Maximum Likelihood and Bayesian Parameter Estimation in Item Response Theory IRT

Author(s):

Lord, Frederic M.

Publication Year:

1984

Report Number:

RR-84-30

Source:

ETS Research Report

Document Type:

Report

Page Count:

23

Subject/Key Words:

Office of Naval Research, Bayesian Statistics, Estimation (Mathematics), Item Response Theory (IRT), Maximum Likelihood Statistics, Statistical Analysis

Abstract

There are currently three main approaches to parameter estimation in item response theory (IRT): (1) joint maximum likelihood, exemplified by LOGIST, yielding maximum likelihood estimates; (2) marginal maximum likelihood, exemplified by BILOG, yielding maximum likelihood estimates of item parameters (ability parameters can be estimated subsequently, using Bayesian procedures); and (3) Bayesian approaches-parameter estimates are usually the mode or mean of the posterior distribution of the parameter estimated. Advantages and disadvantages of these three methods are discussed and compared. (AUTHOR/BW). (23pp.)

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• http://dx.doi.org/10.1002/j.2330-8516.1984.tb00070.x