Open AccessRANDOMIZATION‐BASED INFERENCES ABOUT LATENT VARIABLES FROM COMPLEX SAMPLES
Author(s) -
Mislevy Robert J.
Publication year - 1988
Publication title -
ets research report series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.235
H-Index - 5
ISSN - 2330-8516
DOI - 10.1002/j.2330-8516.1988.tb00310.x
Subject(s) - latent variable , statistics , econometrics , inference , latent class model , variance (accounting) , mathematics , sample (material) , class (philosophy) , latent variable model , computer science , artificial intelligence , chemistry , accounting , chromatography , business
ABSTRACT Standard procedures for drawing inferences from complex samples do not apply when the variable of interest θ cannot be observed directly, but must be inferred from the values of secondary random variables that depend on θ 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 θ been observable. Associated variance estimates account for uncertainty due to both the sampling of respondents from the population and the latency of θ . The approach is illustrated with artificial examples and with data from the 1984 National Assessment for Educational Progress reading survey.