Estimating La tent Variable Interactions and Quadratics: The State of This Art

1996 ◽  
Vol 22 (1) ◽  
pp. 163-183 ◽  
Author(s):  
Robert A. Ping
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
The State ◽  
Detection Techniques ◽  
Structural Equation Analysis ◽  
Recent Advances ◽  
Equation Analysis ◽  
Nonlinear Structural Equation ◽  
Latent Variable Interactions ◽  
Variable Interactions

Because there is little guidance for substantive researchers, the paper reviews the techniques for detecting latent variable interactions and quadratics. After examining plausible research situations where including these nonlinear variables might be appropriate, the paper describes the available detection techniques. Recent advances in nonlinear structural equation analysis are given particular attention, and suggestions for substantive researchers are made.

Generalized Appended Product Indicator Procedure for Nonlinear Structural Equation Analysis

2001 ◽  
Vol 26 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Melanie M. Wall ◽  
Yasuo Amemiya
Keyword(s):  
Social Sciences ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Prevention Study ◽  
Structural Equation Analysis ◽  
Nonlinear Structural ◽  
Equation Analysis ◽  
Standard Error Estimation ◽  
Statistical Issues ◽  
Nonlinear Structural Equation

Interest in considering nonlinear structural equation models is well documented in the behavioral and social sciences as well as in the education and marketing literature. This article considers estimation of polynomial structural models. An existing method is shown to have a limitation that the produced estimator is inconsistent for most practical situations. A new procedure is introduced and defined for a general model using products of observed indicators. The resulting estimator is consistent without assuming any distributional form for the underlying factors or errors. Identification assessment and standard error estimation are discussed. A simulation study addresses statistical issues including comparisons of discrepancy functions and the choice of appended product indicators. Application of the new procedure in a substance abuse prevention study is also reported.

scholarly journals Sustainable Causal Interpretation with Board Characteristics: Caveat Emptor

2020 ◽  
Vol 12 (8) ◽  
pp. 3429 ◽  
Author(s):  
Jong-Min Kim ◽  
Chulhee Jun ◽  
Hope H. Han
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Dependence Structure ◽  
Firm Characteristics ◽  
Causal Interpretation ◽  
Latent Factors ◽  
Caveat Emptor ◽  
Structural Equation Analysis ◽  
Equation Analysis ◽  
Dependence Relationship

The study of a causal interpretation of board and firm characteristics, that is, a hidden dependence relationship on the causal inference among board and firm characteristics, is an important but unaddressed issue in the corporate governance literature. Using diverse advanced statistical methods and focusing on Tobin’s Q, we find that (i) not all board variables previously found to be significant are “robust” to latent variable data analysis, and (ii) those variables that are consistently significant differ markedly in latent structural equation analysis. Our analyses provide researchers interested in board issues with an important caveat: Focusing on the dependence structure of available board variables affected by latent factors may introduce a new horizon in corporate finance.

Integrating Moderation and Mediation

2016 ◽  
Vol 20 (4) ◽  
pp. 721-745 ◽  
Author(s):  
Shruti R. Sardeshmukh ◽  
Robert J. Vandenberg
Keyword(s):  
Structural Equation Modeling ◽  
Research Methods ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Latent Variable Interactions ◽  
Variable Interactions ◽  
Do So

It is increasingly common to test hypotheses combining moderation and mediation. Structural equation modeling (SEM) has been the favored approach to testing mediation hypotheses. However, the biggest challenge to testing moderation hypotheses in SEM was the complexity underlying the modeling of latent variable interactions. We discuss the latent moderated structural equation procedure (LMS) approach to specifying latent variable interactions, which is implemented in Mplus, and offer a simple and accessible way of testing combined moderation and mediation hypotheses using SEM. To do so, we provide sample code for six commonly encountered moderation and mediation cases and relevant equations that can be easily adapted to researchers’ data. By articulating the similarities in the two different approaches, discussing the combination of moderation and mediation, we also contribute to the research methods literature.

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