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 Stata, including its toolbar and shortcuts to frequently used commands, and provide useful tips on how to create and interpret descriptive graphs and table outputs. A range of descriptive statistics is illustrated and applied in Stata, 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.
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.
In Stata, this is best done using the rowmean command. For example, egen commitment = rowmean (com1 com2 com3). This command automatically calculates the mean over the number of nonmissing responses.
- 8.
The logarithm is calculated as follows: If x = y b, then y = log b (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. In Stata, you can generate a log-transformed variable by typing: gen loginc = log(income), whereby loginc refers to the newly created log-transformed variable and income refers to the income variable.
- 9.
If you open Stata in the Windows or Linux operating systems, the toolbar looks a bit different, but is structured along the same lines as discussed in this chapter.
- 10.
- 11.
- 12.
- 13.
Note an ordinary year has 52 weeks and 1 day, while a leap year has 52 weeks and 2 days. This is because 1 week comprises part of 2016 and part of 2017.
References
Agarwal, C. C. (2013). Outlier analysis. New York: Springer.
Agresti, A., & Finlay, B. (2014). Statistical methods for the social sciences (4th ed.). London: Pearson.
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.
Barchard, K. A., & Verenikina, Y. (2013). Improving data accuracy: Electing the best data checking technique. Computers in Human Behavior, 29(50), 1917–1912.
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: Wiley.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: 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: Little, Brown, and Company.
Graham, J. W. (2012). Missing data: Analysis and design. Berlin et al.: Springer.
Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. A global perspective (7th ed.). Upper Saddle River: Pearson.
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: 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: 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: 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.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Mooi, E., Sarstedt, M., Mooi-Reci, I. (2018). Descriptive Statistics. In: Market Research. Springer Texts in Business and Economics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5218-7_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-5218-7_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5217-0
Online ISBN: 978-981-10-5218-7
eBook Packages: Business and ManagementBusiness and Management (R0)