Structural Equation Modeling (SEM) in Social Sciences & Medical Research: A Guide for Improved Analysis

2017 ◽  
Vol 7 (5) ◽  
Author(s):  
Farhat Shaheen ◽  
Naveed Ahmad ◽  
Muhammad Waqas ◽  
Abdul Waheed ◽  
Omer Farooq
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Medical Research ◽  
Structural Equation ◽  
Equation Modeling

scholarly journals Structural Equation Modeling (SEM)-Based Meta-Analysis

2021 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Equation Model ◽  
Equation Modeling ◽  
Future Directions ◽  
Software Packages ◽  
Meta Analyses

Structural equation modeling (SEM) and meta-analysis are two popular techniques in the behavioral, medical, and social sciences. They have their own research communities, terminologies, models, software packages, and even journals. This chapter introduces SEM-based meta-analysis, an approach to conduct meta-analyses using the SEM framework. By conceptualizing studies in a meta-analysis as subjects in a structural equation model, univariate, multivariate, and three-level meta-analyses can be fitted as structural equation models using definition variables. We will review fixed-, random-, and mixed-effects models using the SEM framework. Examples will be used to illustrate the procedures using the metaSEM and OpenMx packages in R. This chapter closes with a discussion of some future directions for research.

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.

scholarly journals Latent Variable Structural Equation Modeling: A Flexible Tool for Establishing Validity and More

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Drew Altschul
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Construct Validity ◽  
Latent Variables ◽  
Latent Variable ◽  
Structural Equation ◽  
Equation Modeling ◽  
Statistical Techniques ◽  
Flexible Tool ◽  
The Past

Petrinovich highlighted many salient issues in the behavioral and social sciences that are of concern to this day, such as insufficient attention to construct validity. Structural equation modeling, particularly with regard to latent variables, is introduced and discussed in this context. Though conceptual issues remain, analytic and statistical techniques have made immense strides in the past three decades since the article was written, and properly used, offer solutions to many problems Petrinovich identified.

scholarly journals Some Reflections on Combining Meta-Analysis and Structural Equation Modeling

2018 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Meta Analysis ◽  
Equation Modeling ◽  
Synthesis Methods ◽  
Software Packages ◽  
Research Questions ◽  
Personal Reflections ◽  
New Research

Meta-analysis and structural equation modeling (SEM) are two of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well-established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Modeling: A Multidisciplinary Journal). In this paper, I will provide some personal reflections on combining meta-analysis and SEM in the forms of meta-analytic SEM (MASEM) and SEM-based meta-analysis. The critical contributions of Becker (1992), Shadish (1992), and Viswesvaran and Ones (1995) in the early development of MASEM are highlighted. Another goal of the paper is to illustrate how meta-analysis can be extended and integrated with other techniques to address new research questions such as the analysis of Big Data. I hope that this paper may stimulate more research development in the area of combining meta-analysis and SEM.

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.

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