Descriptive Statistics

  • Marko Sarstedt
  • Erik Mooi
Part of the Springer Texts in Business and Economics book series (STBE)


We first provide an overview of market research’s workflow. We then discuss efficient strategies to help you structure your project’s database, as well as enter, clean, and easily check the collected data for inconsistencies. In addition, we provide easy strategies that allow you to handle missing data observations before we describe the most common and useful univariate and bivariate descriptive graphs and statistics. Thereafter, we take you through the basics of SPSS and provide useful tips on how to create and interpret descriptive statistics and table outputs. A range of graphs is illustrated and applied in SPSS, including bar charts, histograms, box plots, pie charts, frequency tables, scatter graphs, crosstabs, and correlation tables, all of which are useful for differently scaled variables. We make use of a case study for an easy and meaningful interpretation of the graphs and table outputs. We conclude with recommendations for further readings and a case study with review questions.


Bivariate Description Missing Completely At Random (MCAR) Chart Builder MCAR Test Missing Not At Random (MNAR) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Agarwal, C. C. (2013). Outlier analysis. New York, NY: Springer.CrossRefGoogle Scholar
  2. Agresti, A., & Finlay, B. (2014). Statistical methods for the social sciences (4th ed). Upper Saddle River, NJ: Pearson.Google Scholar
  3. Barchard, K. A., & Verenikina, Y. (2013). Improving data accuracy: Electing the best data checking technique. Computers in Human Behavior, 29(50), 1917–1912.Google Scholar
  4. Barchard, K. A., & Pace, L. A. (2011). Preventing human error: The impact of data entry methods on data accuracy and statistical results. Computers in Human Behavior, 27(5), 1834–1839.CrossRefGoogle Scholar
  5. Baumgartner, H., & Steenkamp, J.-B. E. M. (2001). Response styles in marketing research: A cross-national investigation. Journal of Marketing Research, 38(2), 143–156.CrossRefGoogle Scholar
  6. Carpenter, J. & Kenward, M. (2013). Multiple imputation and its application. New York, NJ: John Wiley.CrossRefGoogle Scholar
  7. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  8. Drolet, A. L., & Morrison, D. G. (2001). Do we really need multiple-item measures in service research? Journal of Service Research, 3(3), 196–204.CrossRefGoogle Scholar
  9. Eekhout, I., de Vet, H. C. W., Twisk, J. W. R., Brand, J. P. L., de Boer, M. R., & Heymans, M. W. (2014). Missing data in a multi-item instrument were best handled by multiple imputation at the item score level. Journal of Clinical Epidemiology, 67(3), 335–342.CrossRefGoogle Scholar
  10. Gladwell, M. (2008). Outliers: The story of success. New York, NY: Little, Brown, and Company.Google Scholar
  11. Graham, J. W. (2012). Missing data: Analysis and design. Berlin et al.: Springer.CrossRefGoogle Scholar
  12. Grotenhuis, M., & Visscher, C. (2014). How to use SPSS syntax: An overview of common commands. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
  13. Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Boston, MA: Cengage.Google Scholar
  14. Harzing, A. W. (2005). Response styles in cross-national survey research: A 26-country study. International Journal of Cross Cultural Management, 6(2), 243–266.CrossRefGoogle Scholar
  15. Johnson, T., Kulesa, P., Lic, I., Cho, Y. I., & Shavitt, S. (2005). The relation between culture and response styles. Evidence from 19 countries. Journal of Cross-Cultural Psychology, 36(2), 264–277.CrossRefGoogle Scholar
  16. Krippendorff, K. (2012). Content analysis: An introduction to its methodology. Thousand Oaks, CA: Sage.Google Scholar
  17. Little, R. J. A. (1998). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association, 83(404), 1198–1202.CrossRefGoogle Scholar
  18. Paulsen, A., Overgaard, S., & Lauritsen, J. M. (2012). Quality of data entry using single entry, double entry and automated forms processing—An example based on a study of patient-reported outcomes. PloS ONE, 7(4), e35087.Google Scholar
  19. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NJ: Wiley.CrossRefGoogle Scholar
  20. Sarstedt, M., Diamantopoulos, A., Salzberger, T., & Baumgartner, P. (2016). Selecting single items to measure doubly-concrete constructs: A cautionary tale. Journal of Business Research, 69(8), 3159–3167.CrossRefGoogle Scholar
  21. Schafer, J. L. (1997). Analysis of incomplete multivariate data. London, UK: Chapman & Hall.CrossRefGoogle Scholar
  22. White, I. R., Royston, P., & Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30(4), 377–399.CrossRefGoogle Scholar

Further Reading

  1. Huck, S. W. (2014). Reading statistics and research (6th ed.). Harlow: Pearson Education.Google Scholar
  2. Levesque, R., Programming and data management for IBM SPSS Statistics 20. Chicago, SPSS, Inc. Available at

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Marko Sarstedt
    • 1
  • Erik Mooi
    • 2
  1. 1.Faculty of Economics and ManagementOtto-von-Guericke- University MagdeburgMagdeburgGermany
  2. 2.Department of Management and MarketingThe University of MelbourneParkville, VICAustralia

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