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Book ChapterDOI

How to Write Up and Report PLS Analyses

01 Jan 2010-pp 655-690
TL;DR: A discussion of key differences and rationale that researchers can use to support their use of PLS is provided, followed by two examples from the discipline of Information Systems.
Abstract: The objective of this paper is to provide a basic framework for researchers interested in reporting the results of their PLS analyses. Since the dominant paradigm in reporting Structural Equation Modeling results is covariance based, this paper begins by providing a discussion of key differences and rationale that researchers can use to support their use of PLS. This is followed by two examples from the discipline of Information Systems. The first consists of constructs with reflective indicators (mode A). This is followed up with a model that includes a construct with formative indicators (mode B).
Citations
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Journal ArticleDOI
TL;DR: In this paper, the heterotrait-monotrait ratio of correlations is used to assess discriminant validity in variance-based structural equation modeling. But it does not reliably detect the lack of validity in common research situations.
Abstract: 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.

12,855 citations


Cites background or methods or result from "How to Write Up and Report PLS Anal..."

  • ...Furthermore, while researchers frequently note that cross-loadings are more liberal in terms of indicating discriminant validity (i.e., the assessment of crossloadings will support discriminant validity when the FornellLarcker criterion fails to do so; Hair et al. 2012a, b; Henseler et al. 2009),…...

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  • ...This result implies that, in the vast majority of situations that lack discriminant validity, empirical 3 Chin (2010) suggests examining the squared loadings and crossloadings instead of the loadings and cross-loadings....

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  • ...…first to propose that each indicator loading should be greater than all of its cross-loadings.3 Otherwise, “the measure in question is unable to discriminate as to whether it belongs to the construct it was intended tomeasure or to another (i.e., discriminant validity problem)” (Chin 2010, p. 671)....

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  • ...The evaluation of the PLS results meets the relevant criteria (Chin 1998, 2010; Götz et al. 2010; Hair et al. 2012a), which Ringle et al. (2010), using this example, presented in detail....

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  • ...…for assessing discriminant validity Reference Recommendation Fornell-Larcker criterion Cross-loadings Barclay, Higgins, and Thompson (1995) ✓ ✓ Chin (1998, 2010) ✓ ✓ Fornell and Cha (1994) ✓ Gefen and Straub (2005) ✓ ✓ Gefen, Straub, and Boudreau (2000) ✓ ✓ Götz, Liehr-Gobbers, and Krafft…...

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Journal ArticleDOI
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 or methods or result from "How to Write Up and Report PLS Anal..."

  • ...…also does not impose any distributional assumptions, researchers cannot rely on the classic inferential framework and thus have to revert to prediction-oriented, non-parametric evaluation criteria as well as resampling procedures to evaluate the partial model structures’ adequacy (e.g., Chin 2010)....

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  • ...…mixture partial least squares (FIMIX-PLS; Hahn et al. 2002; Sarstedt et al. 2011a); (4) guidelines for analyzing moderating effects (Henseler and Chin 2010; Henseler and Fassott 2010); (5) non-linear effects (Rigdon et al. 2010); and (6) hierarchical component models (Lohmöller 1989; Wetzels…...

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  • ...Since predominantly covariance-based SEM techniques have been used to estimate models in marketing, PLSSEM use often requires a more detailed explanation of the rationale for selecting this method (Chin 2010)....

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  • ...In general, PLS-SEM studies should provide information on (1) the population and sample structure, (2) the distribution of the data, (3) the conceptual model, including a description of the inner and outer models, as well as the measurement modes, and (4) the statistical results to corroborate the subsequent interpretation and conclusions (Chin 2010)....

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  • ...…published in 2000 and beyond (165 studies with 242 models).3 Reasons for using PLS-SEM Since predominantly covariance-based SEM techniques have been used to estimate models in marketing, PLSSEM use often requires a more detailed explanation of the rationale for selecting this method (Chin 2010)....

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Journal ArticleDOI
TL;DR: Partial least squares (PLS) is an evolving approach to structural equation modeling (SEM), highlighting its advantages and limitations and providing an overview of recent research on the method across various fields as discussed by the authors.
Abstract: 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

5,191 citations


Cites background from "How to Write Up and Report PLS Anal..."

  • ...additional discussion to explain the rationale behind the decision (Chin, 2010)....

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Book ChapterDOI
TL;DR: Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships as discussed by the authors, and a common goal of PLSSEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior.
Abstract: Partial least squares structural equation modeling (PLS-SEM) has become a popular method for estimating path models with latent variables and their relationships. A common goal of PLS-SEM analyses is to identify key success factors and sources of competitive advantage for important target constructs such as customer satisfaction, customer loyalty, behavioral intentions, and user behavior. Building on an introduction of the fundamentals of measurement and structural theory, this chapter explains how to specify and estimate path models using PLS-SEM. Complementing the introduction of the PLS-SEM method and the description of how to evaluate analysis results, the chapter also offers an overview of complementary analytical techniques. A PLS-SEM application of the widely recognized corporate reputation model illustrates the method.

1,842 citations

References
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Book
01 Dec 1969
TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Abstract: Contents: Prefaces. The Concepts of Power Analysis. The t-Test for Means. The Significance of a Product Moment rs (subscript s). Differences Between Correlation Coefficients. The Test That a Proportion is .50 and the Sign Test. Differences Between Proportions. Chi-Square Tests for Goodness of Fit and Contingency Tables. The Analysis of Variance and Covariance. Multiple Regression and Correlation Analysis. Set Correlation and Multivariate Methods. Some Issues in Power Analysis. Computational Procedures.

115,069 citations


"How to Write Up and Report PLS Anal..." refers background or methods in this paper

  • ...35, similar to Cohen (1988) operational definitions for multiple regression, can be viewed as a gauge for whether a predictor LV has a small, medium, or large effect at the structural level....

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  • ..., Cohen 1990, p. 1309). Instead, closer attention should be paid to the predictiveness of the model. Are the structural paths and loadings of substantial strength as opposed to just statistically significant? Standardized paths should be around 0.20 and ideally above 0.30 in order to be considered meaningful. Meehl (1990) has argued that anything lower may be due to what he has termed the crud factor where “everything correlate to some extent with everything else” (p....

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  • ...approximation by determining the specific portion of the model that has the largest number of predictors for a particular dependent variable and then applying Cohen’s power tables (1988) relative to the effect sizes one wishes to detect....

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Journal ArticleDOI
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


"How to Write Up and Report PLS Anal..." refers methods in this paper

  • ...To that extent, the R-square for dependent LVs, the Stone-Geisser (Stone 1974; Geisser 1975) test for predictive relevance, Fornell and Larcker (1981) average variance extracted measure, and bootcross validation are used to assess predictiveness, while resampling procedures such as jack knifing and bootstrapping are used to examine the stability of estimates....

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01 Jan 1998

10,147 citations


"How to Write Up and Report PLS Anal..." refers background in this paper

  • ...First introduced by Blalock (1964), formative indicators are defined as measures that form or cause the creation or change in an LV (Chin and Gopal 1995, pp. 58–59; Chin 1998b; Jarvis et al. 2003)....

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  • ...A key argument for this position is that the case values for the constructs (i.e., PLS estimated scores) are “inconsistent” relative to CBSEM model analysis because PLS components are aggregates of the observed variables and include measurement error ( Chin 1998b )....

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Journal ArticleDOI
TL;DR: In this article, a generalized form of the cross-validation criterion is applied to the choice and assessment of prediction using the data-analytic concept of a prescription, and examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.
Abstract: SUMMARY A generalized form of the cross-validation criterion is applied to the choice and assessment of prediction using the data-analytic concept of a prescription. The examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.

7,385 citations


"How to Write Up and Report PLS Anal..." refers methods in this paper

  • ...To that extent, the R-square for dependent LVs, the Stone-Geisser (Stone 1974; Geisser 1975) test for predictive relevance, Fornell and Larcker (1981) average variance extracted measure, and bootcross validation are used to assess predictiveness, while resampling procedures such as jack knifing and bootstrapping are used to examine the stability of estimates....

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Journal ArticleDOI
TL;DR: A new latent variable modeling approach is provided that can give more accurate estimates of interaction effects by accounting for the measurement error that attenuates the estimated relationships.
Abstract: The ability to detect and accurately estimate the strength of interaction effects are critical issues that are fundamental to social science research in general and IS research in particular. Within the IS discipline, a significant percentage of research has been devoted to examining the conditions and contexts under which relationships may vary, often under the general umbrella of contingency theory (cf. McKeen et al. 1994, Weill and Olson 1989). In our survey of such studies, the majority failed to either detect or provide an estimate of the effect size. In cases where effect sizes are estimated, the numbers are generally small. These results have led some researchers to question both the usefulness of contingency theory and the need to detect interaction effects (e.g., Weill and Olson 1989). This paper addresses this issue by providing a new latent variable modeling approach that can give more accurate estimates of interaction effects by accounting for the measurement error that attenuates the estimated relationships. The capacity of this approach at recovering true effects in comparison to summated regression is demonstrated in a Monte Carlo study that creates a simulated data set in which the underlying true effects are known. Analysis of a second, empirical data set is included to demonstrate the technique's use within IS theory. In this second analysis, substantial direct and interaction effects of enjoyment on electronic-mail adoption are shown to exist.

5,639 citations