A Note on the Structural Change Test in Highly Parameterized Psychometric Models

Autor: K. B. S. Huth, L. J. Waldorp, J. Luigjes, A. E. Goudriaan, R. J. van Holst, M. Marsman
Přispěvatelé: Psychology Other Research (FMG), Psychologische Methodenleer (Psychologie, FMG), Adult Psychiatry, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Digital Health
Rok vydání: 2022
Předmět:
Zdroj: Psychometrika, 87(3), 1064-1080. Springer New York
PSYCHOMETRIKA, 87(3), 1064-1080. Springer New York
ISSN: 1860-0980
0033-3123
DOI: 10.1007/s11336-021-09834-6
Popis: Equal parameter estimates across subgroups is a substantial requirement of statistical tests. Ignoring subgroup differences poses a threat to study replicability, model specification, and theory development. Structural change tests are a powerful statistical technique to assess parameter invariance. A core element of those tests is the empirical fluctuation process. In the case of parameter invariance, the fluctuation process asymptotically follows a Brownian bridge. This asymptotic assumption further provides the basis for inference. However, the empirical fluctuation process does not follow a Brownian bridge in small samples, and this situation is amplified in large psychometric models. Therefore, common methods of obtaining the sampling distribution are invalid and the structural change test becomes conservative. We discuss an alternative solution to obtaining the sampling distribution—permutation approaches. Permutation approaches estimate the sampling distribution through resampling of the dataset, avoiding distributional assumptions. Hereby, the tests power are improved. We conclude that the permutation alternative is superior to standard asymptotic approximations of the sampling distribution.
Databáze: OpenAIRE
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