Estimating Latent Distributions
- 1 September 1984
- journal article
- Published by Cambridge University Press (CUP) in Psychometrika
- Vol. 49 (3) , 359-381
- https://doi.org/10.1007/bf02306026
Abstract
Consider vectors of item responses obtained from a sample of subjects from a population in which ability θ is distributed with density g (θ‖α), where the α are unknown parameters. Assuming the responses depend on θ through a fully specified item response model, this paper presents maximum likelihood equations for the estimation of the population parameters directly from the observed responses; i.e., without estimating an ability parameter for each subject. Also provided are asymptotic standard errors and tests of fit, computing approximations, and details of four special cases: a non-parametric approximation, a normal solution, a resolution of normal components, and a beta-binomial solution.Keywords
This publication has 18 references indexed in Scilit:
- Marginal Maximum Likelihood Estimation of Item Parameters: Application of an EM AlgorithmPsychometrika, 1981
- Bayes Empirical BayesJournal of the American Statistical Association, 1981
- Visual spatial perception in adolescents and their parents: The X-linked recessive hypothesisBehavior Genetics, 1981
- Estimation in Covariance Components ModelsJournal of the American Statistical Association, 1981
- Nonparametric Maximum Likelihood Estimation of a Mixing DistributionJournal of the American Statistical Association, 1978
- Estimating the Parameters of the Latent Population DistributionPsychometrika, 1977
- Factor Analysis of Dichotomized VariablesPsychometrika, 1975
- Fitting a Response Model for n Dichotomously Scored ItemsPsychometrika, 1970
- Estimating the components of a mixture of normal distributionsBiometrika, 1969
- Estimating True-Score Distributions in Psychological Testing (an Empirical Bayes Estimation Problem)Psychometrika, 1969