Stereotype content across ethnicity and socio-economic status: a case of ethnic groups in Russia

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FEDERAL STATE AUTONOMOUS EDUCATIONAL

INSTITUTION OF TERTIARY EDUCATION

«NATIONAL RESEARCH UNIVERSITY HIGHER SCHOOL OF ECONOMICS»

FACULTY OF SOCIAL SCIENCES

DEPARTMENT OF PSYCHOLOGY

Master's Program «Applied Social Psychology»

Drozdova, Anna

Stereotype content across ethnicity and socio-economic status: a case of ethnic groups in Russia

Master's Thesis

Reviewer ScD,

Tenured Professor

Lebedeva N.M

Supervisor

PhD in Soc. Psych.,

Research Fellow

Grigoryev D.S.

Moscow, 2020



Table of content

Abstract

Theoretical Background

Stereotype Content Model

Stereotype Content Model and Status

Stereotype Content Model and Ethnicity

Multiple Categorization

Hypothesis

Pre-test

Main test

Results

Discussion

Conclusion

References

Appendix 1

Appendix 2

Appendix 3

Appendix 4

Appendix 5

Appendix 6

Appendix 7

Appendix 8

Appendix 9



Abstract

As the structure of society increases in complexity more intricate social categories emerge. People employ stereotyping to make judgements faster and these stereotypes can impair communication in some cases leading to negative consequences for society. This study investigates the interaction between target's ethnicity and their SES in forming stereotypes and aims to clarify which is more significant. 722 Russian participants took part in a 2Ч4 vignette analysis of four ethnic groups and two statuses and filled out a questionnaire intended to assess stereotype content. We formed 6 hypotheses: H1:Competence of targets should be affected by the status of targets; H2: Warmth of targets should be affected by the ethnicity of targets; H3: High-status targets should be evaluated as more competent than warm; H4: Low-status targets should be evaluated as more warm than competent; H5: The consistent information about stereotype content from the status and ethnicity should lead to no changes in the evaluation of targets; H6: The inconsistent information about stereotype content from the status and ethnicity should lead to the additive pattern with changes in the evaluation of targets toward neutral. The results mostly supported the hypotheses. Competence of targets is affected both by the status and ethnicity of targets and warmth dimension affected by ethnicity. High-status targets are evaluated as more competent than warm and low-status targets - as more warm than competent. The additive model fits the data explaining cross-categorization results.

Keywords: SCM, SES, ethnicity, Russia, competence, warmth



Theoretical Background

In the current day people from around the world travel to other countries a lot, both for leisure and work. That means that people from different countries and cultures need to successfully interact with each other, which in some cases can be accompanied by various complications.

Russia is not only big in terms of territory but is also diversity with a wide range of ethnicitiesthat are yet to be studied adequately (Grigoryev, Fiske, &Batkhina, 2019).

Understanding how people form stereotypes and what affects their judgment is crucially important tonegate the negative effects of stereotyping and to developbetter communication among people.

Presently, much research attempts to try to understand both stereotyping on the basis of one feature and more complex cases in which cross-categorization occurs (Nicolas, Fuente & Fiske, 2017). However, most of the research on multiplecategorization though tends to focus oncategories such as gender and race, and are rarely on combining SES with other factors. We hope to find what affects the stereotype content more - perceived ethnicity or perceived SES.

The research question that we pose is whether the perceived SES of a person would significantly affect attitudes towards people from various ethnic groups, and if yes - which dimensions (warmth, competence) are affected.

The aim of this work is to establish to what extent does perceived SES changes stereotype content that participants form about a target.Following the results possible implications for real-life situations to better relationships are discussed.

The theoretical significance of this paper lies in clarifying the interaction between ethnicity and SES and their effect on stereotype content. That will, in turn, help to better understand how cross-categorization works.

The methodological significance - whether the results of stereotype content formed by Russian participants based on target's perceived SES are consistent with stereotypes toward rich/poor found in research in other cultures.

The practical significance- betterunderstanding of the stereotypes content and psychological reasons behind attitudes toward various ethnical groups will help to learn how we can influence and alter them.

Novelty -studying the effect of ethnicity and socioeconomic status interaction on stereotype content in Russia.

I would like to begin this paper by defining two core concepts - socioeconomic status and ethnicity.

According to American Psychological Association socioeconomic status is defined as the “social standing or class of an individual or group” (“Socioeconomic Status,” n.d.) that is commonly measured by income, occupation, education, or a combination of above.

An ethnicity is described as “a large group of people who have the same national, racial, or cultural origins, or the state of belonging to such a group”, and can be used to reference a particular (Cambridge Dictionary, 2020)

Having defined our main terms we can now move to discussing the Stereotype Content Model and, more specifically, research done on the topics of status, ethnicity and cross-categorisation done within the plain of this model.

Stereotype Content Model

Individuals are constantly observed by society and are seen as members groups through the phenomenon - categorization. Social categorization has been widely researched and many models try to understand the process that have emerged (Nicolas, Fuente& Fiske, 2017).

Two of the most popular models claim that stereotype content can be shaped by two forms of observation: the typical roles of the group members or the intergroup relations (Koenig& Eagly, 2019). The observation of roles is the basis of the Social Role Model (SRM), more specifically the behaviour of individuals with a particular role leads to the development of behaviour expectations attributed to such a role. Whereas, the observation of a group member's position in the social structure and their relations to other groups produces stereotypes according to the Stereotype Content Model (SCM).

The main goal of Koenig and Eagly's (2019) study was to clarify the source of stereotypes. For that purpose, they conducted four studies in which they designed vignettes that manipulated role and intergroup relations of fictional groups. They created descriptions of: two roles (warrior, care-taker), two statuses (high, low) and two degrees of interdependence (cooperative and competitive). The role and intergroup information were paired to be consistent or contradictory in the studies. Firstly, the research aimed to assess the validity and compatibility of the SRM and the SCM. The first study found that when presented alone; status, social roles and intergroup relations evoke stereotypes supporting both SRM and SCM. The second and third studies found that when social roles and intergroup relation information were both available stereotypes also formed in line with SRM and SCM (Koenig & Eagly, 2019). The influence of roles and intergroup relations on forming stereotypes is seemingly almost equal, with participants averaging the two kinds of information to form a stereotype. The fourth study addressed the relationship between SRM and SCM, finding that for status and interdependence structural dimensions provided high correlations between their role-level and group-level manifestations. They concluded that groups' positioning in society, which is determined by typical social roles and relationships between groups, is reflected in stereotype content.

Overall, although SRM and SCM yield valid results, neither considers all the information from which stereotypes are formed. Therefore, as the study presents the case for an integrative social structural understanding of stereotype content that is systemic in its analysis of groups in social structure, authors proposed that the two models can be conceptually integrated in the future (Koenig & Eagly, 2019).

I will be focusing on the Stereotype Content Model. It will be more fitting to research the questions set for the current study.

There are a number of models that try to organize stereotype content into dimensions, with most of them agreeing on two general dimensions that can be categorized as vertical and horizontal, although they bear various labels (Abele et al, 2016). The vertical dimension measure is commonly referred to as status, dominance, agency or competence and the horizontal dimension interpreted as warmth, trustworthiness, communion, cooperation-competition, me-them differences (Grigoryan et al., 2020; Tsukamoto; Fiske, 2018). For the sake of consistency and readability I will be referring to those two dimensions as Competence and Warmth throughout this paper, unless otherwise stated.

The stereotype of warmth (trustworthiness, sociability) is based on perceived intentions of the outgroup (Tsukamoto, Fiske, 2018) and formed based on the cooperation or competition between groups (competitive relationships result in a lack of warmth and cooperation - in rating the group as warm) (Koenig & Eagly, 2019; Grigoryan et al., 2020; Fiske, 2015, 2018).

The competence (capability, effectiveness) dimension is formed based on the information about other groups' capabilities (Tsukamoto, Fiske, 2018) and status (Grigoryan et al., 2020), thus demonstrating the perceiver's respect (Fiske et al., 2002).

Some researchers argue that warmth is judged fasterthan competence as it is an initial characteristic; warmth is the intention of an action and competence is the ability to act out the intention (Fiske, 2015).

Other research proposes a different sequence of how stereotype content forms. Perceived social structure (cooperation, status) predicts stereotypes (warmth, competence), which in turn predicts emotional prejudices (pride, pity, contempt, envy), and finally, the emotions predict discrimination (active and passive help and harm) (Fiske, 2018).

Stereotype content has been shown to emerge from social context. The SCM details the connection between stereotype content and social context. More specifically, the perception of a group's competence and warmth can be predicted based on the relative status of the group and how cooperative or competitive group members are (Grigoryan et al., 2020).

The SCM shows that stereotype content relates to intergroup relations; stereotypes of competence are derived from high status and incompetence from low status (Koenig & Eagly, 2019).Overall, the process can be described as a loop in which people assume that the observed societal position of the group, such as the groups' social status and cooperativeness, is reflective of the group's nature. Then, these surmised group characteristics (stereotypes) explain and maintain the groups' current societal positions. Biases contribute to the loop/stereotype logic through; internal attribution (reasoning that the group's status is caused by internal factors), dispositional bias (underestimating the situation) and sample bias (Grigoryan et al., 2020).

Communication norms and inequality support the status stereotype and warmth-competence trait relationship; high status is evaluated as cold-but-competent and low status as incompetent-but-warm (Fiske et al., 2016).Characteristics of intergroup relations can be expressed subtly; explicitly commenting on either an individual's competence (or warmth) and ignoring the other dimension suggesting a lack of the other dimension. Another example of subtlety appears by indicating that one out of two otherwise proportionate groups is a norm and the other group should be compared to it (Bergsieker et al., 2012; Fiske et al., 2016).

This trade-off appears in various encounters across race, gender, status, and class and can be potentially reinforced as humans, in their pursuit of control and order, combine status stereotypes with the need to justify the existing order, thus assessing inequality as legitimate and sometimes desirable (Fiske et al., 2016).

Stereotypes are often equivocal and are linked to rates of inequality in any given country; shown by depictions of the low-income social class as warmer but lower skilled than the higher-income group (Durante & Fiske, 2017; Fiske, 2018).

The degree of ambiguity in stereotypes towards various social groups is related to a nation's economic inequality. Countries with high rates of inequality, are more susceptible to ambivalent stereotypes. Nations with high income-inequality create complicated maps where each group has a varying degree of competence-warmth trade-offin order to justify the existing, often unfair system. For nations in which the income is distributed more equally, the need for such maps is redundant as they view most of the groups as deserving and rate them high on both competence and warmth dimensions, with only a few rare groups rated low on both dimensions (Durante & Fiske, 2017; Fiske, 2017, 2018).

While some can assume that the SCM is just an embodiment of modern liberal views, trying to make sure that outgroups can have some positive aspects, we can actually find that relevant dimensions can be seen in research done in the 20th century, with the content of stereotypes concerning certain groups remaining the same throughout years. SCM dimensions can be seen in the first studies of stereotypes and have been replicated and continuously validated across the 20th and 21st centuries. The patterns can be extrapolated across varying cultures, on an individual and differing group size level (up to nations) and on measures (whether it's societal indicators, self-report or neural) (Cuddy, Fiske &Glick, 2008; Fiske et al.,2002; Fiske, 2015; 2018).

It is also important to mention that in recent years a new type of SCM that fits the data better that the classical two-facet division was proposed (Abele et al., 2016).Abele et al. note how the two-facet model has varying labels and thus conceptualizations - one dimension could be categorized in varying ways, creating confusion. They propose that this situation is a reflection of the multi-faceted nature of the fundamental dimensions (Abele et al., 2016).

They formulated a fundamental facet-model of personality which consists of agency (A) (what was in previous papers often called competence) and communion (C) (warmth); this model was further differentiated into assertiveness and competence as facets of agency and warmth and morality as facets of communion. The model was validated across 6 countries (Germany, France, Australia, Poland, China, USA) and `cross-checked' against pre-existing models of personality such as The Big Five thus integrating into the nomological network. They also performed an exploratory analysis to see how ratings across these four dimensions correspond to self-construal, self-esteem, impression management and values.

Authors have demonstrated the universality of the two-facet dimension model and have provided compelling evidence for the four-factor model. Furthermore, the results of this study elucidate previous research done within the two-factor model paradigm which was incongruent with the majority findings, further justifying a four-facet model.

Thus, the four-dimension model was proposed, the agency dimension was divided into assertiveness and competence and the communion dimension - into warmth and morality.

Competence reflects one's abilities while assertiveness shows more about one's motivation to pursue a goal. Warmth accounts for maintaining social relationships and being friendly, and morality encompasses adherence to social values and ethics (Abele et al., 2016).

Keeping that in mind it is important to mention that further down in the paper I will be using slightly different labels in accordance to the model used by Fiske (Fiske et al., 2002; Grigoryev et al., 2019). The two main dimensions here are warmth and competence that are further divided into morality andsociability and assertiveness andskills, respectively.

Stereotype Content Model and Status

Social class is a stratification system that reflects how material, social, and cultural resources are distributed among groups/individuals according to hierarchy. This significantly affects their lives (Durante & Fiske, 2017).

Within societies members are ranked along a status dimension according to their education, profession, income wealth and other resources - resulting in competent higher-class stereotypes and incompetentlower-class stereotypes (Fiske, 2017).

Competence derived from status is almost universally accepted. Across countries there is a strong positive correlation between a group's trait competence against the judged societal status of the group (Fiske, 2016).

Psychological distance from others emerges among higher ranks in social class enabling increased independent action (agency) for the higher ranked and requiring deference by the lower ranked. This occurs regardless of how permanent rank is, whether from birth (chronic) or gained and lost throughout life (temporary) (Fiske et al., 2016).

The paper by Durante and Fiske (2017) reviews existing research on the ambivalent stereotype content of social classes, how they develop in children and is then further reinforced by current educational systems. They also research how stereotype ambivalence is connected to the level of inequality in a given country. Children acquire the understanding of a rich-poor dichotomy as early as in their preschool years. By the age of 6, children stereotype towards the poor as less competent than rich. Significant here is that children are shown to base their socializing preferences on a competence dimension. Children infer social class by wealth cues as well as race, with more negative attitudes towards poorer classes (Durante & Fiske, 2017).

Federica Durante and colleagues employed the Stereotype Content Model to investigate how people with low and high socioeconomic status (SES) are viewed, both on individual and societal levels (Durante, Tablante, Fiske, 2017). For the American sample the content of stereotypes towards high-SES people tended to be more stable across the nation, while results for low-SES people varied more. They found out that on both levels people with high SES are perceived as more competent than warm, while groups or individuals with low SES are viewed as either equally warm and competent, or more warm than competent. It is interesting to note that in societies that are more unequal the differences between characteristics are more prominent - the alleged incompetence of low-SES people is exaggerated, just as high-SES people tend to be rated even lower on the warmth dimension. Authors highlight that a warmth-competence trade-off helps people to justify the existing social-class system where the social class divide is significant and income inequality is prevalent (Durante et al., 2017).

Similar patterns appear in other research. In their work Wu et al. (Wu, Bai, Fiske, 2018) studied stereotype content and evaluations of rich people in the United States and China. They found that both Chinese and Americans participants viewed the rich generally ambivalently - as more competent and less warm than the middle class. This study also highlighted the difference between the perception of rich in China and the United States: while Chinese respondents tended to view rich as significantly less warm than middle class, Americans rated rich as cold. It is worth noting that even within the rich category there is a divide in degree of favourability depending on specific professions, meaning that all rich people were envied but some of them were subject to contempt and some - to admiration (though specific professions might be different for different cultures). 

Some interesting observations emerge when it comes to measuring implicit stereotypes. While explicitly people tend to display ambivalent or negative wealth stereotypes, when authors assessed implicit stereotype content, people tend to view the rich positively (Wu et al., 2018).

Evidently, there are some differences in perception of various social groups that are country specific - in that case post-communist and capitalist countries. In contrast to previous research Grigoryan et al. (2019) found that participants often identified the working class as a group in society - this was especially the case in post-communist countries. The working-class group was mentioned more by participants from post-communist countries than by those from capitalist countries.

In addition, the perception of working-class groups, which was previously inconclusive or negative, was shown to be more positive in post-communist societies (Grigoryen et al., 2019). Similarly, the lower class was perceived more positively and the upper class more negatively. Participants in post-communist societies perceived the working-class and lower class as warmer, the upper class was assessed as less competent in post-communist countries - which is quite unexpected as much research shows the upper class as competent and cold (Wu et al., 2018; Volpato et al., 2017). There was no difference in the perception of the middle class between participants in capitalist societies and post-communist societies.

Grigoryanet al. (2019) also controlled for perceived group competition, preference for embeddedness over autonomy values and disbelief in meritocracy. A negative correlation was found between perceived group competition and warmth of this group in both capitalist and post-communist countries with stronger correlation in post-communist countries.

This correlation was supported by a stronger preference for embeddedness over autonomy values in post-communist countries. Furthermore, participants' perceptions of the competence-status connection were weakened by disbelief in meritocracy (Grigoryen et al., 2019).

Stereotype Content Model and Ethnicity

Tsukamoto and Fiske (2018) in the pursuit to help explain the psychological reasons behind negative attitudes toward immigrants set to explore the perceptions of in-group national representations and immigrant image.

They found that out-groups stereotyped as competitive (cold/low-warmth) are seen to threaten civic values (political ideology, legal norms, etc.) and are therefore rejected whereas ethnic values (customs and shared culture) are unthreatened - in the context of Americans and immigrants (Tsukamoto, Fiske, 2018). In one of the studies the predicted competition-prejudice relationship was mediated by value threat. In short, immigrants were more likely to be viewed as negative if they had a low-warmth stereotype. This study revealed that the relationship between prejudice towards immigrants and stereotypes about immigrants is not mediated by perceived threat to ethnic values. The authors concluded that stereotypes of immigrants and the threat they prose may depend on the nation's values. This study showed that specifically for American participants prejudice arose from a fear that American shared civic values and political ideologies will be incompatible with immigrants' values. The perceived threat to values was heightened, especially, for participants with a less clear American-immigrant identity distinction.

The research found that the dimension of warmth mattered mostly for the ingroup-outgroup relationship as it was considered an other -focused trait, but much more research on how in-group representations are impacted by differing out-groupsis needed (Tsukamoto, Fiske, 2018).

When it comes to Russia, we can see that even though there is a big number of ethnic groups living in one country, which presents grounds for testing Stereotype Content Model dimensions on a non-American and non-European country, it is still quite understudied (Grigoryev et al., 2019). Various ethnicities have vastly different stereotype content with some of the groups being clear outcasts with low competence and low warmth rating, some staying approximately in the middle on both dimensions and three groups rated as high competence and high warmth.

Multiple Categorization

It is not yet clear how the cross-categorization or multiple categorizations works. A number of approacheshave been proposed; the 3 most commonly used ones are -an additive model, an averaging model and anon-algebraic models.

Additive model - the additive model dictates that the effects of the in-group and out-group dimension are summed.

This means that a person belonging to two in-groups (crossed) will be judged more positively than a person belonging to one in-group (simple) and that membership to two out-groups results in more negative judgement than membership to one out-group. Alternatively, a neutral judgement occurs when an individual is a member of an in-group and out-group simultaneously (Nicolas, Fuente & Fiske, 2017).

In contrast, to the additive model, the averaging model predicts that membership to one in-group results in an equally positive judgement as membership to two in-groups. In other words, in the crossed case (more than one group membership) the effects are averaged. Important to note, that similar to the additive model, membership to an in-group and out-group leads to a neutral evaluation.

Non-algebraic models:

Category dominance states that in the case of crossed categorization all but the dominant category is ignored when making judgements. 

Category conjunction theorizes that a person needs to be in-group on all necessary dimensions in order to be perceived as an in-group. 

The Feature detection model proposes that when people need to evaluate a person belonging to more than one category, they use a top-down strategy. By assessing within which dimensions a person falls they are then evaluated as “in-group like” or “out-group like” based on the number of in-group and out-group categories (5 in-group and 1 out-group = in-group) and category salience (some categories are more important than others). 

Much research has been done on crossed categorization models, yet the results are mixed and often contradict each other (Nicolas, Fuente & Fiske, 2017) implying that there are many limitations that need to be considered. The above-mentioned models should be investigated further, considering a vast number of factors that could complicate how judgment about a multiply-categorized target is formed.

Another type of model is the emergent stereotype model. The emergent stereotype model suggests an alternative theory on the effects of crossed-categorization membership. A classic study (Kunda et al., 1990) revealed that an individual belonging to two categories, may be judged to have novel attributes and stereotypes which emerged from the integration of the two existing categories. Thus, this allows for greater individuation as a person is evaluated as an individual (with a unique set of attributes relevant to the individual) rather than a general-group member.

Despite the lack of clarity within research and limitations inherent in each model, to develop the understanding of crossed-categorization, researchers use the model most applicable to their investigation. Additionally, Nicolas et al. (2017) hypothesize that the different models of crossed categorization and emergence might map onto different levels of individuated processing. 

In the paper Grigoryan (2019) carried out a factorial survey to investigate the correlation between a participant's attitudes toward a target and perceived similarity to the target depending on the participant's group memberships and target's perceived group memberships. Participants did not always categorize the target as `in-group' or `out-group' properly - in cases of status dimensions they tend to categorize targets as in-group, when the target is of higher status, even if they are objectively out-group. Measure of attitudes positively correlated to perceived similarity and not to actual group membership. To learn that she used social media and online platforms of ethnic diasporas to collect the participants (N= 524). The socio-demographic categories seem to be unequally distributed among participants with the majority being Russian with Russian citizenship and `skilled' workers. Grigoryan asked 3 questions: one to measure perceived similarity and two to measure attitudes along a 10-point scale. She created vignettes with 6 ethnicities multiplied by 8 social-demographic dimensions. Participants could be similar to the target along any of the categories.

The process of individuation is complex, yet it is hypothesized that different crossed-categorization and emergence models are connected to different levels of individuation (Nicolas, Fuente & Fiske, 2017). Having said, I will basing my predictions employing the additive model.



Hypothesis

Based on the review of existing research 6 hypotheses concerning the stereotype content of participants from Russia were proposed. The two main dimensions of stereotypes - competence and warmth - are divided into subdimensions of assertiveness andskills and morality and sociability, respectively.

1. Social structure predictions

H1a: Competence of targets should be affected by the status of targets.

H1b: Warmth of targets should be affected by the ethnicity of targets.

2. Compensation effect predictions

H2a: High-status targets should be evaluated as more competent than warm.

H2b: Low-status targets should be evaluated as more warm than competent.

3. Crossed categorization predictions

H3a: The consistent information about stereotype content from the status and ethnicity should lead to no changes in the evaluation of targets.

H3b: The inconsistent information about stereotype content from the status and ethnicity should lead to the additive pattern with changes in the evaluation of targets toward neutral.

Pre-test

Participants

For the pre-test, 334 participants completed the survey (25% completion rate), after cleaning the results 330 of them were used for analysis. The sample included 58% men and 42% women, the age varied from 16 to 83 (M = 26.6, SD = 10.3). 40% of participants were students.

Procedure

The data for both the pre-test and the main part of the study were collected online via social media in 2019 and 2020. Surveys were administered through 1KA (Version 17.05.02). All participants completed the survey voluntarily and did not receive any compensation for it. We recruited participants using two methods - “snowball sampling” technique and paid targeted advertisements on the social network called “VK”. “VK” is the most popular social network in Russia that as of 2019 has an audience of more than 90 million Russian citizens throughout all of Russia.

All materials were administered in Russian.The survey started with a consent form (see Appendix 1), followed by instructions and ways to contact the researcher with questions or comments.

Firstly, participants completed their socio-demographic information. Then participants were presented with a photograph of a target. They were asked:(1)to suggest the nationality of a man in a picture, (2) how hard it was to guess the nationality and (3) to fillout a shortened warmth-competence questionnaire, consisting of 16 words-descriptors of a target. This process comprises one block (see Appendix 2).

Each participant was presented with 6 such blocks overall which differentiated by ethnicity- with the photos of Chechen, Polish, Russian, German, Uzbekistan and Chinese target. Using photographs elicits the same responses across SCM dimensions as using labels (Fiske, 2018). These ethnicities were chosen based on research on stereotype content of ethnic groups in Russia (Grigoryev et al., 2019), so that they represent opposed groups (high warmth-high competence, low warmth-low competence, low warmth-high competence). The photos are of an averaged person's face from each specific ethnicity, they were taken from the website faceresearch.org website (with the creators' consent). In the current paper we aimed to choose faces that would be similar across age and gender, as to not evoke any unneeded stereotypes. The categories of gender and age have their foundations in biology and society (human interdependence can be seen across cultures), and they always appear as descriptors of groups when recollecting groups in society. Vast research supports that the stereotypes which emerge from gender and age perceptions are seemingly universal and generally shared across cultures (Fiske, 2017).

Measures

Participants answered how hard it is for them to guess the nationality on a5-point scale. In the warmth-competence questionnaire, participants rated the target on perceived warmth and competence, using a 5-point Likert scale (1 = not at all; 5 = extremely). In accordance with the SCM instructions, they were asked how, in their opinion, society views each group (Fiske et al., 2002). This scale measures stereotype contentment on four dimensions morality, sociability, skills and assertiveness.

Independentvariable - photos - ethnicity.

Dependent variable - questionnaire answers; stereotypes content.

Statistical Analysis and Results

After clearing the sample of the answers from surveys that were fully completedyet unusable because of invalid answers (had jokes instead of actual answers in open questions, etc.), 332 participants' answers were analysed. The open-question answers were redacted for misspells, extra comments and punctuation marks were removed. Excel 2016 and IBM SPSS Statistics 2019 were used.

To understand how accurately participants guessed the ethnicity of a target we calculated the percentage of correct answers in three ways:

Strict condition - only correct answers are calculated as correct (see table 1)

Table 1 - Pre-test Guessing Ethnicity, Strict Condition

	Chechen	Polish	Russian	German	Uzbek	Chinese
% correct	12,4%	3,6%	67,9%	4,5%	28,8%	28.5%

Middle condition -similar-looking ethnicities were also taken into account (e.g. Caucasian was calculated as a correct answer for a Chechen target, but Estonian was not) (see table 2).



Table 2 - Pre-test Guessing Ethnicity, Middle Condition

	Chechen	Polish	Russian	German	Uzbek	Chinese
% correct	87%	85%	89%	69%	95%	88%

Quadrant condition -ethnicities that fall into the same warmth-competence quadrant according to previous research (Grigoryev et al./ 2019), were calculated as correct (see table 3).

Table 3 - Pre-test Guessing Ethnicity, QuadrantCondition

	Chechen	Polish	Russian	German	Uzbek	Chinese
% correct	22%	5%	76%	12%	16%	54%

As can be seen, Chechen, Russian, Uzbek and Chinese targets are better identified then German and Polish targets. The exception was, in the middle condition German and Polish targets were also identified accurately, arguablydue to the fact that “Russian” was classified as correct identification in this condition.

For further analysis we computed means for each stereotype dimension-morality, sociability, skills and assertiveness. We checked Alpha-Cronbach for each of the fourstereotype dimensions as well as for the warmth and competence dimensions. All values were above 0.8 mark suggesting a relatively high internal consistency of the four facet model, except for the competence scale, which iscompliant with previous research suggesting the need for separate sub-dimensions within the competence dimension (Abele et al., 2016) (see table 4).

Table 4 - Pre-test Alpha-Cronbach

	Moral	Sociability	Warmth	Skills	Assertiveness	Competence
Chechen	.87	.84	.92	.86	.81	.66
Polish	.86	.83	.91	.83	.84	.74
Russian	.90	.89	.94	.85	.81	.71
German	.90	.90	.94	.87	.84	.76
Uzbek	.92	.90	.94	.88	.84	.78
Chinese	.92	.90	.94	.91	.86	.81



Then the correlation between ethnicity identification accuracy and stereotype content was calculated (see table 5)

Table 5 - Pre-test Correlation for accuracy of guessing and stereotype content

	Guess(Strogo)	Guess(Kv)	Guess(Middle)	Morality	Sociability	Skills	Assertiveness
Guess(Strogo)	1
Guess(Kv)	0.70	1
Guess(Middle)	0.15	0.01	1
Morality	-0.08	-0.10	-0.15	1
Sociability	-0.14	-0.12	-0.17	0.79	1
Skills	-0.06	-0.07	-0.15	0.72	0.69	1
Assertiveness	0.20	0.12	0.12	0.12	0.06	0.22	1

Based on the results of the t-test (see Appendix 3, 4, 5, 6) we selected four groups that show most significant difference among all four subdimensions of SCM. The significance of the difference was measured for each of the 6 targets with 15 pairwise comparisons. Hence the members of those pairs that showed robust differences in all of the four scales of comparison (for each of the dependent variable) were chosen for the following main part of the study.

The pre-test showed that, overall,participants collected for the current sample present similar stereotypes of ethnic groups as in previous research. Based on the results of t-test, we decided to keep four ethnicities for the main part of the research - Chechen, Russian, Uzbek and Chinese - as distinct enough groups to present interest when looking for differences.

Main test

Participants

722 participants fully filled out the survey. 19% were male, with the age varying from 14 to 74 (M = 40, SD = 13), 15% were students. 85% identified as Russian. 50% were atheists and 42% are Christians.

Procedure

All materials were administered in Russian.

The survey started with consent form, followed by instructions and how to contact the researcher with questions or comments. Firstly, participants completed their socio-demographic information. Then participants were presented with a photograph of a target. Similarly to the pre-test, they were asked to propose the ethnicity of a man on a picture, how hard it was to guess the ethnicity, and, after that, they filled out a shortened warmth-competence questionnaire. 5 new questions were added: two questions asking how prestigious of a job does the target has and how much he earns, one question to measure perceived similarity to Russians, one question asking how similar targets'goals are compared to Russians and one question asking whether the ethnicity of the target is prestigious in Russia.

Each participant was presented with only one block. There were 8 blocks overall, differing only in the photographs presented.

For photographs we took four images chosen in the pre-test and then photoshopped each one with a reflective vest for the low-SES condition and a business suit for the high-SES condition (4ethnicitiesЧ 2 SES). Images of the vest and the business suit were taken the website of stock images for free use. They were tested to show that they are associated with poverty and wealth respectively (see Appendix 7). One block of the questionnaire can be seen in Appendix 8.

Measures

Independent variables - photos - ethnicity and socio-economic status represented by clothing (working clothes - overalls typical for outdoors city workers in Russia and classical business attire - business suit). According to Fiske et al. (2016) individual's status can be represented through attire and will evoke stereotyping based on perceived social status.

Dependent variables - questionnaire answers; stereotypes content.

The level of how hard it was for the participant to guess the ethnicity and the stereotype content are measured the same as in the pre-test.

New questions are all answered on a 5-point scale. 

Results

Excel 2016 and IBM SPSS Statistics 26 were used.

The data had no missing values. Four observations were removed due to being obviously poorly filled out (included jokes or numbers in fields where text was supposed to be).

To control that the status manipulation worked properly we conducted a one-way between-subjectsANOVA to compare SES of the target (High, Low)with the ratings onprofessional prestige and salary. The effect was statistically significant - ANOVA (F(1,1250)=319, p< .001) andANOVA (F(1,1250)=297, p< .001) respectively, with means for low statusconditions consistently lower than for the high status conditions for these measures, meaning that the manipulation was successful.

Similarly to pre-test we tested how accurately did the respondents guess the ethnicity of a target. That was done in tree ways - with strict, middle and quadrant conditions.

For the strict condition only fully correct answers were calculated as correct (see Table 6).

Table 6 - Main Test Guessing Ethnicity, Strict Condition

% correct	Chechen	Russian	Uzbek	Chinese
High-SES	19%	57%	20%	14%
Low-SES	12%	65%	34%	24%

For the middlecondition,t he category of correct answers was expanded to include not only strictly correct answers but also ethnicities that are close appearance-wise (see Table 7).

Table 7 - Main Test Guessing Ethnicity, Middle Condition

% correct	Chechen	Russian	Uzbek	Chinese
High-SES	100%	88%	91%	99%
Low-SES	84%	93%	94%	90%

For the quadrantconditionethnicitiesfromthe same warmth-competence quadrant (Grigoryev, et al., 2019) as the target were marked as correct (see Table 8).

Table 8 - Main Test Guessing Ethnicity, QuadrantCondition

% correct	Chechen	Russian	Uzbek	Chinese
High-SES	30%	61%	33%	51%
Low-SES	33%	71%	67%	40%

Then we mapped each of the 8 groups into the scatter plot to visualise the results. Here we used two main SCM dimensions - morality and competence, in order to be able to compare the present results with previous research(see Figure 1).

Figure 1 - The Russian SCM Map for Ethnicity and Status



Low-SES Russians score the highest on the warmth scale, while Chinese score highest on the competence scale, followed closely by high-SES Russians and Chechen. Both Chechen groups have the lowest ratings on warmth scale with high status group having the lowest scores. The least competent group is low status Uzbeks followed, unexpectedly by low status Chinese.

Overall, scores for Russian target have moved down on both scales, and Chechen scores, though still low on warmth scale, moved up in both dimensions, when compared to results obtained by Grigoryev and colleagues (2019).

To understand how our groups differ on all four subscales we created a spider diagram which can be seen on Figure 2 (see Appendix 9). Chechens with high SES were viewed as the most assertive group, followed by high-SES Russians. High-SES Chinese seem to be on the same middle level across all four scales (though in case of skills subscale they have the highest score).

Status

The results of the descriptive statistics of the variables are presented in Table 9. Comparative analysis of variables using MANOVA, with status an independent variable, and stereotype content (e.g. assertiveness, skills, morality, and sociability)-as dependent variables showed a significant multivariate main effect for the competence dimension (see Table 9).

After that we conducted post-hoc analysis (Tukey's HSD) to understand the nature of groups' differences. Its' results are presented in Table 9 with subscript letters indicating belonging to a group (different letters indicate groups that have a statistically significant difference). Socioeconomic status of the targets significantly affects the competence scale, but not the warmth scale. Though some inclination towards lower scores in warmth dimension for high-status targets can be observed it is not significant.

Table 9 - MANOVA Effect of Status and Post-hoc Results

		Status	F(1,710)	з2
		Low	High
Competence
Assertiveness	M	2.81a	3.43b	85.5*	0.11
	SD	(0.85)	(0.91)
Skills	M	2.66a	2.91b	20.5*	0.03
	SD	(0.79)	(0.84)
Warmth
Morality	M	2.97a	2.95a	0.1	0.01
	SD	(0.84)	(0.85)
Sociability	M	3.28a	3.17a	1.5	0.01
	SD	(0.91)	(0.86)

Note. * p < .001

Ethnicity

We adhered to a similar procedure, analysing the effect of ethnicity of the target on stereotype content: conducting MANOVA followed by a Tukey's HSD test to see which groups differ between each other (see Table 10).

Table 10 - MANOVA Effect of Ethnicity and Post-hoc Results

		Chechen	Russian	Uzbek	Chinese	F (3. 710)	з2
Competence
Assertiveness	M	3.54a	3.16b	2.92c	2.94bc	17.94*	0.07
	SD	(0.86)	(0.94)	(0.88)	(0.93)
Skills	M	2.64a	2.89b	2.63a	2.98b	9.48*	0.04
	SD	(0.73)	(0.78)	(0.84)	(0.88)
Warmth
Morality	M	2.68a	3.19b	2.88c	3.04bc	12.70*	0.05
	SD	(0.82)	(0.78)	(0.79)	(0.91)
Sociability	M	2.81a	3.55b	3.15c	3.28c	23.53*	0.09
	SD	(0.82)	(0.80)	(0.85)	(0.90)

Note. * p < .001 

Target's ethnicity affects all subscales of SCM. Chinese have the highest score on the skills scale followed closely by Russians. Russians have the highest scores on both subscales of warmth dimension followed by Chinese and Chechen have the lowest scores.

According to the Tukey's HSD test the four groups can be put together in three way depending on the subscale we are referring to. Group affiliation is indicated in Table 10 by small letters. Only Chechens can beisolated clearly into a separate group with highest scores on the assertiveness subscale, other ethnicities, though significantly different, cannot be formed into non-overlapping groups.

The four ethnic groups can be divided into two subsets based on the skills subscale. Chechens and Uzbeks form one group with lower scores and Russians and Chinese form a second subset.

Based on the sociability subscale the sample can be distributed into three subsets. Chechens form one group with lowest score, Russians - another one, with highest score, and Uzbeks and Chinese make the third - middle - group.

Status and Ethnicity Interaction

Following the same suit, we performed comparative analysis of variables using MANOVA, with status and ethnicity interaction as an independent variable, and stereotype content (e.g. assertiveness, skills, morality, and sociability) - as dependent variables. The analysis showed a significant multivariate main effect for three out of four dimensions (all subscales except assertiveness) (see Table 11). After that we conducted the post-hoc testing, the results of which are also displayed in Table 11 as a subscale letters (similar letters indicate the groups that are not statistically significantly different).

Table 11 - MANOVA Effect of Status and Ethnicity Interaction and Post-hoc Results

Chechen

Russian

Uzbek

Chinese

F(3.710)

з2

High

Low

High

Low

High

Low

High

Low

Competence

Assertiveness

M

3.70a

3.35ad

3.51ad

2.79b

3.24bd

2.55b

3.26d

2.64c

1.64

0.01

SD

0.82

0.87

0.96

0.76

0.86

0.76

0.92

0.84

Skills

M

2.58ad

2.71abd

2.95bd

2.82d

2.82abd

2.42a

3.34c

2.65ad

8.32*

0.03

SD

0.75

0.70

0.84

0.70

0.79

0.86

0.80

0.83

Warmth

Morality

M

2.59a

2.79ab

3.06bc

3.32c

2.94ab

2.82ab

3.23bdc

2.86ab

5.78*

0.02

SD

0.85

0.78

0.84

0.69

0.73

0.86

0.88

0.90

Sociability

M

2.66a

3.00ac

3.39bc

3.73b

3.16bc

3.14...

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