Abstract
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.
Electronic supplementary material
The online version of this chapter (https://doi.org/10.1007/978-3-662-56707-4_5) contains additional material that is available to authorized users. You can also download the “Springer Nature More Media App” from the iOS or Android App Store to stream the videos and scan the image containing the “Play button”.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Alternatively, you could also choose one of the many control system versions, including Subversion, Git, and Mecurial, which enable simple branching and project management. These systems work well with version control in centralized and in distributed environments.
- 2.
- 3.
For more information on missing data, see https://www.iriseekhout.com
- 4.
The mode is another measure. However, unlike the median and mean, it is ill-defined, because it can take on multiple values. Consequently, we do not discuss the mode.
- 5.
A similar type of chart is the line chart . In a line chart, measurement points are ordered (typically by their x-axis value) and joined with straight line segments.
- 6.
Note that the terms n−1 in the numerator and denominator cancel each other and are therefore not shown here.
- 7.
The logarithm is calculated as follows: If x = y b, then y = logb(x) where x is the original variable, b the logarithm’s base, and y the exponent. For example, log 10 of 100 is 2. Logarithms cannot be calculated for negative values (such as household debt) and for the value of zero.
References
Agarwal, C. C. (2013). Outlier analysis. New York, NY: Springer.
Agresti, A., & Finlay, B. (2014). Statistical methods for the social sciences (4th ed). Upper Saddle River, NJ: Pearson.
Barchard, K. A., & Verenikina, Y. (2013). Improving data accuracy: Electing the best data checking technique. Computers in Human Behavior, 29(50), 1917–1912.
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.
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.
Carpenter, J. & Kenward, M. (2013). Multiple imputation and its application. New York, NJ: John Wiley.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
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.
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.
Gladwell, M. (2008). Outliers: The story of success. New York, NY: Little, Brown, and Company.
Graham, J. W. (2012). Missing data: Analysis and design. Berlin et al.: Springer.
Grotenhuis, M., & Visscher, C. (2014). How to use SPSS syntax: An overview of common commands. Thousand Oaks, CA: Sage.
Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Boston, MA: Cengage.
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.
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.
Krippendorff, K. (2012). Content analysis: An introduction to its methodology. Thousand Oaks, CA: Sage.
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.
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.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NJ: Wiley.
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.
Schafer, J. L. (1997). Analysis of incomplete multivariate data. London, UK: Chapman & Hall.
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.
Further Reading
Huck, S. W. (2014). Reading statistics and research (6th ed.). Harlow: Pearson Education.
Levesque, R., Programming and data management for IBM SPSS Statistics 20. Chicago, SPSS, Inc. Available at http://www.spsstools.net/en/resources/spss-programming-book/
SticiGui at http://www.stat.berkeley.edu/~stark/SticiGui/Text/correlation.htm
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Sarstedt, M., Mooi, E. (2019). Descriptive Statistics. In: A Concise Guide to Market Research. Springer Texts in Business and Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56707-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-662-56707-4_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-56706-7
Online ISBN: 978-3-662-56707-4
eBook Packages: Business and ManagementBusiness and Management (R0)