Alexander Robitzsch

Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal Designs

Mark Lustig, Oliver Lüdtke, Alexander Robitzsch, Simon Grund

Missing data are common in longitudinal designs and are often addressed with multiple imputation (MI), either as single-level MI, which treats repeated measures as separate variables, or multilevel MI, which treats repeated measures as nested within participants. Previous research has shown that …

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Revisiting Long-Term Trends in Germany’s Upper Secondary Mathematics Achievement: A Chain Linking Analysis from FIMS 1964 to TIMSS 1996

Carolin Wosch, Alexander Robitzsch, Aiso Heinze, Tobias Rolfes

Public discourse in many countries reflects a persistent perception of declining educational achievement over the decades, particularly among upper secondary graduates in mathematics at the transition to higher education. Assessing such claims requires evidence of long-term trends. International large-scale assessments (ILSAs), including …

Stabilizing Propensity Score Weighting Estimators: An Evaluation of Truncation, Trimming, and Shrinkage Approaches

Jingyu Zhang, Oliver Lüdtke, Alexander Robitzsch

Propensity score weighting is widely used to estimate the average treatment effect (ATE) in observational studies. Two common weighting estimators are inverse probability weighting (IPW) and weighted regression. Both methods can become unstable when estimated propensity scores approach 0 or 1, producing …

Flexible Multiple Imputation of Missing Data in Time-Structured Longitudinal Designs

Mark Lustig, Oliver Lüdtke, Alexander Robitzsch, Simon Grund

Missing data are common in longitudinal designs and are often addressed with multiple imputation (MI), either as single-level MI, which treats repeated measures as separate variables, or multilevel MI, which treats repeated measures as nested within participants. Previous research has shown that …

A review and evaluation of doubly robust approaches for estimating average treatment effects

Jingyu Zhang, Oliver Lüdtke, Alexander Robitzsch

In nonexperimental studies, obtaining an unbiased estimate of the average treatment effect (ATE) typically requires two key assumptions: that all relevant covariates are measured (i.e., no unmeasured confounding) and that the statistical model used for covariate adjustment is correctly specified. Two common …

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Evaluating a Bayesian Approach for Estimating Moderator Effects in Parameter-Based Meta-Analytic Structural Equation Modeling

Julian F. Lohmann, Oliver Lüdtke, Alexander Robitzsch

Meta-analytic structural equation modeling (MASEM) enables meta-analytic investigations of multivariate models. A common research objective in meta-analyses is identifying study-level moderators that explain heterogeneity across primary studies. Several MASEM approaches have been extended to include moderators; however, research evaluating and comparing these …

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Confidence Interval Estimation for RMSD and MD Item Fit Statistics

Alexander Robitzsch

Item response theory (IRT) models are widely used in the social sciences to analyze multivariate discrete data that include cognitive test items. In many applications, the performance of two groups is compared using IRT modeling. The assessment of differential item functioning (DIF) …

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Supplementary Material: Bias-corrected RMSD Item Fit Statistic via SIMEX

Alexander Robitzsch

Bias-Corrected Root Mean Square Deviation Estimators

Alexander Robitzsch

The root mean square deviation (RMSD) is a widely used item fit statistic in item response models. However, the sample RMSD is known to exhibit positive bias in small samples. To address this, seven alternative bias-corrected RMSD estimators are proposed and evaluated …

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Comparing Weighted RMSD, Weighted MD, Infit, and Outfit Item Fit Statistics Under Uniform Differential Item Functioning

Alexander Robitzsch

In educational large-scale assessment studies, uniform differential item functioning (DIF) across countries often challenges the application of a common item response model, such as the two-parameter logistic (2PL) model, to all participating countries. DIF occurs when certain items provide systematic advantages or …

de (code pays fourni par la source)

Evaluating a Bayesian Approach for Estimating Moderator Effects in Parameter-Based Meta-Analytic Structural Equation Modeling

Julian F. Lohmann, Oliver Lüdtke, Alexander Robitzsch

Meta-analytic structural equation modeling (MASEM) enables meta-analytic investigations of multivariate models. A common research objective in meta-analyses is identifying study-level moderators that explain heterogeneity across primary studies. Several MASEM approaches have been extended to include moderators, however research evaluating and comparing these …

Supplementary Material: A Note on Using Scale Sum Scores in Path Analysis

Alexander Robitzsch