Computerized Classification Testing Under the Generalized Graded Unfolding Model

Abstract

The generalized graded unfolding model (GGUM) has been recently developed to describe item responses to Likert items (agree-disagree) in attitude measurement. In this study, the authors (a) developed two item selection methods in computerized classification testing under the GGUM, the current estimate/ability confidence interval method and the cut score/sequential probability ratio test method and (b) evaluated their accuracy and efficiency in classification through simulations. The results indicated that both methods were very accurate and efficient. The more points each item had and the fewer the classification categories, the more accurate and efficient the classification would be. However, the latter method may yield a very low accuracy in dichotomous items with a short maximum test length. Thus, if it is to be used to classify examinees with dichotomous items, the maximum text length should be increased.Keywords unfolding model, computerized classification testing, computerized adaptive testing, mutual information, sequential probability ratio test Likert items (or ''agree-disagree'' items) have been widely used in attitude measurement. Respondents are instructed to select a category that best describes their attitude toward statements. The ideal point process (Coombs, 1964) has been proposed to describe the underlying mental process. It assumes that a person chooses to agree or disagree with an attitude statement (an item), according to the distance between

Computerized Classification Testing Under the One-Parameter Logistic Response Model With Ability-Based Guessing

“…Compared with the SPRT, the ACI had a shorter test length, a larger bank usage, a lower item exposure rate, but a larger forced classification rate. Thus, the ACI seemed to be slightly more efficient than the SPRT, but at the expense of a larger forced classification, which is consistent with the literature (Wang & Liu, in press).…”

“…Actually, efficiency and accuracy cannot be separated, that is, when comparing one criterion (e.g., efficiency) between the SPRT and the ACI, the other criterion (e.g., accuracy) has to be kept equal between methods. Wang and Liu (in press) compared the performances of the SPRT and ACI under the generalized graded unfolding model (Roberts, Donoghue, & Laughlin, 2000) and observed that the ACI requires a shorter test length than the SPRT to achieve approximately the same accuracy in classification, but at the expense of a higher forced classification rate.…”

Fitting item response unfolding models to Likert-scale data using mirt in R

“…The unfolding process has attracted great interests in constructs of personality, attitudes, job performance, vocational interests, leadership, emotion measurements, and so forth [ 7 – 13 ]. The unfolding model has also seen a wide variety of applications in that it has been applied to computerized adaptive testing [ 14 , 15 ], response styles [ 16 ], computerized classification testing [ 17 ], multilevel data analysis [ 18 ], multidimensional latent scaling [ 19 , 20 ], and random threshold modeling [ 15 ].…”

Constructing a Scale of Attitudes toward School Science Using the General Graded Unfolding Model

“…In a study by Wang and Liu (2011) aimed to develop two methods of item selection in computerized classification testing using the GGUM, the current estimate/ability confidence interval method and the cut-off score/sequential probability ratio test method were used. They evaluated the accuracy and efficiency of both methods in classification through simulation.…”