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400021 SE Factorial Survey Course (2019S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 04.02.2019 09:00 to Th 28.02.2019 17:00
- Deregistration possible until Su 31.03.2019 09:00
Details
max. 15 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Lehrende: Prof. Katrin Auspurg (LMU München)
- Monday 25.03. 09:00 - 12:00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
- Monday 25.03. 14:00 - 16:00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
- Tuesday 26.03. 08:30 - 11:30 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
- Tuesday 26.03. 14:00 - 16:00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
- Wednesday 27.03. 09:00 - 12:00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
- Wednesday 27.03. 14:00 - 16:00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
- Thursday 28.03. 09:00 - 12:00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
- Thursday 28.03. 14:00 - 16:00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
- Friday 29.03. 09:00 - 12:00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien
Information
Aims, contents and method of the course
Assessment and permitted materials
Course Requirements / Assignments
Regular attendance and participation in the class [10%]
Satisfactory work on daily assignments (be also prepared to present some solutions to exercises to the other participants) [30%]
Preparation of a short research proposal [60%]
Regular attendance and participation in the class [10%]
Satisfactory work on daily assignments (be also prepared to present some solutions to exercises to the other participants) [30%]
Preparation of a short research proposal [60%]
Minimum requirements and assessment criteria
Course Prerequisites
Participants should have basic knowledge of questionnaire design and experimental methods, methodical knowledge of data management and quantitative data analyses (e.g. linear regression techniques, coding of variables, merging of data sets). For most practical analyses, the statistical software package Stata will be used. Although a short introduction to Stata will be provided, participants should be familiar with Stata (or a similar software package, such as R or SPSS) before the course starts.
Participants should have basic knowledge of questionnaire design and experimental methods, methodical knowledge of data management and quantitative data analyses (e.g. linear regression techniques, coding of variables, merging of data sets). For most practical analyses, the statistical software package Stata will be used. Although a short introduction to Stata will be provided, participants should be familiar with Stata (or a similar software package, such as R or SPSS) before the course starts.
Examination topics
Reading list
Main Readings
Auspurg, Katrin, and Thomas Hinz (2015): Factorial Survey Experiments. Series: Quantitative Applications in the Social Sciences. Volume 175. SAGE.
Mutz, Diana C. (2011): Vignette Treatments. In: Mutz, Diana C.: Population-Based Survey Experiments. Princeton and Oxford: Princeton University Press: Chapter Four (54-67).
Rossi, Peter .H. and Anderson, Andy B. (1982): The Factorial Survey Approach: An Introduction. In: Rossi, P.H./Nock, S.L. (Eds.): Measuring Social Judgments. The Factorial Survey Approach. Beverly Hills: 15-67.
Wallander, Lisa (2009): 25 years of factorial surveys in sociology: a review. In: Social Science Research 38: 505-520.
Auspurg, Katrin, and Thomas Hinz (2015): Factorial Survey Experiments. Series: Quantitative Applications in the Social Sciences. Volume 175. SAGE.
Mutz, Diana C. (2011): Vignette Treatments. In: Mutz, Diana C.: Population-Based Survey Experiments. Princeton and Oxford: Princeton University Press: Chapter Four (54-67).
Rossi, Peter .H. and Anderson, Andy B. (1982): The Factorial Survey Approach: An Introduction. In: Rossi, P.H./Nock, S.L. (Eds.): Measuring Social Judgments. The Factorial Survey Approach. Beverly Hills: 15-67.
Wallander, Lisa (2009): 25 years of factorial surveys in sociology: a review. In: Social Science Research 38: 505-520.
Association in the course directory
Last modified: Mo 07.09.2020 15:47
Survey experiments are frequently used for investigating individuals’ social attitudes, opinions, and behavioral intentions. In particular, there is an increasing use of methods that integrate multi-factorial experimental set-ups into surveys, such as factorial surveys methods (sometimes referred to as vignette analyses). Respondents are asked to evaluate fictitious situations, objects or persons. By systematically varying attributes of the descriptions (e.g., the educational background of a described person), it is possible to determine their influence on respondents’ stated choices, decisions or attitudes. For example, when evaluating fair earnings, should men and women earn the same wages? What would be a fair return to higher education? Do all respondents employ similar evaluation rules or are there differences across social groups? Researchers’ controlled experimental variation of stimuli allows a reliable evaluation of the impact of each attribute. Moreover, the method allows direct tests of decision processes and theories. As the experiment is embedded into a survey questionnaire, it is possible to easily reach a heterogeneous sample population. The variety of possible applications and the appealing possibilities to test social and economic theories are important reasons for the method being more and more often used in the social sciences.
This course gives a theoretical and practical overview of factorial survey methods and also some information on related experimental survey methods (conjoint analysis, choice experiments). Participants will get practical insights into all single steps that are needed to design factorial survey experiments, starting with the development of vignettes, continuing with the selection of an experimental design, drafting and programming of questionnaires (for online and paper and pencil surveys), up to special methods for data analyses (such as multilevel regressions). For practical exercises, participants might select a research question related to their own research (e.g. PhD thesis). Participants will find the course particularly useful if they want to learn about survey-experimental designs; want to deepen their knowledge of experimental designs and quantitative statistical methods; and/or want to learn how to analyse data from multifactorial survey experiments and how to evaluate the quality of such data.