Understanding Relationships Between Groups

Another way to understand how citizens view stereotyped groups is to consider how the stereotyped group is seen to be similar to, or different from, other groups. For example, to better understand the contours of stereotypes regarding female politicians, it would be useful to understand in what ways they differ from the group women. Researchers can measure group stereotypes and apply two key psychological concepts—subgroup and subtype (Richards & Hewstone, 2001)—to identify how groups overlap and differ from one another. If a researcher measures stereotypes of two groups and observes that the smaller group shares many characteristics with the larger group, the smaller group is a subgroup of the larger group. For example, mothers share significant stereotype content with the group women—both are warm, caring, and kind. In contrast, if measures indicate that the smaller group deviates greatly in stereotype content from the larger group, it constitutes a subtype, or a new stereotypical category. For example, working women differ greatly from the category women in being perceived as aggressive (Clifton, McGrath, & Wick, 1976; Heilman, Block, & Martell, 1995; Hugenberg, Blusiewicz, & Sacco, 2010).

Subgroups and subtypes can be established empirically by using the explicit methodologies described above, such as open-ended responses (Katz & Braly, 1933) or closed-ended trait lists (Schneider & Bos, 2011, 2014). In these studies, comparisons can be made between the target group and other related groups, such as comparing female politicians to women, politicians, male politicians, and female professionals. Using such comparisons, Schneider and Bos (2014) found that female politicians—the target group of interest—did not share the qualities typically ascribed to women (e.g., warm, empathetic), and also were less likely than other groups to be seen as possessing the male stereotypical qualities related to leadership (Schneider & Bos, 2014). Female politicians, they concluded, were a subtype of the larger group women and of the larger group politicians. This subtype was created by perceivers to accommodate the perceived differences between female politicians and the superordinate groups. Male politicians, on the other hand, shared substantial stereotype content with the superordinate group men, and thus constituted a subgroup of men.

In another example, Schneider and Bos (2011) used the same methodology to understand the relationship of Black politicians to other key groups. Rather than being viewed as similar to African Americans (e.g., as poor, lazy, unintelligent, violence-prone; Peffley et al., 1997), Black politicians are seen as ambitious and educated (Schneider & Bos, 2011). Importantly, Black politicians are perceived more positively than Blacks, since their level of success helps them overcome the poor or low-class stereotypes applied to Blacks as a group. In this way, Black politicians constitute a subtype of African Americans. In addition, the analysis found that compared to generic politicians, Black politicians were seen as more liberal, more Democratic, and significantly more capable of handling issues like civil and equal rights, affirmative action for Blacks, and race relations.

Understanding the relationship of Black and female politicians to other relevant groups directs an understanding of stereotype consequences (Schneider & Bos, 2011, 2014). In particular, subtyping allows perceivers to “fence off” those in the subtyped group from the larger group (Richards & Hewstone, 2001), leaving the original stereotype of the superordinate group intact. Thus, showing that Black politicians are a subtype indicates that the success of Black politicians will do little to change the negative stereotypes of Blacks (Kunda & Oleson, 1995). In sum, the measurement of stereotypes with respect to other key groups is essential in uncovering the significant consequences of stereotype content and structure.

Measuring Intersectional Stereotypes

Because citizens are often evaluating policies that benefit multiple groups, or candidates with multiple identities, understanding the stereotypes of intersectional groups is crucial. Stereotypes of female Republicans, for example, may differ in important ways from stereotypes of both Republicans and female politicians. As with any measurement, understanding from a theoretical perspective how two types of stereotypes could combine is paramount to measuring the stereotypes with good content validity. The intersection of two stereotypes may result in ones dominating the other, ones dominating the other in some instances but not others, or a parallel processing model where the two stereotypes combine to influence judgments (Kunda & Thagard, 1996; Schneider & Bos, 2016).

Measuring the content of intersectional stereotypes to understand which model best fits the unique stereotype combination has its challenges. Researchers may manipulate two stereotypes using a vignette in an experimental design in order to understand the effects of the stereotype interactions on ratings of particular traits or characteristics (Huddy & Capelos, 2002; King & Matland, 2003). Another method may be to ask a comparative question of who the respondent thinks would be more likely to have a particular trait. In the example of the intersection of party and gender stereotypes, this could result in asking whether a Republican woman or a Republican man might best be described with a particular trait (Sanbonmatsu & Dolan, 2009). A method with higher external validity would be to examine the effects of party and gender separately and in combination on trait ratings of Congressional candidates using a regression and controlling for other relevant factors (Hayes, 2011).

We argue, however, that the use of either an open-ended methodology or a trait selection method with a comprehensive list of traits is the best method for uncovering stereotypic content and endorsement. It is only through methods that allow respondents to dictate the content of stereotypical assumptions that researchers can begin to understand how stereotypes intersect. For instance, using a comprehensive list of traits where respondents had to rate how well a particular trait described a typical politician from a gender and party combined group, the intersection of party and gender was best able to explain respondents’ trait selections (Schneider & Bos, 2016). In particular, male Republicans were seen as uniquely low on empathy ratings and their ability to handle issues related to helping others. In many cases, female Democrats were more likely than both women and Democrats to be seen as capable of handling issues like the wage gap, child welfare, and women’s rights. In sum, gender has a different effect for Democrats, with female Democrats often being seen as similar to male Democrats, than it does for Republicans, with female Republicans being seen as more centrist than their male party counterparts. A measurement design that allowed respondents to choose traits from an exhaustive list allowed for these conclusions.

In a second test, we used an open-ended methodology to uncover the existence of emerging traits. By asking respondents to list attributes and issues that applied to either a base group (Republican, Democrat, female, or male) or a conjunctive category (the party and gender intersection), unique terms appearing to describe the conjunctive category but not the base group could be identified. For example, family values emerged as an issue that female Republican politicians would be good at handling but not female politicians nor Republican politicians. While the open-ended methodology is time-consuming, its ability to reveal terms that are helpful for understanding the intersectionality of two stereotypes cannot be replicated with other methods.

In sum, it is imperative that researchers select explicit closed or open-ended measures with intention in order to overcome the challenges of low content validity and social desirability. When researchers carefully consider the content of the stereotypes under examination, the relationship between the content of one stereotype to another (e.g., subtype or subgroup), and the intersections between group stereotypes, they can create better measures of stereotypes that are more reliable and valid.

Implicit Measures of Stereotype Endorsement

Implicit measures provide alternative methods of measuring stereotypes that help alleviate concerns about social desirability bias. While there are several types of implicit measures, the most common is the Implicit Association Test (IAT; Greenwald & Banaji, 1995). During a typical IAT, respondents are shown various stimuli from two social groups and two evaluative or stereotypical concepts. Each social group is assigned a key. When a word from the evaluative category or stereotypical concept is shown, respondents hit the key that corresponds to the group associated with the category. The speed with which respondents select the correct key theoretically corresponds to how closely respondents associate the social group with the evaluative concept or stereotype. This approach is often used to measure racial bias. For example, respondents are shown the face of a Black person or the face of a White person. They would also be shown words with either positive (e.g., happy) or negative (e.g., crime) valence. These categories would then be grouped, so that Black positive makes one group and White negative makes another (alternative groups would be Black negative and White positive). The key that respondents choose more quickly in response to a picture or word suggests stronger associations between the two categories in the respondent’s mind, and the associations are, in turn, assumed to link to attitudes. Respondents who choose the White positive key more quickly than the Black positive key when shown either a white face or a positive word are inferred to more strongly associate White with positive than Black with positive (Greenwald, McGhee, & Schwartz, 1998). The IAT has been used to demonstrate stereotype content; that is, Brown et al. (2011) showed that men are associated with stability and women with change. They used these implicit connections to show that female political candidates are preferred under conditions of threat, when a change to the system is warranted. In another study, Mo (2015) used the IAT to argue the existence of implicit bias toward female candidates and also to demonstrate that both implicit and explicit attitudes significantly predict voters’ choices.

Another implicit measure often used to measure racial biases is an affective attitude test, where respondents are primed with a stimulus and then are asked to categorize a word (e.g., Fazio, Jackson, Dunton, & Williams, 1995). Using the same type of example as above, this means that respondents would be primed with a picture of either a Black or White person, see a word with positive or negative valence, and then categorize the word as either positive or negative. In instances where being shown a Black person or face leads to more positive word categorizations, it is inferred that the respondent holds more favorable attitudes toward Black people.

Additional measures used by researchers to implicitly measure racial bias include word fragment completion tasks, measures of abstract language usage (where more abstract language indicates behaviors consistent with expectations), and sentence completion measures, which rely on the theory that people explain events more when the events are inconsistent with their expectations (Fazio & Olson, 2003).

Implicit stereotype measures are subject to concerns about their reliability and validity (Cunningham, Preacher, & Banaji, 2001; De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009). One critique focuses primarily on the stability of measures over time; respondents may exhibit different levels of bias when given the same test at different times (Cunningham, Preacher, & Banaji, 2001). Another key critique of these measures is that they do not always produce similar results (De Houwer et al., 2009). A respondent could take an IAT and an affective attitude test, and the corresponding levels of bias could be meaningfully different. One approach to addressing the first two critiques is to use latent variable models, which measure each construct (e.g., racial bias) with multiple measures (Cunningham, Preacher, & Banaji, 2001). Research using latent variable analyses conducted with three separate implicit measures suggests that once measurement error is accounted for, implicit measures show a relatively high degree of validity and reliability.

A third key concern about implicit measures is that they do not necessarily reflect real-world behaviors or actual attitudes. Even if a respondent’s scores on the IAT suggest significant levels of bias toward a group, that bias is not necessarily reflected in the way that respondents treat members of the group in real life. Scholars have argued the results of an IAT cannot be a valid measure of an attitude if there is no behavioral evidence to support that the attitude exists to begin with (Borsboom, Mellenbergh, & van Heerden, 2004; De Houwer et al., 2009). Other researchers, however, have found a correlation between racial IAT responses and behavioral cues, such as time spent speaking or smiling depending on the race of the researcher administering the IAT (McConnell & Liebold, 2001). One explanation for this is that implicit and explicit measures tap different dimensions of underlying attitudes, and so the presence of bias in an implicit (explicit) test does not necessarily mean one should expect to see similar levels of results in an explicit (implicit) test.

Measuring Stereotype Activation and Application

Citizens may know or understand a group stereotype that is not activated because stereotype activation is distinct from having knowledge of a group stereotype. Stereotypes are activated when the situation or context increases the accessibility of a group stereotype. For example, a female candidate who emphasizes motherhood may activate voters’ feminine stereotypes.

Experimental methods are most conducive to measuring stereotype activation. Experiments have been used to examine the activation of feminine stereotypes (Bauer, 2015, 2017). In one study, respondents read about a male or female candidate who was described as having feminine traits (Bauer, 2015). After viewing this information, respondents rated the candidate with regard to implicit stereotypes along the power and warmth dimensions (e.g., placing a candidate on a scale from 1 to 7 with ends like strong−weak and hard−soft). When participants read about a female candidate described as having feminine traits, feminine stereotype activation occurred whereby the female candidate was perceived as possessing feminine traits compared to when she was simply described without trait information.

When an activated stereotype influences evaluations of a group member, stereotype application has occurred. Researchers can measure stereotype application in an experiment. After stereotype activation is demonstrated, the researcher tests whether the activated stereotype influences other dependent measures. For example, once feminine stereotypes were activated, feminine stereotypes negatively affected evaluations of female, but not male, candidates (Bauer, 2015). This was true both in an experimental study using fictitious candidates and in a real campaign where feminine stereotypes were activated through advertising (Bauer, 2015).

An alternative to observing stereotype activation in experiments is observational panel studies. Dolan (2014) used a two-wave panel study where she measured trait and issue gender-stereotype evaluations for specific candidates at multiple points and observed their relationship to various dependent measures. Additional waves could allow researchers to more carefully examine when gender stereotypes are activated throughout the campaign, what causes such activation, and the specific effects of stereotype activation.

In conclusion, it is important for social scientists to create reliable and valid measures of group stereotypes in order to test theories regarding important questions, such as how stereotypes affect evaluations of candidates from a stereotyped group (such as African Americans or women) or opinions about a policy that may have a particular group as its perceived beneficiary. In creating good measures, social desirability must be considered, as should the content of the group’s stereotypes relative to, and in combination with, stereotypes of other groups. A variety of explicit and implicit measures can be considered in gauging stereotypes to help researchers create more accurate measures of stereotypes. Researchers should carefully consider measures and how to observe stereotype content, stereotype activation, and stereotype application in their work. Doing so is important because our ability to accurately measure stereotypes is a necessary step in understanding when and how people use stereotypes to navigate the political arena.

Further Reading

Devine, P. G., & Baker, S. M. (1991). Measurement of racial stereotype subtyping. Personality and Social Psychology Bulletin, 17(1), 44–50.Find this resource:

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