In the realities of measurement in social and behavioral sciences, in addition to the characteristic(s) of the respondents targeted by the measurement, other influences (other characteristics of the respondents and the items) can be reflected by the responses to the items in a measure. In the current study, different levels of deviations from strict unidimensionality in measures and the accuracy of parameter estimates of widely used unidimensional latent variable measurement models were further investigated. Of interest were unidimensionality violations in measures intended/designed as unidimensional (when the items primarily reflect a dominant latent dimension, as intended in a unidimensional measure, but also reflect, to a smaller degree, some additional influences). In the simulated conditions of interest, varying degrees of systematic error (bias) in the unidimensional model item and person parameters estimates were demonstrated (e.g., factor loadings overestimation and measurement error underestimation). The strength of the relevant relations and the size of bias were examined. If the size of these systematic distortions is uncommunicated, various negative consequences can ensue for substantive research and applied measurement (in relation to the reliability, validity, and fairness of research/measurement outcomes), when the model estimates are used. The utility of the approach employed in the study was discussed.
Revisiting Strong Assumptions in Psychological Constructs Measurement: Further Studying Deviations from Unidimensionality and Bias in Latent Variable Measurement Models
