Distinguishing among parametric item response models for polychotomous ordered data
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Distinguishing among parametric item response models for polychotomous ordered data
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1994
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Abstract
Several item response models have been proposed
for fitting Likert-type data. Thissen & Steinberg (1986)
classified most of these models into difference models
and divide-by-total models. Although they have different
mathematical forms, divide-by-total and difference
models with the same number of parameters seem to
provide very similar fit to the data. The ideal observer
method was used to compare two models with the same
number of parameters-Samejima’s (1969) graded response
model (a difference model) and Thissen &
Steinberg’s (1986) extension of Masters’ (1982) partial
credit model (a divide-by-total model-to investigate
whether difference models or divide-by-total models
should be preferred for fitting Likert-type data. The
models were found to be very similar under the conditions
investigated, which included scale lengths from 5
to 25 items (five-option items were used) and calibration
samples of 250 to 3,000. The results suggest that
both models fit approximately equally well in most
practical applications. Index terms: graded response
model, IRT, Likert scales, partial credit model, polychotomous
models, psychometrics.
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Maydeu-Olivares, Albert, Drasgow, Fritz & Mead, Alan D. (1994). Distinguishing among parametric item response models for polychotomous ordered data. Applied Psychological Measurement, 18, 245-256. doi:10.1177/014662169401800305
Suggested citation
Maydeu-Olivares, Albert; Drasgow, Fritz; Mead, Alan D.. (1994). Distinguishing among parametric item response models for polychotomous ordered data. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/117007.
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