The Experts below are selected from a list of 123081 Experts worldwide ranked by ideXlab platform
Marko Sarstedt - One of the best experts on this subject based on the ideXlab platform.
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partial least squares Structural Equation Modeling in hrm research
2020Co-Authors: Christian M Ringle, Marko Sarstedt, Rebecca Mitchell, Siegfried P GuderganAbstract:Partial least squares Structural Equation Modeling (PLS-SEM) has become a key multivariate analysis technique that human resource management (HRM) researchers frequently use. While most disciplines...
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a new criterion for assessing discriminant validity in variance based Structural Equation Modeling
2015Co-Authors: Marko Sarstedt, Christian M Ringle, Joerg HenselerAbstract:Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based Structural Equation Modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based Structural Equation Modeling.
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partial least squares Structural Equation Modeling pls sem
2014Co-Authors: Joseph F Hair, Marko Sarstedt, Lucas Hopkins, Volker G KuppelwieserAbstract:Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to Structural Equation Modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitations/implications – While rese...
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a primer on partial least squares Structural Equation Modeling pls sem
2013Co-Authors: Joseph F Hair, Christian M Ringle, Tomas G M Hult, Marko SarstedtAbstract:Chapter 1: An Introduction to Structural Equation Modeling What Is Structural Equation Modeling? Considerations in Using Structural Equation Modeling Structural Equation Modeling With Partial Least Squares Path Modeling PLS-SEM, CB-SEM, and Regressions Based on Sum Scores Organization of Remaining Chapters Chapter 2: Specifying the Path Model and Examining Data Stage 1: Specifying the Structural Model Stage 2: Specifying the Measurement Models Stage 3: Data Collection and Examination Case Study Illustration: Specifying the PLS-SEM Model Path Model Creation Using the SmartPLS Software Chapter 3: Path Model Estimation Stage 4: Model Estimation and the PLS-SEM Algorithm Case Study Illustration: PLS Path Model Estimation (Stage 4) Chapter 4: Assessing PLS-SEM Results Part I: Evaluation of Reflective Measurement Models Overview of Stage 5: Evaluation of Measurement Models Stage 5a: Assessing Results of Reflective Measurement Models Case Study Illustration-Reflective Measurement Models Running the PLS-SEM Algorithm Reflective Measurement Model Evaluation Chapter 5: Assessing PLS-SEM Results Part II: Evaluation of the Formative Measurement Models Stage 5b: Assessing Results of Formative Measurement Models Bootstrapping Procedure Bootstrap Confidence Intervals Case Study Illustration-Evaluation of Formative Measurement Models Chapter 6: Assessing PLS-SEM Results Part III: Evaluation of the Structural Model Stage 6: Assessing PLS-SEM Structural Model Results Case Study Illustration-How Are PLS-SEM Structural Model Results Reported? Chapter 7: Mediator and Moderator Analysis Mediation Moderation Chapter 8: Outlook on Advanced Methods Importance-Performance Map Analysis Hierarchical Component Models Confirmatory Tetrad Analysis Dealing With Observed and Unobserved Heterogeneity Consistent Partial Least Squares
Mike W L Cheung - One of the best experts on this subject based on the ideXlab platform.
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some reflections on combining meta analysis and Structural Equation Modeling
2019Co-Authors: Mike W L CheungAbstract:Meta-analysis and Structural Equation Modeling (SEM) are 2 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 and SEM-based meta-analysis. The critical contributions of Becker (1992), Shadish (1992), and Viswesvaran and Ones (1995) in the early development of meta-analytic SEM 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.
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metasem an r package for meta analysis using Structural Equation Modeling
2015Co-Authors: Mike W L CheungAbstract:The metaSEM package provides functions to conduct univariate, multivariate, and three-level meta-analyses using a Structural Equation Modeling (SEM) approach via the \texttt{OpenMx} package in R statistical platform. It also implements the two-stage SEM approach to conducting fixed- and random-effects meta-analytic SEM on correlation or covariance matrices. This paper briefly outlines the theories and their implementations. It provides a summary on how meta-analyses can be formulated as Structural Equation models. The paper closes with a conclusion on several relevant topics to this SEM-based meta-analysis. Several examples are used to illustrate the procedures in the supplementary material.
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meta analytic Structural Equation Modeling a two stage approach
2005Co-Authors: Mike W L Cheung, Wai ChaAbstract:To synthesize studies that use Structural Equation Modeling (SEM), researchers usually use Pearson correlations (univariate r), Fisher z scores (univariate z), or generalized least squares (GLS) to combine the correlation matrices. The pooled correlation matrix is then analyzed by the use of SEM. Questionable inferences may occur for these ad hoc procedures. A 2-stage Structural Equation Modeling (TSSEM) method is proposed to incorporate meta-analytic techniques and SEM into a unified framework. Simulation results reveal that the univariate-r, univariate-z, and TSSEM methods perform well in testing the homogeneity of correlation matrices and estimating the pooled correlation matrix. When fitting SEM, only TSSEM works well. The GLS method performed poorly in small to medium samples.
Christian M Ringle - One of the best experts on this subject based on the ideXlab platform.
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partial least squares Structural Equation Modeling in hrm research
2020Co-Authors: Christian M Ringle, Marko Sarstedt, Rebecca Mitchell, Siegfried P GuderganAbstract:Partial least squares Structural Equation Modeling (PLS-SEM) has become a key multivariate analysis technique that human resource management (HRM) researchers frequently use. While most disciplines...
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an assessment of the use of partial least squares Structural Equation Modeling pls sem in hospitality research
2017Co-Authors: Faiza Ali, Christian M Ringle, Marko Sarsted, Mostafa S Rasoolimanesh, Kisang RyuAbstract:Purpose Structural Equation Modeling (SEM) depicts one of the most salient research methods across a variety of disciplines, including hospitality management. While for many researchers, SEM is equivalent to carrying out covariance-based SEM, recent research advocates the use of partial least squares Structural Equation Modeling (PLS-SEM) as an attractive alternative. We systematically examine how PLS-SEM has been applied in major hospitality research journals with the aim of providing important guidance and, if necessary, opportunities for realignment in future applications. As PLS-SEM in hospitality research is still in an early stage of development, critically examining its use holds considerable promise in order to counteract misapplications which otherwise might reinforce over time. Design/methodology/approach We reviewed all PLS-SEM studies published in six SSCI-indexed hospitality management journals between 2001 and 2015. Tying in with prior studies in the field, our review covers reasons for usin...
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advanced issues in partial least squares Structural Equation Modeling
2017Co-Authors: Joseph F Hai, Christian M Ringle, Marko Sarsted, Siegfried P GudergaAbstract:Written as an extension of A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)Second Edition, this easy-to-understand, practical guide covers advanced content on PLS-SEM to help students and researchers apply techniques to research problems and accurately interpret results. Authors Joseph F. Hair, Jr., Marko Sarstedt, Christian Ringle, and Siegfried P. Gudergan provide a brief overview of basic concepts before moving to the more advanced material. Offering extensive examples on SmartPLS 3 software and accompanied by free downloadable data sets, the book emphasizes that any advanced PLS-SEM approach should be carefully applied to ensure that it fits the appropriate research context and the data characteristics that underpin the research.
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a new criterion for assessing discriminant validity in variance based Structural Equation Modeling
2015Co-Authors: Marko Sarstedt, Christian M Ringle, Joerg HenselerAbstract:Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based Structural Equation Modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based Structural Equation Modeling.
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Structural Equation Modeling with the smartpls
2014Co-Authors: Christian M Ringle, Dirceu Da Silva, Diogenes De Souza BidoAbstract:The objective of this article is to present a didactic example of Structural Equation Modeling using the software SmartPLS 2.0 M3. The program mentioned uses the method of Partial Least Squares and seeks to address the following situations frequently observed in marketing research: Absence of symmetric distributions of variables measured by a theory still in its beginning phase or with little “consolidation”, formative models, and/or a limited amount of data. The growing use of SmartPLS has demonstrated its robustness and the applicability of the model in the areas that are being studied.
Suzanne Jak - One of the best experts on this subject based on the ideXlab platform.
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maximum likelihood estimation in meta analytic Structural Equation Modeling
2016Co-Authors: Frans J Oort, Suzanne JakAbstract:Meta-analytic Structural Equation Modeling (MASEM) involves fitting models to a common population correlation matrix that is estimated on the basis of correlation coefficients that are reported by a number of independent studies. MASEM typically consist of two stages. The method that has been found to perform best in terms of statistical properties is the two-stage Structural Equation Modeling, in which maximum likelihood analysis is used to estimate the common correlation matrix in the first stage, and weighted least squares analysis is used to fit Structural Equation models to the common correlation matrix in the second stage. In the present paper, we propose an alternative method, ML MASEM, that uses ML estimation throughout. In a simulation study, we use both methods and compare chi-square distributions, bias in parameter estimates, false positive rates, and true positive rates. Both methods appear to yield unbiased parameter estimates and false and true positive rates that are close to the expected values. ML MASEM parameter estimates are found to be significantly less bias than two-stage Structural Equation Modeling estimates, but the differences are very small. The choice between the two methods may therefore be based on other fundamental or practical arguments. Copyright © 2016 John Wiley & Sons, Ltd.
Joerg Henseler - One of the best experts on this subject based on the ideXlab platform.
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a new criterion for assessing discriminant validity in variance based Structural Equation Modeling
2015Co-Authors: Marko Sarstedt, Christian M Ringle, Joerg HenselerAbstract:Discriminant validity assessment has become a generally accepted prerequisite for analyzing relationships between latent variables. For variance-based Structural Equation Modeling, such as partial least squares, the Fornell-Larcker criterion and the examination of cross-loadings are the dominant approaches for evaluating discriminant validity. By means of a simulation study, we show that these approaches do not reliably detect the lack of discriminant validity in common research situations. We therefore propose an alternative approach, based on the multitrait-multimethod matrix, to assess discriminant validity: the heterotrait-monotrait ratio of correlations. We demonstrate its superior performance by means of a Monte Carlo simulation study, in which we compare the new approach to the Fornell-Larcker criterion and the assessment of (partial) cross-loadings. Finally, we provide guidelines on how to handle discriminant validity issues in variance-based Structural Equation Modeling.
