Measurement invariance of body image measures by age, gender, sexual orientation, race, weight status, and age: The U.S. Body Project I
- PMID: 35247868
- PMCID: PMC9167237
- DOI: 10.1016/j.bodyim.2022.01.015
Measurement invariance of body image measures by age, gender, sexual orientation, race, weight status, and age: The U.S. Body Project I
Abstract
Despite growing interest in comparing body image experiences across diverse groups, limited work has examined whether body image measures operate similarly across different populations, raising important questions about the appropriateness of comparing scale means across demographic groups. This study employed measurement invariance testing to evaluate whether such comparisons are appropriate with existing body image measures. Specifically, multi-group confirmatory factor analysis was conducted using a community sample of 11,620 men and women to test increasing levels of invariance (configural, metric, scalar) across five key demographic variables (age group, gender, sexual orientation, race, weight status) for five commonly used body image measures (the Sociocultural Attitudes Towards Appearance Questionnaire-4, the Body Surveillance subscale of the Objectified Body Consciousness Scale, the Appearance Evaluation and Overweight Preoccupation subscales of the Multidimensional Body-Self Relations Questionnaire, and the Body Image Quality of Life Inventory). Results provided evidence of scalar (i.e., strong) invariance for all five measures across age, gender, sexual orientation, race, and weight status groups, indicating that the latent factors captured by these measures have the same meaning across demographic groups. Findings therefore support the comparison of scale/subscale means across multiple demographic groups for these body image measures.
Keywords: Body Image; Body Mass Index; Gender; Psychometrics; Race; Sexual orientation.
Copyright © 2022 Elsevier Ltd. All rights reserved.
Conflict of interest statement
Conflict of Interest
There are no conflicts of interest.
References
-
- Berinsky AJ, Huber GA, & Lenz GS (2012). Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Political Analysis, 20, 351–368. 10.1093/pan/mpr057 - DOI
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