This study compared Stepwise Logistic Regression (Stepwise-LR) and three machine learning (ML) methods--Classification and Regression Trees (CART), Random Forest (RF), and Generalized Boosted Modeling (GBM) for estimating propensity scores (PS) applied in causal inference. A simulation study was conducted considering factors of the sample size,…

Over the past decade, statisticians have developed methods to improve generalizations from nonrandom samples using propensity score methods. While these methods contribute to generalization research, their effectiveness is limited by small sample sizes. Small area estimation is a class of model-based methods that address the imprecision due to…

This study examined the false positive (FP) rates and sensitivity of Bayesian fit indices to structural misspecification in Bayesian structural equation modeling. The impact of measurement quality, sample size, model size, the magnitude of misspecified path effect, and the choice or prior on the performance of the fit indices was also…

Rasch common-item equating is often used in high-stakes testing to maintain equivalent passing standards across test administrations. If unaddressed, item parameter drift poses a major threat to the accuracy of Rasch common-item equating. We compared the performance of well-established and newly developed drift detection methods in small and large…

This article describes an extension to the use of heteroskedastic ordered probit (HETOP) models to estimate latent distributional parameters from grouped, ordered-categorical data by pooling across multiple waves of data. We illustrate the method with aggregate proficiency data reporting the number of students in schools or districts scoring in…

Regression mixture models are a statistical approach used for estimating heterogeneity in effects. This study investigates the impact of sample size on regression mixture's ability to produce "stable" results. Monte Carlo simulations and analysis of resamples from an application data set were used to illustrate the types of problems that…

This study investigated the extent to which class-specific parameter estimates are biased by the within-class normality assumption in nonnormal growth mixture modeling (GMM). Monte Carlo simulations for nonnormal GMM were conducted to analyze and compare two strategies for obtaining unbiased parameter estimates: relaxing the within-class normality…

The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, three-step BCH approach, and LTB approach) based on diverse sample size conditions. To carry out this research, two simulation studies were conducted with two different models, a latent class…

In this study, it was aimed to investigate the impact of different missing data handling methods on DINA model parameter estimation and classification accuracy. In the study, simulated data were used and the data were generated by manipulating the number of items and sample size. In the generated data, two different missing data mechanisms…

Taking more than one measurement of the same variable also hosts the possibility of contamination from error sources, both singly and in combination as a result of interactions. Therefore, although the internal consistency of scores received from measurement tools is examined by itself, it is necessary to ensure interrater or intra-rater agreement…

Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score-based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods…

A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and…

Within the framework of item response theory (IRT), there are two recent lines of work on the estimation of classification accuracy (CA) rate. One approach estimates CA when decisions are made based on total sum scores, the other based on latent trait estimates. The former is referred to as the Lee approach, and the latter, the Rudner approach,…

The purpose of this study was to investigate the effect of complex structure on dimensionality assessment in noncompensatory multidimensional item response models using dimensionality assessment procedures based on DETECT (dimensionality evaluation to enumerate contributing traits) and NOHARM (normal ogive harmonic analysis robust method). Five…

To better understand the statistical properties of the deterministic inputs, noisy "and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the…