On the evaluation of structural equation models
TL;DR: In this article, structural equation models with latent variables are defined, critiqued, and illustrated, and an overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence.
Abstract: Criteria for evaluating structural equation models with latent variables are defined, critiqued, and illustrated. An overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence. Model assessment is considered to be a complex process mixing statistical criteria with philosophical, historical, and theoretical elements. Inevitably the process entails some attempt at a reconcilation between so-called objective and subjective norms.
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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 background from "On the evaluation of structural equ..."
...DOI 10.2753/MTP1069-6679190202 Structural equation modeling (SEM) first appeared in the marketing literature in the early 1980s (e.g., Bagozzi 1994; Bagozzi and Yi 1988; Fornell and Larcker 1981a, 1981b), but in recent years, its application has become quite widespread....
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TL;DR: In this paper, the authors addressed the nature and functioning of relationships of interpersonal trust among managers and professionals in organizations, the factors influencing trust's development, and the implications of trust for behavior and performance.
Abstract: This study addressed the nature and functioning of relationships of interpersonal trust among managers and professionals in organizations, the factors influencing trust's development, and the implications of trust for behavior and performance Theoretical foundations were drawn from the sociological literature on trust and the social-psychological literature on trust in close relationships An initial test of the proposed theoretical framework was conducted in a field setting with 194 managers and professionals
6,473 citations
TL;DR: In this paper, the authors report an empirical assessment of a model of service encounters that simultaneously considers the direct effects of quality, satisfaction, and value on consumers' behavioral intentions, and further suggest that indirect effects of the service quality and value constructs enhanced their impact on behavioral intentions.
6,176 citations
Cites methods from "On the evaluation of structural equ..."
...Construct reliability was calculated using the procedures outlined by Fornell and Larcker (1981) which include the examination of the parameter estimates, their associated t-values, and assessing the average variance extracted for each construct (Anderson and Gerbing, 1988; Bagozzi and Yi, 1988)....
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TL;DR: In this paper, a multidimensional measure of psychological empowerment in the workplace has been developed and validated using second-order confirmatory factor analysis with two complementary samples to demonstrate the convergent and discriminant validity of four dimensions of empowerment.
Abstract: This research begins to develop and validate a multidimensional measure of psychological empowerment in the workplace Second-order confirmatory factor analyses were conducted with two complementary samples to demonstrate the convergent and discriminant validity of four dimensions of empowerment and their contributions to an overall construct of psychological empowerment Structural equations modeling was used to examine a nomological network of psychological empowerment in the workplace Tested hypotheses concerned key antecedents and consequences of the construct Initial support for the construct validity of psychological empowerment was found Directions for future research are discussed
5,629 citations
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
Additional excerpts
...50 Bagozzi and Yi 1988 J. of the Acad. Mark. Sci. (2012) 40:414–433 429...
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References
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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: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Abstract: The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.
47,133 citations
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Abstract: The problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion. These terms are a valid large-sample criterion beyond the Bayesian context, since they do not depend on the a priori distribution.
38,681 citations
01 Jan 2005
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Abstract: The problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion. These terms are a valid large-sample criterion beyond the Bayesian context, since they do not depend on the a priori distribution.
36,760 citations
TL;DR: In this article, a general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models, and the importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models.
Abstract: Factor analysis, path analysis, structural equation modeling, and related multivariate statistical methods are based on maximum likelihood or generalized least squares estimation developed for covariance structure models. Large-sample theory provides a chi-square goodness-of-fit test for comparing a model against a general alternative model based on correlated variables. This model comparison is insufficient for model evaluation: In large samples virtually any model tends to be rejected as inadequate, and in small samples various competing models, if evaluated, might be equally acceptable. A general null model based on modified independence among variables is proposed to provide an additional reference point for the statistical and scientific evaluation of covariance structure models. Use of the null model in the context of a procedure that sequentially evaluates the statistical necessity of various sets of parameters places statistical methods in covariance structure analysis into a more complete framework. The concepts of ideal models and pseudo chi-square tests are introduced, and their roles in hypothesis testing are developed. The importance of supplementing statistical evaluation with incremental fit indices associated with the comparison of hierarchical models is also emphasized. Normed and nonnormed fit indices are developed and illustrated.
16,420 citations