nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Structural Equation Modeling

verfasst von : Hans Baumgartner, Bert Weijters

Erschienen in: Handbook of Market Research

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This chapter presents an overview of the process of structural equation modeling, involving the steps of model specification, model estimation, overall fit evaluation, model respecification, and local fit assessment (including interpreting the parameters of the model). Various extensions of the core structural equation model are described to enable more general representations of measurement and latent variable models as well as applications of the model to heterogeneous populations. An empirical example is provided to illustrate the process of structural equation modeling and to demonstrate some of the complexities that may arise in practical applications.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Modeling Marketing Dynamics Using Vector Autoregressive (VAR) Models

Nächstes Kapitel Partial Least Squares Structural Equation Modeling

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.CrossRef

Bagozzi, R. P. (1980). Causal models in marketing. New York: Wiley.

Bandalos, D. L., & Finney, S. J. (2001). Item parceling issues in structural equation modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 269–296). Mahwah: Erlbaum.

Baumgartner, H., & Weijters, B. (2019). Measurement in marketing. Foundations and Trends® in Marketing, 12(4), 278–400.CrossRef

Baumgartner, H., & Weijters, B. (forthcoming). Dealing with common method variance in international marketing research. Journal of International Marketing. in press.

Baumgartner, H., Weijters, B., & Pieters, R. (2021). The biasing effect of common method variance: Some clarifications. Journal of the Academy of Marketing Science, 49(2), 221–235. https://doi.org/10.1007/s11747-020-00766-8.

Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. MIS Quarterly, 35(2), 359–372.CrossRef

Bollen, K. A. (2012). Instrumental variables in sociology and the social sciences. Annual Review of Sociology, 38, 37–72.CrossRef

Bollen, K. A. (2018). Model implied instrumental variables (MIIVs): An alternative orientation to structural equation modeling. Multivariate Behavioral Research, 54(1), 31–46.CrossRef

Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken: Wiley.

Bollen, K. A., Harden, J. J., Ray, S., & Zavisca, J. (2014). BIC and alternative Bayesian information criteria in the selection of structural equation models. Structural Equation Modeling, 21(1), 1–19.CrossRef

Chapman, C., & Feit, E. M. D. (2019). R for marketing research and analytics (2nd ed.). Springer: Cham.CrossRef

Cortina, J. M., Markell-Goldstein, H. M., Green, J. P., & Chang, Y. (2021). How are we testing interactions in latent variable models? Surging forward or fighting shy? Organizational Research Methods, 24(1), 26–54.CrossRef

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.CrossRef

Diamantopoulos, A. (2011). Incorporating formative measures into covariance-based structural equation models. MIS Quarterly, 35(June), 335–358.CrossRef

Diamantopoulos, A., & Winklhofer, H. (2001). Index construction with formative indicators: An alternative to scale development. Journal of Marketing Research, 38(May), 269–277.CrossRef

Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61(12), 1203–1218.CrossRef

Diop, E. B., Zhao, S., & Duy, T. V. (2019). An extension of the technology acceptance model for understanding travelers’ adoption of variable message signs. PLoS One, 14(4), e0216007.CrossRef

Edwards, J. R. (2011). The fallacy of formative measurement. Organizational Research Methods, 14(2), 370–388.CrossRef

Fisher, Z. F., Bollen, K. A., Gates, K., & Ronkko, M. (2020). MlIVsem: Model implied instrumental variable (MIIV) estimation of structural equation models. R package version 0.5.5.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRef

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks: Sage.

Howell, R. D., Breivik, E., & Wilcox, J. B. (2007). Reconsidering formative measurement. Psychological Methods, 12(2), 205–218.CrossRef

Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453.CrossRef

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRef

Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199–218.CrossRef

Kenny, D. A., & Judd, C. M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201–210.CrossRef

Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65(December), 457–474.CrossRef

Kline, R. B. (2013). Reverse arrow dynamics: Feedback loops and formative measurement. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 39–76). Greenwich: Information Age Publishing.

MacCallum, R. C. (1986). Specification searches in covariance structure modeling. Psychological Bulletin, 100(1), 107–120.CrossRef

MacCallum, R. C., & Browne, M. W. (1993). The use of causal indicators in covariance structure models: Some practical issues. Psychological Bulletin, 114, 533–541.CrossRef

MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modification in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111(3), 490–504.CrossRef

MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542–555.CrossRef

MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. Journal of Applied Psychology, 90(4), 710–730.CrossRef

MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(June), 293–334.CrossRef

MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7(1), 83–104.CrossRef

Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11(3), 320–341.CrossRef

Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110.CrossRef

McNeish, D. (2020). Should we use F-tests for model fit instead of chi-square in over-identified structural equation models? Organizational Research Methods, 23, 487–510.CrossRef

Muthén, B. (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 1–33). Mahwah: Erlbaum.

Muthén, B., & Asparouhov, T. (2011). Beyond multilevel regression modeling: Multilevel analysis in a general latent variable framework. In J. Hox & J. K. Roberts (Eds.), Handbook of advanced multilevel analysis (pp. 15–40). New York: Taylor and Francis.

Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335.CrossRef

Muthén, L. K., & Muthén, B. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9(4), 599–620.CrossRef

Niemand, T., & Mai, R. (2018). Flexible cutoff values for fit indices in the evaluation of structural equation models. Journal of the Academy of Marketing Science, 46, 1148–1172.CrossRef

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.CrossRef

Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.CrossRef

Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344.CrossRef

Rhemtulla, M., Brosseau-Liard, A. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373.CrossRef

Rhemtulla, M., van Bork, R., & Borsboom, D. (2020). Worse than measurement error: Consequences of inappropriate latent variable measurement models. Psychological Methods, 25(1), 30–45.CrossRef

Rönkko, M., McIntosh, C. N., Antonakis, J., & Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47–48(November), 9–27.CrossRef

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.CrossRef

Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514.CrossRef

Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in crossnational consumer research. Journal of Consumer Research, 25(June), 78–90.CrossRef

Sterba, S. K. (2011). Implications of parcel-allocation variability for comparing fit of item-solutions and parcel-solutions. Structural Equation Modeling, 18(4), 554–577.CrossRef

Sterba, S. K., & Pek, J. (2012). Individual influence on model selection. Psychological Methods, 17(4), 582–599.CrossRef

Sterba, S. K., & Rights, J. D. (2017). Effects of parceling on model selection: Parcel-allocation variability in model ranking. Psychological Methods, 22(1), 47–68.CrossRef

Umbach, N., Naumann, K., Brandt, H., & Kelava, A. (2017). Fitting nonlinear structural equation models in R with package nlsem. Journal of Statistical Software, 77(7), 1–20. https://doi.org/10.18637/iss.v077.i07.CrossRef

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(January), 4–69.CrossRef

Weijters, B., & Baumgartner, H. (2019). Analyzing Policy Capturing Data Using Structural Equation Modeling for Within-Subject Experiments (SEMWISE). Organizational Research Methods, 22(3), 623–648.CrossRef

Weijters, B., & Baumgartner, H. (forthcoming). On the use of balanced item parceling to counter acquiescence bias in structural equation models. Organizational Research Methods. in press.

Wilcox, J. B., Howell, R. D., & Breivik, E. (2008). Questions about formative measurement. Journal of Business Research, 61, 1219–1228.CrossRef

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913–934.CrossRef

Wooldridge, J. M. (2016). Introductory econometrics: A modern approach (6th ed.). Boston: Cengage Learning.

Yang, M., Jiang, G., & Yuan, K.-H. (2018). The performance of ten modified rescaled statistics as the number of variables increases. Structural Equation Modeling, 25(3), 414–438.CrossRef

Titel: Structural Equation Modeling
verfasst von: Hans Baumgartner
Bert Weijters
Verlag: Springer International Publishing
Buch: Handbook of Market Research
Print ISBN: 978-3-319-57411-0

Electronic ISBN: 978-3-319-57413-4

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-319-57413-4_14