Examining the Performance of the Trifactor Model for Multiple Raters

2021 ◽  
Vol 46 (1) ◽  
pp. 53-67
Author(s):  
James Soland ◽  
Megan Kuhfeld
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Item Response Theory ◽  
Item Response ◽  
Structural Equation ◽  
Convergence Rates ◽  
Model Fit ◽  
Equation Modeling ◽  
Simulation Studies ◽  
The Social

Researchers in the social sciences often obtain ratings of a construct of interest provided by multiple raters. While using multiple raters provides a way to help avoid the subjectivity of any given person’s responses, rater disagreement can be a problem. A variety of models exist to address rater disagreement in both structural equation modeling and item response theory frameworks. Recently, a model was developed by Bauer et al. (2013) and referred to as the “trifactor model” to provide applied researchers with a straightforward way of estimating scores that are purged of variance that is idiosyncratic by rater. Although the intent of the model is to be usable and interpretable, little is known about the circumstances under which it performs well, and those it does not. We conduct simulation studies to examine the performance of the trifactor model under a range of sample sizes and model specifications and then compare model fit, bias, and convergence rates.

An Innovative Application of Composite-Based Structural Equation Modeling in Hospitality Research With Empirical Example

2020 ◽  
pp. 193896552095175
Author(s):  
Noppadol Manosuthi ◽  
Jin-Soo Lee ◽  
Heesup Han
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Model Fit ◽  
Equation Modeling ◽  
Simulation Studies ◽  
Path Modeling ◽  
Parameter Recovery ◽  
Performance Map ◽  
Map Analysis ◽  
Oriented Approach

Partial least squares path modeling (PLS-PM) and generalized structured component analysis (GSCA) are two key estimators derived from a full-fledged composite-based structural equation modeling (SEM). The analyses of PLS-PM and GSCA have been recently extended to mimic factor-based SEM, and the extended approaches are called PLSC and GSCAM, respectively. Simulation studies have confirmed that the relative performance of PLS-PM is comparable with that of GSCA. Similarly, GSCAM, PLSC, and the traditional factor-based SEM perform equally well in parameter recovery. Although composite-based SEM perfectly fits into the current research landscape that focuses on a prediction-oriented approach, empirical research in the hospitality context that uses PLS-PM, GSCA, PLSC, and GSCAM estimators is extremely rare. To encourage hospitality researchers to adopt these methodologies, we demonstrate an illustrative example using PLS-PM, GSCA, PLSC, and GSCAM based on the confirmatory composite analysis (CCA) procedure. Measurement and structural invariances, applications of model fit, PLSpredict, and importance-performance map analysis are incorporated into our example. Finally, practical management in the hospitality field based on this methodology is discussed.

scholarly journals Application of Structural Equation Modeling to the Social Sciences: A Brief Guide for Researchers

2016 ◽  
Vol 37 (3) ◽  
pp. 99-123
Author(s):  
Vaithehy Shanmugam ◽  
John E. Marsh
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Latent Variables ◽  
Structural Equation ◽  
Research Question ◽  
Theoretical Models ◽  
Equation Modeling ◽  
Cognitive Behavioural ◽  
The Social ◽  
Testing Complex

Emanating from a family of statistical techniques used for the analysis of multivariate data to measure latent variables and their interrelationships, structural equation modeling (SEM) is briefly introduced. The basic tenets of SEM, the principles of model creation, identification, estimation and evaluation are outlined and a four-step procedure for applying SEM to test an evidence-based model of eating disorders (transdiagnostic cognitive-behavioural theory; Fairburn, Cooper, & Shafran, 2003) using previously obtained data on eating psychopathology within an athletic population (Shanmugam, Jowett, & Meyer, 2011) is presented and summarized. Central issues and processes underpinning SEM are discussed and it is concluded that SEM offers promise for testing complex, integrated theoretical models and advances of research within the social sciences, with the caveat that it should be restricted to situations wherein there is a pre-existing substantial base of empirical evidence and a strong conceptual understanding of the theory undergirding the research question.

Reflections on SEM

2021 ◽  
Vol 52 (SI) ◽  
pp. 101-113
Author(s):  
Joseph F. Hair
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Final Section ◽  
Early Years ◽  
Equation Modeling ◽  
Statistical Tool ◽  
Structural Relationships ◽  
The Social ◽  
Almost All

For almost 40 years structural equation modeling (SEM) has been the statistical tool of choice for the assessing measurement and structural relationships in the social sciences. During the initial 30 years almost all applications of SEM utilized what has become known as covariance-based SEM. But in the past ten years an alternative structural equation modeling method, composite-based SEM, has increasingly been applied. In fact, a substantial number of social sciences scholars consider composite-based SEM the method of choice for structural equation modeling applications. In this paper, I provide an overview of the evolution of SEM, from the early years when factor-based SEM was the dominant method to the more recent years as composite-based methods have become much more prevalent. I also summarize several relevant composite-based topics including the emergence of composite-based SEM, confirmatory composite analysis (CCA), and a new method of generalized structured component analysis (GSCA). In the final section I propose some observations about current developments and future opportunities for composite-based SEM methods.

An Investigation of the Structure of the Social Optimism Scale with Respect to the Dimensionality Problem

2008 ◽  
Vol 29 (4) ◽  
pp. 223-230 ◽  
Author(s):  
Karl Schweizer ◽  
Wolfgang Rauch
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Independent Method ◽  
Model Fit ◽  
Equation Modeling ◽  
Observational Methods ◽  
Structural Homogeneity ◽  
Alternative Structures ◽  
Satisfactory Model ◽  
The Social

This paper reports an investigation of the structure of the social optimism scale. Research on structural homogeneity in other scales that are also composed of equal numbers of positively and negatively worded items suggests two alternative structures: a general optimism dimension or the combination of two specific dimensions representing social optimism and social pessimism. Since positively and negatively worded items can be regarded as two different observational methods, the combination of a general optimism dimension and two independent method dimensions needs to be considered. In order to achieve a satisfactory model fit, the three subscales of the social optimism scale have to be modeled as additional, uncorrelated dimensions. Investigating the different proposed models with structural equation modeling provides support for the position of a general optimism dimension and two independent method dimensions.

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