Measurement equivalence of the Marijuana Effect Expectancies Questionnaire-Brief across sex, race/ethnicity, and their co-occurring social identities for Black, Latinx, and non-Latinx white youth in the Adolescent Brain Cognitive Development (ABCD) Study.

Abstract

Objective: The primary aim of this study was to assess and adjust for measurement non-equivalence (bias) by sex, race/ethnicity, and co-occurring social identities (sex x race/ethnicity) for the Marijuana Effect Expectancies Questionnaire-Brief (MEEQ-B) among Black, Latinx, and Non-Latinx white youth. The second aim was to determine how group comparisons change after accounting for possible measurement bias.

Method: Black, Latinx, and Non-Latinx white youth from the Adolescent Brain and Cognitive Development Study Follow-up 3 (n=8,982; mean age=12.91; SD=0.65; 47.28% female; 15.03% Black, 22.93% Latinx, 62.04% Non-Latinx white) completed the MEEQ-B. Moderated nonlinear factor analysis (MNLFA) generated positive and negative expectancies factor scores accounting for non-equivalence. Analyses contrasted group differences by sex, race/ethnicity, and these co-occurring social identities using original (unadjusted) versus MNLFA-generated scores adjusted for measurement non-equivalence.

Results: Measurement non-equivalence was observed for positive and negative expectancies across sex, race/ ethnicity and their co-occurring social identities. MNLFA revealed between-group differences at the factor and item level. Further, comparisons of original (unadjusted) and MNLFA-generated adjusted scores revealed that unadjusted scores underestimated or did not detect some group differences in positive expectancies identified using adjusted scores, and unadjusted scores underestimated how much lower negative expectancies were in Black and Latinx relative to non-Latinx white youth.

Conclusions: Results highlight the need for caution when interpreting scores of a measure like the MEEQ-B that has not undergone measurement equivalence testing and demonstrate how failing to adjust for non-equivalence can result in biased estimates of positive and negative expectancies, particularly when used with diverse populations.