A Monte Carlo Test for Longitudinal Structural Equation Models in Small Samples

2020 ◽  
pp. 1-17
Author(s):  
Sarfaraz Serang
Keyword(s):  
Monte Carlo ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Small Samples ◽  
Monte Carlo Test

scholarly journals Analytical power calculations for structural equation modeling: A tutorial and Shiny app

2020 ◽  
Author(s):  
Suzanne Jak ◽  
Terrence D. Jorgensen ◽  
Mathilde G. E. Verdam ◽  
Frans J. Oort ◽  
Louise Elffers
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Statistical Significance ◽  
Equation Modeling ◽  
Ratio Test ◽  
Power Calculations ◽  
Shiny App ◽  
Computationally Intensive

Abstract Conducting a power analysis can be challenging for researchers who plan to analyze their data using structural equation models (SEMs), particularly when Monte Carlo methods are used to obtain power. In this tutorial, we explain how power calculations without Monte Carlo methods for the χ2 test and the RMSEA tests of (not-)close fit can be conducted using the Shiny app “power4SEM”. power4SEM facilitates power calculations for SEM using two methods that are not computationally intensive and that focus on model fit instead of the statistical significance of (functions of) parameters. These are the method proposed by Satorra and Saris (Psychometrika 50(1), 83–90, 1985) for power calculations of the likelihood ratio test, and that described by MacCallum, Browne, and Sugawara (Psychol Methods 1(2) 130–149, 1996) for RMSEA-based power calculations. We illustrate the use of power4SEM with examples of power analyses for path models, factor models, and a latent growth model.

scholarly journals Correcting measurement error bias in interaction models with small samples

2006 ◽  
Vol 3 (2) ◽  
Author(s):  
Josep Bisbe ◽  
Germà Coenders ◽  
Willem Saris ◽  
Joan Batista-Foguet
Keyword(s):  
Measurement Error ◽  
Least Squares ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Ordinary Least Squares ◽  
Alternative Methods ◽  
Small Samples ◽  
Least Squares Regression ◽  
Interaction Terms ◽  
Error Bias

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression.

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