Abstract
In this chapter, we explore some other commonly used but less ‘traditional’ statistical procedures. While these procedures are commonly reported in behavioural and social research, they tend not to be well-covered in standard statistical texts. Procedures discussed and illustrated include: reliability analysis & classical item analysis (useful for assessing measurement quality); data screening & missing value analysis (useful for preliminary explorations looking for anomalous data patterns); confidence intervals (useful for assessing the precision of statistical estimates); bootstrapping and jackknifing (useful for estimating errors associated with statistic estimates where traditional methods are not available or do not work); time series analysis (useful for understanding data patterns over time, with or without an intervention); confirmatory factor analysis (useful for evaluating theorised factor structures); structural equation models (useful for evaluating theorised causal models); and meta-analysis (useful for exploring data patterns evident in samples of published, and occasionally unpublished, research).
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References
References for Procedure 8.1
Athanasou, J. A. (1997). Introduction to educational testing. Wentworth Falls: Social Science Press, ch. 11 and 14.
Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. Beverly Hills: Sage Publications.
Cooksey, R. W., & McDonald, G. (2019). Surviving and thriving in postgraduate research (2nd ed.). Singapore: Springer, ch. 18.
Gwet, K. L. (2012). Handbook of inter-rater reliability (3rd ed.). Gaithersburg: Advanced Analytics, LLC.
SPSS. (1998). Using SPSS for item analysis: More reliable test assessment using statistics, SPSS White Paper. http://www.spsstools.net/Syntax/ItemAnalysis/UsingSPSSforItemAnalysis.pdf. Accessed 16 Sept 2019.
Thorndike, R. L., & Thorndike, R. M. (1997). Reliability. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 755–790). Oxford: Pergamon Press.
Waltz, C. F., Strickland, O. L., & Lenz, E. R. (2010). Measurement in nursing and health research (4th ed.). New York: Springer Publishing.
Useful Additional Reading for Procedure 8.1
Allen, P., Bennett, K., & Heritage, B. (2019). SPSS Statistics: A practical guide (4th ed.). Cengage Learning Australia Pty: South Melbourne, ch. 16.
George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). New York: Routledge, ch. 18.
Everitt, B. S. (1995). Making sense of statistics in psychology: A second level course. Oxford: Oxford University Press, ch. 13.
Murphy, K. R., & Davidshofer, C. O. (2004). Psychological testing: Principles and applications (6th ed.). Englewood Cliffs: Prentice-Hall, ch. 6, 7, and 10.
Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill, ch. 7. [a classic text in measurement theory].
Rosenthal, R., & Rosnow, R. L. (1991). Essentials of behavioral research: Methods and data analysis (2nd ed.). New York: McGraw-Hill, ch. 3.
Thorndike, T. L. (1982). Applied psychometrics. Boston: Houghton-Mifflin, ch. 6. [another classic text in measurement theory].
Walsh, W. B., & Betz, N. E. (2000). Tests and assessment (4th ed.). Englewood Cliffs: Prentice-Hall, ch. 3.
References for Procedure 8.2
Allison, P. D. (2001). Missing data. Thousand Oaks: Sage Publications.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage Publications, ch. 6.
Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River: Pearson Education, ch. 2.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education, ch. 4.
Useful Additional Reading for Procedure 8.2
Beaton, A. F. (1997). Missing scores in survey research. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 763–766). Oxford: Pergamon Press.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah: Lawrence Erlbaum Associates, ch. 4, 10 & 11.
Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage Publications, ch. 6.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks: Sage Publications, ch. 3A, 3B.
References for Procedure 8.3
Cumming, G., & Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement, 61(4), 532–574.
Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed.). Los Angeles: Sage Publications, section 2.8 and 2.99.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London: Sage Publications, ch. 2, 6, 7, 9.
Smithson, M. J. (2000). Statistics with confidence. London: Sage Publications, ch. 5.
Smithson, M. J. (2003). Confidence intervals. Thousand Oaks: Sage Publications.
Thompson, B. (2002). What future quantitative social science research could look like: Confidence intervals for effect sizes. Educational Researcher, 31(3), 25–32.
Useful Additional Reading for Procedure 8.3
Agresti, A. (2018). Statistical methods for the social sciences (5th ed.). Boston: Pearson, Ch. 5.
Dracup, C. (2005). Confidence intervals. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (Vol. 1, pp. 366–375). Chichester: Wiley.
Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York: The Guilford Press, ch. 7.
References for Procedure 8.4
Byrne, B. M. (2016). Structural equation modelling with AMOS: Basic concepts, applications, and programming (3rd ed.). New York: Routledge.
Canty, A. J., & Davison, A. C. (2005). Bootstrap inference. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (Vol. 1, pp. 169–176). Chichester: Wiley.
Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R (pp. 226–227, 298–301). London: Sage Publications.
Mooney, C. Z., & Duval, R. D. (1993). Bootstrapping: A nonparametric approach to statistical inference. Newbury Park: Sage Publications.
Rodgers, J. L. (2005). Jackknife. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (Vol. 2, pp. 1005–1007). Chichester: Wiley.
Useful Additional Reading for Procedure 8.4
Dracup, C. (2005). Confidence intervals. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science (Vol. 1, pp. 366–375). Chichester: Wiley.
Field, A. (2018). Discovering statistics using SPSS for Windows (5th ed., pp. 265–268). Los Angeles: Sage Publications.
Mooney, C. (2004). Bootstrapping. In M. S. Lewisbeck, A. Bryman, & T. F. Liao (Eds.), The SAGE encyclopedia of social science research methods (Vol. 1, pp. 75–78). Thousand Oaks: Sage Publications.
Westfall, P. H., & Young, S. (1993). Resampling-based multiple testing: Examples and methods for p-value adjustment. New York: Wiley.
References for Procedure 8.5
Allen, P. G., & Fildes, R. (2001). Econometric forecasting. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for practitioners and researchers (pp. 303–362). Boston: Kluwer Academic Publishers.
Bauwens, L., Laurent, S., & Rombouts, J. V. K. (2006). Multivariate GARCH models: A survey. Journal of Applied Econometrics, 21(1), 79–109.
Bowerman, BL O’Connell, RT & Koehler, AB 2005, Forecasting, time series, and regression: An applied approach, 4, Brooks/Cole, Belmont, Parts III and IV.
Coghlan, A. (2018). A little book of R for time series (Release 0.2). https://buildmedia.readthedocs.org/media/pdf/a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf. Accessed 18 Sept 2019.
Cromwell, J. B., Labys, W. C., & Terraza, M. (1994a). Univariate tests for time series models. Thousand Oaks: Sage Publications.
Cromwell, J. B., Hannan, M. J., Labys, W. C., & Terraza, M. (1994b). Multivariate tests for time series models. Thousand Oaks: Sage Publications.
Crosbie, J. (1993). Interrupted time-series analysis with brief single-subject data. Journal of Consulting and Clinical Psychology, 61(6), 966–974.
Glass, G. V., Willson, V. L., & Gottman, J. M. (2008). Design and analysis of time-series experiments. Charlotte: Information Age Publishing.
McCleary, R., & Hay, R. A., Jr. (1980). Applied time series analysis for the social sciences. Beverly Hills: Sage Publications.
McDowell, D., McCleary, R., Meidinger, E. E., & Hay, R. A., Jr. (1980). Interrupted time series analysis. Beverly Hills: Sage Publications.
Ostrom, C. W., Jr. (1978). Time series analysis: Regression techniques. Beverly Hills: Sage Publications.
Sayrs, L. W. (1989). Pooled time series analysis. Newbury Park: Sage Publications.
Sharpley, C. F. (1997). Single case research: Measuring change. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 451–456). Oxford: Pergamon Press.
StataCorp, L. P. (2011). Stata time-series reference manual: Release 12. College Station: Stata Press.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education, ch. 17.
Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented regression analysis of interrupted time series studies in medication use research. Journal of Clinical Pharmacy and Therapeutics, 27, 299–309.
Useful Additional Reading for Procedure 8.5
Gottman, J. M. (1981). Time series analysis: A comprehensive introduction for social scientists. London: Cambridge University Press.
Hamilton, L. C. (2013). Statistics with Stata: Version 12. Boston: Brooks/Cole, ch. 12.
Huitema, B. (2004). Analysis of interrupted time-series experiments using ITSE: A critique. Understanding Statistics, 3(1), 27–46.
IBM Corporation. (2017). IBM SPSS Forecasting 25, available as downloadable user’s guide. ftp://public.dhe.ibm.com/software/analytics/spss/documentation/statistics/25.0/en/client/Manuals/IBM_SPSS_Forecasting.pdf. Downloaded 19 Sept 2019.
References for Procedure 8.6
Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: The Guilford Press.
Bryant, F. B., & Yarnold, P. R. (1995). Principal components analysis and exploratory and confirmatory factor analysis. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate statistics (pp. 99–136). Washington, DC: American Psychological Association.
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). New York: Routledge.
Geiser, C. (2013). Data analysis with MPlus. New York: The Guilford Press, section 3.4.
Kline, R. B. (2015). Principles and practice of structural equation modelling (4th ed.). New York: The Guilford Press, ch. 9, 12, 13.
Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling (4th ed.). New York: Routledge, ch. 6.
Useful Additional Reading for Procedure 8.6
Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River: Pearson Education, ch. 12.
Long, J. S. (1983). Confirmatory factor analysis. Beverley Hills: Sage Publications.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks: Sage Publications, ch. 11A, 11B.
References for Procedure 8.7
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). New York: Routledge.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah: Lawrence Erlbaum Associates, ch. 12.
Geiser, C. (2013). Data analysis with MPlus. New York: The Guilford Press.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modelling (SEM). Los Angeles: Sage Publications.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40, 414–433.
Klem, L. (2000). Structural equation modeling. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding more multivariate statistics (pp. 227–260). Washington, DC: American Psychological Association (APA).
Kline, R. B. (2015). Principles and practice of structural equation modelling (4th ed.). New York: The Guilford Press, ch. 6, 7, 8, 10.
Ringle, C. M., Wende, S., & Will, S. (2005). SmartPLS 2.0 (M3) Beta. Hamburg: University of Hamburg.
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). A critical look at the use of PLS-SEM in MIS Quarterly. MIS Quarterly, 36(1), iii–xiv and S3–S8.
Rosseel, Y. (2012). lavaan: An R package for structural equation modelling. Journal of Statistical Software, 48(2), 1–36.
Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling (4th ed.). New York: Routledge.
Useful Additional Reading for Procedure 8.7
Hair, J. F., Black, B., Babin, B., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River: Pearson Education, ch. 11, 12.
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.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1–12.
Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. New York: Guilford Press.
Long, J. S. (1983). Covariance structure analysis. Newbury Park: Sage Publications.
Meyers, L. S., Gamst, G. C., & Guarino, A. (2017). Applied multivariate research: Design and interpretation (3rd ed.). Thousand Oaks: Sage Publications, ch. 12A to 14B.
Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). South Melbourne: Wadsworth Thomson Learning, ch. 18 and 19.
Sellin, N., & Keeves, J. P. (1997). Path analysis with latent variables. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 632–640). Oxford: Pergamon Press.
Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). New York: Pearson Education, ch. 14.
Thompson, B. (2000). Ten commandments of structural equation modeling. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding more multivariate statistics (pp. 261–284). Washington, DC: American Psychological Association (APA).
Tuijnman, A. C., & Keeves, J. P. (1997). Path analysis and linear structural relations analysis. In J. P. Keeves (Ed.), Educational research, methodology, and measurement: An international handbook (2nd ed., pp. 621–632). Oxford: Pergamon Press.
Ullman, J. B., & Bentler, P. M. (2004). Structural equation modeling. In M. Hardy & A. Bryman (Eds.), Handbook of data analysis (pp. 431–458). London: Sage Publications.
References for Procedure 8.8
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester: Wiley.
Chen, D.-G., & Peace, K. E. (2013). Applied meta-analysis using R. London: Chapman & Hall/CRC.
Cooksey, R. W., & McDonald, G. (2019). Surviving and thriving in postgraduate research (2nd ed.). Singapore: Springer.
Cooper, H., Hedges, L. V., & Valentine, J. V. (2009). The handbook of research synthesis and meta-analysis (2nd ed.). New York: Russell Sage Foundation.
Everitt, B. S., & Hothorn, T. (2006). A handbook of statistical analyses using R. Boca Raton: Chapman & Hall/CRC.
Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.
Hedges, L., & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando: Academic, ch. 1 provides a good foundation.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48.
Wolf, F. (1986). Meta-analysis. Beverly Hills: Sage Publications.
Useful Additional Reading for Procedure 8.8
Durlak, J. A. (1995). Understanding meta-analysis. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate statistics (pp. 319–352). Washington, DC: American Psychological Association.
Glass, G. V., McGaw, B., & Smith, M. L. (1981). Meta-analysis in social research. Beverly Hills: Sage Publications.
Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research findings (2nd ed.). Thousand Oaks: Sage Publications.
Konstantopoulos, S., & Hedges, L. (2004). Meta-analysis. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 281–300). Thousand Oaks: Sage Publications.
Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks: Sage Publications.
Rosenthal, R. (1984). Meta-analytic procedures for social research. Beverly Hills: Sage Publications.
Rosenthal, R., & Rosnow, R. L. (1991). Essentials of behavioral research: Methods and data analysis (2nd ed.). New York: McGraw-Hill, ch. 22.
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Cooksey, R.W. (2020). Other Commonly Used Statistical Procedures. In: Illustrating Statistical Procedures: Finding Meaning in Quantitative Data . Springer, Singapore. https://doi.org/10.1007/978-981-15-2537-7_8
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