Investigation of Item Selection Methods According to Test Termination Rules in CAT Applications

Year 2019, Volume: 10 Issue: 3, 315 - 326, 04.09.2019

Sema Sulak Hülya Kelecioğlu

https://doi.org/10.21031/epod.530528

Cited By: 2

Abstract

In this
research, computerized adaptive testing item selection methods were
investigated in regard to ability estimation methods and test termination
rules. For this purpose, an item pool including 250 items and 2000 people were
simulated (M = 0, SD = 1). A total of thirty computerized adaptive testing
(CAT) conditions were created according to item selection methods (Maximum
Fisher Information, a-stratification, Likelihood Weight Information Criterion,
Gradual Information Ratio, and Kullback-Leibler), ability estimation methods
(Maximum Likelihood Estimation, Expected a Posteriori Distribution), and test
termination rules (40 items, SE < .20 and SE < .40). According to the
fixed test-length stopping rule, the SE values that were obtained by using the
Maximum Likelihood Estimation method were found to be higher than the SE values
that were obtained by using the Expected a Posteriori Distribution ability
estimation method. When ability estimation was Maximum Likelihood, the highest
SE value was obtained from a-stratification item selection method when the test
length is smaller then 30. Whereas, Kullback-Leibler item selection method
yielded the highest SE value when the test length is larger then 30. According
to Expected a Posteriori ability estimation method, the highest SE value was
obtained from a-stratification item selection method in all test lengths. In
the conditions where test termination rule was SE < .20, and Maximum
Likelihood Ability Estimation method was used, the lowest and highest average
number of items were obtained from the Gradual Information Ratio and Maximum
Fisher Information item selection method, respectively. Furthermore, when the
SE is lower than .20 and Expected a Posteriori ability estimation method was
utilized, the lowest average number of items was obtained through
Kullback-Leibler, and the highest was obtained through Likelihood Weight
Information Criterion item selection method. In the conditions where the test
termination rule was SE < .40, and ability estimation method was Maximum
Likelihood Estimation, the maximum and minimum number of items were obtained by
using Maximum Fisher Information and Kullback-Leibler item selection methods
respectively. Additionally, when Expected a Posteriori ability estimation was
used, the maximum and minimum number of items were obtained via Maximum Fisher
Information and a-stratification item selection methods. For the cases where
the stopping rule was SE < .20 and SE < .40 and Maximum Likelihood
Estimation method was used, the average number of items were found to be
highest in all item selection methods.

Keywords

Computerized adaptive testing, maximum fisher information, a-stratification, likelihood weight information criterion, gradual information ratio, kullback-leibler

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