Using structural equation modeling to asses two theoretical models that explain dropout

1996 ◽  
Author(s):  
G. Lawrence Farmer
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Theoretical Models ◽  
Equation Modeling

scholarly journals Testing IB theories with meta-analytic structural equation modeling

2016 ◽  
Vol 26 (4) ◽  
pp. 472-492 ◽  
Author(s):  
Ryan W. Tang ◽  
Mike W.-L. Cheung
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Transaction Cost Economics ◽  
Theoretical Models ◽  
Equation Modeling ◽  
Methodological Issues ◽  
Optimal Method ◽  
Internal Factors ◽  
Content Type ◽  
Rigorous Approach

Purpose The purpose of this paper is to illustrate how international business (IB) researchers can benefit from meta-analytic structural equation modeling (MASEM) by introducing a statistically rigorous approach (i.e. two-stage meta-analytic structural equation modeling or TSSEM) and comparing it with a conventional approach (i.e. the univariate-r approach). The illustration and comparison present a methodological overview of MASEM that will assist IB researchers in selecting an optimal method. Design/methodology/approach In this paper, the MASEM method is elaborated upon, and methodological issues are addressed, by comparing the TSSEM and the univariate-r approaches using an empirical illustration. In this illustrative example, which is based on transaction cost economics, the effects of a firm’s internal factors on its levels of commitment in an international entry strategy are examined. Findings The MASEM method can help IB researchers to test and build on IB theories by synthesizing findings in the extant literature because this method reflects the theoretical complexity of IB (e.g. intercorrelationships among factors). Comparing the two approaches of MASEM, it is found in this study that due to its statistical rigorousness TSSEM has methodological advantages in helping IB researchers test theoretical models. Originality/value This is the first study to introduce MASEM into the discipline of IB strategies. In this paper, the authors introduce an advanced research method and illustrate two ways of using it.

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.

Theory Development in Information Systems Research Using Structural Equation Modeling

2010 ◽  
pp. 77-94 ◽  
Author(s):  
Nicholas Roberts ◽  
Varun Grover
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Theory Development ◽  
Theoretical Models ◽  
Equation Modeling ◽  
Is Research ◽  
Significant Potential ◽  
Systems Research ◽  
Information Systems Research ◽  
Is Theory

Structural equation modeling (SEM) techniques have significant potential for assessing and modifying theoretical models. There have been 171 applications of SEM in IS research, published in major journals, most of which have been after 1994. Despite SEM’s surging popularity in the IS field, it remains a complex tool that is often mechanically used but difficult to effectively apply. The purpose of this study is to review previous applications of SEM in IS research and to recommend guidelines to enhance the use of SEM to facilitate theory development. The authors review and evaluate SEM applications, both component-based (e.g., PLS) and covariance-based (e.g., LISREL), according to prescribed criteria. Areas of improvement are suggested which can assist application of this powerful technique in IS theory development.

The Use of Structural Equation Modeling in IS Research

2011 ◽  
pp. 44-65 ◽  
Author(s):  
Kun S. Im ◽  
Varun Grover
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Theory Development ◽  
Theoretical Models ◽  
Research Tool ◽  
Equation Modeling ◽  
Is Research ◽  
Significant Potential ◽  
Is Theory

The structural equation modeling (SEM) technique has significant potential as a research tool for assessing and modifying theoretical models. There have been 139 applications of SEM in IS research published in major journals, most of which have been after 1994. However, despite its increasing use in the field, it remains a complex tool that is often difficult to apply effectively. The purpose of this study is to evaluate the previous IS applications of SEM and to suggest guidelines to realize the potential of SEM in IS research. The 72 empirical applications of SEM gathered from leading IS journals are reviewed and evaluated according to prescribed criteria. Avenues for improvement are suggested which can facilitate application of this important technique in IS theory development and testing.

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.

scholarly journals Issues in Solving the Problem of Effect Size Heterogeneity in Meta-Analytic Structural Equation Modeling: A Commentary and Simulation Study on Yu, Downes, Carter, and O’Boyle (2016)

2017 ◽  
Author(s):  
Mike W.-L. Cheung
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Parametric Bootstrap ◽  
Theoretical Models ◽  
Equation Modeling ◽  
Organizational Studies ◽  
Full Information ◽  
Correlation Matrices

This paper is in press at Journal of Applied Psychology.Abstract:Meta-analytic structural equation modeling (MASEM) is becoming increasingly popular for testing theoretical models from a pool of correlation matrices in management and organizational studies. One limitation of the conventional MASEM approaches is that the proposed structural equation models are only tested on the average correlation matrix. It remains unclear how far the proposed models can be generalized to other populations when the correlation matrices are heterogeneous. Recently, Yu, Downes, Carter, and O’Boyle (2016) proposed a full information MASEM approach to address this limitation by fitting structural equation models from the correlation matrices generated from a parametric bootstrap. However, their approach suffers from several conceptual issues and technical errors. In this study, we reran some of the simulations in Yu et al. by correcting all of the errors in their original studies. The findings showed that bootstrap credible intervals (CVs) work reasonably well, while test statistics and goodness-of-fit indices do not. We advise researchers on what they can and cannot achieve by applying the full information MASEM approach. We recommend fitting MASEM with the TSSEM approach, which works well for the simulation studies. If researchers want to inspect the heterogeneity of the parameters, they may use the bootstrap CVs from the full information MASEM approach. All of these analyses were implemented in the open-source R statistical platform; researchers can easily apply and verify the findings. This paper concludes with several future directions to address the issue of heterogeneity in MASEM.

Testing the Assumption of Uncorrelated Errors for Short Scales by Means of Structural Equation Modeling

2014 ◽  
Vol 35 (4) ◽  
pp. 201-211 ◽  
Author(s):  
André Beauducel ◽  
Anja Leue
Keyword(s):  
Structural Equation Modeling ◽  
Negative Correlation ◽  
Structural Equation ◽  
Error Score ◽  
Classical Test Theory ◽  
Test Theory ◽  
Equation Modeling ◽  
Eysenck Personality Questionnaire ◽  
Personality Questionnaire ◽  
True Score

It is shown that a minimal assumption should be added to the assumptions of Classical Test Theory (CTT) in order to have positive inter-item correlations, which are regarded as a basis for the aggregation of items. Moreover, it is shown that the assumption of zero correlations between the error score estimates is substantially violated in the population of individuals when the number of items is small. Instead, a negative correlation between error score estimates occurs. The reason for the negative correlation is that the error score estimates for different items of a scale are based on insufficient true score estimates when the number of items is small. A test of the assumption of uncorrelated error score estimates by means of structural equation modeling (SEM) is proposed that takes this effect into account. The SEM-based procedure is demonstrated by means of empirical examples based on the Edinburgh Handedness Inventory and the Eysenck Personality Questionnaire-Revised.

The Measurement Invariance of the Short Dark Triad

2020 ◽  
Vol 41 (4) ◽  
pp. 207-218
Author(s):  
Mihaela Grigoraș ◽  
Andreea Butucescu ◽  
Amalia Miulescu ◽  
Cristian Opariuc-Dan ◽  
Dragoș Iliescu
Keyword(s):  
Structural Equation Modeling ◽  
Item Response ◽  
Structural Equation ◽  
Dark Triad ◽  
Theory And Practice ◽  
Equation Modeling ◽  
Scale Level ◽  
High Stake ◽  
Differential Functioning ◽  
Item Functioning

Abstract. Given the fact that most of the dark personality measures are developed based on data collected in low-stake settings, the present study addresses the appropriateness of their use in high-stake contexts. Specifically, we examined item- and scale-level differential functioning of the Short Dark Triad (SD3; Paulhus & Jones, 2011 ) measure across testing contexts. The Short Dark Triad was administered to applicant ( N = 457) and non-applicant ( N = 592) samples. Item- and scale-level invariances were tested using an Item Response Theory (IRT)-based approach and a Structural Equation Modeling (SEM) approach, respectively. Results show that more than half of the SD3 items were flagged for Differential Item Functioning (DIF), and Exploratory Structural Equation Modeling (ESEM) results supported configural, but not metric invariance. Implications for theory and practice are discussed.

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