Score‐based tests for parameter instability in ordinal factor models.
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| Title: | Score‐based tests for parameter instability in ordinal factor models. |
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| Authors: | Classe, Franz (AUTHOR), Debelak, Rudolf (AUTHOR), Kern, Christoph (AUTHOR) |
| Source: | British Journal of Mathematical & Statistical Psychology. Nov2025, Vol. 78 Issue 3, p996-1024. 29p. |
| Subjects: | Item response theory, Parameter estimation, Factor analysis, Statistical software, Error probability, Statistical hypothesis testing, Test scoring |
| Abstract: | We present a novel approach for computing model scores for ordinal factor models, that is, graded response models (GRMs) fitted with a limited information (LI) estimator. The method makes it possible to compute score‐based tests for parameter instability for ordinal factor models. This way, rapid execution of numerous parameter instability tests for multidimensional item response theory (MIRT) models is facilitated. We present a comparative analysis of the performance of the proposed score‐based tests for ordinal factor models in comparison to tests for GRMs fitted with a full information (FI) estimator. The new method has a good Type I error rate, high power and is computationally faster than FI estimation. We further illustrate that the proposed method works well with complex models in real data applications. The method is implemented in the lavaan package in R. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | We present a novel approach for computing model scores for ordinal factor models, that is, graded response models (GRMs) fitted with a limited information (LI) estimator. The method makes it possible to compute score‐based tests for parameter instability for ordinal factor models. This way, rapid execution of numerous parameter instability tests for multidimensional item response theory (MIRT) models is facilitated. We present a comparative analysis of the performance of the proposed score‐based tests for ordinal factor models in comparison to tests for GRMs fitted with a full information (FI) estimator. The new method has a good Type I error rate, high power and is computationally faster than FI estimation. We further illustrate that the proposed method works well with complex models in real data applications. The method is implemented in the lavaan package in R. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00071102 |
| DOI: | 10.1111/bmsp.12392 |
