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.

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 Meta-Analytic Structural Equation Modeling

2021 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Structural Equation Modeling ◽  
Correlation Matrix ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Equation Modeling ◽  
Average Correlation ◽  
Correlation Matrices ◽  
Factor Analytic Model ◽  
Special Cases

Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. When there is moderate to high heterogeneity, moderators such as study characteristics may be used to explain the heterogeneity in the data. On the other hand, SEM includes several special cases, including the general linear model, path model, and confirmatory factor analytic model. SEM allows researchers to test hypothetical models with empirical data. Meta-analytic structural equation modeling (MASEM) is a statistical approach combining the advantages of both meta-analysis and SEM for fitting structural equation models on a pool of correlation matrices. There are usually two stages in the analyses. In the first stage of analysis, a pool of correlation matrices is combined to form an average correlation matrix. In the second stage of analysis, proposed structural equation models are tested against the average correlation matrix. MASEM enables researchers to synthesize researching findings using SEM as the research tool in primary studies. There are several popular approaches to conduct MASEM, including the univariate-r, generalized least squares, two-stage SEM (TSSEM), and one-stage MASEM (OSMASEM). MASEM helps to answer the following key research questions: (a) Are the correlation matrices homogeneous? (b) Do the proposed models fit the data? (c) Are there moderators that can be used to explain the heterogeneity of the correlation matrices? The MASEM framework has also been expanded to analyze large datasets or big data with or without the raw data.

scholarly journals Meta-Analytic Structural Equation Modeling

2020 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Structural Equation Modeling ◽  
Correlation Matrix ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Meta Analysis ◽  
Equation Modeling ◽  
Average Correlation ◽  
Correlation Matrices ◽  
Factor Analytic Model ◽  
Special Cases

Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. When there is non-trial heterogeneity, moderators such as study characteristics may be used to explain the heterogeneity in the data. On the other hand, SEM includes several special cases, including the general linear model, path model, and confirmatory factor analytic model. SEM allows researchers to test hypothetical models with empirical data. Meta-analytic structural equation modeling (MASEM) is a statistical approach combining the advantages of both meta-analysis and SEM for fitting structural equation models on a pool of correlation matrices. There are usually two stages in the analyses. In the first stage of analysis, a pool of correlation matrices is combined to form an average correlation matrix. In the second stage of analysis, proposed structural equation models are tested against the average correlation matrix. MASEM enables researchers to synthesize researching findings using SEM as the research tool in primary studies. There are several popular approaches to conduct MASEM, including the univariate-r, generalized least squares, two-stage SEM (TSSEM), and one-stage MASEM (OSMASEM). MASEM helps to answer the following key research questions: (1) Are the correlation matrices homogeneous? (2) Do the proposed models fit the data? (3) Are there moderators that can be used to explain the heterogeneity of the correlation matrices? The MASEM framework has also been expanded to analyze large datasets or big data with or without the raw data.

scholarly journals Meta-Analytic Structural Equation Modeling with Moderating Effects on SEM Parameters

2018 ◽  
Author(s):  
Suzanne Jak ◽  
Mike W.-L. Cheung
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Meta Analysis ◽  
Theoretical Models ◽  
Equation Model ◽  
Equation Modeling ◽  
Future Research ◽  
Moderator Variable ◽  
One Stage ◽  
Empirical Performance

Meta-analytic structural equation modeling (MASEM) is an increasingly popular meta-analytic technique that combines the strengths of meta-analysis and structural equation modeling. MASEM facilitates the evaluation of complete theoretical models (e.g., path models or factor analytic models), accounts for sampling covariance between effect sizes, and provides measures of overall fit of the hypothesized model on meta-analytic data. We propose a novel MASEM method, One-Stage MASEM, which is better suitable to explain study-level heterogeneity than existing methods. One-Stage MASEM allows researchers to incorporate continuous or categorical moderators into the MASEM, in which any parameter in the structural equation model (e.g., path coefficients and factor loadings) can be modeled by the moderator variable, while the method does not require complete data for the primary studies included in the meta-analysis. We illustrate the new method on two real datasets, evaluate its empirical performance via a computer simulation study, and provide user-friendly R-functions and annotated syntax to assist researchers in applying One-Stage MASEM. We close the paper by presenting several future research directions.

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.

Impulsivity and Aggression: A Meta-analysis Using the UPPS Model of Impulsivity

2019 ◽  
Author(s):  
Konrad Bresin
Keyword(s):  
Structural Equation Modeling ◽  
Sensation Seeking ◽  
Structural Equation ◽  
Meta Analysis ◽  
The Other ◽  
Equation Modeling ◽  
Negative Urgency ◽  
Two Stage ◽  
Trait Impulsivity

Trait impulsivity has long been proposed to play a role in aggression, but the results across studies have been mixed. One possible explanation for the mixed results is that impulsivity is a multifaceted construct and some, but not all, facets are related to aggression. The goal of the current meta-analysis was to determine the relation between the different facets of impulsivity (i.e., negative urgency, positive urgency, lack of premeditation, lack of perseverance, and sensation seeking) and aggression. The results from 93 papers with 105 unique samples (N = 36, 215) showed significant and small-to-medium correlations between each facet of impulsivity and aggression across several different forms of aggression, with more impulsivity being associated with more aggression. Moreover, negative urgency (r = .24, 95% [.18, .29]), positive urgency (r = .34, 95% [.19, .44]), and lack of premeditation (r = .23, 95% [.20, .26]) had significantly stronger associations with aggression than the other scales (rs < .18). Two-stage meta-analytic structural equation modeling showed that these effects were not due to overlap among facets of impulsivity. These results help advance the field of aggression research by clarifying the role of impulsivity and may be of interest to researchers and practitioners in several disciplines.

The Model of Goal-Directed Behavior in Tourism and Hospitality: A Meta-analytic Structural Equation Modeling Approach

2021 ◽  
pp. 004728752199124
Author(s):  
Weisheng Chiu ◽  
Heetae Cho
Keyword(s):  
Structural Equation Modeling ◽  
Cultural Differences ◽  
Structural Equation ◽  
Meta Analysis ◽  
Equation Modeling ◽  
Causal Relationships ◽  
Eastern Culture ◽  
Analytic Review ◽  
Tourism And Hospitality ◽  
Goal Directed Behavior

The model of goal-directed behavior (MGB) has been widely utilized to explore consumer behavior in the fields of tourism and hospitality. However, prior studies have demonstrated inconsistent findings with respect to the causal relationships of the MGB variables. To address this issue, we conducted a meta-analytic review based on studies that had previously applied MGB. Moreover, we compared the cultural differences that emerged within MGB. By reviewing and analyzing 37 studies with 39 samples ( N = 14,581), this study found that among the causal relationships within MGB, positive anticipated emotion was the most influential determinant in the formation of consumer desire. In addition, different patterns of causal relationships between Eastern culture and Western culture were identified within MGB. This article is the first meta-analysis to address the application of MGB in tourism and hospitality and, thus, contributes to the theoretical advancement of MGB.

The Roles of Initial Mathematics, Reading, and Cognitive Skills in Subsequent Mathematics Performance: A Meta-Analytic Structural Equation Modeling Approach

2021 ◽  
pp. 003465432110545
Author(s):  
Xin Lin ◽  
Sarah R. Powell
Keyword(s):  
Working Memory ◽  
Structural Equation Modeling ◽  
Cognitive Skills ◽  
Structural Equation ◽  
Meta Analysis ◽  
Time Lag ◽  
Mathematics Performance ◽  
Equation Modeling ◽  
Word Problem Solving ◽  
Significant Moderator

In the present meta-analysis, we systematically investigated the relative contributions of students’ initial mathematics, reading, and cognitive skills on subsequent mathematics performance measured at least 3 months later. With one-stage meta-analytic structural equation modeling, we conducted analyses based on 580,437 students from 265 independent samples and 250 studies. Findings suggested fluency in both mathematics and reading, as well as working memory, yielded greater impacts on subsequent mathematics performance. Age emerged as a significant moderator in the model, such that the effects of comprehensive mathematics and working memory on subsequent mathematics increased with age, whereas attention and self-regulation’s impacts declined with age. Time lag between assessments also emerged as a significant moderator, such that the effects of word-problem solving and word recognition accuracy decreased as the time lag increased, whereas vocabulary, attention, and self-regulation’s effects increased as the time lag increased.

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