Thomas J. Leeper > Teaching > Public Opinion, Political Psychology, and Citizenship > Problem Set 4

Problem Set 4: Experimentation


The purpose of this problem set is to assess your understanding of one key method of quantitative public opinion research: experimental design and analysis.

Your Task

  1. In your own words, explain the “fundamental problem of causal inference” and how experiments provide a solution to that problem.

  2. A researcher wants to understand how a televised party leaders debate affected citizens’ vote intentions and considers two alternative research designs. The first design involves interviewing a representative sample of citizens, asking whether they watched the debate, and comparing vote intentions among viewers and non-viewers. The second design involves recruiting a non-representative sample of citizens into a laboratory session where one half of participants are randomly assigned to watch the debate and the other half is randomly assigned to watch a non-political program. Vote intentions are measured at the end of the laboratory session. Discuss the trade-offs involved in these designs, including what would be required to obtain an estimate of the causal effect of the debate on vote intentions in each design.

  3. How do we know if randomization “worked”? In other words, how do we know that experimental groups are identical to one another except for the difference in the experimentally manipulated variable?

  4. Consider an experiment on 500 individuals in which one group is randomly assigned to read a treatment message from David Cameron support the “Remain” position in the upcoming European Union referendum and another group is assigned to a control condition that receives no information. Measures of opinions for the European Union are recorded for both groups on a 0 to 1 scale, with higher scores indicating greater favorability toward British membership in the European Union.

a. Assuming the treatment group mean score was 0.68 and the control group mean score was .51, what is the average treatment effect? Is this substantively large or small?

b. Assuming the t-statistic for the mean-difference is 1.76, should we consider this effect to be statistically large and distinguishable from zero?

  1. The statistical power of a two-sample t-test (which is, in essence, the power of a posttest-only, two-group experimental design) is influenced by four things: the size of each experimental group, the difference-in-means (i.e., difference in mean values of the outcome in the two groups), the variance of the outcome measure, and alpha (the significance level or “Type 1” error probability).

a. If alpha (the Type 1 error probability) is 0.05, how often should we expect to find a “statistically significant” effect size when one is not present?

b. If you increase the size of your treatment groups in an experiment while the expected effect size remains unchanged, what happens to the power of your experiment? Are you more or less likely to obtain a “false negative” result? What about “false positives”?

c. Imagine we are expecting to find a small effect but we can only collect a small number of observations in our experiment, so the minimum detectable effect size in our study is larger than the effect size we would expect to observe given our theory. If our experiment reveals an effect that is statistically distinguishable from zero, what are the two possible interpretations of this result?

  1. Consider an experiment in which the effect of a treatment is measured using a single survey question. Assuming a given effect size, and that we cannot change alpha or the size of the experimental groups, what practical action can we do to increase the power of our experimental design?

Submission Instructions

Please submit your answers as a PDF document via Moodle. It should be no more than 4 pages, single-spaced, in Times New Roman font size 12, on A4 paper with standard 2.54cm margins. This problem set is self-assessed. A solution set will be provided on the course website and the activity will be discussed in class.


Group feedback will be provided during class. If you would like more specific individual feedback on your work, please ask the instructor during office hours.