PLS-SEM for Multivariate Analysis: A Practical Guide to Educational Research using SmartPLS
https://doi.org/10.35877/454RI.eduline2861
Keywords:
PLS-SEM, smartPLS, partial least squares, structural equation modelling, multivariate analysis
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Abstract
Implementation of PLS-SEM in educational research has developed significantly, but there are variations in the presentation of the analysis results. This study aims to provide practical understanding to researchers who intend to utilize PLS-SEM in multivariate analysis to enhance the recognition and validity of the resultant research outcomes using SmartPLS. This research is a literature study that conducts content analysis of relevant books and publications. The research results present PLS-SEM analysis using SmartPLS on reflective and formative research models with first-order and second-order approaches through measurement model evaluation (outer model) and structural model evaluation (inner model) with various criteria. Evaluation of the reflective measurement model consists of reflective indicator loadings, internal consistency reliability, convergent validity, and discriminant validity. The review of the formative measurement model consists of convergent validity, collinearity, and statistical significance of weights. The structural model evaluation consists of the collinearity test, significance value, f square, R square, Q square, SRMR, PLSpredict, and robustness checks. Therefore, this study can provide guidance using SmartPLS in conducting PLS-SEM analysis and presenting acceptable analysis results.
References
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Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models. Long Range Planning, 45(5), 359–394. https://doi.org/https://doi.org/10.1016/j.lrp.2012.10.001
Berman, A. (1971). Theory of Incomplete Models of Dynamic Structures. AIAA Journal, 9(8), 1481–1487.
Cheah, J.-H., Ting, H., Ramayah, T., Memon, M. A., Cham, T.-H., & Ciavolino, E. (2019). A comparison of five reflective–formative estimation approaches: reconsideration and recommendations for tourism research. Quality & Quantity, 53(3), 1421–1458. https://doi.org/10.1007/s11135-018-0821-7
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/https://doi.org/10.4324/9780203771587
Creswell, J. W. (2013). Research Design: Qualitative, Quantitative, and Mixed Method Approaches (4th ed.). Sage Publications, Inc.
Creswell, J. W., & Creswell, D. J. (2018). Research Design: Qualitative, Quantitative adn Mixed Methods Approaches (5th ed.). Sage Publications, Inc.
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/https://doi.org/10.1016/j.techfore.2021.121092
Djamba, Y. K., & Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches (7th ed.). Pearson Education Limited. https://doi.org/10.2307/3211488
Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., & Kyngäs, H. (2014). Qualitative Content Analysis: A Focus on Trustworthiness. SAGE Open, 4(1), 2158244014522633. https://doi.org/10.1177/2158244014522633
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101
George, M. W. (2008). The Elements of Library Research. In What Every Student Needs to Know. Princeton University Press. https://doi.org/doi:10.1515/9781400830411
Grech, V., & Calleja, N. (2018). WASP (Write a Scientific Paper): Multivariate analysis. Early Human Development, 123, 42–45. https://doi.org/https://doi.org/10.1016/j.earlhumdev.2018.04.012
Gregory, R. J. (2015). Psychological Testing:History, Principles, and Applications (7th ed.). Pearson Education Limited.
Groenwold, R. H. H., & Dekkers, O. M. (2023). Is it a risk factor, a predictor, or even both? The multiple faces of multivariable regression analysis. European Journal of Endocrinology, 188(1), E1–E4. https://doi.org/10.1093/ejendo/lvac012
Haenlein, M., & Kaplan, A. M. (2004). A Beginner’s Guide to Partial Least Squares Analysis. Understanding Statistics, 3(4), 283–297. https://doi.org/10.1207/s15328031us0304_4
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.). SAGE Publications Inc.
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, CA. https://doi.org/10.1016/j.lrp.2013.01.002
Hair, Joe F, Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, Joe 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. https://doi.org/10.1108/EBR-11-2018-0203
Hair, Joseph F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling with R. Springer.
Hair, Joseph F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications Inc.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928
Henseler, J., Hubona, G., & Ray, P. A. (2017). Partial Least Squares Path Modeling: Updated Guidelines BT - Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (H. Latan & R. Noonan (eds.); pp. 19–39). Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_2
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. https://doi.org/10.1007/s11747-014-0403-8
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
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Kenny, D. A. (2018). Moderator variables. https://davidakenny.net/cm/moderation.htm
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. https://doi.org/https://doi.org/10.1111/isj.12131
Kwok, O.-M., Cheung, M. W. L., Jak, S., Ryu, E., & Wu, J.-Y. (2018). Editorial: Recent Advancements in Structural Equation Modeling (SEM): From Both Methodological and Application Perspectives. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01936
Lachowicz, M. J., Preacher, K. J., & Kelley, K. (2018). A Novel Measure of Effect Size for Mediation Analysis. Psychological Methods, 23(2), 244–261. https://doi.org/dx.doi.org/10.1037/met0000165
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Poon, W.-Y., & Tang, F.-C. (2002). Multisample Analysis of Multivariate Ordinal Categorical Variables. Multivariate Behavioral Research, 37(4), 479–500. https://doi.org/10.1207/S15327906MBR3704_03
Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2012). On the value relevance of customer satisfaction. Multiple drivers and multiple markets. Journal of the Academy of Marketing Science, 40(4), 509–525. https://doi.org/10.1007/s11747-011-0247-4
Rigdon, E. E. (2012). Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods. Long Range Planning, 45(5), 341–358. https://doi.org/https://doi.org/10.1016/j.lrp.2012.09.010
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s Comments: A Critical Look at the Use of PLS-SEM in “MIS Quarterly.” MIS Quarterly, 36(1), iii–xiv. https://doi.org/10.2307/41410402
Sarstedt, M., & Cheah, J. H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. Journal of Marketing Analytics, 7(3), 196–202. https://doi.org/10.1057/s41270-019-00058-3
Sarstedt, M., Hair, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003
Sarstedt, M., & Ringle, C. M. (2020). Structural Equation Models: From Paths to Networks (Westland 2019). Psychometrika, 85(3), 841–844. https://doi.org/10.1007/s11336-020-09719-0
Sarstedt, M., Ringle, C. M., Cheah, J. H., Ting, H., Moisescu, O. I., & Radomir, L. (2019). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554. https://doi.org/10.1177/1354816618823921
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling BT - Handbook of Market Research (C. Homburg, M. Klarmann, & A. E. Vomberg (eds.); pp. 1–47). Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-2
Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012). Long Range Planning, 47(3), 154–160. https://doi.org/https://doi.org/10.1016/j.lrp.2014.02.007
Schermelleh-engel, K., & Moosbrugger, H. (2003). Evaluating the Fit of Structural Equation Models : Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online, 8(2), 23–74. http://www.mpr-online.de
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. https://doi.org/10.1108/EJM-02-2019-0189
Slocum, T. A., & Rolf, K. R. (2021). Features of Direct Instruction: Content Analysis. Behavior Analysis in Practice, 14(3), 775–784. https://doi.org/10.1007/s40617-021-00617-0
Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Subhaktiyasa, P. G., Sintari, S. N. N., Andriana, K. R. F., Sumaryani, N. P., Werang, B. W., & Sudiarta, I. N. (2024). The Effect of Tri Hita Karana on Adaptability, Consistency, Involvement, and Mission in Organizational. Jurnal Ilmiah Manajemen Dan Bisnis, 9(1), 1–10. https://doi.org/https://doi.org/10.38043/jimb. v7i2.4679
Subhaktiyasa, P. G., Sutrisna, I. G. P. A. F., Sumaryani, N. P., & Sunita, N. W. (2024). Entrepreneurial Intentions among Medical Laboratory Technology Students : Effect of Education and Self-Efficacy. Journal of Education and Learning, 18(3), 719–726. https://doi.org/https://doi.org/10.11591/edulearn.v18i3.21467
Tuckman, B. W., & Harper, B. E. (2012). Conducting Educational Research (6th ed.). Published by Rowman & Littlefi eld Publishers, Inc. www.rowmanlittlefi eld.com
Zeng, N., Liu, Y., Gong, P., Hertogh, M., & König, M. (2021). Do right PLS and do PLS right : A critical review of the application of PLS-SEM in construction management. Frontiers of Engineering Management, 8(3), 356–369. https://doi.org/https://doi.org/10.1007/s42524-021-0153-5
Bayonne, E., Marin-garcia, J. A., & Alfalla-luque, R. (2020). Partial Least Squares ( PLS ) in Operations Management Research : Insights from a Systematic Literature Review. Journal of Industrial Engineering and Management, 13(3), 565–597. https://doi.org/https://doi.org/10.3926/jiem.3416
Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models. Long Range Planning, 45(5), 359–394. https://doi.org/https://doi.org/10.1016/j.lrp.2012.10.001
Berman, A. (1971). Theory of Incomplete Models of Dynamic Structures. AIAA Journal, 9(8), 1481–1487.
Cheah, J.-H., Ting, H., Ramayah, T., Memon, M. A., Cham, T.-H., & Ciavolino, E. (2019). A comparison of five reflective–formative estimation approaches: reconsideration and recommendations for tourism research. Quality & Quantity, 53(3), 1421–1458. https://doi.org/10.1007/s11135-018-0821-7
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/https://doi.org/10.4324/9780203771587
Creswell, J. W. (2013). Research Design: Qualitative, Quantitative, and Mixed Method Approaches (4th ed.). Sage Publications, Inc.
Creswell, J. W., & Creswell, D. J. (2018). Research Design: Qualitative, Quantitative adn Mixed Methods Approaches (5th ed.). Sage Publications, Inc.
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/https://doi.org/10.1016/j.techfore.2021.121092
Djamba, Y. K., & Neuman, W. L. (2014). Social Research Methods: Qualitative and Quantitative Approaches (7th ed.). Pearson Education Limited. https://doi.org/10.2307/3211488
Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., & Kyngäs, H. (2014). Qualitative Content Analysis: A Focus on Trustworthiness. SAGE Open, 4(1), 2158244014522633. https://doi.org/10.1177/2158244014522633
Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1), 101–107. https://doi.org/10.1093/biomet/61.1.101
George, M. W. (2008). The Elements of Library Research. In What Every Student Needs to Know. Princeton University Press. https://doi.org/doi:10.1515/9781400830411
Grech, V., & Calleja, N. (2018). WASP (Write a Scientific Paper): Multivariate analysis. Early Human Development, 123, 42–45. https://doi.org/https://doi.org/10.1016/j.earlhumdev.2018.04.012
Gregory, R. J. (2015). Psychological Testing:History, Principles, and Applications (7th ed.). Pearson Education Limited.
Groenwold, R. H. H., & Dekkers, O. M. (2023). Is it a risk factor, a predictor, or even both? The multiple faces of multivariable regression analysis. European Journal of Endocrinology, 188(1), E1–E4. https://doi.org/10.1093/ejendo/lvac012
Haenlein, M., & Kaplan, A. M. (2004). A Beginner’s Guide to Partial Least Squares Analysis. Understanding Statistics, 3(4), 283–297. https://doi.org/10.1207/s15328031us0304_4
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.). SAGE Publications Inc.
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, CA. https://doi.org/10.1016/j.lrp.2013.01.002
Hair, Joe F, Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. https://doi.org/https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, Joe 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. https://doi.org/10.1108/EBR-11-2018-0203
Hair, Joseph F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling with R. Springer.
Hair, Joseph F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications Inc.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209. https://doi.org/10.1177/1094428114526928
Henseler, J., Hubona, G., & Ray, P. A. (2017). Partial Least Squares Path Modeling: Updated Guidelines BT - Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (H. Latan & R. Noonan (eds.); pp. 19–39). Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_2
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. https://doi.org/10.1007/s11747-014-0403-8
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
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Kenny, D. A. (2018). Moderator variables. https://davidakenny.net/cm/moderation.htm
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. https://doi.org/https://doi.org/10.1111/isj.12131
Kwok, O.-M., Cheung, M. W. L., Jak, S., Ryu, E., & Wu, J.-Y. (2018). Editorial: Recent Advancements in Structural Equation Modeling (SEM): From Both Methodological and Application Perspectives. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01936
Lachowicz, M. J., Preacher, K. J., & Kelley, K. (2018). A Novel Measure of Effect Size for Mediation Analysis. Psychological Methods, 23(2), 244–261. https://doi.org/dx.doi.org/10.1037/met0000165
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Poon, W.-Y., & Tang, F.-C. (2002). Multisample Analysis of Multivariate Ordinal Categorical Variables. Multivariate Behavioral Research, 37(4), 479–500. https://doi.org/10.1207/S15327906MBR3704_03
Raithel, S., Sarstedt, M., Scharf, S., & Schwaiger, M. (2012). On the value relevance of customer satisfaction. Multiple drivers and multiple markets. Journal of the Academy of Marketing Science, 40(4), 509–525. https://doi.org/10.1007/s11747-011-0247-4
Rigdon, E. E. (2012). Rethinking Partial Least Squares Path Modeling: In Praise of Simple Methods. Long Range Planning, 45(5), 341–358. https://doi.org/https://doi.org/10.1016/j.lrp.2012.09.010
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor’s Comments: A Critical Look at the Use of PLS-SEM in “MIS Quarterly.” MIS Quarterly, 36(1), iii–xiv. https://doi.org/10.2307/41410402
Sarstedt, M., & Cheah, J. H. (2019). Partial least squares structural equation modeling using SmartPLS: a software review. Journal of Marketing Analytics, 7(3), 196–202. https://doi.org/10.1057/s41270-019-00058-3
Sarstedt, M., Hair, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211. https://doi.org/10.1016/j.ausmj.2019.05.003
Sarstedt, M., & Ringle, C. M. (2020). Structural Equation Models: From Paths to Networks (Westland 2019). Psychometrika, 85(3), 841–844. https://doi.org/10.1007/s11336-020-09719-0
Sarstedt, M., Ringle, C. M., Cheah, J. H., Ting, H., Moisescu, O. I., & Radomir, L. (2019). Structural model robustness checks in PLS-SEM. Tourism Economics, 26(4), 531–554. https://doi.org/10.1177/1354816618823921
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling BT - Handbook of Market Research (C. Homburg, M. Klarmann, & A. E. Vomberg (eds.); pp. 1–47). Springer International Publishing. https://doi.org/10.1007/978-3-319-05542-8_15-2
Sarstedt, M., Ringle, C. M., Henseler, J., & Hair, J. F. (2014). On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012). Long Range Planning, 47(3), 154–160. https://doi.org/https://doi.org/10.1016/j.lrp.2014.02.007
Schermelleh-engel, K., & Moosbrugger, H. (2003). Evaluating the Fit of Structural Equation Models : Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psychological Research Online, 8(2), 23–74. http://www.mpr-online.de
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. https://doi.org/10.1108/EJM-02-2019-0189
Slocum, T. A., & Rolf, K. R. (2021). Features of Direct Instruction: Content Analysis. Behavior Analysis in Practice, 14(3), 775–784. https://doi.org/10.1007/s40617-021-00617-0
Stone, M. (1974). Cross-Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Subhaktiyasa, P. G., Sintari, S. N. N., Andriana, K. R. F., Sumaryani, N. P., Werang, B. W., & Sudiarta, I. N. (2024). The Effect of Tri Hita Karana on Adaptability, Consistency, Involvement, and Mission in Organizational. Jurnal Ilmiah Manajemen Dan Bisnis, 9(1), 1–10. https://doi.org/https://doi.org/10.38043/jimb. v7i2.4679
Subhaktiyasa, P. G., Sutrisna, I. G. P. A. F., Sumaryani, N. P., & Sunita, N. W. (2024). Entrepreneurial Intentions among Medical Laboratory Technology Students : Effect of Education and Self-Efficacy. Journal of Education and Learning, 18(3), 719–726. https://doi.org/https://doi.org/10.11591/edulearn.v18i3.21467
Tuckman, B. W., & Harper, B. E. (2012). Conducting Educational Research (6th ed.). Published by Rowman & Littlefi eld Publishers, Inc. www.rowmanlittlefi eld.com
Zeng, N., Liu, Y., Gong, P., Hertogh, M., & König, M. (2021). Do right PLS and do PLS right : A critical review of the application of PLS-SEM in construction management. Frontiers of Engineering Management, 8(3), 356–369. https://doi.org/https://doi.org/10.1007/s42524-021-0153-5
Published
2024-08-01
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How to Cite
Putu Gede Subhaktiyasa. (2024). PLS-SEM for Multivariate Analysis: A Practical Guide to Educational Research using SmartPLS. EduLine: Journal of Education and Learning Innovation, 4(3), 353-365. https://doi.org/10.35877/454RI.eduline2861
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