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- W4210364041 abstract "Abstract. Common model fit indices behave poorly in structural equation models for experience sampling data which typically contain many manifest variables. In this article, we propose a block-wise fit assessment for large models as an alternative. The entire model is estimated jointly, and block-wise versions of common fit indices are then determined from smaller blocks of the variance-covariance matrix using simulated degrees of freedom. In a first simulation study, we show that block-wise fit indices, contrary to global fit indices, correctly identify correctly specified latent state-trait models with 49 occasions and N = 200. In a second simulation, we find that block-wise fit indices cannot identify misspecification purely between days but correctly rejects other misspecified models. In some cases, the block-wise fit is superior in judging the strength of the misspecification. Lastly, we discuss the practical use of block-wise fit evaluation and its limitations." @default.
- W4210364041 created "2022-02-08" @default.
- W4210364041 creator A5019558950 @default.
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- W4210364041 date "2022-01-01" @default.
- W4210364041 modified "2023-09-29" @default.
- W4210364041 title "Block-Wise Model Fit for Structural Equation Models With Experience Sampling Data" @default.
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- W4210364041 doi "https://doi.org/10.1027/2151-2604/a000482" @default.
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