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2018 | OriginalPaper | Chapter

5. Descriptive Statistics

Authors : Erik Mooi, Marko Sarstedt, Irma Mooi-Reci

Published in: Market Research

Publisher: Springer Singapore

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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.

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previous chapter Getting Data

next chapter Hypothesis Testing & ANOVA

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.

There are multivariate techniques that consider three, or more, variables simultaneously in order to detect outliers. See Hair et al. (2010) for an introduction, and Agarwal (2013) for a more detailed methodological discussion.

For more information on missing data, see https://www.iriseekhout.com

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.

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.

Note that the terms n−1 in the numerator and denominator cancel each other and are therefore not shown here.

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.

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.

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.

http://www.stata.com/manuals14/ddatatypes.pdf

http://www.stata.com/manuals14/u.pdf

http://www.stata.com/manuals14/dformat.pdf

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.

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Title: Descriptive Statistics
Authors: Erik Mooi
Marko Sarstedt
Irma Mooi-Reci
Publisher: Springer Singapore
Book: Market Research
Print ISBN: 978-981-10-5217-0

Electronic ISBN: 978-981-10-5218-7

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-981-10-5218-7_5