Fitting a polytomous item response model to Likert-type data
1990
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Fitting a polytomous item response model to Likert-type data
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1990
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Abstract
This study examined the application of the MML-EM
algorithm to the parameter estimation problems of the
normal ogive and logistic polytomous response models
for Likert-type items. A rating-scale model was developed
based on Samejima’s (1969) graded response
model. The graded response model includes a separate
slope parameter for each item and an item response
parameter. In the rating-scale model, the item response
parameter is resolved into two parameters: the
item location parameter, and the category threshold
parameter characterizing the boundary between response
categories. For a Likert-type questionnaire,
where a single scale is employed to elicit different responses
to the items, this item response model is expected
to be more useful for analysis because the item
parameters can be estimated separately from the
threshold parameters associated with the points on a
single Likert scale. The advantages of this type of
model are shown by analyzing simulated data and data
from the General Social Surveys. Index terms: EM
algorithm, General Social Surveys, graded response
model, item response model, Likert scale, marginal
maximum likelihood, polytomous item response model,
rating-scale model.
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Muraki, Eiji. (1990). Fitting a polytomous item response model to Likert-type data. Applied Psychological Measurement, 14, 59-71. doi:10.1177/014662169001400106
Suggested citation
Muraki, Eiji. (1990). Fitting a polytomous item response model to Likert-type data. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/107784.
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