Key Topics
GESIS Training offers courses on all aspects of empirical social research. Our focus is on Data Analysis, Survey Methodology, Computational Social Science, and Research Data Management.
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Data Analysis
We offer courses that teach the theoretical foundations and practical applications of a wide range of data analysis methods. The underlying data sources range from surveys to experimental studies to digital behavioral data obtained, for example, from social media or smartphone sensors (Computational Social Science). At GESIS Training, we offer courses for beginners who have no or very little prior knowledge in data analysis methods, as well as for researchers who are looking to advance their knowledge in this area. Most courses in data analysis are related to quantitative data, but you will also find courses with a qualitative focus.
If you have not worked much with quantitative data so far, we recommend that you first attend an introductory course on univariate and bivariate statistics and an introductory course on statistical software such as Stata or R. An introductory course on regression analysis is also well-suited for beginners.
Depending on your research question, you can then attend courses on, for example, structural equation modeling, panel data analysis, or methods of causal inference.
You can find an overview of all courses that are currently offered in Data Analysis here.
Survey Methodology
In the empirical social sciences, surveys are a traditional and the most frequently used method of data collection. We offer courses that deal with conducting surveys and evaluating their quality. The Total Survey Error framework helps identify different sources of errors that can occur in surveys and negatively affect data quality. These errors are related to measurement and representation. Measurement errors can result, for example, from poor questionnaire design or undesirable respondent behavior. Representation errors result, among others, from sampling errors, selective sample dropouts, or inadequate sample adjustments. You can learn how to avoid such errors in our courses.
Do you not yet have any experience conducting surveys yet? Then we recommend that you first attend a course on introduction to survey design and a course on questionnaire development. Afterward, you can choose from a variety of courses on different topics depending on your specific needs.
The Summer School in Survey Methodology allows you to combine many courses covering Survey Methodology topics in a short time.
You can find an overview of all courses that are currently offered in Survey Methodology here.
Computational Social Science
The field of Computational Social Science (CSS) studies the same sociocultural phenomena as all other social sciences but is characterized by the use of new types of data ("Digitial Behavioral Data"), large amounts of data ("Big Data"), and computationally intensive technologies to process and analyze these data. CSS thus lies at the intersection of the social sciences and computer science. Our course offerings include courses that address the collection of new types or large amounts of structured and unstructured data, as well as analysis procedures that either relate to these data or focus on more traditional data using new computational techniques. Examples of collecting new types of data include web scraping, APIs, or sensors. Computational analysis techniques cover, for example, network analysis as well as machine and deep learning for textual and audiovisual data.
A good foundation for taking advanced courses is an introduction to Python. The courses on web scraping and social media data collection using APIs also tend to have a low entry barrier. These courses prepare you well for success in advanced courses in the field, such as those on network analysis or text analysis.
Like Survey Methodology, CSS has its own format at GESIS Training, the Fall Seminar in Computational Social Science. It offers a variety of courses ranging from broad introductions to the field suitable for beginners to specialized courses on cutting-edge topics geared towards advanced participants.
You can find an overview of all courses that are currently offered in Computational Social Science here.
Research Data Management
Research Data Management (RDM) is a key aspect in complying with the rules of good scientific practice as well as in implementing funding requirements in the context of Open Data according to the FAIR Data Principles. RDM includes all measures and strategies for planning, collecting, processing, archiving, and preserving data long-term. Moreover, RDM covers the access to and reuse of data, standards for data documentation, and legal issues related to secure data handling. To support researchers and data managers in conducting good RDM, GESIS offers a series of courses on various aspects of (research) data management such as data cleaning and organization.
You can find an overview of all courses that are currently offered in Research Data Management here.