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Christian M Ringle - One of the best experts on this subject based on the ideXlab platform.
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treating Unobserved Heterogeneity in pls sem a multi method approach
2017Co-Authors: Christian M Ringle, Marko Sarstedt, Joseph F HairAbstract:Accounting for Unobserved Heterogeneity has become a key concern to ensure the validity of results when applying partial least squares structural equation modeling (PLS-SEM). Recent methodological research in the field has brought forward a variety of latent class techniques that allow for identifying and treating Unobserved Heterogeneity. This chapter raises and discusses key aspects that are fundamental to a full and adequate understanding of how to apply these techniques in PLS-SEM. More precisely, in this chapter, we introduce a systematic procedure for identifying and treating Unobserved Heterogeneity in PLS path models using a combination of latent class techniques. The procedure builds on the FIMIX-PLS method to decide if Unobserved Heterogeneity has a critical impact on the results. Based on these outcomes, researchers should use more recently developed latent class methods, which have been shown to perform superior in recovering the segment-specific model estimates. After introducing these techniques, the chapter continues by discussing the means to identify explanatory variables that characterize the latent segments. Our discussion also broaches the issue of measurement invariance testing, which is a fundamental requirement for a subsequent comparison of parameters across groups by means of a multigroup analysis.
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identifying and treating Unobserved Heterogeneity with fimix pls part ii a case study
European Business Review, 2016Co-Authors: Lucy Matthews, Joseph F Hair, Marko Sarstedt, Christian M RingleAbstract:Purpose Part I of this article (European Business Review, Volume 28, Issue 1) offered an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social sciences researchers. This paper aims to provide an example that explains how to identify and treat Unobserved Heterogeneity in PLS-SEM by using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software (Part II). Design/methodology/approach This case study illustrates the application of FIMIX-PLS using a popular corporate reputation model. Findings The case study demonstrates the capability of FIMIX-PLS to identify whether Unobserved Heterogeneity significantly affects structural model relationships. Furthermore, it shows that FIMIX-PLS is particularly useful for determining the number of segments to extract from the data. Research limitations/implications Since the introduction of FIMIX-PLS, a range of alternative latent class techniques has appeared. These techniques address some of the limitations of the approach relating to, for example, its failure to handle Heterogeneity in measurement models, or its distributional assumptions. This research discusses alternative latent class techniques and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation. Originality/value This article is the first to offer researchers, who have not been exposed to the method, an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique, the paper offers a step-by-step tutorial on how to use FIMIX-PLS by using the SmartPLS 3 software.
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identifying and treating Unobserved Heterogeneity with fimix pls part i method
European Business Review, 2016Co-Authors: Joseph F Hair, Marko Sarstedt, Lucy Matthews, Christian M RingleAbstract:Purpose – The purpose of this paper is to provide an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat Unobserved Heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat Unobserved Heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applicati...
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Identifying and treating Unobserved Heterogeneity with FIMIX-PLS: part I – method
European Business Review, 2016Co-Authors: Joseph F Hair, Marko Sarstedt, Lucy M. Matthews, Christian M RingleAbstract:Purpose – The purpose of this paper is to provide an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat Unobserved Heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat Unobserved Heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applicati...
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discovering Unobserved Heterogeneity in structural equation models to avert validity threats
Management Information Systems Quarterly, 2013Co-Authors: Janmichael Becker, Christian M Ringle, Franziska VolcknerAbstract:A large proportion of information systems research is concerned with developing and testing models pertaining to complex cognition, behaviors, and outcomes of individuals, teams, organizations, and other social systems that are involved in the development, implementation, and utilization of information technology. Given the complexity of these social and behavioral phenomena, Heterogeneity is likely to exist in the samples used in IS studies. While researchers now routinely address observed Heterogeneity by introducing moderators, a priori groupings, and contextual factors in their research models, they have not examined how Unobserved Heterogeneity may affect their findings. We describe why Unobserved Heterogeneity threatens different types of validity and use simulations to demonstrate that Unobserved Heterogeneity biases parameter estimates, thereby leading to Type I and Type II errors. We also review different methods that can be used to uncover Unobserved Heterogeneity in structural equation models. While methods to uncover Unobserved Heterogeneity in covariance-based structural equation models (CB-SEM) are relatively advanced, the methods for partial least squares (PLS) path models are limited and have relied on an extension of mixture regression--finite mixture partial least squares (FIMIX-PLS) and distance measure-based methods--that have mismatches with some characteristics of PLS path modeling. We propose a new method--prediction-oriented segmentation (PLSPOS)--to overcome the limitations of FIMIX-PLS and other distance measure-based methods and conduct extensive simulations to evaluate the ability of PLS-POS and FIMIX-PLS to discover Unobserved Heterogeneity in both structural and measurement models. Our results show that both PLS-POS and FIMIX-PLS perform well in discovering Unobserved Heterogeneity in structural paths when the measures are reflective and that PLS-POS also performs well in discovering Unobserved Heterogeneity in formative measures. We propose an Unobserved Heterogeneity discovery (UHD) process that researchers can apply to (1) avert validity threats by uncovering Unobserved Heterogeneity and (2) elaborate on theory by turning Unobserved Heterogeneity into observed Heterogeneity, thereby expanding theory through the integration of new moderator or contextual variables.
Marko Sarstedt - One of the best experts on this subject based on the ideXlab platform.
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treating Unobserved Heterogeneity in pls sem a multi method approach
2017Co-Authors: Christian M Ringle, Marko Sarstedt, Joseph F HairAbstract:Accounting for Unobserved Heterogeneity has become a key concern to ensure the validity of results when applying partial least squares structural equation modeling (PLS-SEM). Recent methodological research in the field has brought forward a variety of latent class techniques that allow for identifying and treating Unobserved Heterogeneity. This chapter raises and discusses key aspects that are fundamental to a full and adequate understanding of how to apply these techniques in PLS-SEM. More precisely, in this chapter, we introduce a systematic procedure for identifying and treating Unobserved Heterogeneity in PLS path models using a combination of latent class techniques. The procedure builds on the FIMIX-PLS method to decide if Unobserved Heterogeneity has a critical impact on the results. Based on these outcomes, researchers should use more recently developed latent class methods, which have been shown to perform superior in recovering the segment-specific model estimates. After introducing these techniques, the chapter continues by discussing the means to identify explanatory variables that characterize the latent segments. Our discussion also broaches the issue of measurement invariance testing, which is a fundamental requirement for a subsequent comparison of parameters across groups by means of a multigroup analysis.
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identifying and treating Unobserved Heterogeneity with fimix pls part ii a case study
European Business Review, 2016Co-Authors: Lucy Matthews, Joseph F Hair, Marko Sarstedt, Christian M RingleAbstract:Purpose Part I of this article (European Business Review, Volume 28, Issue 1) offered an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social sciences researchers. This paper aims to provide an example that explains how to identify and treat Unobserved Heterogeneity in PLS-SEM by using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software (Part II). Design/methodology/approach This case study illustrates the application of FIMIX-PLS using a popular corporate reputation model. Findings The case study demonstrates the capability of FIMIX-PLS to identify whether Unobserved Heterogeneity significantly affects structural model relationships. Furthermore, it shows that FIMIX-PLS is particularly useful for determining the number of segments to extract from the data. Research limitations/implications Since the introduction of FIMIX-PLS, a range of alternative latent class techniques has appeared. These techniques address some of the limitations of the approach relating to, for example, its failure to handle Heterogeneity in measurement models, or its distributional assumptions. This research discusses alternative latent class techniques and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation. Originality/value This article is the first to offer researchers, who have not been exposed to the method, an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique, the paper offers a step-by-step tutorial on how to use FIMIX-PLS by using the SmartPLS 3 software.
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identifying and treating Unobserved Heterogeneity with fimix pls part i method
European Business Review, 2016Co-Authors: Joseph F Hair, Marko Sarstedt, Lucy Matthews, Christian M RingleAbstract:Purpose – The purpose of this paper is to provide an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat Unobserved Heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat Unobserved Heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applicati...
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Identifying and treating Unobserved Heterogeneity with FIMIX-PLS: part I – method
European Business Review, 2016Co-Authors: Joseph F Hair, Marko Sarstedt, Lucy M. Matthews, Christian M RingleAbstract:Purpose – The purpose of this paper is to provide an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat Unobserved Heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat Unobserved Heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applicati...
Joseph F Hair - One of the best experts on this subject based on the ideXlab platform.
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treating Unobserved Heterogeneity in pls sem a multi method approach
2017Co-Authors: Christian M Ringle, Marko Sarstedt, Joseph F HairAbstract:Accounting for Unobserved Heterogeneity has become a key concern to ensure the validity of results when applying partial least squares structural equation modeling (PLS-SEM). Recent methodological research in the field has brought forward a variety of latent class techniques that allow for identifying and treating Unobserved Heterogeneity. This chapter raises and discusses key aspects that are fundamental to a full and adequate understanding of how to apply these techniques in PLS-SEM. More precisely, in this chapter, we introduce a systematic procedure for identifying and treating Unobserved Heterogeneity in PLS path models using a combination of latent class techniques. The procedure builds on the FIMIX-PLS method to decide if Unobserved Heterogeneity has a critical impact on the results. Based on these outcomes, researchers should use more recently developed latent class methods, which have been shown to perform superior in recovering the segment-specific model estimates. After introducing these techniques, the chapter continues by discussing the means to identify explanatory variables that characterize the latent segments. Our discussion also broaches the issue of measurement invariance testing, which is a fundamental requirement for a subsequent comparison of parameters across groups by means of a multigroup analysis.
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identifying and treating Unobserved Heterogeneity with fimix pls part ii a case study
European Business Review, 2016Co-Authors: Lucy Matthews, Joseph F Hair, Marko Sarstedt, Christian M RingleAbstract:Purpose Part I of this article (European Business Review, Volume 28, Issue 1) offered an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social sciences researchers. This paper aims to provide an example that explains how to identify and treat Unobserved Heterogeneity in PLS-SEM by using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software (Part II). Design/methodology/approach This case study illustrates the application of FIMIX-PLS using a popular corporate reputation model. Findings The case study demonstrates the capability of FIMIX-PLS to identify whether Unobserved Heterogeneity significantly affects structural model relationships. Furthermore, it shows that FIMIX-PLS is particularly useful for determining the number of segments to extract from the data. Research limitations/implications Since the introduction of FIMIX-PLS, a range of alternative latent class techniques has appeared. These techniques address some of the limitations of the approach relating to, for example, its failure to handle Heterogeneity in measurement models, or its distributional assumptions. This research discusses alternative latent class techniques and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation. Originality/value This article is the first to offer researchers, who have not been exposed to the method, an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique, the paper offers a step-by-step tutorial on how to use FIMIX-PLS by using the SmartPLS 3 software.
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identifying and treating Unobserved Heterogeneity with fimix pls part i method
European Business Review, 2016Co-Authors: Joseph F Hair, Marko Sarstedt, Lucy Matthews, Christian M RingleAbstract:Purpose – The purpose of this paper is to provide an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat Unobserved Heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat Unobserved Heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applicati...
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Identifying and treating Unobserved Heterogeneity with FIMIX-PLS: part I – method
European Business Review, 2016Co-Authors: Joseph F Hair, Marko Sarstedt, Lucy M. Matthews, Christian M RingleAbstract:Purpose – The purpose of this paper is to provide an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat Unobserved Heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat Unobserved Heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applicati...
Lucy Matthews - One of the best experts on this subject based on the ideXlab platform.
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identifying and treating Unobserved Heterogeneity with fimix pls part ii a case study
European Business Review, 2016Co-Authors: Lucy Matthews, Joseph F Hair, Marko Sarstedt, Christian M RingleAbstract:Purpose Part I of this article (European Business Review, Volume 28, Issue 1) offered an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social sciences researchers. This paper aims to provide an example that explains how to identify and treat Unobserved Heterogeneity in PLS-SEM by using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software (Part II). Design/methodology/approach This case study illustrates the application of FIMIX-PLS using a popular corporate reputation model. Findings The case study demonstrates the capability of FIMIX-PLS to identify whether Unobserved Heterogeneity significantly affects structural model relationships. Furthermore, it shows that FIMIX-PLS is particularly useful for determining the number of segments to extract from the data. Research limitations/implications Since the introduction of FIMIX-PLS, a range of alternative latent class techniques has appeared. These techniques address some of the limitations of the approach relating to, for example, its failure to handle Heterogeneity in measurement models, or its distributional assumptions. This research discusses alternative latent class techniques and calls for the joint use of FIMIX-PLS and PLS prediction-oriented segmentation. Originality/value This article is the first to offer researchers, who have not been exposed to the method, an introduction to FIMIX-PLS. Based on a state-of-the-art review of the technique, the paper offers a step-by-step tutorial on how to use FIMIX-PLS by using the SmartPLS 3 software.
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identifying and treating Unobserved Heterogeneity with fimix pls part i method
European Business Review, 2016Co-Authors: Joseph F Hair, Marko Sarstedt, Lucy Matthews, Christian M RingleAbstract:Purpose – The purpose of this paper is to provide an overview of Unobserved Heterogeneity in the context of partial least squares structural equation modeling (PLS-SEM), its prevalence and challenges for social science researchers. Part II – in the next issue (European Business Review, Vol. 28 No. 2) – presents a case study, which illustrates how to identify and treat Unobserved Heterogeneity in PLS-SEM using the finite mixture PLS (FIMIX-PLS) module in the SmartPLS 3 software. Design/methodology/approach – The paper merges literatures from various disciplines, such as management information systems, marketing and statistics, to present a state-of-the-art review of FIMIX-PLS. Based on this review, the paper offers guidelines on how to apply the technique to specific research problems. Findings – FIMIX-PLS offers a means to identify and treat Unobserved Heterogeneity in PLS-SEM and is particularly useful for determining the number of segments to extract from the data. In the latter respect, prior applicati...
Peter Arcidiacono - One of the best experts on this subject based on the ideXlab platform.
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conditional choice probability estimation of dynamic discrete choice models with Unobserved Heterogeneity
Econometrica, 2011Co-Authors: Peter Arcidiacono, Robert A MillerAbstract:We adapt the expectation–maximization algorithm to incorporate Unobserved Heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The Unobserved Heterogeneity can be time-invariant or follow a Markov chain. By developing a class of problems where the difference in future value terms depends on a few conditional choice probabilities, we extend the class of dynamic optimization problems where CCP estimators provide a computationally cheap alternative to full solution methods. Monte Carlo results confirm that our algorithms perform quite well, both in terms of computational time and in the precision of the parameter estimates.
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ccp estimation of dynamic discrete choice models with Unobserved Heterogeneity
2008 Meeting Papers, 2008Co-Authors: Robert C Miller, Peter ArcidiaconoAbstract:We adapt the Expectation-Maximization (EM) algorithm to incorporate Unobserved Heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The Unobserved Heterogeneity can be time-invariant, fully transitory, or follow a Markov chain. By exploiting finite dependence, we extend the class of dynamic optimization problems where CCP estimators provide a computationally cheap alternative to full solution methods. We also develop CCP estimators for mixed discrete/continuous problems with Unobserved Heterogeneity. Further, when the unobservables affect both dynamic discrete choices and some other outcome, we show that the probability distribution of the Unobserved Heterogeneity can be estimated in a first stage, while simultaneously accounting for dynamic selection. The probabilities of being in each of the Unobserved states from the first stage are then taken as given and used as weights in the second stage estimation of the dynamic discrete choice parameters. Monte Carlo results for the three experimental designs we develop confirm that our algorithms perform quite well, both in terms of computational time and in the precision of the parameter estimates.
