Aguinis, H., Gottfredson, R. K., & Joo, H. (2013). Best-practice recommendations for defining, identifying, and handling outliers. Organizational Research Methods, 16(2), 270–301.
Altman, D. G. (1985). Comparability of randomised groups. Journal of the Royal Statistical Society: Series D (The Statistician), 34(1), 125–136.
Berger, V. W. (2006). A review of methods for ensuring the comparability of comparison groups in randomized clinical trials. Reviews on Recent Clinical Trials, 1(1), 81–86.
Blanca Mena, M. J., Alarcón Postigo, R., Arnau Gras, J., Bono Cabré, R., & Bendayan, R. (2017). Non-normal data: Is ANOVA still a valid option? Psicothema, 2017, Vol. 29, Num. 4, p. 552-557.
Boehmke, B., & Greenwell, B. (2019). Hands-on machine learning with r. Chapman; Hall/CRC.
Buuren, S. van. (2018). Flexible imputation of missing data. CRC press.
Cambridge Dictionary. (2021). Concatenate.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences new york. In NY: Academic Press.
Cohen, P., West, S. G., & Aiken, L. S. (2014). Applied multiple regression/correlation analysis for the behavioral sciences. Psychology press.
Cook, R. D., & Weisberg, S. (1982). Residuals and influence in regression. New York: Chapman; Hall.
Cousineau, D., & Chartier, S. (2010). Outliers detection and treatment: A review. International Journal of Psychological Research, 3(1), 58–67.
Dinno, A. (2015). Nonparametric pairwise multiple comparisons in independent groups using dunn’s test. The Stata Journal, 15(1), 292–300.
Dong, Y., & Peng, C.-Y. J. (2013). Principled missing data methods for researchers. SpringerPlus, 2(1), 1–17.
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage Publications.
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press.
Ginkel, J. R. van, Linting, M., Rippe, R. C., & Voort, A. van der. (2020). Rebutting existing misconceptions about multiple imputation as a method for handling missing data. Journal of Personality Assessment, 102(3), 297–308.
Grandstrand, O. (2004). Durbin-watson statistic. In B. Lewis-Beck Michael S (Ed.), The SAGE encyclopedia of social science research methods. Thousand Oaks, California.
Greenhouse, S. W., & Geisser, S. (1959). On methods in the analysis of profile data. Psychometrika, 24(2), 95–112.
Grolemund, G., & Wickham, H. (2011). Dates and times made easy with lubridate. Journal of Statistical Software, 40(3), 1–25.
Henson, R. K. (2001). Understanding internal consistency reliability estimates: A conceptual primer on coefficient alpha. Measurement and Evaluation in Counseling and Development, 34(3), 177–189.
Hoaglin, D. C., & Welsch, R. E. (1978). The hat matrix in regression and ANOVA. The American Statistician, 32(1), 17–22.
Hochberg, Y. (1974). Some generalizations of the t-method in simultaneous inference. Journal of Multivariate Analysis, 4(2), 224–234.
Hochberg, Y. (1988). A sharper bonferroni procedure for multiple tests of significance. Biometrika, 75(4), 800–802.
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.
Huynh, H., & Feldt, L. S. (1976). Estimation of the box correction for degrees of freedom from sample data in randomized block and split-plot designs. Journal of Educational Statistics, 1(1), 69–82.
Jakobsen, J. C., Gluud, C., Wetterslev, J., & Winkel, P. (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials–a practical guide with flowcharts. BMC Medical Research Methodology, 17(1), 1–10.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 159–174.
Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766.
Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1202.
Müller, K., & Wickham, H. (2021). Column data types.
Nunally, J. C. (1967). Psychometric theory. New york: Mc Graw-Hill.
Nunally, J. C. (1978). Psychometric theory (2nd edition). New york: Mc Graw-Hill.
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592.
Schafer, J. L. (1999). Multiple imputation: A primer. Statistical Methods in Medical Research, 8(1), 3–15.
Silge, J., & Robinson, D. (2017). Text mining with r: A tidy approach. "O’Reilly Media, Inc.".
Stevens, J. P. (2012). Applied multivariate statistics for the social sciences. Routledge.
Stobierski, T. (2021). Data wrangling: What it is and why it’s important. Harvard Business School Online.
Tomarken, A. J., & Serlin, R. C. (1986). Comparison of ANOVA alternatives under variance heterogeneity and specific noncentrality structures. Psychological Bulletin, 99(1), 90.
West, S. G., Taylor, A. B., Wu, W., et al. (2012). Model fit and model selection in structural equation modeling. Handbook of Structural Equation Modeling, 1, 209–231.
Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(1), 1–23.
Wickham, H. (2021). The tidyverse style guide.
Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media, Inc.
Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. Chapman; Hall/CRC.
Yang, Z., Wang, X., & Su, C. (2006). A review of research methodologies in international business. International Business Review, 15(6), 601–617.