Single Course Fee: 800.00 EUR (tax exempt)
Instructor: Ashley Amaya
A key tool of methodological research is the split-ballot experiment, in which randomly selected subgroups of a sample receive different questions, different response formats, or different modes of data collection. In theory, such experiments can combine the clarity of experimental designs with the inferential power of representative samples. All too often, though, such experiments use flawed designs that leave serious doubts about the meaning or generalizability of the findings. The purpose of this course is to consider the issues involved in the design and analysis of data from experiments embedded in surveys. It covers the purposes of experiments in surveys, examines several classic survey experiments in detail, and takes a close look at some of the pitfalls and issues in the design of such studies. These pitfalls include problems (such as the confounding of the experimental variables) that jeopardize the comparability of the experimental groups, problems (such as nonresponse) that cast doubts on the generality of the results, and problems in determining the reliability of the results. The course will also consider some of the design decisions that almost always arise in planning experiments — issues such as identifying the appropriate error term for significance tests and including necessary comparison groups.
By the end of the course, students will…
Grading will be based on:
Three online quizzes (45%)
Three exercises (45%)
Participation in online discussions (10%)
Dates of when assignment will be due are indicated in the syllabus. Extensions will be granted sparingly and are at the instructor's discretion.
At least one prior course in data analysis. Ability to use SAS or STATA
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Stuart, E. A., & Rubin, D. B (2008). Best practices in quasiexperimental designs: Matching methods for causal inference. In J. Osborne (Ed.), Best practices in quantitative methods (pp. 155-176). Thousand Oaks, CA: Sage Publications.
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Shadish, W. R., Cook, T.D., & Campbell, D. T. (2002). Experimental & quasi-experimental designs for generalized causal inference. Chapters 1-3.
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Tourangeau, R. (2004). Design considerations for questionnaire development. In S. Presser, J. Rothgeb, M. Couper, J. Lessler, E. Martin, J. Martin, and E. Singer (Eds.), Methods for Testing and Evaluating Survey Questionnaires (pp. 209-224). New York: John Wiley & Sons.
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Tourangeau, R., Smith, T.W., and Rasinski, K. (1997). Motivation to report sensitive behaviors in surveys: Evidence from a bogus pipeline experiment. Journal of Applied Social Psychology, 27, 209-222.
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Van den Brakel, J. (2008). Design-based analysis of embedded experiments with applications in the Dutch Labour Force Survey. Journal of the Royal Statistical Society, Series A, 171, 581–613.
Weekly online meetings & assignments: