Comparison of ability metrics obtained under two latent trait theory procedures
1983
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Comparison of ability metrics obtained under two latent trait theory procedures
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1983
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Abstract
Both the BICAL and LOGIST computer programs
implement a maximum likelihood procedure for jointly
estimating the item and ability parameters. The two
programs differ, however, with respect to (1) the anchoring
procedures used to overcome the metric indeterminancy
of the paradigm; (2) the item characteristic
curve models employed; and (3) how the examinees
are grouped within the estimation process. Three simulated
sets of item response data based upon a known
underlying ability metric were used to investigate the
metric recovery capabilities of the two computer programs.
The results showed that both programs recovered
a transformation of the underlying metric via
a common equation, but the elements used in this
equation were program specific. The transformation of
the metric yielded by BICAL to the underlying metric
depended only upon the item characteristic curve parameters,
whereas the LOGIST transformation also depended
upon the frequency distribution of the estimated
ability scores over the underlying ability metric.
The empirical results indicate that both transformations
are quite sensitive to errors in the average value of the
obtained item discrimination indices. Because LOGIST
groups examinees by ability levels and BICAL
does so by raw score levels, the variability of the
transformed ability estimates yielded by BICAL were
smaller than those from LOGIST. The results suggest
that when comparing results yielded by the two computer
programs, particular attention should be paid to
the characteristics of the obtained metrics.
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Baker, Frank B. (1983). Comparison of ability metrics obtained under two latent trait theory procedures. Applied Psychological Measurement, 7, 97-110. doi:10.1177/014662168300700112
Suggested citation
Baker, Frank B.. (1983). Comparison of ability metrics obtained under two latent trait theory procedures. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/101631.
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