Guides design, estimation, and diagnostics for list experiments (item count technique, ICT). Use when (1) deciding whether a list experiment is warranted for a sensitive question, (2) designing the control list or choosing baseline items, (3) selecting between design variants (single, double, placebo), (4) choosing an estimator (difference-in-means, multivariate NLSreg/MLreg, combined), (5) testing the identifying assumptions (no design effect, no floor/ceiling), (6) assessing or diagnosing mechanical inflation or artificial deflation, or (7) interpreting list experiment results in relation to direct question estimates. Covers the full pipeline from pre-design sensitivity assessment through statistical inference and power analysis.
list R package.list R package (Blair, Chou & Imai), which provides a unified interface for difference-in-means, NLSreg, MLreg, combined estimator, and Bayesian MCMC hierarchical models, along with all standard diagnostic tests.ict.test() in Blair & Imai's (2012) list package.ictreg().list package's simulation tools support this. Rule of thumb: assume effective sample sizes 5–10× below what a direct question study would require.list package cited: Is the list R package (Blair, Chou & Imai) cited as the implementation source?