Structural Equation Modeling
TL;DR: The theory of SEM, which allows for the analysis of independent observations for both unrelated and family data, the available software for SEM, and an example of SEM analysis are reviewed.
Abstract: Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.
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TL;DR: The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
Abstract: Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.
11,624 citations
Cites methods from "Structural Equation Modeling"
...The technique focuses on estimating a set of model parameters in such a way that the difference between the theoretical covariance matrix and the estimated covariance matrix is minimized (e.g., Rigdon 1998)....
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TL;DR: An extensive search in the 30 top ranked marketing journals allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010), and a critical analysis of these articles addresses the following key methodological issues: reasons for using PLS, data and model characteristics, outer and inner model evaluations, and reporting.
Abstract: Most methodological fields undertake regular critical reflections to ensure rigorous research and publication practices, and, consequently, acceptance in their domain. Interestingly, relatively little attention has been paid to assessing the use of partial least squares structural equation modeling (PLS-SEM) in marketing research—despite its increasing popularity in recent years. To fill this gap, we conducted an extensive search in the 30 top ranked marketing journals that allowed us to identify 204 PLS-SEM applications published in a 30-year period (1981 to 2010). A critical analysis of these articles addresses, amongst others, the following key methodological issues: reasons for using PLS-SEM, data and model characteristics, outer and inner model evaluations, and reporting. We also give an overview of the interdependencies between researchers’ choices, identify potential problem areas, and discuss their implications. On the basis of our findings, we provide comprehensive guidelines to aid researchers in avoiding common pitfalls in PLS-SEM use. This study is important for researchers and practitioners, as PLS-SEM requires several critical choices that, if not made correctly, can lead to improper findings, interpretations, and conclusions.
5,328 citations
Cites background from "Structural Equation Modeling"
...2008; Bagozzi 1994; Hulland 1999), as it allows authors to test complete theories and concepts (Rigdon 1998)....
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...Structural equation modeling (SEM) has become a quasi-standard in marketing research (e.g., Babin et al. 2008; Bagozzi 1994; Hulland 1999), as it allows authors to test complete theories and concepts (Rigdon 1998)....
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TL;DR: This work presents an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases and uses several applied examples to illustrate the flexibility of this framework.
Abstract: Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover, these methods have each appeared in isolation, so a unified framework that integrates the existing methods, as well as new multilevel mediation models, is lacking. Here we show that a multilevel structural equation modeling (MSEM) paradigm can overcome these 2 limitations of mediation analysis with MLM. We present an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases. We use several applied examples and accompanying software code to illustrate the flexibility of this framework and to show that different substantive conclusions can be drawn using MSEM versus MLM.
2,595 citations
Cites background from "Structural Equation Modeling"
...We address several additional models (for 2-2-1, 1-1-1, 1-1-2, 1-2-2, 2-1-2, and 1-2-1 data) in Appendix B....
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...Additional discussion of the general model can be found in Kaplan (2009) and Kaplan et al. (2009)....
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01 Jan 2013
TL;DR: This paper is written to address this knowledge gap and help beginners to understand how PLS-SEM can be used in marketing research.
Abstract: SmartPLS is one of the prominent software applications for Partial Least Squares Structural Equation Modeling (PLS-SEM). It was developed by Ringle, Wende & Will (2005). The software has gained popularity since its launch in 2005 not only because it is freely available to academics and researchers, but also because it has a friendly user interface and advanced reporting features. Although an extensive number of journal articles have been published on the topic of PLS modeling, the amount of instructional materials available for this software is limited. This paper is written to address this knowledge gap and help beginners to understand how PLS-SEM can be used in marketing research.
1,553 citations
Cites background from "Structural Equation Modeling"
...Prior research suggests that a sample size of 100 to 200 is usually a good starting point in carrying out path modeling (Hoyle, 1995)....
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TL;DR: This paper discusses a recent development in partial least squares (PLS) path modeling, namely goodness-of-fit indices, and estimates PLS path models with simulated data, and contrasts their values with fit indices commonly used in covariance-based structural equation modeling.
Abstract: 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.
1,102 citations
References
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TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.
Abstract: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.
80,095 citations
"Structural Equation Modeling" refers background in this paper
...More detail on mediation models can be found elsewhere (5, 6)....
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TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Abstract: This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...
76,383 citations
"Structural Equation Modeling" refers background in this paper
...Hu and Bentler (9) categorize these fit statistics as “comparative” or “absolute....
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...Hu and Bentler (9) categorize these fit statistics as “comparative” or “absolute.”...
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...Hu LT, Bentler PM, (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives....
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TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...
56,555 citations
TL;DR: The extent to which method biases influence behavioral research results is examined, potential sources of method biases are identified, the cognitive processes through which method bias influence responses to measures are discussed, the many different procedural and statistical techniques that can be used to control method biases is evaluated, and recommendations for how to select appropriate procedural and Statistical remedies are provided.
Abstract: Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
52,531 citations
Book•
27 May 1998TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Abstract: Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation and pitfalls of structural equation modelling (SEM) in the social sciences. The book covers introductory techniques including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods such as the evaluation of non-linear effects, the analysis of means in convariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the book offers clear instructions on the preparation and screening of data, common mistakes to avoid and widely used software programs (Amos, EQS and LISREL). The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
42,102 citations