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Partial Least Squares (PLS-SEM): Eine Analyse mithilfe von plspm in R

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Quantitative Forschung in Masterarbeiten

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Zusammenfassung

Für die Durchführung einer PLS-SEM-Analyse stehen unterschiedliche Software-Lösungen zur Verfügung. Hierunter fallen verschiedene, auf PLS-SEM spezialisierte Angebote. Darüber hinaus ist eine Analyse auch mit R unter Heranziehung z. B. des Paketes plspm möglich. Für eine Anwendung mit R spricht insbesondere die Integration aller Datenanalyseschritte von der Datenbereinigung und -aufbereitung, über explorative Datenanalysen bis hin zur eigentlichen Durchführung des PLS-Algorithmus mit der Gütebeurteilung der Messmodelle sowie des Strukturmodells. Als nachteilig gegenüber spezialisierten Software-Lösungen werden die komplexeren Anforderungen im Coding empfunden. Aus Masterandensicht stellt die Verwendung von unbekannten R Codes z. T. ein Hemmnis dar. Der vorliegende Beitrag zeigt eine schrittweise PLS-SEM-Analyse mit R anhand eines konkreten Beispiels aus einer Masterarbeit und gibt eine Hilfestellung für die eigene Umsetzung.

Wir danken Joachim Schwarz, Oliver Gansser und Marco Zimmer für den Austausch zu diesem Artikel.

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Literatur

  • Allaire, J. J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., & Iannone, R. (2021): rmarkdown: Dynamic Documents for R. R package version 2.8. https://rmarkdown.rstudio.com

    Google Scholar 

  • Becker, J.-M., Ringle, C.M., Sarstedt, M., & Völckner, F. (2015): How collinearity affects mixture regression results, Marketing Letters, 26(4), 643–659.

    Article  Google Scholar 

  • Birkinshaw, J., Morrison, A., & Hulland, J. (1995): Structural and Competitive Determinants of a Global Integration Strategy, Strategic Management Journal, 16(8), 637–655.

    Article  Google Scholar 

  • Boßow-Thies, S., & Panten, G. (2009): Analyse kausaler Wirkungszusammenhänge mit Hilfe von Partial Least Squares (PLS), in: Albers, S., Klapper, D., Konradt, U., Walter, A., Wolf, J. (Hrsg.): Methodik der empirischen Forschung, 3. Auflage, Wiesbaden: Springer Verlag, 365–380.

    Chapter  Google Scholar 

  • Chin, W. W. (1998): The Partial Least Squares Approach to Structural Equation Modeling, in: Marcoulides, G. A.: Modern Methods for Business Research, New Jersey: Lawrence Erlbaum, 295–336.

    Google Scholar 

  • Chin, W. W. (2003): PLS-Graph 3.0, Soft Modeling Inc, Houston.

    Google Scholar 

  • Cinelli, C., Ferwerda, J., & Hazlett, C. (2020): sensemakr: Sensitivity Analysis Tools for Regression Models. R package version 0.1.3. https://CRAN.R-project.org/package=sensemakr

  • Cronbach, L. J. (1951): Coefficient Alpha and the Internal Structure of Tests, Psychometrika, 16(3), 297–324.

    Article  Google Scholar 

  • Davis, F. D. (1989): Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology, MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012): Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective, Journal of the Academy of Marketing Science, 40(3), 434–449.

    Article  Google Scholar 

  • Diamantopoulos, A., & Winklhofer, H. M. (2001): Index Construction with Formative Indicators – An Alternative to Scale Development, Journal of Marketing Research, 38(2), 269–277.

    Article  Google Scholar 

  • Fornell, C., & Bookstein, L. (1982): Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory, Journal of Marketing Research, 19(4), 440–452.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981): Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Fox, J., & Weisberg, S. (2019): An R Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/

  • Geisser, S. (1974): A predictive approach to the random effects model, Biometrika, 61(1), 101–107.

    Article  Google Scholar 

  • 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 edition., Thousand Oaks: Sage Publications, Inc.

    Google Scholar 

  • Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019): When to use and how to report the results of PLS-SEM, European Business Review, 31(1), 2–24.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015): A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, 43(1), 115–135.

    Article  Google Scholar 

  • Hulland, J. (1999): Use of Partial Least Squares (PLS) in Strategic Management Research – A Review of four Recent Studies, Strategic Management Journal, 20(2), 195–204.

    Article  Google Scholar 

  • Jöreskog, K. G. (1971): Simultaneous factor analysis in several populations, Psychometrika, Vol. 36 (4), 409–426.

    Article  Google Scholar 

  • Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2021): semTools: Useful tools for structural equation modeling. R package version 0.5-4. https://CRAN.R-project.org/package=semTools

  • Leiner, D. J. (2019): Too Fast, too Straight, too Weird: Non-Reactive Indicators for Meaningless Data in Internet Surveys, Survey Research Methods, 13(3), 229–248.

    Google Scholar 

  • Mason, C. H., & Perreault, W. D. (1991): Collinearity, power, and interpretation of multiple regression analysis, Journal of Marketing Research, 28(3), 268–280.

    Article  Google Scholar 

  • Monecke, A., & Leisch, F. (2012): semPLS: structural equation modeling using partial least squares, Journal of Statistical Software, 48(3), 1–32.

    Article  Google Scholar 

  • Nunnally, J. C., & Bernstein, I. H. (1994): Psychometric Theory, New York: McGraw-Hill, 1994.

    Google Scholar 

  • Pruim, R., Kaplan, D. T., & Horton, N. J. (2017): The mosaic Package: Helping Students to ‘Think with Data’ Using R, in: The R Journal, 9(1), 77–102.

    Google Scholar 

  • R Core Team (2020): R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • Ringle, C. M., Wende, S., & Becker, J.-M. (2015): SmartPLS 3. Boenningstedt: SmartPLS GmbH.

    Google Scholar 

  • Rönkkö, M. (2021): matrixpls: Matrix-based Partial Least Squares Estimation. R package version 1.0.13. https://github.com/mronkko/matrixpls

  • Sanchez, G. (2013): PLS Path Modeling with R, Trowchez Editions. http://www.gastonsanchez.com/PLS_Path_Modeling_with_R.pdf

  • Sanchez, G., Trinchera, L., & Russolillo, G. (2015): plspm: Tools for Partial Least Squares Path Modeling (PLS-PM). R package version 0.4.9. https://github.com/gastonstat/plspm

  • Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017): Partial Least Squares Structural Equation Modeling, in: Homburg, C., Klarmann, M., Vomberg, A. (ed.), Handbook of Market Research, Berlin: Springer International Publishing, 1–29.

    Google Scholar 

  • Schneider, S., & Boßow-Thies, S. (2021): Partial Least Squares (PLS-SEM): Analyse der Einflussfaktoren auf die Akzeptanz mobiler Kundenkarten in der Generation der Digital Natives, in: Boßow-Thies, S., Krol, B. (Hrsg.), Quantitative Forschung in Masterarbeiten – Best-Practice-Beispiele wirtschaftswissenschaftlicher Studienrichtungen, Springer Gabler, Wiesbaden.

    Google Scholar 

  • Seltin, N., & Keeves, J.P. (1994): Path Analysis with Latent Variables, in: Husen, T., Postlethwaite, T. N. (ed.), International Encyclopedia 0, Education, 2nd edition, Oxford, 4352–4359.

    Google Scholar 

  • Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016): The Elephant in the Room: Predictive Performance of PLS Models, Journal of Business Research, 69(10), 4552–4564.

    Article  Google Scholar 

  • Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J. H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019): Predictive model assessment in PLS-SEM: guidelines for using PLSpredict, European Journal of Marketing, 53 (11), 2322–2347.

    Article  Google Scholar 

  • Stone, M. (1974): Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society, 36(2), 111–147.

    Google Scholar 

  • Werts, C. E., Linn R. L., & Jöreskog, K. G. (1974): Interclass Reliability Estimates: Testing Structural Assumptions, Educational and Psychological Measurement, 34, 24–33.

    Article  Google Scholar 

  • Xie, Y., Allaire, J. J., & Grolemund, G. (2018): R Markdown: The Definitive Guide. Chapman and Hall/CRC. https://bookdown.org/yihui/rmarkdown

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Authors and Affiliations

  1. FOM Hochschule für Oekonomie & Management, Hamburg, Deutschland

    Silvia Boßow-Thies

  2. FOM Hochschule für Oekonomie & Management, Essen, Deutschland

    Bianca Krol

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  1. Silvia Boßow-Thies
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  2. Bianca Krol
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Correspondence to Silvia Boßow-Thies .

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Editors and Affiliations

  1. FOM Hochschule für Oekonomie & Management, Hamburg, Deutschland

    Silvia Boßow-Thies

  2. FOM Hochschule für Oekonomie & Management, Essen, Deutschland

    Bianca Krol

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Boßow-Thies, S., Krol, B. (2022). Partial Least Squares (PLS-SEM): Eine Analyse mithilfe von plspm in R. In: Boßow-Thies, S., Krol, B. (eds) Quantitative Forschung in Masterarbeiten. FOM-Edition. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-35831-0_15

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  • DOI: https://doi.org/10.1007/978-3-658-35831-0_15

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