TY - JOUR
T1 - How big is big enough? Sample size requirements for CAST item parameter estimation
AU - Chuah, Siang Chee
AU - Drasgow, Fritz
AU - Luecht, Richard
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - Adaptive tests offer the advantages of reduced test length and increased accuracy in ability estimation. However, adaptive tests require large pools of precalibrated items. This study looks at the development of an item pool for 1 type of adaptive administration: the computer-adaptive sequential test. An important issue is the sample size required for adequate estimation of item response theory item parameters. The authors simulated responses of 300, 500, and 1,000 respondents per item, estimated item parameters with the BILOG program, and then evaluated the adequacy of the parameter estimates. The results suggest that sample sizes as small as 300 respondents per item are adequate for estimating ability and classifying examinees as masters or nonmasters.
AB - Adaptive tests offer the advantages of reduced test length and increased accuracy in ability estimation. However, adaptive tests require large pools of precalibrated items. This study looks at the development of an item pool for 1 type of adaptive administration: the computer-adaptive sequential test. An important issue is the sample size required for adequate estimation of item response theory item parameters. The authors simulated responses of 300, 500, and 1,000 respondents per item, estimated item parameters with the BILOG program, and then evaluated the adequacy of the parameter estimates. The results suggest that sample sizes as small as 300 respondents per item are adequate for estimating ability and classifying examinees as masters or nonmasters.
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U2 - 10.1207/s15324818ame1903_5
DO - 10.1207/s15324818ame1903_5
M3 - Article
AN - SCOPUS:33746414744
SN - 0895-7347
VL - 19
SP - 241
EP - 255
JO - Applied Measurement in Education
JF - Applied Measurement in Education
IS - 3
ER -