Randomization-Based Inferences about Latent Variables from Complex Samples

摘要

Standard procedures for drawing inferences from complex samples do not apply when the variable of interest theta cannot be observed directly, but must be inferred from the values of secondary random variables that depend on theta stochastically. Examples are examinee proficiency variables in item response theory models and class memberships in latent class models. This paper uses Rubin's multiple imputation approach to approximate sample statistics that would have been obtained, had theta been observable. Associated variance estimates account for uncertainty due to both the sampling of respondents from the population and the latency of theta. The approach is illustrated with artificial examples and with data from the 1984 National Assessment for Educational Progress reading survey. (KR)