The knowledge or random guessing model for matching tests
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The knowledge or random guessing model for matching tests
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1992
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Abstract
The knowledge or random guessing (KRG)
model was applied to matching tests. A matching
test typically consists of two lists of alternatives.
The response alternatives in the first list might
consist of several terms to be defined, and the
question alternatives in the second list would then
consist of the definitions. Examinees are instructed
to match the question alternatives to the response
alternatives. According to the KRG model, if an
examinee knows the correct answer, the correct
answer will be chosen; however, if the examinee
does not know the correct match, he/she will select
the question alternative by guessing at random.
Reliability formulas for the number of correct
matchings based on the KRG model are given by
Zimmerman and Williams (1982). Before applying
these formulas, an appropriate statistical test
should be used to test whether the model holds.
A goodness-of-fit test is developed that is especially
sensitive to the assumption of random guessing.
Moreover, a simplified version of the model is
presented in which the alternatives are ordered
according to a Guttman scale. Three examples are
given in which the model is applied to real data.
It appears that in many cases examinees use coping
strategies that violate the assumption of random
guessing. A suggestion is made for the development
of a somewhat more complex model that
takes into account examinee coping strategies and
that can be considered an extension of the KRG
model. Index terms: achievement testing, guessing
in matching tests, knowledge or random guessing
model, matching tests.
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Previously Published Citation
Van der Ven, A. H & Gremmen, F. M. (1992). The knowledge or random guessing model for matching tests. Applied Psychological Measurement, 16, 177-194. doi:10.1177/014662169201600207
Suggested citation
Van der Ven, A. H. G. S.; Gremmen, F. M.. (1992). The knowledge or random guessing model for matching tests. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/115647.
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