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• W4254804673 abstract "Free Access References Daniel J. Denis, Daniel J. DenisSearch for more papers by this author Book Author(s):Daniel J. Denis, Daniel J. DenisSearch for more papers by this author First published: 27 March 2020 https://doi.org/10.1002/9781119549963.refs AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. References Agresti, A. (2002). Categorical Data Analysis. New York: Wiley. Wiley Online LibraryGoogle Scholar 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, 1173– 1182. CrossrefCASPubMedWeb of Science®Google Scholar Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York: Routledge. Wiley Online LibraryGoogle Scholar Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. New Jersey: Lawrence Erlbaum Associates. Google Scholar Crawley, M. J. (2013). The R Book. New York: Wiley. Google Scholar Dalgaard, P. (2008). Introductory Statistics with R. New York: Springer. CrossrefWeb of Science®Google Scholar Denis, D. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. New York: Wiley. Google Scholar Denis, D. (2019). SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics. New York: Wiley. Google Scholar Everitt, B. & Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R. New York: Springer. CrossrefWeb of Science®Google Scholar Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster Analysis. New York: Oxford University Press. Google Scholar Fiedler, K., Schott, M., & Meiser, T. (2011). What mediation analysis can (not) do. Journal of Experimental Social Psychology, 47(6), 1231– 1236. CrossrefWeb of Science®Google Scholar Fitzmaurice, G. M. (2011). Applied Longitudinal Analysis. New York: Wiley. Wiley Online LibraryGoogle Scholar Fox, J. (2016). Applied Regression Analysis & Generalized Linear Models. New York: Sage. Google Scholar Galili, T. (2019). Hierarchical cluster analysis on famous data sets – enhanced with the dendextend package. Retrieved on September 2, 2019. https://cran.r-project.org/web/packages/dendextend/vignettes/Cluster_Analysis.html Google Scholar Guttag, J. V. (2013). Introduction to Computation and Programming Using Python. Cambridge: MIT Press. Google Scholar Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer. CrossrefGoogle Scholar Hays, W. L. (1994). Statistics. Fort Worth, TX: Harcourt College Publishers. Google Scholar Hennig, C. (2015). What are the true clusters? Pattern Recognition Letters, 64, 53– 62. CrossrefWeb of Science®Google Scholar Hintze, J. L. & Nelson, R. D. (1998). Violin plots: A box plot-density trace synergism. The American Statistician, 52(2), 181– 184. CrossrefWeb of Science®Google Scholar Holtz, Y. (2018). The R Graph Gallery . Document Retrieved on September 2, 2019. www.r-graph-gallery.com Google Scholar Hosmer, D. & Lemeshow, S. (1989). Applied Logistic Regression. New York: Wiley. Web of Science®Google Scholar Howell, D. C. (2002). Statistical Methods for Psychology. Pacific Grove, CA: Duxbury Press. Google Scholar Izenman, A. J. (2008). Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. New York: Springer. CrossrefWeb of Science®Google Scholar James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. New York: Springer. CrossrefGoogle Scholar Johnson, R. A. & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. New Jersey: Pearson Prentice Hall. Google Scholar Jolliffe, I. T. (2002). Principal Component Analysis. New York: Springer. Google Scholar Kassambara, A. (2017). Machine Learning Essentials: Practical Guide in R. STHDA. Google Scholar Kirk, R. E. (2012). Experimental Design: Procedures for the Behavioral Sciences. Pacific Grove, CA: Brooks/Cole Publishing Company. Wiley Online LibraryGoogle Scholar Kuhn, T. S. (2012). The Structure of Scientific Revolutions: 50th Anniversary Edition. Chicago: University of Chicago Press. CrossrefGoogle Scholar MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis. New York: Lawrence Erlbaum Associates. Google Scholar MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. In: L. LeCam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1. Berkeley: University of California Press, pp. 281– 297. Google Scholar Matloff, N. (2011). The Art of R Programming. San Francisco: No Starch Press. Google Scholar McCullagh, P. & Nelder, J. A. (1990). Generalized Linear Models. New York: Chapman & Hall. Google Scholar Mulaik, S. A. (2009). The Foundations of Factor Analysis. New York: McGraw-Hill. CrossrefGoogle Scholar Olson, C. L. (1976). On choosing a test statistic in multivariate analysis of variance. Psychological Bulletin, 83, 579– 586. CrossrefWeb of Science®Google Scholar Petrocelli, J. V. (2003). Hierarchical multiple regression in counseling research: Common problems and possible remedies. Measurement and Evaluation in Counseling and Development, 36, 9– 22. CrossrefWeb of Science®Google Scholar Rencher, A. C. (1998). Multivariate Statistical Inference and Applications. New York: Wiley. Google Scholar Rencher, A. C. & Christensen, W. F. (2012). Methods of Multivariate Analysis. New York: Wiley. Wiley Online LibraryWeb of Science®Google Scholar Schumacker, R. E. (2016). Using R with Multivariate Statistics. New York: Sage. Google Scholar Searle, S. R., Casella, G., & McCulloch, C. E. (2006). Variance Components. New York: Wiley. Google Scholar Stigler, S. M. (1986). The History of Statistics: The Measurement of Uncertainty Before 1900. London: Belknap Press. Google Scholar Stoltzfus, J. C. (2011). Logistic regression: A brief primer. Academic Emergency Medicine, 18, 1099– 1104. Wiley Online LibraryPubMedWeb of Science®Google Scholar Tabachnick, B. G. & Fidell, L. S. (2001). Using Multivariate Statistics. New York: Pearson Education. PubMedWeb of Science®Google Scholar Warner, R. M. (2013). Applied Statistics: From Bivariate Through Multivariate Techniques. London: Sage. Google Scholar Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. Springer: New York. CrossrefWeb of Science®Google Scholar Wickham, H. (2014). Advanced R. New York: Chapman & Hall/CRC The R Series. CrossrefGoogle Scholar Yuan, K. & Bentler, P. (2000). Inferences on correlation coefficients in some classes of nonnormal distributions. Journal of Multivariate Analysis, 72(2), 230– 248. CrossrefWeb of Science®Google Scholar Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science ReferencesRelatedInformation" @default.
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