Exploratory Data Analysis

Target group and prerequisites:

PhD candidates working (or planning to work) with quantitative data sets. Participants should be familiar with basic concepts in descriptive statistics as, for example, taught in introductory statistics courses.

Description of the seminar:

The aim of the seminar is to provide the participants with guidance and examples for an exploratory data analysis that represents an important first step before conducting any empirical research analysis. To correctly understand and interpret empirical results, it is of the essence to have a very good understanding of the quality and the characteristics of the employed data set. During the seminar, methods from descriptive statistics and multivariate data analysis will be presented in a hands-on way to inspire participants to inspect, clean and understand their data.

Content:

1. Measurement scales
2. Missing data (briefly; a separate specific workshop will be offered 2 weeks later. See here)
3. Measures of central tendency
4. Outlier detection and treatment
5. Dispersion parameters, skewness, and kurtosis
6. Graphical inspection of data (uni- and multivariate)
7. Measures of concentration
8. Measures of bivariate association

Learning objectives:

After attending the seminar, you will be able to

• use a toolbox of methods to properly examine and portray your data
• select the appropriate graphical method to examine the characteristics of your data or relationships of interest
• identify univariate, bivariate, and multivariate outliers
• test your data for the assumptions underlying the most common estimation techniques
• determine the best method of data transformation given a specific problem

Assignment (mandatory for obtaining 1 ECTS):

To obtain 1 ECTS, participants are required to hand in an assignment based on an exploratory analysis of their data set. For PhD candidates that do not dispose of an own data set to be analyzed, data sets will be provided. The assignment contains a set of questions that need to be addressed by providing a written document containing different tables, graphs, and figures, as well as their interpretations. Participants are free to use their preferred statistical software.

References:

Cleff T. (2014) Exploratory Data Analysis in Business and Economics, Springer

Hair J.F., Black W., Babin B., Anderson R. (2019) Multivariate Data Analysis, Pearson

Wooldridge J. Introductory Econometrics: A Modern Approach, South Western College Publishing