11 August 2023
(Non-)Probability Samples in the Social Sciencesonline course
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.
Carina Cornesse is a post-doctoral researcher in survey methodology at the University of Mannheim, Germany.
Olga Maslovskaya is an Assistant Professor in Social Statistics and Demography at the University of Southampton, UK
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
- 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.
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,
- Certificate of attendance issued upon completion.
The University of Mannheim acknowledges the workload for regular attendance, satisfactory work on daily assignments and for submitting a paper of 5000 words to the lecturer(s) by 15 October at the latest with 4 ECTS (70 EUR administration fee).
EUR 500: Student/PhD student rate.
EUR 750: Academic/non-profit rate.
The rates include the tuition fee and the course materials.