Bayesian power equivalence in latent growth curve models

Autor:	Timo von Oertzen, Angelika Stefan
Přispěvatelé:	Psychologische Methodenleer (Psychologie, FMG)
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Statistics and Probability Bayesian probability power analysis structural equation modelling 01 natural sciences Structural equation modeling 010104 statistics & probability 0504 sociology Arts and Humanities (miscellaneous) Frequentist inference Econometrics Humans Computer Simulation Longitudinal Studies 0101 mathematics Equivalence (measure theory) General Psychology Statistical hypothesis testing Likelihood Functions Models Statistical 05 social sciences 050401 social sciences methods Bayes factor Bayes Theorem General Medicine Original Articles sample size Power analysis Sample size determination Linear Models Original Article design analysis Factor Analysis Statistical Mindfulness
Zdroj:	The British Journal of Mathematical and Statistical Psychology British Journal of Mathematical & Statistical Psychology, 73(S1), 180-193. Wiley-Blackwell British Journal of Mathematical and Statistical Psychology
ISSN:	2044-8317 0007-1102
Popis:	Longitudinal studies are the gold standard for research on time‐dependent phenomena in the social sciences. However, they often entail high costs due to multiple measurement occasions and a long overall study duration. It is therefore useful to optimize these design factors while maintaining a high informativeness of the design. Von Oertzen and Brandmaier (2013, Psychology and Aging, 28, 414) applied power equivalence to show that Latent Growth Curve Models (LGCMs) with different design factors can have the same power for likelihood‐ratio tests on the latent structure. In this paper, we show that the notion of power equivalence can be extended to Bayesian hypothesis tests of the latent structure constants. Specifically, we show that the results of a Bayes factor design analysis (BFDA; Schönbrodt & Wagenmakers (2018, Psychonomic Bulletin and Review, 25, 128) of two power equivalent LGCMs are equivalent. This will be useful for researchers who aim to plan for compelling evidence instead of frequentist power and provides a contribution towards more efficient procedures for BFDA.
Databáze:	OpenAIRE
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