GESIS Training Courses
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Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

Course 4: (Non-)Probability Samples in the Social Sciences

About
Location:
Online via Zoom
 
Course duration:
10:00-17:00 CEST
General Topics:
Course Level:
Format:
Software used:
None
Duration:
Language:
Fees:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
 
Keywords
Additional links
Lecturer(s): Carina Cornesse, Olga Maslovskaya

About the lecturer - Carina Cornesse

About the lecturer - Olga Maslovskaya

Course description

The main objective of the course is to provide students with a full overview of the history, theoretical foundations, critical arguments, and accumulated empirical evidence surrounding the debate about probability and nonprobability sample surveys. A focus will be on real-world examples of why and how the choice of sample type matters, including topics such as election polling debacles, mispredictions during the pandemic (e.g., willingness to get vaccinated), and the role that surveys can have in supporting versus debunking fake news.
 
In addition to discussing these topics, the course will provide students with an in-depth understanding of the conditions under which probability and nonprobability samples can provide useful data to answer social scientific research questions (e.g., Total Survey Error framework adaptations, fit-for-purpose designs, causal inference logic), including hands-on recommendations and exercises on how to design your own (hypothetical) research study.
 
Furthermore, we will discuss the benefits and challenges of different approaches to probability and nonprobability sampling (e.g., simple vs. stratified random sampling, snowball vs. respondent-driven sampling, social media and river sampling) as well as recent insights on sample weighting and data integration techniques (e.g., propensity score weighting and blended calibration). All material and discussions will be hands-on and intuitive.
 
A detailed syllabus with course times and literature will soon be available for download here.


Target group

Participants will find the course useful if:
  • they would like to get a full picture of the debate about probability and nonprobability sample surveys.
  • they plan to design their own research study and need to choose a sample type.


  • Learning objectives

    By the end of the course participants will:
  • have a full overview of the history, theoretical foundations, critical arguments, and accumulated empirical evidence surrounding the debate about probability and nonprobability sample surveys.
  • possess the necessary skills to evaluate whether any given sample is fit for the purpose of answering a particular research question.
  • be able to choose an appropriate sample type when designing their own social scientific research studies.
  •  
    Organizational structure of the course
    This is a five-day course with a total amount of 30 hours of virtual class time. Participants can expect a mix of interactive teaching, group exercises, and opportunity for individual consultation. Exercises are designed to deepen the understanding of the course material and to apply it to hypothetical and real-world research settings. This will not require using any statistical software (although participants will have the opportunity to work with data voluntarily using their preferred software). To get the most out of the course, students are strongly encouraged to participate pro-actively in small-group projects, present their thoughts and work output in class, and critically discuss the course material with the lecturer and classmates.


    Prerequisites

  • Basic knowledge of introductory statistics (e.g., descriptive statistics, basic regression analysis)
  • Basic conceptual understanding of survey data collection (e.g., survey lifecycle, Total Survey Error framework)
  • Basic understanding of sampling theory and/or survey weighting procedures is desirable, but not strictly necessary
  •  
    Software and hardware requirements
    None