Open AccessSAMPLING VARIANCES AND COVARIANCES OF PARAMETER ESTIMATES IN ITEM RESPONSE THEORY
Author(s) -
Lord Frederic M.,
Wingersky Marilyn S.
Publication year - 1982
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.2333-8504.1982.tb01318.x
Subject(s) - mathematics , covariance , statistics , covariance matrix , variance (accounting) , item response theory , sampling (signal processing) , law of total covariance , set (abstract data type) , estimation of covariance matrices , covariance intersection , computer science , filter (signal processing) , psychometrics , accounting , business , computer vision , programming language
This paper develops a possible method for computing the asymptotic sampling variance‐covariance matrix of joint maximum likelihood estimates in item response theory when both item parameters and abilities are unknown. For a set of artificial data, results are compared with empirical values; also with the variance‐covariance matrices found by the usual formulas for the case where the abilities are known, or where the item parameters are known. The results are consistent with the conjecture that the new method is asymptotically correct except for errors due to grouping.