PLS-Based Structural Equation Modelling: An Alternative Approach to Estimating Complex Relationships Between Unobserved Constructs

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Methods for Researching Professional Learning and Development

Abstract

A traditional approach to test complex relationships between different unobserved constructs included in theoretical models is to apply covariance-based structural equation modelling (CB-SEM). This chapter aims at introducing an alternative approach to estimating structural equation models that has not yet widely been used in research on professional learning and development or in research on learning in general: Partial-least squares structural equation modelling (PLS-SEM). PLS-SEM is based on ordinary least square regression analysis and uses an iterative algorithm to find parameter estimates. This estimation approach has several advantages including fewer statistical assumptions. In addition, PLS-SEM allows for the incorporation of both lower order and higher order formative constructs as well as for estimating rather complex models, which is not always possible with CB-SEM. The conceptual explanation of this particular SEM technique will be illustrated using a replication study of a published research study, focussing on the influence of learner factors and learning context on different professional learning activities.

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References

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

    Article  Google Scholar 

  • Bauer, J. (2022). A primer to latent profile and latent class analysis. In M. Goller, E. Kyndt, S. Paloniemi, & C. DamÅŸa (Eds.), Methods for researching professional learning and development: Challenges, applications, and empirical illustrations (pp. 243–268). Springer.

    Google Scholar 

  • Barclay, D. W., Higgins, C. A., & Thompson, R. (1995). The partial least squares approach to causal modeling: Personal computer adoption and use as illustration. Technology Studies, 2, 285–309.

    Google Scholar 

  • Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605–634. https://doi.org/10.1146/annurev.psych.53.100901.135239

    Article  Google Scholar 

  • Bollen, K. A., & Ting, K.-F. (2000). A tetrad test for causal indicators. Psychological Methods, 5(1), 3–22. https://doi.org/10.1037/1082-989X.5.1.3

    Article  Google Scholar 

  • Butler, J., & Brooker, R. (1998). The learning context within technical and further education colleges as perceived by apprentices and their workplace supervisors. Journal of Vocational Education & Training, 50(1), 79–96.

    Article  Google Scholar 

  • Cangialosi, N., Odoardi, C., & Battistelli, A. (2020). Learning climate and innovative work behavior, the mediating role of the learning potential of the workplace. Vocations and Learning, 13(2), 263–280. https://doi.org/10.1007/s12186-019-09235-y

    Article  Google Scholar 

  • Cerasoli, C. P., Alliger, G. M., Donsbach, J. S., Mathieu, J. E., Tannenbaum, S. I., & Orvis, K. A. (2017). Antecedents and outcomes of informal learning behaviors: A meta-analysis. Journal of Business and Psychology, 33, 203–230.

    Article  Google Scholar 

  • Cheng, E. W. L., & Ho, D. C. K. (2001). The influence of job and career attitudes on learning motivation and transfer. Career Development International, 6(1), 20–28. https://doi.org/10.1108/13620430110381007

    Article  Google Scholar 

  • Chin, W. W. (1995). Partial least squares is to LISREL as principal components analysis is to common factor analysis. Technology Studies, 2, 315–319.

    Google Scholar 

  • Chin, W. W., & Dibbern, J. (2010). An introduction to a permutation based procedure for multi-group PLS analysis: Results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA. In V. Esposito Vinzi, W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 171–193). Springer. https://doi.org/10.1007/978-3-540-32827-8_8

    Chapter  Google Scholar 

  • Chin, W. W., & Newsted, P. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–341). Sage.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.).

    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. https://doi.org/10.1509/jmkr.38.2.269.18845

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Dimitrov, D. M. (2010). Testing for factorial invariance in the context of construct validation. Measurement and Evaluation in Counseling and Development, 43(2), 121–149. https://doi.org/10.1177/0748175610373459

    Article  Google Scholar 

  • Eraut, M. (2004). Informal learning in the workplace. Studies in Continuing Education, 26(2), 247–273. https://doi.org/10.1080/158037042000225245

    Article  Google Scholar 

  • Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (Eds.). (2010). Handbook of partial least squares: Concepts, methods and applications. Springer.

    Google Scholar 

  • Esposito Vinzi, V., Trinchera, L., Squillacciotti, S., & Tenenhaus, M. (2008). REBUS-PLS: A response-based procedure for detecting unit segments in PLS path modelling. Applied Stochastic Models in Business and Industry, 24, 439–458.

    Article  Google Scholar 

  • Facteau, J. D., Dobbins, G. H., Russell, J. E. A., Ladd, R. T., & Kudisch, J. D. (1995). The influence of general perceptions of the training environment on pretraining motivation and perceived training transfer. Journal of Management, 21, 1–25.

    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. https://doi.org/10.2307/3151312

    Article  Google Scholar 

  • Gegenfurtner, A. (2013). Dimensions of motivation to transfer: A longitudinal analysis of their influence on retention, transfer, and attitude change. Vocations and Learning, 6(2), 187–205. https://doi.org/10.1007/s12186-012-9084-y

    Article  Google Scholar 

  • Goller, M. (2017). Human agency at work: An active approach towards expertise development. Springer VS.

    Book  Google Scholar 

  • Goller, M., Harteis, C., Gijbels, D., & Donche, V. (2020). Engineering students' learning during internships: Exploring the explanatory power of the job demands-control-support model. Journal of Engineering Education, 109(2), 307–324. https://doi.org/10.1002/jee.20308

    Article  Google Scholar 

  • Goller, M., & Paloniemi, S. (Eds.). (2017). Agency at work: An agentic perspective on professional learning and development. Springer.

    Google Scholar 

  • Götz, O., Liehr-Gobbers, K., & Krafft, M. (2010). Evaluation of structural equation models using the partial least squares (PLS) approach. In V. Esposito Vinzi (Ed.), Handbook of partial least squares: Concepts, methods and applications (pp. 691–711). Springer.

    Chapter  Google Scholar 

  • Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12), 1238–1249. https://doi.org/10.1016/j.jbusres.2008.01.012

    Article  Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A primer on partial least squares structural equations modeling (PLS-SEM) (2nd ed.). SAGE.

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2018). Multivariate data analysis (8th ed.). Cengage.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2012). Using partial least squares path modeling in international advertising research: Basic concepts and recent issues. In S. Okazaki (Ed.), Handbook of research in international advertising (pp. 252–276). Edward Elgar.

    Google Scholar 

  • Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580. https://doi.org/10.1007/s00180-012-0317-1

    Article  Google Scholar 

  • Hilkenmeier, F., Bohndick, C., Bohndick, T., & Hilkenmeier, J. (2020). Assessing distinctiveness in multidimensional instruments without access to raw data–a manifest Fornell-Larcker criterion. Frontiers in Psychology, 11, 1–9. https://doi.org/10.3389/fpsyg.2020.00223

    Article  Google Scholar 

  • Hilkenmeier, F., Goller, M. & Schaper, N. (2021). The differential influence of learner factors and learning context on different professional learning activities. Vocations and Learning, 14(3), 411–438. https://doi.org/10.1007/s12186-021-09266-4

  • Hyland, M. (1981). Introduction to theoretical psychology. Macmillan Education UK. https://doi.org/10.1007/978-1-349-16464-6

  • Janz, B. D., & Prasarnphanich, P. (2003). Understanding the antecedents of effective knowledge management: The importance of a knowledge-centered culture. Decision Sciences, 34, 351–384.

    Article  Google Scholar 

  • 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. https://doi.org/10.1086/376806

    Article  Google Scholar 

  • Jöreskog, K. G. (1970). Estimation and testing of simplex models. British Journal of Mathematical and Statistical Psychology, 23(2), 121–145. https://doi.org/10.1111/j.2044-8317.1970.tb00439.x

    Article  Google Scholar 

  • Jöreskog, K. G., & Sörbom, D. (1979). Advances in factor analysis and structural equation models. Abt Books.

    Google Scholar 

  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.

    Google Scholar 

  • Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261.

    Article  Google Scholar 

  • Kyndt, E., & Baert, H. (2013). Antecedents of employees’ involvement in work-related learning: A systematic review. Review of Educational Research, 83, 273–313. https://doi.org/10.3102/0034654313478021

    Article  Google Scholar 

  • Kyndt, E., Govaerts, N., Dochy, F., & Baert, H. (2011). The learning intention of low-qualified employees: A key for participation in lifelong learning and continuous training. Vocations and Learning, 4(3), 211–229. https://doi.org/10.1007/s12186-011-9058-5

    Article  Google Scholar 

  • Latan, H., & Noonan, R. (Eds.). (2017). Partial least squares path modeling. Springer.

    Google Scholar 

  • Leicher, V., Mulder, R. H., & Bauer, J. (2013). Learning from errors at work: A replication study in elder care nursing. Vocations and Learning, 6(2), 207–220. https://doi.org/10.1007/s12186-012-9090-0

    Article  Google Scholar 

  • Lohmöller, J.-B. (1989). Latent variable path modeling with partial least squares. Physica-Verlag HD.

    Book  Google Scholar 

  • Maurer, T. J., Weiss, E. M., & Barbeite, F. G. (2003). A model of involvement in work-related learning and development activity: The effects of individual, situational, motivational, and age variables. Journal of Applied Psychology, 88(4), 707–724. https://doi.org/10.1037/0021-9010.88.4.707

    Article  Google Scholar 

  • Rai, A., Goodhue, D. L., Henseler, J., & Thompson, R. (2013). To PLS or not to PLS: That is the question. AMCIS 2013 Proceedings. Retrieved from http://aisel.aisnet.org/amcis2013/Panels/PanelSubmissions/2

  • 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. https://doi.org/10.1016/j.ijresmar.2009.08.001

    Article  Google Scholar 

  • Ridgon, E. E. (2016). Choosing PLS path modeling as analytical method in European management research: A realist perspective. European Management Journal, 34(6), 598–605. https://doi.org/10.1016/j.emj.2016.05.006

    Article  Google Scholar 

  • Ringle, C. M., Sarstedt, M., Schlittgen, R., & Taylor, C. R. (2013). PLS path modeling and evolutionary segmentation. Journal of Business Research, 66, 1318–1324.

    Article  Google Scholar 

  • Rönkko, M. (2014). The effects of chance correlation on partial least squares path modeling. Organizational Research Methods, 17(2), 164–181. https://doi.org/10.1177/1094428114525667

    Article  Google Scholar 

  • Sanchez, G. (2013). PLS path modeling with R. http://gastonsanchez.com/PLS_Path_Modeling_with_R.pdf

  • Sarstedt, M., Hair, J. F., Ringle, C. M., Thiele, K. O., & Gudergan, S. P. (2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010. https://doi.org/10.1016/j.jbusres.2016.06.007

    Article  Google Scholar 

  • Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications to development research (pp. 399–419). SAGE.

    Google Scholar 

  • Shmueli, G., Ray, S., Valasquez Estrada, J. M., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552–4564. https://doi.org/10.1016/j.jbusres.2016.03.049

    Article  Google Scholar 

  • Tenenhaus, M., Amato, S., & Vinzi, V. E. (2004). A global goodness-of-fit index for PLS structural equation modeling. Proceedings of the XLII SIS Scientific Meeting, 739–742.

    Google Scholar 

  • Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159–205. https://doi.org/10.1016/j.csda.2004.03.005

    Article  Google Scholar 

  • Tynjälä, P. (2013). Toward a 3-P model of workplace learning: A literature review. Vocations and Learning, 6, 11–36. https://doi.org/10.1007/s12186-012-9091-z

    Article  Google Scholar 

  • Vaughan, K. (2008). Workplace learning: A literature review. NZCER Press.

    Google Scholar 

  • Wold, H. O. A. (1975). Path models with latent variables: The NIPALS approach. In H. M. Blalock, A. Aganbegian, F. M. Borodkin, R. Boudon, & V. Capecchi (Eds.), Quantitative sociology: International perspectives on mathematical and statistical modeling (pp. 307–357). Academic.

    Chapter  Google Scholar 

  • Zwaan, R., Etz, A., Lucas, R., & Donnellan, M. (2018). Making replication mainstream. Behavioral and Brain Sciences, 41, E120. https://doi.org/10.1017/S0140525X17001972

    Article  Google Scholar 

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Goller, M., Hilkenmeier, F. (2022). PLS-Based Structural Equation Modelling: An Alternative Approach to Estimating Complex Relationships Between Unobserved Constructs. In: Goller, M., Kyndt, E., Paloniemi, S., DamÅŸa, C. (eds) Methods for Researching Professional Learning and Development. Professional and Practice-based Learning, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-031-08518-5_12

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