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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
International Journal of Human Resource Management, 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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Partial Least Squares structural equation modeling pls sem
European Business Review, 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
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goodness of fit indices for Partial Least Squares path modeling
Computational Statistics, 2013Co-Authors: Jorg Henseler, Marko SarstedtAbstract:This paper discusses a recent development in Partial Least Squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoFrel), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoFrel are not suitable for model validation. However, the GoF can be useful to assess how well a PLS path model can explain different sets of data.
Jorg Henseler - One of the best experts on this subject based on the ideXlab platform.
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Testing measurement invariance of composites using Partial Least Squares
International Marketing Review, 2016Co-Authors: Jorg Henseler, Christian M RingleAbstract:Purpose – Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as Partial Least Squares (PLS) path modeling. Design/methodology/approach – A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications. Findings – The MICOM procedure appropriately identifies no, Partial, and full measurement invariance. Research limitations/implications – The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account. Originality/value – The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.
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consistent Partial Least Squares path modeling
Management Information Systems Quarterly, 2015Co-Authors: Theo K Dijkstra, Jorg HenselerAbstract:This paper resumes the discussion in information systems research on the use of Partial Least Squares (PLS) path modeling and shows that the inconsistency of PLS path coefficient estimates in the case of reflective measurement can have adverse consequences for hypothesis testing. To remedy this, the study introduces a vital extension of PLS: consistent PLS (PLSc). PLSc provides a correction for estimates when PLS is applied to reflective constructs: The path coefficients, inter-construct correlations, and indicator loadings become consistent. The outcome of a Monte Carlo simulation reveals that the bias of PLSc parameter estimates is comparable to that of covariance-based structural equation modeling. Moreover, the outcome shows that PLSc has advantages when using non-normally distributed data. We discuss the implications for IS research and provide guidelines for choosing among structural equation modeling techniques.
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goodness of fit indices for Partial Least Squares path modeling
Computational Statistics, 2013Co-Authors: Jorg Henseler, Marko SarstedtAbstract:This paper discusses a recent development in Partial Least Squares (PLS) path modeling, namely goodness-of-fit indices. In order to illustrate the behavior of the goodness-of-fit index (GoF) and the relative goodness-of-fit index (GoFrel), we estimate PLS path models with simulated data, and contrast their values with fit indices commonly used in covariance-based structural equation modeling. The simulation shows that the GoF and the GoFrel are not suitable for model validation. However, the GoF can be useful to assess how well a PLS path model can explain different sets of data.
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handbook of Partial Least Squares
2010Co-Authors: Vincenzo Esposito Vinzi, Jorg Henseler, Wynne W Chin, Huiwen WangAbstract:The new volume of Computational Statistics represents a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. The handbook covers the broad area of PLS methods -from regression to structural equation modeling applications, software and interpretation of results. It features papers on the use and the analysis of latent variables and indicators by means of the PLS path modeling approach from the design of the causal network to model assessment and improvement. Within the PLS framework, the handbook also addresses advanced topics such as the analysis of multi-block, multi-group and multi-structured data, the use of categorical indicators, the study of interaction effects, the integration of classification issues, the validation aspects and the comparison between the PLS approach and covariance based structural equation modeling. Most chapters comprise a thorough discussion of applications to marketing and related areas, some tutorials focus on key aspects of PLS analysis with a didactic approach. This handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.
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on the convergence of the Partial Least Squares path modeling algorithm
Computational Statistics, 2010Co-Authors: Jorg HenselerAbstract:This paper adds to an important aspect of Partial Least Squares (PLS) path modeling, namely the convergence of the iterative PLS path modeling algorithm. Whilst conventional wisdom says that PLS always converges in practice, there is no formal proof for path models with more than two blocks of manifest variables. This paper presents six cases of non-convergence of the PLS path modeling algorithm. These cases were estimated using Mode A combined with the factorial scheme or the path weighting scheme, which are two popular options of the algorithm. As a conclusion, efforts to come to a proof of convergence under these schemes can be abandoned, and users of PLS should triangulate their estimation results.
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
International Journal of Human Resource Management, 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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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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Testing measurement invariance of composites using Partial Least Squares
International Marketing Review, 2016Co-Authors: Jorg Henseler, Christian M RingleAbstract:Purpose – Research on international marketing usually involves comparing different groups of respondents. When using structural equation modeling (SEM), group comparisons can be misleading unless researchers establish the invariance of their measures. While methods have been proposed to analyze measurement invariance in common factor models, research lacks an approach in respect of composite models. The purpose of this paper is to present a novel three-step procedure to analyze the measurement invariance of composite models (MICOM) when using variance-based SEM, such as Partial Least Squares (PLS) path modeling. Design/methodology/approach – A simulation study allows us to assess the suitability of the MICOM procedure to analyze the measurement invariance in PLS applications. Findings – The MICOM procedure appropriately identifies no, Partial, and full measurement invariance. Research limitations/implications – The statistical power of the proposed tests requires further research, and researchers using the MICOM procedure should take potential type-II errors into account. Originality/value – The research presents a novel procedure to assess the measurement invariance in the context of composite models. Researchers in international marketing and other disciplines need to conduct this kind of assessment before undertaking multigroup analyses. They can use MICOM procedure as a standard means to assess the measurement invariance.
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a primer on Partial Least Squares structural equation modeling pls sem
Published in 2016 in Los Angeles by SAGE, 2014Co-Authors: Joseph F Hai, Christian M Ringle, Tomas G M Hul, Marko SarstedAbstract:With applications using SmartPLS (www.smartpls.com)—the primary software used in Partial Least Squares structural equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.
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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
Volker G Kuppelwieser - One of the best experts on this subject based on the ideXlab platform.
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Partial Least Squares structural equation modeling pls sem
European Business Review, 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...
D L Massart - One of the best experts on this subject based on the ideXlab platform.
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the radial basis functions Partial Least Squares approach as a flexible non linear regression technique
Analytica Chimica Acta, 1996Co-Authors: Beata Walczak, D L MassartAbstract:A new approach founded on Radial Basis Functions (RBF) and Partial Least Squares (PLS) is proposed to model non-linear chemical systems. Its performance is demonstrated for two simulated examples and compared with those of Multilayer Feedforward Network (MLP), Radial Basis Function Network (RBFN), and Spline-PLS. Good performance and a guaranteed learning algorithm of the RBF-PLS approach makes it an attractive alternative for the earlier established methods.
