Measurement Invariance of the Modified Yale Food Addiction Scale 2.0 Across Gender and Racial Groups. (March 2020)

• Record Type: Journal Article
• Authors: Carr, Meagan M.; Schulte, Erica M.; Saules, Karen K.; Gearhardt, Ashley N.
• Is Part Of: Assessment. Volume 27:Number 2(2020)
• Page Start: 356
• Page End: 364
• Publication Date: 2020-03
• Subjects: eating inventory -- disordered eating -- ethnic differences -- race -- gender -- measurement invariance; Psychological tests -- Periodicals; Psychodiagnostics -- Periodicals; Personality tests -- Periodicals; 150.287
• DOI: 10.1177/1073191118786576
• Languages: English
• ISSNs: 1073-1911

Abstract

Objective: Food addiction reflects a substance use disorder framework, suggesting certain foods (e.g., high-fat, high-sugar foods) may trigger an addictive-like eating response in vulnerable individuals. This study explored whether the Modified Yale Food Addiction Scale 2.0 (mYFAS 2.0), a newly validated and shortened measure of food addiction, is appropriate for use in heterogeneous samples. Assessment of mYFAS 2.0 invariance is essential, as this measure was developed for use as a brief screener in large epidemiological samples that are likely demographically diverse.Method: Multigroup confirmatory factor analysis examined measurement invariance across racial/ethnic (White, Black, and Hispanic) and gender (male, female) groups. Participants were recruited through Qualtrics qBus, which uses demographic quotas to recruit a sample representative of the U.S. census reference population. Individuals were included in analyses if they identified their race/ethnicity as White, Black, or Hispanic ( N = 923).Results: Results supported full and partial measurement invariance across racial and gender groups, respectively.Discussion: Results increase confidence in the generalizability of findings using the mYFAS 2.0 and indicate that observed differences in prevalence rates, such as the higher rates of food addiction observed for women and Hispanic individuals, are likely due to true differences in the population rather than due to measurement bias.