Wording effects in assessment: missing the trees for the forest
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Date
2022
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Abstract
This article examines wording effects when positive and negative worded items are includedin psychological assessment. Wordings effects have been analyzed in the literature usingstatistical approaches based on population homogeneity assumptions (i.e. CFA, SEM), com-monly adopting the bifactor model to separate trait variance and wording effects. This art-icle presents an alternative approach by explicitly modeling population heterogeneitythrough a latent profile model, based on the idea that a subset of individuals exhibits word-ing effects. This kind of mixture model allows simultaneously to classify respondents, sub-stantively characterize the differences in their response profiles, and report respondents’results in a comparable manner. Using the Rosenberg’s self-esteem scale data from the LISSPanel (N¼6,762) in three studies, we identify a subgroup of participants who respond dif-ferentially according to item-wording and examine the impact of its responses in the esti-mation of the RSES measurement model, in terms of global and individual fit, under one-factor and bifactor models.The results of these analyses support the interpretation of wording effects in terms of a the-oretically-proposed differential pattern of response to positively and negatively wordeditems, introducing a valuable tool for examining the artifactual or substantive interpretationsof such wording effects.
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Keywords
Wording effects, latent profile analysis, Rosenberg's self-esteem scale, confirmatory factor analysis, iteratively reweighted least squares