All accountants are boring and introverted, they are brilliant at math, and love crunching numbers all day. These are just a handful of some popular, harmful stereotypes often attributed to accountants (Imada et al., 1980). Stereotypes are a form of heuristics that humans use to make sense of a world wrought with information-overload (Hilton & Hippel, 1996). Rather than being constantly overwhelmed by novel information and situations, individuals are categorized into groups based on specific attributes.
The Stereotype Content Model (SCM) is a renowned framework with which to classify the ways stereotypes are conceptualized (Fiske et al., 2002). There is strong empirical and theoretical evidence to suggest that stereotypes are conceptualized on a two-dimensional framework consisting of warmth and competence. Warmth concerns how well social bonds are upheld or “getting along” and competence refers to the proficiency with which tasks are accomplished or “getting ahead” (Abele et al., 2021). Previous research has demonstrated impressive reliability of the model to categorize stereotypes of gender, ethnicity, race, class, age, and disability-outgroups. Although some occupational components (e.g., black professionals, migrant workers, business women) are included alongside other stereotypes mentioned above (Cuddy & Fiske, 2002), there has yet to be a sole focus on occupational stereotypes using the SCM.
Humans spend a considerable portion of their lives working and constantly interact with individuals in occupations. The U.S. Bureau of Labor Statistics divides occupations into 23 categories, each associated with dozens of specific jobs (Standard Occupational Classification System, 2020). Just like accountants, each occupation comes with stereotypes that enable classification of the individual workers. Occupational stereotypes have been defined as “a preconceived attitude about a particular occupation, about people who are employed in that occupation or about one’s suitability for that occupation” (King et al., 2006, p. 1145). In other words, occupational stereotypes concern how people distinguish the motives of individuals of a particular profession (Abele et al., 2020). Given that information-reduction strategies would be necessary to evaluate how individuals should interact with the hundreds of types of workers in our society, the following is proposed:
- H1: Occupations will show distinct, reliable differences on the dimensions of warmth and competence.
Proposed Method
150 participants will be recruited from Baruch College’s Undergraduate SONA research pool. Participants will be shown the 23 occupational categories from the U.S. Bureau of Labor Statistics. Examples of occupational categories are legal, healthcare practitioners, construction, and management occupations. Participants will be asked to rate these groups on scales reflecting warmth and competence.
Both competence and warmth will be measured on a 5-point Likert scale ranging from 1 (not at all) to 5 (extremely). Competence. Competence will be assessed using a 5-item scale adapted from Fiske and colleagues (2002). Participants will be asked to rate how society views the competency of each occupational group. An example item is, “As viewed by society, how intelligent are members of this occupational group?” Warmth. Warmth will be assessed using a 4-item scale adapted from Fiske and colleagues (2002). Participants will be asked to rate how society views the warmth of each occupational group. An example item is, “As viewed by society, how sincere are members of this occupational group?”
Proposed Analyses
To test our hypothesis, the distribution of the 23 occupational categories on the two dimensions of warmth and competence will be classified using cluster analysis. This will be conducted using an exploratory hierarchical cluster analysis followed by a confirmatory k-means cluster analysis. First, the best-fit for number of clusters will be determined using Ward’s method of squared Euclidian distance (Ward, 1963). The agglomeration schedule produced by the analysis along with theoretical reasoning will be used to determine the best-fit for numbers of clusters. Second, depending on the results of the hierarchical cluster analysis, k-means cluster analysis will be used to examine the warmth and competence groupings of cluster memberships. Third, each cluster will be plotted on the two-dimensions of warmth and competence (see Figure 1). Along with cluster membership scores, each occupational category will be categorized on the distance from the respective cluster center.
Discussion
The present study aims to classify 23 occupational categories along the dimensions of warmth and competence, as put forth by the Stereotype Content Model. Considerable work has been done to understand a litany of stereotypes using the two-dimensional framework, but the literature in this domain lacks substantially on classification of occupational stereotypes (Fiske et al., 2002). This is rather surprising given the extensive implications that occupational stereotypes have on our society. Stereotypes of difficult math in occupations have historically resulted in less women applying to STEM fields (Bonot, 2007) and the high turnover rate in the food service has been attributed to negative stereotypes of individuals working in that industry (Wildes, 2007).
By clustering occupations on the SCM we can begin to further elucidate the public perception of occupations and classify the occupational stereotypes attached to those individuals in each industry. However, this study is simply the first step to reliably classify occupational stereotypes and is not without limitations. First, cluster analysis does not provide us with a level of statistical significance and would need to be paired with consecutive studies to ensure reliable classification. Follow-up studies would need to focus on interrater reliability of the tentative occupational groupings. Second, it is difficult to ensure that we are capturing how the participant believes society views this occupation, not their individual views. This may conflate the measurements to include individual and societal stereotypes.
There are distinct future directions in this novel domain of research that will ensure a reliable classification of occupational stereotypes. First, as mentioned above, follow-up studies must be conducted to assess the interrater reliability of the groupings. Second, after assessing interrater reliability, other empirical methods should be utilized to confirm the occupational groupings. A nascent research direction would be to group ONET KSAO measurements into dimensions of warmth and competence. Measurements of cooperation, dependability, and integrity could serve as indicators of warmth while achievement, innovation, and analytical thinking may be used to classify the competence of the occupation. Comparing these ONET groupings to the participant ratings could serve to confirm that the participants were not rating their personal stereotypes of occupations, but instead societal stereotypes.
References
Abele, A. E., Ellemers, N., Fiske, S. T., Koch, A., & Yzerbyt, V. (2021). Navigating the social world: Toward an integrated framework for evaluating self, individuals, and groups. Psychological Review, 128(2), 290.
Fiske, S. T., Cuddy, A. J., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition. Journal of personality and social psychology, 82(6), 878.
He, J. C., Kang, S. K., Tse, K., & Toh, S. M. (2019). Stereotypes at work: Occupational stereotypes predict race and gender segregation in the workforce. Journal of Vocational Behavior, 115, 103318.
Hilton, J. L., & Von Hippel, W. (1996). Stereotypes. Annual review of psychology, 47(1), 237-271.
Imada, A. S., Fletcher, C., & Dalessio, A. (1980). Individual correlates of an occupational stereotype: A reexamination of the stereotype of accountants. Journal of Applied Psychology, 65(4), 436.
King, E. B., Mendoza, S. A., Madera, J. M., Hebl, M. R., & Knight, J. L. (2006). What’s in a name? A multiracial investigation of the role of occupational stereotypes in selection decisions. Journal of Applied Social Psychology, 36(5), 1145-1159.
Standard Occupational Classification System. United States Department of Labor. (2020). Retrieved 23 May 2022, from https://www.bls.gov/soc/2018/major_groups.htm.
Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58(301), 236-244.
Wildes, V. J. (2007). Attracting and retaining food servers: How internal service quality moderates occupational stigma. International Journal of Hospitality Management, 26(1), 4-19.