Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/261928 
Year of Publication: 
2022
Citation: 
[Journal:] BRQ Business Research Quarterly [ISSN:] 2340-9436 [Volume:] 25 [Issue:] 3 [Publisher:] Sage Publishing [Place:] London [Year:] 2022 [Pages:] 283-294
Publisher: 
Sage Publishing, London
Abstract: 
New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great potential to facilitate group comparison in the case of an extensive sample. This article presents exploratory data analysis methods useful for inspecting variation across groups in crucial variables and detecting heterogeneity. The exploratory data analysis methods (introduced by Tukey in his seminal book of 1977) encompass a set of statistical tools aimed to extract information from data using simple graphical tools. In this article, some of the exploratory data analysis methods like the boxplot and scatterplot are revisited and enhanced using modern graphical computational devices (as, for example, the heat-map) and their use illustrated with Spanish Social Security data. We explore how earnings vary across several factors like age, gender, type of occupation, and contract, and in particular, the gender gap in salaries is visualized in various dimensions relating to the type of occupation. The exploratory data analysis methods are also applied to assessing and refining competing regressions by plotting residuals-versus-fitted values. The methods discussed should be useful to researchers to assess heterogeneity in data, across-group variation, and classical diagnostic plots of residuals from alternative models fits.
Subjects: 
EDA
ggplot
heat-maps
large data set
R
JEL: 
C55
J01
J08
Y10
C80
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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