Interrelationships between latent state-trait theory and generalizability theory within a structural equation modeling framework.

2021 ◽  
Author(s):  
Walter P. Vispoel ◽  
Guanlan Xu ◽  
Wei S. Schneider
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Generalizability Theory ◽  
Equation Modeling ◽  
Modeling Framework ◽  
Trait Theory

scholarly journals Improving generalizability coefficient estimate accuracy: A way to incorporate auxiliary information

2018 ◽  
Vol 11 (2) ◽  
pp. 205979911879139 ◽  
Author(s):  
Zhehan Jiang ◽  
Kevin Walker ◽  
Dexin Shi ◽  
Jian Cao
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Generalizability Theory ◽  
Theory Model ◽  
Bias Reduction ◽  
Equation Modeling ◽  
Coefficient Estimate ◽  
Auxiliary Variables ◽  
Modeling Approach ◽  
Generalizability Coefficient

Initially proposed by Marcoulides and further expanded by Raykov and Marcoulides, a structural equation modeling approach can be used in generalizability theory estimation. This article examines the utility of incorporating auxiliary variables into the structural equation modeling approach when missing data is present. In particular, the authors assert that by adapting a saturated correlates model strategy to structural equation modeling generalizability theory models, one can reduce any biased effects caused by missingness. Traditional approaches such as an analysis of variance do not possess such a feature. This article provides detailed instructions for adding auxiliary variables into a structural equation modeling generalizability theory model, demonstrates the corresponding benefits of bias reduction in generalizability coefficient estimate via simulations, and discusses issues relevant to the proposed approach.

scholarly journals Analyzing factorial survey data with structural equation models

2021 ◽  
pp. 004912412110431
Author(s):  
Bert Weijters ◽  
Eldad Davidov ◽  
Hans Baumgartner
Keyword(s):  
Structural Equation Modeling ◽  
Survey Data ◽  
Structural Equation ◽  
Multilevel Models ◽  
Structural Equation Models ◽  
Social Science Research ◽  
Science Research ◽  
Equation Modeling ◽  
Factorial Survey ◽  
Modeling Framework

In factorial survey designs, respondents evaluate multiple short descriptions of social objects (vignettes) that experimentally vary different levels of attributes of interest. Analytical methods (including individual-level regression analysis and multilevel models) estimate the weights (or utilities) assigned to the levels of the different attributes by participants to arrive at an overall response to the vignettes. In the current paper, we explain how data from factorial surveys can be analyzed in a structural equation modeling framework using an approach called structural equation modeling for within-subject experiments. We review the use of factorial surveys in social science research, discuss typically used methods to analyze factorial survey data, introduce the structural equation modeling for within-subject experiments approach, and present an empirical illustration of the proposed method. We conclude by describing several extensions, providing some practical recommendations, and discussing potential limitations.

Response shift was identified over multiple occasions with a structural equation modeling framework

2009 ◽  
Vol 62 (11) ◽  
pp. 1181-1188 ◽  
Author(s):  
Ruth Barclay-Goddard ◽  
Lisa M. Lix ◽  
Robert Tate ◽  
Leah Weinberg ◽  
Nancy E. Mayo
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Response Shift ◽  
Equation Modeling ◽  
Modeling Framework

scholarly journals Rock the MIC: The Matrix of Implied Causation, a Tool for Experimental Design and Model Checking

2020 ◽  
Vol 3 (3) ◽  
pp. 286-299
Author(s):  
Timothy R. Brick ◽  
Drew H. Bailey
Keyword(s):  
Structural Equation Modeling ◽  
Experimental Design ◽  
Model Checking ◽  
Structural Equation ◽  
Equation Modeling ◽  
Causal Process ◽  
Path Modeling ◽  
Modeling Framework ◽  
Additional Tool ◽  
The Matrix

Path modeling and the extended structural equation modeling framework are in increasingly common use for statistical analysis in modern behavioral science. Path modeling, including structural equation modeling, provides a flexible means of defining complex models in a way that allows them to be easily visualized, specified, and fitted to data. Although causality cannot be determined simply by fitting a path model, researchers often use such models as representations of underlying causal-process models. Indeed, causal implications are a vital characteristic of a model’s explanatory value, but these implications are rarely examined directly. When models are hypothesized to be causal, they can be differentiated from one another by examining their causal implications as defined by a combination of the model assumptions, data, and estimation procedure. However, the implied causal relationships may not be immediately obvious to researchers, especially for intricate or long-chain causal structures (as in longitudinal panel designs). We introduce the matrix of implied causation (MIC) as a tool for easily understanding and reporting a model’s implications for the causal influence of one variable on another. With examples from the literature, we illustrate the use of MICs in model checking and experimental design. We argue that MICs should become a routine element of interpretation when models with complex causal implications are examined, and that they may provide an additional tool for differentiating among models with otherwise similar fit.

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