Abstract
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.
De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 35.
Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Upper Saddle River, NJ: Pearson. ch. 7.
Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioural sciences (10th ed.). Belmont, CA: Wadsworth Cengage. ch. 15.
Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 9.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 4A.
Steinberg, W. J. (2011). Statistics alive (2nd ed.). Los Angeles: Sage. ch. 34–36.
Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York: The Guilford Press. ch. 5.
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.
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.
Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 12.
Chen, P. Y., & Popovich, P. M. (2002). Correlation: Parametric and nonparametric approaches. Thousand Oaks, CA: Sage.
De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 36.
Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage. ch. 8.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage. ch. 6.
Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioural sciences (10th ed.). Belmont, CA: Wadsworth Cengage. ch. 15.
Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 9.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 4B.
Steinberg, W. J. (2011). Statistics alive (2nd ed.). Los Angeles: Sage. ch. 34–36.
Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York: The Guilford Press. ch. 5.
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.
References for Procedure 6.2
Everitt, B. S. (1977). The analysis of contingency tables. London: Chapman & Hall. ch. 3.
Liebetrau, A. M. (1983). Measures of association. Beverly Hills, CA: Sage.
Reynolds, H. T. (1984). Analysis of nominal data (2nd ed.). Beverly Hills, CA: Sage.
Useful Additional Readings for Procedure 6.2
Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Boston: Pearson. Ch. 8.
Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 6 and 7.
Chen, P. Y., & Popovich, P. M. (2002). Correlation: Parametric and nonparametric approaches. Thousand Oaks, CA: Sage.
De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 36.
Gibbons, J. D. (1993). Nonparametric measures of association. Newbury Park, CA: Sage.
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).
Howell, D. C. (2013). Statistical methods for psychology (7th ed.). Belmont, CA: Cengage Wadsworth. ch. 6.
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.
Miles, J., & Shevlin, M. (2001). Applying regression & correlation: A guide for students and researchers. London: Sage. ch. 1.
Useful Additional Readings for Procedure 6.3
Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Boston: Pearson. Ch. 9.
Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 12.
De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 37.
Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage. ch. 9.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage. ch. 7.
George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 15.
Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Upper Saddle River, NJ: Pearson. ch. 8.
Gravetter, F. J., & Wallnau, L. B. (2017). Statistics for the behavioural sciences (10th ed.). Belmont, CA: Wadsworth Cengage. ch. 16.
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.
Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 9.
Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data analysis: A model-comparison approach (3rd ed.). New York: Routledge. ch. 5.
Lewis-Beck, M. S. (1995). Data analysis: An introduction. Thousand Oaks, CA: Sage. ch. 6.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 4A, 4B.
Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). South Melbourne, VIC: Wadsworth Thomson Learning. ch. 2.
Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E. (1986). Understanding regression analysis: An introductory guide. Beverly Hills, CA: Sage. ch. 1.
Steinberg, W. J. (2011). Statistics alive (2nd ed.). Los Angeles: Sage. ch. 37–38.
Stolzenberg, R. M. (2004). Multiple regression analysis. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 165–207). London: Sage.
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.
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.
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.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education. ch. 5.
Useful Additional Readings for Procedure 6.4
Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Boston: Pearson. Ch. 11, 12.
Allen, P., Bennett, K., & Heritage, B. (2019). SPSS statistics: A practical guide (4th ed.). South Melbourne, VIC: Cengage Learning Australia Pty. ch. 13.
Argyrous, G. (2011). Statistics for research: With a guide to SPSS (3rd ed.). London: Sage. ch. 13.
De Vaus, D. (2002). Analyzing social science data: 50 key problems in data analysis. London: Sage. ch. 46–49.
Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage. ch. 9, sections 9.9 onward.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage. ch. 7, sections 7.6 onward.
George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 16.
Glass, G. V., & Hopkins, K. D. (1996). Statistical methods in education and psychology (3rd ed.). Upper Saddle River, NJ: Pearson. ch. 8.
Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. ch. 2.
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.
Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 15.
Lewis-Beck, M. S. (1995). Data analysis: An introduction. Thousand Oaks, CA: Sage. ch. 6.
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.
Miles, J., & Shevlin, M. (2001). Applying regression & correlation: A guide for students and researchers. London: Sage. ch. 2–5.
Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). South Melbourne, VIC: Wadsworth Thomson Learning. ch. 5.
Schroeder, L. D., Sjoquist, D. L., & Stephan, P. E. (1986). Understanding regression analysis: An introductory guide. Beverly Hills, CA: Sage.
Spicer, J. (2005). Making sense of multivariate data analysis. Thousand Oaks, CA: Sage. ch. 4.
Steinberg, W. J. (2011). Statistics alive (2nd ed.). Los Angeles: Sage. ch. 37–38.
Stolzenberg, R. M. (2004). Multiple regression analysis. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 165–207). London: Sage.
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.
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.
Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). South Melbourne, VIC: Wadsworth Thomson Learning. ch. 7 and 9.
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.
Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. ch. 2.
Howell, D. C. (2013). Statistical methods for psychology (8th ed.). Belmont, CA: Cengage Wadsworth. ch. 15.
Judd, C. M., McClelland, G. H., & Ryan, C. S. (2017). Data analysis: A model-comparison approach (3rd ed.). New York: Routledge. ch. 6.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 5A.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education. ch. 5.
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.
References for Procedure 6.5
Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage. ch. 18.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage. ch. 17.
Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
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.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education. ch. 13.
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.
Dunteman, G. H. (1989). Principal components analysis. Newbury Park, CA: Sage.
George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 20.
Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. ch. 4.
Kim, J., & Mueller, C. W. (1978a). Introduction to factor analysis. Beverly Hills, CA: Sage.
Kim, J., & Mueller, C. W. (1978b). Factor analysis. Beverly Hills, CA: Sage.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 10A, 10B.
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.
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.
References for Procedure 6.6
Aldenderfer, M. S., & Blashfield, R. K. (1984). Cluster analysis. Beverly Hills, CA: Sage.
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.
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.
Everitt, B. S., & Hothorn, T. (2006). A handbook of statistical analyses using R. Boca Raton, FL: Chapman & Hall/CRC.
Lorr, M. (1983). Cluster analysis for social scientists. San Francisco, CA: Jossey-Bass Publishers.
Revelle, W. R. (1978). ICLUST: A cluster analytic approach to exploratory and confirmatory scale construction. Behavior Research Methods & Instrumentation, 10, 739–742.
Revelle, W. R. (1979). Hierarchical cluster analysis and the internal structure of tests. Multivariate Bahavioral Research, 14, 57–74.
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.
Useful Additional Readings for Procedure 6.6
Anderberg, M. R. (1973). Cluster analysis for applications. New York: Academic.
Bailey, K. D. (1994). Typologies and taxonomies: An introduction to classification techniques. Newbury Park, CA: Sage.
Everitt, B. S. (1980). Cluster analysis (2nd ed.). London: Heinemann Educational Books.
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.
George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 21.
Grimm, L. G., & Yarnold, P. R. (Eds.). (2000). Reading and understanding more multivariate statistics. Washington, DC: American Psychological Association. ch. 5.
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.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 17A, 17B.
References for Procedure 6.7
Everitt, B. S., & Hothorn, T. (2006). A handbook of statistical analyses using R. Boca Raton, FL: Chapman & Hall/CRC.
Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills, CA: Sage.
Schiffman, S. S., Reynolds, M. L., & Young, F. W. (1981). Introduction to multidimensional scaling: Theory, methods, and applications. New York: Academic.
Useful Additional Readings for Procedure 6.7
Clausen, S. E. (1998). Applied correspondence analysis: An introduction. Thousand Oaks, CA: Sage.
Davison, M. L. (1983). Multidimensional scaling. New York: Wiley.
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.
George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge. ch. 19.
Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association. ch. 5.
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.
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.
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.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 16A, 16B.
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.
Weller, S. (1990). Metric scaling correspondence analysis. Thousand Oaks, CA: Sage.
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.
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.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education. ch. 12.
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.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks, CA: Sage. ch. 7A, 7B.
Thompson, B. (1984). Canonical correlation analysis: Uses and interpretation. Beverly Hills, CA: Sage.
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.
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Cooksey, R.W. (2020). Correlational Statistics for Characterising Relationships. In: Illustrating Statistical Procedures: Finding Meaning in Quantitative Data . Springer, Singapore. https://doi.org/10.1007/978-981-15-2537-7_6
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