Applied Data Analysis   

What an excellent and enriching experience! It's great how Professor Hofmann's lectures integrate theoretical knowledge and practical applications, and in his afternoon consultation sessions, he helped me resolve issues that I had with my individual research. — participant from the Netherlands

This course provides an intuitive and applied introduction to statistical concepts. Participants acquire the basic quantitative research tools they need to become active and statistically savvy social scientist, who can address their own empirical questions and produce new and exciting insights. We discuss the underlying theories of descriptive and univariate statistics, measures of association between variables, multiple regression, and statistical inference, but the focus is on application, implementation, and interpretation. Participants will learn how to conduct their own statistical analyses using Stata.

This course provides an introduction to quantitative methods. Prior knowledge of quantitative data analysis or the use of statistical software are not assumed. Participants with a basic background in statistics and mathematics should consider taking Regression Analysis instead.


This two-week, 35-hour course runs Monday-Friday, 9:00 am-12:30 pm, July 1-12, 2019.


Tobias Hofmann (picture), University of Utah

Detailed Description

The course provides an intuitive and applied introduction to the quantitative research tools that social scientists use to address and answer such diverse empirical questions as: What factors explains income inequality? Why do conflicts escalate? Do political institutions or government ideology influence social and economic policies?

As theory and practice go hand in hand, the aim is for participants to become familiar with basic statistical concepts as well as to acquire practical quantitative skills. Focusing on putting theory into practice, we do not only study statistical techniques and discuss how to appropriately select them, but execute them, examine their assumptions, and interpret statistical findings. Throughout the course, participants have the opportunity to extensively practice how to apply statistical theory using the popular statistical software Stata. Through a number of replication exercise, you will learn how to use Stata to conduct your own data analyses. By systematically probing the analyses of others, you acquire the skills that allow you to produce new and exciting insights as well as to summarize and present them.

The first part of the course introduces the basic elements of univariate descriptive statistics, the concepts of central tendency and dispersion, and tabular and graphical methods for displaying data. We discuss how to collect quantitative data and learn and practice how to use Stata to visualize data with graphs and to describe data with numbers and tables. The second part covers the basics of probability theory and different sampling distributions of discrete and random variables and introduces the statistical inference techniques that come from these distributions. In the final part, we turn to hypothesis testing. We focus on the analyses of variance and covariance as well as regression methods for the analysis of relationships among two or more variables.

By providing an introduction to basics statistical concepts and the foundations of data analysis, this course also functions as a 'launch pad' for the more advanced quantitative courses and the topics they cover, such as advanced multiple regression and multilevel, time series, and event history analysis. Participants are not assumed to have any prior knowledge of quantitative data analysis or the use of statistical software.


There are no prerequisites for this course.


Participants are expected to bring a WiFi-enabled laptop computer. Access to data, temporary licenses for the course software, and installation support will be provided by the Methods School.

Core Readings

Acock, Alan C. 2018. A Gentle Introduction to Stata. 6th edition. College Station, TX: Stata Press.

Suggested Readings

Agresti, Alan, and Barbara Finlay. 2008. Statistical Methods for the Social Sciences. 4th edition. Upper Saddle River, NJ: Prentice-Hall.

Lewis-Beck, Michael S. 1995. Data Analysis: An Introduction. Thousand Oaks, CA: Sage Publications.

Lewis-Beck, Michael S. 1989. Applied Regression: An Introduction. Thousand Oaks, CA: Sage Publications.

Freedman, David, Robert Pisani, and Roger Purves. 2007. Statistics. 4th edition. New York, NY: W. W. Norton & Company.

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