A Monte Carlo study was conducted to compare the performance of a level-specific (LS) fit evaluation with that of a simultaneous (SI) fit evaluation in multilevel confirmatory factor analysis (MCFA) models. We extended previous studies by examining their performance under MCFA models with different factor structures across levels. In addition,…

Item analysis is performed by developers as an integral part of the scale development process. Thus, items are excluded from the scale depending on the item analysis prior to the factor analysis. Existing item discrimination indices are calculated based on correlation, yet items with different response patterns are likely to have a similar item…

Factor mixture modeling (FMM) is generally complex with both unobserved categorical and unobserved continuous variables. We explore the potential of item parceling to reduce the model complexity of FMM and improve convergence and class enumeration accordingly. To this end, we conduct Monte Carlo simulations with three types of data, continuous,…

To evaluate the fit of a confirmatory factor analysis model, researchers often rely on fit indices such as SRMR, RMSEA, and CFI. These indices are frequently compared to benchmark values of 0.08, 0.06, and 0.96, respectively, established by Hu and Bentler (1999). However, these indices are affected by model characteristics and their sensitivity to…

Chi-square tests based on maximum likelihood (ML) estimation of covariance structures often incorrectly over-reject the null hypothesis: [sigma] = [sigma(theta)] when the sample size is small. Reweighted least squares (RLS) avoids this problem. In some models, the vector of parameter must contain means, variances, and covariances, yet whether RLS…

A simulation study was conducted to explore the impact of differential item functioning (DIF) on general factor difference estimation for bifactor, ordinal data. Common analysis misspecifications in which the generated bifactor data with DIF were fitted using models with equality constraints on noninvariant item parameters were compared under data…

Prior studies investigating the effects of non-normality in structural equation modeling typically induced non-normality in the indicator variables. This procedure neglects the factor analytic structure of the data, which is defined as the sum of latent variables and errors, so it is unclear whether previous results hold if the source of…

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the…

In exploratory factor analysis, although the researchers decide which items belong to which factors by considering statistical results, the decisions taken sometimes can be subjective in case of having items with similar factor loadings and complex factor structures. The aim of this study was to examine the validity of classifying items into…

When estimating an average treatment effect with observational data, it's possible to get an unbiased estimate of the causal effect if all confounding variables are observed and reliably measured. In education, confounding variables are often latent constructs. Covariate selection methods used in causal inference applications assume that all…

Response time models (RTMs) are of increasing interest in educational and psychological testing. This article focuses on the lognormal model for response times, which is one of the most popular RTMs. Several existing statistics for testing normality and the fit of factor analysis models are repurposed for testing the fit of the lognormal model. A…

Exploratory factor analysis (EFA) is widely used by researchers in the social sciences to characterize the latent structure underlying a set of observed indicator variables. One of the primary issues that must be resolved when conducting an EFA is determination of the number of factors to retain. There exist a large number of statistical tools…

Response-time models are of increasing interest in educational and psychological testing. This paper focuses on the lognormal model for response times (van der Linden, 2006), which is one of the most popular response-time models. Several existing statistics for testing normality and the fit of factor-analysis models are repurposed for testing the…

Kaufman Domains of Creativity Scale (K-DOCS) is a self-report of creative behavior in five distinct domains. The present study aims to translate K-DOCS into Russian and evaluate its psychometric properties. The psychometric analysis was performed on a sample of adults recruited through Yandex Toloka (N = 1011; M[subscript age] = 35.94,…

Purpose: This study aims to investigate the orthogonality assumption, which restricts the use of Bifactor item response theory under different conditions. Method: Data of the study have been obtained in accordance with the Bifactor model. It has been produced in accordance with two different models (Model 1 and Model 2) in a simulated way.…