The Hidden Role of Dominance in Career Interests: A Bifactor Analysis of Agentic and Communal Goal Orientations

Abstract

Agentic and communal goal orientations are widely used to predict career interests. However, the number of dimensions that underlie measures of goal orientations remains unclear. Across two studies, using exploratory structural equation modeling (ESEM) and bifactor confirmatory factor analysis, we found that communal goal orientation was unidimensional. However, agentic goal orientations comprised a single global agentic factor that represents a competence dimension plus two domain-specific factors: dominance and self-direction. Structural equation modeling indicated that gender differences in goal orientations, as well as the indirect effects of gender on career interest via goal orientations, were small. However, goal orientations exhibited sizeable direct effects on career interests, with agentic dominance goals the strongest predictor of organizational fit (Study 1 with 318 U.S. college students) and career interests (Study 2 with 789 U.S. MTurk workers). Future studies should consider the multidimensional structure of agentic goals and examine how dominance goals may help us better understand gender differences, social roles, and career preferences.

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Correspondence to Abigail M. Folberg.

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Should this paper be accepted, all stimulus materials, data, and Mplus commands will be made publicly available on Open Science Framework. Further, the authors have no conflict of interest to declare. We conducted this research in accordance with the American Psychological Association’s Guidelines for the Responsible Conduct of Research and the research was approved by the IRB at the University of Nebraska, Omaha.

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Folberg, A.M., Kercher, K. & Ryan, C.S. The Hidden Role of Dominance in Career Interests: A Bifactor Analysis of Agentic and Communal Goal Orientations. Sex Roles 83, 193–210 (2020). https://doi.org/10.1007/s11199-019-01104-1

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Keywords

  • Communal goal orientations
  • Agentic goal orientations
  • Dominance
  • Gender roles
  • Bifactor modeling
  • Structural equation modeling