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Below results based on the criteria 'survey experiment'
Total number of records returned: 2
Measuring Political Support and Issue Ownership Using Endorsement Experiments, with Application to the Militant Groups in Pakistan
To measure the levels of support for political actors (e.g., candidates and parties) and the strength of their issue ownership, survey experiments are often conducted in which respondents are asked to express their opinion about a particular policy endorsed by a randomly selected political actor. These responses are contrasted with those from a control group that receives no endorsement. This survey methodology is particularly useful for studying sensitive political attitudes. We develop a Bayesian hierarchical measurement model for such endorsement experiments, demonstrate its statistical properties through simulations, and use it to measure support for Islamist militant groups in Pakistan. Our model uses item response theory to estimate support levels on the same scale as the ideal points of respondents. The model also estimates the strength of political actors' issue ownership for specic policies as well as the relationship between respondents' characteristics and support levels. Our analysis of a recent survey experiment in Pakistan reveals three key patterns. First, citizens' attitudes towards militant groups are geographically clustered. Second, once these regional differences are taken into account, respondents' characteristics have little predictive power for their support levels. Finally, militant groups tend to receive less support in the areas where they operate.
What Can We Learn with Statistical Truth Serum? Design and Analysis of the List Experiment
item count technique
Due to the inherent sensitivity of many survey questions, a number of researchers have adopted an indirect questioning technique known as the list experiment (or the item count technique) in order to minimize bias due to dishonest or evasive responses. However, standard practice with the list experiment requires a large sample size, is not readily adaptable to regression or multivariate modeling, and provides only limited diagnostics. This paper addresses all three of these issues. First, the paper presents design principles for the standard list experiment (and the double list experiment) to minimize bias and reduce variance as well as providing sample size formulas for the planning of studies. Additionally, this paper investigates the properties of a number of estimators and introduces an easy-to-use piecewise estimator that reduces necessary sample sizes in many cases. Second, this paper proves that standard-procedure list experiment data can be used to estimate the probability that an individual holds the socially undesirable opinion/behavior. This allows multivariate modeling. Third, this paper demonstrates that some violations of the behavioral assumptions implicit in the technique can be diagnosed with the list experiment data. The techniques in this paper are illustrated with examples from American politics.