Bibliographie

Ouvrages de référence

• American Psychological Association. (2020). Publication manual of the American Psychological Association (7^e éd.). APA.
• Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Sage.
• Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. Wiley.
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2^e éd.). Lawrence Erlbaum.
• Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. Chapman and Hall.
• Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5^e éd.). Sage.
• George, D., & Mallery, P. (2019). IBM SPSS Statistics 25 step by step: A simple guide and reference (15^e éd.). Routledge.
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8^e éd.). Cengage.
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3^e éd.). Sage.
• Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3^e éd.). Guilford Press.
• Kline, R. B. (2016). Principles and practice of structural equation modeling (4^e éd.). Guilford Press.
• McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum.
• Menard, S. (1995). Applied logistic regression analysis. Sage.
• Nunnally, J. C. (1967). Psychometric theory. McGraw-Hill.
• Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3^e éd.). McGraw-Hill.
• Spector, P. E. (1997). Job satisfaction: Application, assessment, causes, and consequences. Sage.
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7^e éd.). Pearson.
• West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (dir.), Structural equation modeling: Concepts, issues, and applications (p. 56-75). Sage.

Articles

• Arena, M., & Azzone, G. (2009). Identifying organizational drivers of internal audit effectiveness. International Journal of Auditing, 13(1), 43-60.
• Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182.
• Bartlett, M. S. (1954). A note on the multiplying factors for various χ² approximations. Journal of the Royal Statistical Society: Series B, 16(2), 296-298.
• Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185-216.
• Carifio, J., & Perla, R. J. (2007). Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes. Journal of Social Sciences, 3(3), 106-116.
• Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245-276.
• Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (dir.), Modern methods for business research (p. 295-336). Lawrence Erlbaum.
• Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309-319.
• Cochran, W. G. (1954). Some methods for strengthening the common χ² tests. Biometrics, 10(4), 417-451.
• Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10, article 7.
• Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297-334.
• Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7-29.
• Delaney, J. T., & Huselid, M. A. (1996). The impact of human resource management practices on perceptions of organizational performance. Academy of Management Journal, 39(4), 949-969.
• Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297-316.
• Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299.
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
• Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18(3), 233-239.
• Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.
• Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408-420.
• Hayes, A. F., & Coutts, J. J. (2020). Use omega rather than Cronbach's alpha for estimating reliability. But… Communication Methods and Measures, 14(1), 1-24.
• Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2-20.
• 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.
• Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565-580.
• Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179-185.
• 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.
• 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.
• Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141-151.
• Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
• Kim, H.-Y. (2013). Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52-54.
• Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.
• 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.
• Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 1-55.
• Mihret, D. G., & Yismaw, A. W. (2007). Internal audit effectiveness: An Ethiopian public sector case study. Managerial Auditing Journal, 22(5), 470-484.
• Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625-632.
• 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.
• Podsakoff, P. M., MacKenzie, S. B., Moorman, R. H., & Fetter, R. (1990). Transformational leader behaviors and their effects on followers' trust in leader, satisfaction, and organizational citizenship behaviors. The Leadership Quarterly, 1(2), 107-142.
• Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891.
• Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592.
• 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.
• Streiner, D. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99-103.
• Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206.

Logiciels

• Hayes, A. F. (2022). PROCESS macro for SPSS and SAS (version 4.2) [logiciel]. https://www.processmacro.org
• IBM Corp. (2022). IBM SPSS Statistics for Windows (version 29.0) [logiciel]. IBM Corp.
• Ringle, C. M., Wende, S., & Becker, J.-M. (2022). SmartPLS 4 [logiciel]. SmartPLS GmbH. https://www.smartpls.com

Kenny (2018), cité au module 9 pour les repères de taille d'effet des modérations, est une ressource en ligne de David A. Kenny (davidakenny.net) reprise par Hair et al. (2022).