A log-linear model (LLM) is a well-known statistical method to examine the relationship among categorical variables. This study investigated the performance of LLM in detecting differential item functioning (DIF) for polytomously scored items via simulations where various sample sizes, ability mean differences (impact), and DIF types were…

Measurement invariance is crucial for making valid comparisons across different groups (Kline, 2016; Vandenberg, 2002). To address the challenges associated with invariance testing such as large sample size requirements, the complexity of the model, etc., applied researchers have incorporated parcels. Parcels have been shown to alleviate skewness,…

It has been pointed out in the literature that misspecification of the level-1 error covariance structure in latent growth modeling (LGM) has detrimental impacts on the inferences about growth parameters. Since correct covariance structure is difficult to specify by theory, the identification needs to rely on a specification search, which,…

Equivalence assessment is becoming an increasingly important topic in many application areas including behavioral and social sciences research. Although there exist more powerful tests, the two one-sided tests (TOST) procedure is a technically transparent and widely accepted method for establishing statistical equivalence. Alternatively, a direct…

Various tests to check the homogeneity of variance assumption have been proposed in the literature, yet there is no consensus as to their robustness when the assumption of normality does not hold. This simulation study evaluated the performance of 14 tests for the homogeneity of variance assumption in one-way ANOVA models in terms of Type I error…

This research examines the effects of single-value response style contamination on measures of model fit and model convergence issues. A simulation study examines the effects resulting from percentage of contamination, number of manifest, number of reverse coded items, magnitude of standardized factor loadings, response scale granularity, and…

Education experiments frequently assign students to treatment or control conditions within schools. Longitudinal components added in these studies (e.g., students followed over time) allow researchers to assess treatment effects in average rates of change (e.g., linear or quadratic). We provide methods for a priori power analysis in three-level…

Randomized experiments can provide key insights for improving educational technologies, but many students may experience conditions associated with inferior learning outcomes in these experiments. Multiarmed bandit (MAB) algorithms can address this issue by accumulating evidence from the experiment as it runs and modifying the experimental design…

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…

Although the research methodology literature includes empirical benchmarks for effect sizes and intraclass correlations to help researchers determine adequate sample sizes through power analysis, it does not include similar benchmarks that would assist proper planning for attrition. To help fill this void, this paper describes how researchers can…

The aim of the present research study was to compare the findings from the nonparametric MSA, DIMTEST and DETECT and the parametric dimensionality determining methods in various simulation conditions by utilizing exploratory and confirmatory methods. For this purpose, various simulation conditions were established based on number of dimensions,…

This study presents a new approach to synthesizing differential item functioning (DIF) effect size: First, using correlation matrices from each study, we perform a multigroup confirmatory factor analysis (MGCFA) that examines measurement invariance of a test item between two subgroups (i.e., focal and reference groups). Then we synthesize, across…

This meta-analysis intends to determine the impact of cooperative learning model research findings on the mathematics learning outcomes of Indonesian vocational high school students. Based on moderator variables, such as grade level and student sample size, identify efficacy and publication bias measures. Data from descriptive analysis, which…

Cognitive diagnostic models (CDMs) are a family of psychometric models designed to provide categorical classifications for multiple latent attributes. CDMs provide more granular evidence than other psychometric models and have potential for guiding teaching and learning decisions in the classroom. However, CDMs have primarily been conducted using…

A total of 1023 selected articles published in 2016-2019 related to mobile learning were examined and classified according to the categories in this research: 40% of these articles used quantitative approaches, 18% of them used mixed, and 13% of them were literature reviews. The published studies were analyzed according to research model, sample…