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.

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.

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.

Theoretical and Applied Sources for Teachers of Structural Equation Modeling

1994 ◽  
Vol 21 (3) ◽  
pp. 179-181 ◽  
Author(s):  
James B. Hittner ◽  
Kenneth M. Carpenter
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Journal Articles ◽  
Graduate Course ◽  
Software Packages ◽  
The Social ◽  
Modeling Software ◽  
Linear Structural Relations

We describe software and present theoretical and applied sources for teaching a graduate course in structural equation modeling. We recommend Linear Structural Relations (LISREL; Jöreskog & Sörbom, 1989) as the primary structural equation modeling software because it is the most generally applicable and widely available of the appropriate software packages, and it is fully supported by the Statistical Package for the Social Sciences (SPSS, 1988). We also suggest relevant background readings, recommend principal textbook sources and review articles, and advocate reading empirical journal articles to accompany the core texts. These sources are offered as part of a comprehensive teaching approach designed to impart an appreciation for and working knowledge of LISREL-based structural equation modeling.

scholarly journals Inteligência emocional em enfermeiros: a escala Trait Meta-Mood Scale

2017 ◽  
Vol 30 (2) ◽  
pp. 204-209 ◽  
Author(s):  
María del Carmen Giménez-Espert ◽  
Vicente Javier Prado-Gascó
Keyword(s):  
Social Sciences ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
The Social ◽  
Modeling Software ◽  
Mood Scale

Resumo Objetivo Nesse contexto, o objetivo desse estudo é duplo. Primeiro, almeja-se explorar as propriedades psicométricas da TMMS-24 em uma amostra de enfermeiros espanhóis e em segundo lugar fornecer alguns percentis para interpretar os níveis de IE em enfermeiros espanhóis. Métodos Um desenho de estudo descritivo correlacional foi utilizado para avaliar as propriedades psicométricas da TMMS-24. Este estudo foi realizado com uma amostra de 530 enfermeiros de 11 hospitais espanhóis da Comunidade Valenciana. Os critérios de inclusão foram enfermeiros ativos (temporário, interino ou permanente) nos centros selecionados que haviam previamente consentido em participar. A idade dos participantes variou de 22 a 64 anos (X= 44,13; DP = 11,58). 75,6% eram mulheres (401), 53,8% (285) eram funcionários permanentes, 28,4% (151) eram substitutos e 17,8% (94) tinham contrato temporário. A análise estatística foi realizada através do programa SPSS (Statistical Package for the Social Sciences, Versão 22), além dos programas EQS (Structural Equation Modeling Software, Versão 6.2) e FACTOR. Resultados Os resultados indicam que as propriedades psicométricas da TMMS-24 são adequadas e seu uso parece ser justificado. Por último, são apresentados percentis para interpretar os níveis de inteligência emocional em enfermeiros espanhóis. Conclusão O instrumento tem várias aplicações potenciais para gerentes de enfermagem preocupados com o ambiente de trabalho de saúde e com enfermagem. Primeiro, o estudo apoia o uso da TMMS-24 no contexto de enfermagem na Espanha. Em segundo lugar, o estudo também apoia o uso da TMMS-24 para avaliar a IE em enfermeiros. Terceiro, a avaliação da prática de enfermagem atual, a partir de uma perspectiva de autoavaliação, pode determinar as necessidades de treinamento e avaliar a eficácia da formação e das intervenções para melhorar a IE. Em quarto lugar, a existência do instrumento e dos percentis facilita a interpretação das pontuações obtidas e permite uma rápida comparação com outras amostras de enfermeiros.

Meta-Accuracy and Perceived Reciprocity From the Perception-Meta-Perception Social Relations Model

2019 ◽  
Vol 50 (1) ◽  
pp. 24-37
Author(s):  
Ben Porter ◽  
Camilla S. Øverup ◽  
Julie A. Brunson ◽  
Paras D. Mehta
Keyword(s):  
Structural Equation Modeling ◽  
Social Relations ◽  
Latent Variables ◽  
Structural Equation ◽  
Multilevel Structural Equation Modeling ◽  
Equation Modeling ◽  
Complete Understanding ◽  
Social Relations Model ◽  
Flexible Framework ◽  
Multilevel Structural Equation

Abstract. Meta-accuracy and perceptions of reciprocity can be measured by covariances between latent variables in two social relations models examining perception and meta-perception. We propose a single unified model called the Perception-Meta-Perception Social Relations Model (PM-SRM). This model simultaneously estimates all possible parameters to provide a more complete understanding of the relationships between perception and meta-perception. We describe the components of the PM-SRM and present two pedagogical examples with code, openly available on https://osf.io/4ag5m . Using a new package in R (xxM), we estimated the model using multilevel structural equation modeling which provides an approachable and flexible framework for evaluating the PM-SRM. Further, we discuss possible expansions to the PM-SRM which can explore novel and exciting hypotheses.

Building a Model of Family Caregiving for Children with Emotional Disorders

1997 ◽  
Vol 5 (3) ◽  
pp. 138-148 ◽  
Author(s):  
Thomas P. Mcdonald ◽  
Thomas K. Gregoire ◽  
John Poertner ◽  
Theresa J. Early
Keyword(s):  
Structural Equation Modeling ◽  
Emotional Disorders ◽  
Problem Behaviors ◽  
Family Caregiving ◽  
Latent Variables ◽  
Structural Equation ◽  
Empirical Literature ◽  
Equation Modeling ◽  
Special Challenge ◽  
And Coping

In this article we describe the results of an ongoing effort to better understand the caregiving process in families of children with severe emotional problems. We make two assumptions. First, we assume that these families are essentially like other families but are faced with a special challenge in raising and caring for their special children while at the same time performing the multiple tasks and demands faced by all families. Second, we assume that public policy and programs must be supportive of the care of these children in their own homes and communities whenever possible. The purpose of this article is to present a model of family caregiving that draws broadly from available theory and empirical literature in multiple fields and to subject this model to empirical testing. We use structural equation modeling with latent variables to estimate an empirical model based on the theoretical model. Results of the model testing point to the importance of the child's external problem behaviors and the family's socioeconomic status and coping strategies as determinants of caregiver stress. Other findings highlight difficulties in measuring and modeling the complex mediating process, which includes formal and informal supports, perceptions, and coping behaviors. The use of structural equation modeling can benefit our efforts to support families by making explicit our theories about the important dimensions of this process and the relationship between these dimensions, which can then be subjected to measurement and validation.

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