Conceptualizing two-variable disturbances preventing good model fit in confirmatory factor analysis as item-level method effects instead of correlated residuals avoids violating the principle that residual variation is unique for each item. The possibility of representing such a disturbance by a method factor of a bifactor measurement model was…

Regression factor score predictors have the maximum factor score determinacy, that is, the maximum correlation with the corresponding factor, but they do not have the same inter-correlations as the factors. As it might be useful to compute factor score predictors that have the same inter-correlations as the factors, correlation-preserving factor…

This dissertation compares the performance of equivalence test (EQT) and null hypothesis test (NHT) procedures for identifying invariant and noninvariant factor loadings under a range of experimental manipulations. EQT is the statistically appropriate approach when the research goal is to find evidence of group similarity rather than group…

This study investigated the small sample biasness of the ordered logit model parameters under multicollinearity using Monte Carlo simulation. The results showed that the level of biasness associated with the ordered logit model parameters consistently decreases for an increasing sample size while the distribution of the parameters becomes less…

Composite reliability, or coefficient omega, can be estimated using structural equation modeling. Composite reliability is usually estimated under the basic independent clusters model of confirmatory factor analysis (ICM-CFA). However, due to the existence of cross-loadings, the model fit of the exploratory structural equation model (ESEM) is…

Item parameter drift (IPD) is the systematic differentiation of parameter values of items over time due to various reasons. If it occurs in computer adaptive tests (CAT), it causes errors in the estimation of item and ability parameters. Identification of the underlying conditions of this situation in CAT is important for estimating item and…

The purpose of this study was to investigate the Type I Error findings and power rates of the methods used to determine dimensionality in unidimensional and bidimensional psychological constructs for various conditions (characteristic of the distribution, sample size, length of the test, and interdimensional correlation) and to examine the joint…

Determining the number of factors is a critical first step in exploratory factor analysis. Although various criteria and methods for determining the number of factors have been evaluated in the usual between-subjects R-technique factor analysis, there is still question of how these methods perform in within-subjects P-technique factor analysis. A…

In this study, we propose aggregating test scores with unidimensional within-test structure and multidimensional across-test structure based on a 2-level, 1-factor model. In particular, we compare 6 score aggregation methods: average of standardized test raw scores (M1), regression factor score estimate of the 1-factor model based on the…

The item-position effect describes how an item's position within a test, that is, the number of previous completed items, affects the response to this item. Previously, this effect was represented by constraints reflecting simple courses, for example, a linear increase. Due to the inflexibility of these representations our aim was to examine…

Researchers continue to be interested in efficient, accurate methods of estimating coefficients of covariates in mixture modeling. Including covariates related to the latent class analysis not only may improve the ability of the mixture model to clearly differentiate between subjects but also makes interpretation of latent group membership more…

In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and…

Exploratory factor analysis (EFA) is used routinely in the development and validation of assessment instruments. One of the most significant challenges when one is performing EFA is determining how many factors to retain. Parallel analysis (PA) is an effective stopping rule that compares the eigenvalues of randomly generated data with those for…

Most computerized adaptive tests (CATs) have been studied using the framework of unidimensional item response theory. However, many psychological variables are multidimensional and might benefit from using a multidimensional approach to CATs. This study investigated the accuracy, fidelity, and efficiency of a fully multidimensional CAT algorithm…

In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…