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Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed. [ABSTRACT FROM AUTHOR]

Wording effects associated with positively and negatively worded items have been found in many scales. Such effects may threaten construct validity and introduce systematic bias in the interpretation of results. A variety of models have been applied to address wording effects, such as the correlated uniqueness model and the correlated traits and correlated methods model. This study presents the multilevel bifactor approach to handling wording effects of mixed-format scales used in a multilevel context. The Students Confident in Mathematics scale is used to illustrate this approach. Results from comparing a series of models showed that positive and negative wording effects were present at both the within and the between levels. When the wording effects were ignored, the within-level predictive validity of the Students Confident in Mathematics scale was close to that under the multilevel bifactor model. However, at the between level, a lower validity coefficient was observed when ignoring the wording effects. Implications for applied researchers are discussed. [ABSTRACT FROM AUTHOR]

An experimental study (n = 288 general and special education teachers) examining the effects of altering the referent ("students with mild disabilities," "students with severe disabilities," or "students with disabilities") on a four-item scale (Negative Effect of Inclusion) indicated that wording changes had little effect on the scale's psychometric properties (e.g., factor pattern coefficients). Changes did result in a shift in the mean level of the attitude scale. Regression coefficients between the scale and type of teacher, total years of teaching experience, years of experience at current school, and training in inclusion were not significantly altered by changing the referent. Gender was the only predictor that exhibited a lack of invariance in its regression coefficients across questionnaire forms that differed in referent. For most of the bivariate relationships examined in this study, the same conclusions would be drawn no matter which of the three referents were used. [ABSTRACT FROM AUTHOR]

Investigates the structure of the Child Behavior Checklist/4-18 used in psychometrics. Use of confirmatory factor analysis; Instruments and procedures; Factor model and statistical analyses; Fit indexes for First-Order factor analysis models.

Examines a study conducted in the University of South Florida which contrasts the effects of randomly distributing items versus grouping items together on students' end-of-term course evaluations. Background of the study; Need for further research.