Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.

https://link.springer.com/content/pdf/10.3758/BRM.40.3.879.pdf

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

Thank you for downloading using multivariate statistics. As you may know, people have look hundreds times for their favorite novels like this using multivariate statistics, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop. using multivariate statistics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the using multivariate statistics is universally compatible with any devices to read.

Using Multivariate Statistics

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.

/pdf/a-new-criterion-for-assessing-discriminant-validity-in-4xs4c94ene.pdf

A new criterion for assessing discriminant validity in variance-based structural equation modeling

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.

PLS-SEM: Indeed a Silver Bullet

This study evaluated the sensitivity of maximum likelihood (ML)-, generalized least squares (GLS)-, and asymptotic distribution-free (ADF)-based fit indices to model misspecification, under conditions that varied sample size and distribution. The effect of violating assumptions of asymptotic robustness theory also was examined. Standardized root-mean-square residual (SRMR) was the most sensitive index to models with misspecified factor covariance(s), and Tucker-Lewis Index (1973; TLI), Bollen's fit index (1989; BL89), relative noncentrality index (RNI), comparative fit index (CFI), and the MLand GLS-based gamma hat, McDonald's centrality index (1989; Me), and root-mean-square error of approximation (RMSEA) were the most sensitive indices to models with misspecified factor loadings. With ML and GLS methods, we recommend the use of SRMR, supplemented by TLI, BL89, RNI, CFI, gamma hat, Me, or RMSEA (TLI, Me, and RMSEA are less preferable at small sample sizes). With the ADF method, we recommend the use of SRMR, supplemented by TLI, BL89, RNI, or CFI. Finally, most of the ML-based fit indices outperformed those obtained from GLS and ADF and are preferable for evaluating model fit.

Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification

In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one.Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner’s guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software.Highlights of the Second Edition include:• Review of latent change (growth) analysis models at an introductory level• Coverage of the popular Mplus program• Updated examples of LISREL and EQS• Downloadable resources that contains all of the text’s LISREL, EQS, and Mplus examples.A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

A First Course in Structural Equation Modeling

Contents: Preface. G.A. Marcoulides, Applied Generalizability Theory Models. K.M. Schmidt McCollam, Latent Trait and Latent Class Models. M.E. Lunz, J.M. Linacre, Measurement Designs Using Multifacet Rasch Modeling. Z. Drezner, T. Drezner, Applied Location Theory Models. A.C. Soteriou, S.A. Zenios, Data Envelopment Analysis: An Introduction and an Application to Bank Branch Performance Assessment. S. Salhi, Heuristic Search Methods. R.H. Heck, Factor Analysis: Exploratory and Confirmatory Approaches. S.L. Hershberger, Dynamic Factor Analysis. E.E. Ridgon, Structural Equation Modeling. W.W. Chin, The Partial Least Squares Approach for Structural Equation Modeling. D. Kaplan, Methods for Multilevel Data Analysis. J.J. McArdle, Modeling Longitudinal Data by Latent Growth Curve Methods. P.K. Wood, Structural and Configural Models for Longitudinal Categorical Data.

Modern Methods for Business Research

Contents: G.A. Marcoulides, R.E. Schumacker, Introduction. W. Wothke, Models for Multitrait-Multimethod Matrix Analysis. K.G. Joreskog, F. Yang, Nonlinear Structural Equation Models: The Kenny-Judd Model With Interaction Effects. J.J. McArdle, F. Hamagami, Multilevel Models From a Multiple Group Structural Equation Perspective. J.B. Willett, A.G. Sayer, Cross-Domain Analyses of Change Over Time: Combining Growth Modeling and Covariance Structure Analysis. S.L. Hershberger, P.C.M. Molenaar, S.E. Corneal, A Hierarchy of Univariate and Multivariate Structural Times Series Models. Y-F. Yung, P.M. Bentler, Bootstrapping Techniques in Analysis of Mean and Covariance Structures. K.A. Bollen, A Limited-Information Estimator for LISREL Models With or Without Heteroscedastic Errors. J.L. Arbuckle, Full Information Estimation in the Presence of Incomplete Data. L.J. Williams, H. Bozdogan, L. Aiman-Smith, Inference Problems With Equivalent Models. H.W. Marsh, J.R. Balla, K-T. Hau, An Evaluation of Incremental Fit Indices: A Clarification of Mathematical and Empirical Properties.

Advanced structural equation modeling : issues and techniques

The purpose of this article is twofold: first, to propose and test a model concerning how an organization's culture affects organizational performance; and second, to demonstrate the application of LISREL modeling methodology to estimate and test this model. Organizational culture is hypothesized to consist of three interrelated dimensions: a sociocultural system of the perceived functioning of the organization's strategies and practices, an organizational value system, and the collective beliefs of the individuals working within the organization. Organizational culture is operationalized by several latent variables: organizational structure and purpose, organizational values, task organization, climate, and individual values and beliefs. These variables, in turn, are hypothesized to affect organizational performance. Analysis of data from 392 respondents who participated in the study confirms the fit of the proposed model to the data. The model presented in the study represents an initial attempt to desc...

Organizational Culture and Performance: Proposing and Testing a Model

Contents: Preface. B.O. Muthen, Latent Variable Mixture Modeling. K-H. Yuan, P.M. Bentler, A Unified Approach to Multigroup Structural Equation Modeling With Nonstandard Samples. B. Jo, B.O. Muthen, Modeling of Intervention Effects With Noncompliance: A Latent Variable Approach for Randomized Trials. R.H. Heck, Multilevel Modeling With SEM. K. Jedidi, A. Ansari, Bayesian Structural Equation Models for Multilevel Data. F. Yang-Wallentin, K.G. Joreskog, Robust Standard Errors and Chi-Squares for Interaction Models. F. Li, T.E. Duncan, S.C. Duncan, F. Yang-Wallentin, A.C. Acock, H. Hops, Interaction Models in Latent Growth Curves. F. Hamagami, J.J. McArdle, Advanced Studies of Individual Differences Linear Dynamic Models for Longitudinal Data Analysis. G.A. Marcoulides, Z. Drezner, Specification Searches in Structural Equation Modeling With a Genetic Algorithm. D.L. Bandalos, S.J. Finney, Item Parceling Issues in Structural Equation Modeling. T. Raykov, S. Penev, The Problem of Equivalent Structural Equation Models: An Individual Residual Perspective.

George A. Marcoulides

Papers

A First Course in Structural Equation Modeling

Modern Methods for Business Research

Advanced structural equation modeling : issues and techniques

Organizational Culture and Performance: Proposing and Testing a Model

New developments and techniques in structural equation modeling