ArticleEducational and psychological measurement2018
Summed Score Likelihood-Based Indices for Testing Latent Variable Distribution Fit in Item Response Theory.
Article in Educational and psychological measurement, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Estimating Latent Distribution of Item Response Theory Using Kernel Density Method.Psychometrika · 2026Article
- The generalized Hausman test for detecting non-normality in the latent variable distribution of the two-parameter IRT model.The British journal of mathematical and statistical psychology · 2025Article
- Article
- Random Item Response Data Generation Using a Limited-Information Approach: Applications to Assessing Model Complexity.Psychometrika · 2025Article
- Modified Item-Fit Indices for Dichotomous IRT Models with Missing Data.Applied psychological measurement · 2022Article
- The Lack of Robustness of a Statistic Based on the Neyman-Pearson Lemma to Violations of Its Underlying Assumptions.Applied psychological measurement · 2022Article
- OpenMx: A Modular Research Environment for Item Response Theory Method Development.Applied psychological measurement · 2020Article
- Analyzing the Fit of IRT Models With the Hausman Test.Frontiers in psychology · 2020Article
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2 authors.
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Abstract
In standard item response theory (IRT) applications, the latent variable is typically assumed to be normally distributed. If the normality assumption is violated, the item parameter estimates can become biased. Summed score likelihood-based statistics may be useful for testing latent variable distribution fit. We develop Satorra-Bentler type moment adjustments to approximate the test statistics' tail-area probability. A simulation study was conducted to examine the calibration and power of the unadjusted and adjusted statistics in various simulation conditions. Results show that the proposed indices have tail-area probabilities that can be closely approximated by central chi-squared random variables under the null hypothesis. Furthermore, the test statistics are focused. They are powerful for detecting latent variable distributional assumption violations, and not sensitive (correctly) to other forms of model misspecification such as multidimensionality. As a comparison, the goodness-of-fit statistic
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