Long-standing difficulties in estimating item
parameters in item response theory (IRT) have been
addressed recently with the application of Bayesian
estimation models. The potential of these methods
is enhanced by their availability in the BILOG computer
program. This study investigated the ability
of BILOG to recover known item parameters under
varying conditions. Data were simulated for a two-parameter
logistic IRT model under conditions of
small numbers of examinees and items, and different
variances for the prior distributions of discrimination
parameters. The results suggest that for
samples of at least 250 examinees and 15 items,
BILOG accurately recovers known parameters using
the default variance. The quality of the estimation
suffers for smaller numbers of examinees under the
default variance, and for larger prior variances in
general. This raises questions about how practitioners
select a prior variance for small numbers of
examinees and items. Index terms: BILOG, item
parameter estimation, item response theory, parameter
recovery, prior distributions, simulation.
Harwell, Michael R & Janosky, Janine E. (1991). An empirical study of the effects of small datasets and varying prior variances on item parameter estimation in BILOG. Applied Psychological Measurement, 15, 279-291. doi:10.1177/014662169101500308
Harwell, Michael R.; Janosky, Janine E..
An empirical study of the effects of small datasets and varying prior variances on item parameter estimation in BILOG.
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