Correlational Statistics for Characterising Relationships

This chapter discusses and illustrates correlational statistics for characterising relationships. The purposes of the procedures and fundamental concepts reviewed in this chapter are quite varied ranging from providing a simple summary of the relationship between two variables to facilitating an understanding of complex relationships among many variables. A statistical relationship is a pattern or an association which exists between two or more variables. We employ the statistical concept of correlation to summarise, in a single number, the nature of this patterned relationship or association between two variables. No matter how many variables are involved or how sophisticated the analysis is, all correlational procedures depend upon measuring and then analysing the relationships between pairs of variables. In this chapter, you will explore various procedures (e.g. contingency tables, correlation; partial and semi-partial correlation, simple and multiple regression, exploratory factor analysis, cluster analysis, multidimensional scaling and canonical correlation) that can be employed to answer simple or complex relational or associational questions about data like those posed above. In addition, you will find a more detailed discussion of the fundamental concepts of correlation and partial and semi-partial correlation which will provide necessary foundation material for understanding the discussions to come later in the chapter.

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References

Useful Additional Readings for Fundamental Concept III

• Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 12. MATHGoogle Scholar
• De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 35. Google Scholar
• Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Upper Saddle River, NJ: Pearson. ch. 7. Google Scholar
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioural sciences (10th ed.). Belmont, CA: Wadsworth Cengage. ch. 15. Google Scholar
• Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 9. Google Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 4A. Google Scholar
• Steinberg, W. J. (2011). Statistics alive (2nd ed.). Los Angeles: Sage. ch. 34–36. Google Scholar
• Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York: The Guilford Press. ch. 5. Google Scholar

Reference for Procedure 6.1

• Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Upper Saddle River, NJ: Pearson. ch. 7. Google Scholar

Useful Additional Readings for Procedure 6.1

• Allen, P., Bennett, K., & Heritage, B. (2019). SPSS statistics: A practical guide (4th ed.). South Melbourne, VIC: Cengage Learning Australia Pty. ch. 12. Google Scholar
• Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 12. MATHGoogle Scholar
• Chen, P. Y., & Popovich, P. M. (2002). Correlation: Parametric and nonparametric approaches. Thousand Oaks, CA: Sage. BookGoogle Scholar
• De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 36. Google Scholar
• Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage. ch. 8. MATHGoogle Scholar
• Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage. ch. 6. Google Scholar
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioural sciences (10th ed.). Belmont, CA: Wadsworth Cengage. ch. 15. Google Scholar
• Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 9. Google Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 4B. Google Scholar
• Steinberg, W. J. (2011). Statistics alive (2nd ed.). Los Angeles: Sage. ch. 34–36. Google Scholar
• Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York: The Guilford Press. ch. 5. Google Scholar
• Thorndike, R. M. (1997). Correlation methods. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 484–493). Oxford: Pergamon Press. Google Scholar

References for Procedure 6.2

• Everitt, B. S. (1977). The analysis of contingency tables. London: Chapman & Hall. ch. 3. BookMATHGoogle Scholar
• Liebetrau, A. M. (1983). Measures of association. Beverly Hills, CA: Sage. BookGoogle Scholar
• Reynolds, H. T. (1984). Analysis of nominal data (2nd ed.). Beverly Hills, CA: Sage. BookGoogle Scholar

Useful Additional Readings for Procedure 6.2

• Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Boston: Pearson. Ch. 8. Google Scholar
• Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 6 and 7. MATHGoogle Scholar
• Chen, P. Y., & Popovich, P. M. (2002). Correlation: Parametric and nonparametric approaches. Thousand Oaks, CA: Sage. BookGoogle Scholar
• De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 36. Google Scholar
• Gibbons, J. D. (1993). Nonparametric measures of association. Newbury Park, CA: Sage. BookGoogle Scholar
• Hardy, M. (2004). Summarizing distributions. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 35–64). London: Sage. (particularly the section on bivariate distributions). ChapterGoogle Scholar
• Howell, D. C. (2013). Statistical methods for psychology (7th ed.). Belmont, CA: Cengage Wadsworth. ch. 6. Google Scholar

References for Procedure 6.3

• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. ch. 1 and 2. Google Scholar
• Miles, J., & Shevlin, M. (2001). Applying regression & correlation: A guide for students and researchers. London: Sage. ch. 1. Google Scholar

Useful Additional Readings for Procedure 6.3

• Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Boston: Pearson. Ch. 9. Google Scholar
• Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 12. MATHGoogle Scholar
• De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 37. Google Scholar
• Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage. ch. 9. MATHGoogle Scholar
• Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage. ch. 7. Google Scholar
• George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 15. BookGoogle Scholar
• Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Upper Saddle River, NJ: Pearson. ch. 8. Google Scholar
• Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioural sciences (10th ed.). Belmont, CA: Wadsworth Cengage. ch. 16. Google Scholar
• Hardy, M. A., & Reynolds, J. (2004). Incorporating categorical information into regression models: The utility of dummy variables. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 209–236). London: Sage. ChapterGoogle Scholar
• Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 9. Google Scholar
• Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data analysis: A model-comparison approach (3rd ed.). New York: Routledge. ch. 5. BookGoogle Scholar
• Lewis-Beck, M. S. (1995). Data analysis: An introduction. Thousand Oaks, CA: Sage. ch. 6. BookGoogle Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 4A, 4B. Google Scholar
• Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). South Melbourne, VIC: Wadsworth Thomson Learning. ch. 2. MATHGoogle Scholar
• Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E. (1986). Understanding regression analysis: An introductory guide. Beverly Hills, CA: Sage. ch. 1. BookGoogle Scholar
• Steinberg, W. J. (2011). Statistics alive (2nd ed.). Los Angeles: Sage. ch. 37–38. Google Scholar
• Stolzenberg, R. M. (2004). Multiple regression analysis. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 165–207). London: Sage. ChapterGoogle Scholar

References for Procedure 6.4

• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. ch. 3, 4, 5 and 8. Google Scholar
• Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Pearson Education. ch. 4. Google Scholar
• Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data analysis: A model-comparison approach (3rd ed.). New York: Routledge. ch. 6, 7, 8 and 13. BookGoogle Scholar
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education. ch. 5. Google Scholar

Useful Additional Readings for Procedure 6.4

• Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Boston: Pearson. Ch. 11, 12. Google Scholar
• Allen, P., Bennett, K., & Heritage, B. (2019). SPSS statistics: A practical guide (4th ed.). South Melbourne, VIC: Cengage Learning Australia Pty. ch. 13. Google Scholar
• Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 13. MATHGoogle Scholar
• De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 46–49. Google Scholar
• Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage. ch. 9, sections 9.9 onward. MATHGoogle Scholar
• Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage. ch. 7, sections 7.6 onward. Google Scholar
• George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 16. BookGoogle Scholar
• Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Upper Saddle River, NJ: Pearson. ch. 8. Google Scholar
• Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. ch. 2. Google Scholar
• Hardy, M. A., & Reynolds, J. (2004). Incorporating categorical information into regression models: The utility of dummy variables. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 209–236). London: Sage. ChapterGoogle Scholar
• Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 15. Google Scholar
• Lewis-Beck, M. S. (1995). Data analysis: An introduction. Thousand Oaks, CA: Sage. ch. 6. BookGoogle Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 5A, 5B, 6A, 6B. Google Scholar
• Miles, J., & Shevlin, M. (2001). Applying regression & correlation: A guide for students and researchers. London: Sage. ch. 2–5. Google Scholar
• Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). South Melbourne, VIC: Wadsworth Thomson Learning. ch. 5. MATHGoogle Scholar
• Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E. (1986). Understanding regression analysis: An introductory guide. Beverly Hills, CA: Sage. BookGoogle Scholar
• Spicer, J. (2005). Making sense of multivariate data analysis. Thousand Oaks, CA: Sage. ch. 4. BookGoogle Scholar
• Steinberg, W. J. (2011). Statistics alive (2nd ed.). Los Angeles: Sage. ch. 37–38. Google Scholar
• Stolzenberg, R. M. (2004). Multiple regression analysis. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 165–207). London: Sage. ChapterGoogle Scholar
• Tatsuoka, M. M. (1997). Regression analysis of quantified data. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 648–657). Oxford: Pergamon Press. Google Scholar

References for Fundamental Concepts IV

• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. ch. 3, 5. Google Scholar
• Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). South Melbourne, VIC: Wadsworth Thomson Learning. ch. 7 and 9. MATHGoogle Scholar

Useful Additional Readings for Fundamental Concepts IV

• Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Upper Saddle River, NJ: Pearson. ch. 8. Google Scholar
• Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. ch. 2. Google Scholar
• Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 15. Google Scholar
• Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data analysis: A model-comparison approach (3rd ed.). New York: Routledge. ch. 6. BookGoogle Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 5A. Google Scholar
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education. ch. 5. Google Scholar
• Thorndike, R. M. (1997). Correlation methods. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 484–493). Oxford: Pergamon Press. Google Scholar

References for Procedure 6.5

• Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage. ch. 18. MATHGoogle Scholar
• Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage. ch. 17. Google Scholar
• Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. MATHGoogle Scholar
• Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Pearson Education. ch. 3. Google Scholar
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education. ch. 13. Google Scholar

Useful Additional Readings for Procedure 6.5

• Allen, P., Bennett, K., & Heritage, B. (2019). SPSS statistics: A practical guide (4th ed.). South Melbourne, VIC: Cengage Learning Australia Pty. ch. 15. Google Scholar
• Dunteman, G. H. (1989). Principal components analysis. Newbury Park, CA: Sage. BookGoogle Scholar
• George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 20. BookGoogle Scholar
• Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. ch. 4. Google Scholar
• Kim, J., & Mueller, C. W. (1978a). Introduction to factor analysis. Beverly Hills, CA: Sage. BookGoogle Scholar
• Kim, J., & Mueller, C. W. (1978b). Factor analysis. Beverly Hills, CA: Sage. BookGoogle Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 10A, 10B. Google Scholar
• Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: The use of factor analysis for instrument development in health care research. Thousand Oaks, CA: Sage. BookGoogle Scholar
• Spearitt, D. (1997). Factor analysis. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 528–539). Oxford: Pergamon Press. Google Scholar

References for Procedure 6.6

• Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Beverly Hills, CA: Sage. BookMATHGoogle Scholar
• Blashfield, R., & Aldenderfer, M. S. (1988). The methods and problems of cluster analysis. In J. R. Nesselroade & R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 447–474). New York: Plenum Press. ChapterGoogle Scholar
• Cooksey, R. W., & Soutar, G. N. (2006). Coefficient beta and hierarchical item clustering: An analytical procedure for establishing and displaying the dimensionality and homogeneity of summated scales. Organizational Research Methods, 9(1), 78–98. ArticleGoogle Scholar
• Everitt, B. S., & Hothorn, T. (2006). A handbook of statistical analyses using R. Boca Raton, FL: Chapman & Hall/CRC. BookMATHGoogle Scholar
• Lorr, M. (1983). Cluster analysis for social scientists. San Francisco, CA: Jossey-Bass Publishers. Google Scholar
• Revelle, W. R. (1978). ICLUST: A cluster analytic approach to exploratory and confirmatory scale construction. Behavior Research Methods & Instrumentation, 10, 739–742. ArticleGoogle Scholar
• Revelle, W. R. (1979). Hierarchical cluster analysis and the internal structure of tests. Multivariate Bahavioral Research, 14, 57–74. ArticleGoogle Scholar
• Revelle, W. R. (2019). Package: ‘psych’: Procedures for psychological, psychometric and personality research. Department of Psychology, Northwestern University. http://cran.r-project.org/web/packages/psych/psych.pdf. Accessed 9th Sept 2019.
• SYSTAT Software Inc. (2009). SYSTAT 13: Statistics I. Chicago, IL: SYSTAT Software Inc. Google Scholar

Useful Additional Readings for Procedure 6.6

• Anderberg, M. R. (1973). Cluster analysis for applications. New York: Academic. MATHGoogle Scholar
• Bailey, K. D. (1994). Typologies and taxonomies: An introduction to classification techniques. Newbury Park, CA: Sage. BookGoogle Scholar
• Everitt, B. S. (1980). Cluster analysis (2nd ed.). London: Heinemann Educational Books. MATHGoogle Scholar
• Everitt, B. S. (1997). Cluster analysis. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 466–472). Oxford: Pergamon Press. Google Scholar
• George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 21. BookGoogle Scholar
• Grimm, L. G., & Yarnold, P. R. (Eds.). (2000). Reading and understanding more multivariate statistics. Washington, DC: American Psychological Association. ch. 5. Google Scholar
• Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Pearson Education. ch. 9. Google Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 17A, 17B. Google Scholar

References for Procedure 6.7

• Everitt, B. S., & Hothorn, T. (2006). A handbook of statistical analyses using R. Boca Raton, FL: Chapman & Hall/CRC. BookMATHGoogle Scholar
• Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills, CA: Sage. BookGoogle Scholar
• Schiffman, S. S., Reynolds, M. L., & Young, F. W. (1981). Introduction to multidimensional scaling: Theory, methods, and applications. New York: Academic. MATHGoogle Scholar

Useful Additional Readings for Procedure 6.7

• Clausen, S. E. (1998). Applied correspondence analysis: An introduction. Thousand Oaks, CA: Sage. BookGoogle Scholar
• Davison, M. L. (1983). Multidimensional scaling. New York: Wiley. MATHGoogle Scholar
• Dunn-Rankin, P., & Zhang, S. (1997). Scaling methods. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 790–798). Oxford: Pergamon Press. Google Scholar
• George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 19. BookGoogle Scholar
• Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. ch. 5. Google Scholar
• Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Prentice Hall. ch. 10. Google Scholar
• Henry, G. (1997). Correspondence analysis. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 493–497). Oxford: Pergamon Press. Google Scholar
• Hwang, H., Tomiuk, M. A., & Takane, Y. (2009). Correspondence analysis, multiple correspondence analysis, and recent developments. In R. E. Milsap & A. Maydeu-Olivares (Eds.), The SAGE handbook of quantitative methods in psychology (pp. 219–242). London: Sage. Google Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 16A, 16B. Google Scholar
• Takane, Y., Jung, S., & Oshima-Takane, Y. (2009). Multidimensional scaling. In R. E. Milsap & A. Maydeu-Olivares (Eds.), The SAGE handbook of quantitative methods in psychology (pp. 243–264). London: Sage. Google Scholar
• Weller, S. (1990). Metric scaling correspondence analysis. Thousand Oaks, CA: Sage. BookGoogle Scholar

References for Procedure 6.8

• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. ch. 16, which discusses set correlation. Google Scholar
• Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Prentice Hall. ch. 5. Google Scholar
• Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education. ch. 12. Google Scholar

Useful Additional Readings for Procedure 6.8

• Grimm, L. G., & Yarnold, P. R. (Eds.). (2000). Reading and understanding more multivariate statistics. Washington, DC: American Psychological Association. ch. 9. Google Scholar
• Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 7A, 7B. Google Scholar
• Thompson, B. (1984). Canonical correlation analysis: Uses and interpretation. Beverly Hills, CA: Sage. BookGoogle Scholar
• Thomson, J. D., & Keeves, J. P. (1997). Canonical analysis. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 461–466). Oxford: Pergamon Press. Google Scholar