Model Fit Indices for Random Effects Models: Translating Model Fit Ideas from Latent Growth Curve Models

被引：1

作者：

Zhang, Ziwei ^{[1
]}

Rohloff, Corissa T. ^{[1
]}

Kohli, Nidhi ^{[1
,2
]}

机构：

[1] Univ Minnesota, Minneapolis, MN 55455 USA

[2] Univ Minnesota, 166 Educ Sci Bldg, 56 East River Rd, Minneapolis, MN 55455 USA

来源：

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL | 2023年 / 30卷 / 05期

关键词：

Latent growth curve model; linear and nonlinear models; overall model fit indices; random effects model; RANDOM COEFFICIENT MODELS; LEARNING-DISABILITIES; STUDENTS;

D O I：

10.1080/10705511.2022.2138893

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The latent growth curve modeling (LGM) and random effects modeling (REM) frameworks are analytically and empirically equivalent for intrinsically linear models and used interchangeably for intrinsically nonlinear models. However, while LGM provides overall model fit indices, REM does not. Overall model fit indices are useful because they evaluate how well a specified model fits data. This paper proposes to translate model fit concepts from LGM to REM to help researchers compute overall model fit indices, including the model chi-square (chi 2), comparative fit index (CFI), root mean squared error of approximation (RMSEA), and standardized root mean squared residual (SRMR). Three empirical examples were used as illustrations.

引用

页码：822 / 830

页数：9

共 32 条

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