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How to cite this paper
APA: Afthanorhan, A., Awang, Z & Aimran, N. (2020). An extensive comparison of CB-SEM and PLS-SEM for reliability and validity. International Journal of Data and Network Science, 4(4), 357-364.
Chicago/Turabian: Afthanorhan, A., Awang, Z & Aimran, N. 2020. "An extensive comparison of CB-SEM and PLS-SEM for reliability and validity." International Journal of Data and Network Science 4, no. 4 (2020): 357-364.
AMA: Afthanorhan, A., Awang, Z & Aimran, N. An extensive comparison of CB-SEM and PLS-SEM for reliability and validity. International Journal of Data and Network Science. 2020;4(4):357-364.
References
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Aimran, A. N., Ahmad, S., Afthanorhan, A., & Awang, Z. (2017). The development of comparative bias index. Paper pre-sented at the AIP Conference Proceedings,1870 doi:10.1063/1.4995935
Aguirre-Urreta, M. I., & Marakas, G. M. (2014). A rejoinder to Rigdon et al.(2014). Information Systems Research, 25(4), 785-788.
Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M. (2016). Omission of causal indicators: consequences and implica-tions for measurement. Measurement: Interdisciplinary Research and Perspectives, 14(3), 75-97.
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086-1120.
Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 421-458.
Bainter, S. A., & Bollen, K. A. (2015). Moving forward in the debate on causal indicators: Rejoinder to comments. Meas-urement: Interdisciplinary Research & Perspectives, 13(1), 63-74.
Bentler, P. M. (1982). Confirmatory factor analysis via noniterative estimation: A fast, inexpensive method. Journal of Marketing Research, 19(4), 417-424.
Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods & Re-search, 17(3), 303-316.
Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. Mis Quarterly, 35(2), 359-372.
Bollen, K. A. (2019). Model Implied Instrumental Variables (MIIVs): An alternative orientation to structural equation modeling. Multivariate Behavioral Research, 54(1), 31-46.
Bollen, K. A., & Diamantopoulos, A. (2015). In Defense of Causal–Formative Indicators: A Minority Report.
Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In Handbook of causal analysis for social research (pp. 301-328). Springer Netherlands.
Brown, G. T. (2006). Teachers' conceptions of assessment: Validation of an abridged version. Psychological Reports, 99(1), 166-170.
Dijkstra, T. K. (2010). Latent variables and indices: Herman World’s basic design and partial least squares. In Handbook of partial least squares (pp. 23-46). Springer Berlin Heidelberg.
Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS quarterly Management infor-mation systems quarterly, 39(2), 297-316.
Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155.
Evermann, J., & Tate, M. (2010). Testing models or fitting models? Identifying model misspecification in PLS.
Gates, K. M., Fisher, Z. F., & Bollen, K. A. (2019). Latent variable GIMME using model implied instrumental variables (MIIVs). Psychological methods.
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Comparing PLS to regression and LISREL: A response to Marcou-lides, Chin, and Saunders. MIS Quarterly, 36(3), 703-716.
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation mod-eling (PLS-SEM). Sage Publications.
Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., ... & Calantone, R. J. (2014). Common beliefs and reality about PLS comments on Rönkkö and Evermann (2013). Organizational Research Methods, 1094428114526928.
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.
Jöreskog, K. G., & Wold, H. O. (1982). Systems under indirect observation: Causality, structure, prediction (Vol. 139). North Holland.
Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford publications.
Lohmöller, J. B. (1989). Predictive vs. structural modeling: Pls vs. ml. In Latent variable path modeling with partial least squares (pp. 199-226). Physica, Heidelberg.
Majid, N. A., Zainol, F. A., Daud, W. N. W., & Afthanorhan, A. (2019). Cooperative entrepreneurship in Malaysian sec-ondary schools: A review of current practices. The Journal of Social Sciences Research, 5(3), 812-818.
Marcoulides, G. A., & Saunders, C. (2006). Editor's comments: PLS: a silver bullet? MIS quarterly, iii-ix.
McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239-270.
McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organization-al Research Methods, 1094428114529165.
Mertens, W., Pugliese, A., & Recker, J. (2017). Models with Latent Concepts and Multiple Relationships: Structural Equation Modeling. In Quantitative Data Analysis (pp. 37-59). Springer, Cham.
Mohamad, M., Afthanorhan, A., Awang, Z., & Mohammad, M. (2019). Comparison between CB-SEM and PLS-SEM: Testing and confirming the maqasid syariah quality of life measurement model. Journal of Social Sciences Research, 5(3), 608-614. doi:10.32861/jssr.53.608.614
Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J., & Chen, F. (2001). Monte Carlo experiments: Design and implementa-tion. Structural Equation Modeling, 8(2), 287-312.
Raykov, T., & Marcoulides, G. A. (2012). A first course in structural equation modeling. Routledge.
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and vari-ance-based SEM. International Journal of Research in Marketing, 26(4), 332-344.
Rönkkö, M., & Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 1094428112474693.
Rönkkö, 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, 9-27.
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.
Schumacker, R. E., & Lomax, R. G. (2010). Structural equation modeling. NY. Routlege.
Skrondal, A., & Laake, P. (2001). Regression among factor scores. Psychometrika, 66(4), 563-575.
Westland, J. C. (2015). Structural equation modeling: from paths to networks (pp. 47–60).
Aimran, A. N., Ahmad, S., Afthanorhan, A., & Awang, Z. (2017). The development of comparative bias index. Paper pre-sented at the AIP Conference Proceedings,1870 doi:10.1063/1.4995935
Aguirre-Urreta, M. I., & Marakas, G. M. (2014). A rejoinder to Rigdon et al.(2014). Information Systems Research, 25(4), 785-788.
Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M. (2016). Omission of causal indicators: consequences and implica-tions for measurement. Measurement: Interdisciplinary Research and Perspectives, 14(3), 75-97.
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086-1120.
Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 421-458.
Bainter, S. A., & Bollen, K. A. (2015). Moving forward in the debate on causal indicators: Rejoinder to comments. Meas-urement: Interdisciplinary Research & Perspectives, 13(1), 63-74.
Bentler, P. M. (1982). Confirmatory factor analysis via noniterative estimation: A fast, inexpensive method. Journal of Marketing Research, 19(4), 417-424.
Bollen, K. A. (1989). A new incremental fit index for general structural equation models. Sociological Methods & Re-search, 17(3), 303-316.
Bollen, K. A. (2011). Evaluating effect, composite, and causal indicators in structural equation models. Mis Quarterly, 35(2), 359-372.
Bollen, K. A. (2019). Model Implied Instrumental Variables (MIIVs): An alternative orientation to structural equation modeling. Multivariate Behavioral Research, 54(1), 31-46.
Bollen, K. A., & Diamantopoulos, A. (2015). In Defense of Causal–Formative Indicators: A Minority Report.
Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In Handbook of causal analysis for social research (pp. 301-328). Springer Netherlands.
Brown, G. T. (2006). Teachers' conceptions of assessment: Validation of an abridged version. Psychological Reports, 99(1), 166-170.
Dijkstra, T. K. (2010). Latent variables and indices: Herman World’s basic design and partial least squares. In Handbook of partial least squares (pp. 23-46). Springer Berlin Heidelberg.
Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS quarterly Management infor-mation systems quarterly, 39(2), 297-316.
Edwards, J. R., & Bagozzi, R. P. (2000). On the nature and direction of relationships between constructs and measures. Psychological Methods, 5(2), 155.
Evermann, J., & Tate, M. (2010). Testing models or fitting models? Identifying model misspecification in PLS.
Gates, K. M., Fisher, Z. F., & Bollen, K. A. (2019). Latent variable GIMME using model implied instrumental variables (MIIVs). Psychological methods.
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Comparing PLS to regression and LISREL: A response to Marcou-lides, Chin, and Saunders. MIS Quarterly, 36(3), 703-716.
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation mod-eling (PLS-SEM). Sage Publications.
Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.
Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., ... & Calantone, R. J. (2014). Common beliefs and reality about PLS comments on Rönkkö and Evermann (2013). Organizational Research Methods, 1094428114526928.
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.
Jöreskog, K. G., & Wold, H. O. (1982). Systems under indirect observation: Causality, structure, prediction (Vol. 139). North Holland.
Kline, R. B. (2016). Principles and practice of structural equation modeling. Guilford publications.
Lohmöller, J. B. (1989). Predictive vs. structural modeling: Pls vs. ml. In Latent variable path modeling with partial least squares (pp. 199-226). Physica, Heidelberg.
Majid, N. A., Zainol, F. A., Daud, W. N. W., & Afthanorhan, A. (2019). Cooperative entrepreneurship in Malaysian sec-ondary schools: A review of current practices. The Journal of Social Sciences Research, 5(3), 812-818.
Marcoulides, G. A., & Saunders, C. (2006). Editor's comments: PLS: a silver bullet? MIS quarterly, iii-ix.
McDonald, R. P. (1996). Path analysis with composite variables. Multivariate Behavioral Research, 31(2), 239-270.
McIntosh, C. N., Edwards, J. R., & Antonakis, J. (2014). Reflections on partial least squares path modeling. Organization-al Research Methods, 1094428114529165.
Mertens, W., Pugliese, A., & Recker, J. (2017). Models with Latent Concepts and Multiple Relationships: Structural Equation Modeling. In Quantitative Data Analysis (pp. 37-59). Springer, Cham.
Mohamad, M., Afthanorhan, A., Awang, Z., & Mohammad, M. (2019). Comparison between CB-SEM and PLS-SEM: Testing and confirming the maqasid syariah quality of life measurement model. Journal of Social Sciences Research, 5(3), 608-614. doi:10.32861/jssr.53.608.614
Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J., & Chen, F. (2001). Monte Carlo experiments: Design and implementa-tion. Structural Equation Modeling, 8(2), 287-312.
Raykov, T., & Marcoulides, G. A. (2012). A first course in structural equation modeling. Routledge.
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and vari-ance-based SEM. International Journal of Research in Marketing, 26(4), 332-344.
Rönkkö, M., & Evermann, J. (2013). A critical examination of common beliefs about partial least squares path modeling. Organizational Research Methods, 1094428112474693.
Rönkkö, 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, 9-27.
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.
Schumacker, R. E., & Lomax, R. G. (2010). Structural equation modeling. NY. Routlege.
Skrondal, A., & Laake, P. (2001). Regression among factor scores. Psychometrika, 66(4), 563-575.
Westland, J. C. (2015). Structural equation modeling: from paths to networks (pp. 47–60).