Comparison of CAT Item Selection Criteria for Polytomous Items

Choi, Seung W.; Swartz, Richard J.

doi:10.1177/0146621608327801

Cited by 80 publications

(69 citation statements)

References 27 publications

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“…In the Minimum Expected Posterior Variance method, the information function replaces with the posterior variance of the θ levels to be estimated according to the possible answers (Choi and Swartz, 2009). Van der Linden (1998) stated that the MEPV method is an alternative to MEI for small samples.…”

Section: Minimum Expected Posterior Variance (Mepv)mentioning

confidence: 99%

Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items

Aybek¹,

Demirtasli²

2017

International Journal of Research in Education and Science

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This article aims to provide a theoretical framework for computerized adaptive tests (CAT) and item response theory models for polytomous items. Besides that, it aims to introduce the simulation and live CAT software to the related researchers. Computerized adaptive test algorithm, assumptions of item response theory models, nominal response model, partial credit and generalized partial credit models and graded response model are described carefully to reach that aim. Likewise, item selection methods, such as maximum Fisher information, maximum expected information, minimum expected posterior variance, maximum expected posterior weighted-information, and ability prediction methods, such as expected a posteriori and maximum a posteriori, are expounded as well as stopping rules for the computerized adaptive tests.

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Section: Minimum Expected Posterior Variance (Mepv)mentioning

confidence: 99%

Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items

Aybek¹,

Demirtasli²

2017

International Journal of Research in Education and Science

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“…Specifically, for a single student with latent trait, z, at each step in the 20 See Montgomery & Cutler (2013) for an introduction to this literature aimed at survey researchers in political science. Choi & Swartz (2009) provide an overview of CAT item selection methods for ordinal items.…”

Section: E Ciently Selecting Bridgesmentioning

confidence: 99%

“…where p q (y i+1 q = k|y i ) is the posterior predicted probability, prior to answering question q, that the student's answer to question q achieves a score of k, Var(z|y i , y i+1 q = k) is the posterior variance of the student's latent ability, conditional on her previous performance and achieving a score of k on question q, and Q i is the set of remaining potential questions at the ith step of the process (Choi & Swartz 2009). …”

Section: E Ciently Selecting Bridgesmentioning

confidence: 99%

Evaluating and Improving Item Response Theory Models for Cross-National Expert Surveys

2015

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“…These results are not surprising given that the maximum item information function for most of the PROMIS Fatigue items falls in this range; the bank has fewer items that are maximally informative in the lower range of the θ scale, making it more challenging to accurately estimate the trait levels of individuals with little fatigue. Choi and Swartz (2009) acknowledged similar coverage problems in the bank of depression items upon which their CAT simulation study was based. The researchers noted that it is more challenging to generate items targeting the lower range for the construct of depression.…”

Section: Impact Of Lid On Trait Estimationmentioning

confidence: 99%

“…The current study considered two administration conditions: a fullbank administration of 95 items and a fixed-length CAT administration of 20 items. The CAT modeled in the study would be considered long (Choi & Swartz, 2009) and represents the maximum number of items allowed in an adaptive administration of the PROMIS Fatigue instrument. In health-outcomes settings where instruments are administered to the very ill, young children, and the elderly, respondent burden is of significant concern and short CATs are desirable .…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Detecting Local Item Dependence in Polytomous Adaptive Data

Mislevy

Rupp

Harring

2012

J Educational Measurement

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iii Table 6. Results summary for tracked item pairs in the CONV condition with no LID .. 91 1 For simplicity, the term "individual" will be used throughout the document to denote the person who provides the responses to a data-collection instrument; the term is assumed to be synonymous with alternative terms such as "examinee" and "respondent," though it is acknowledged that each has domainspecific connotations. Along these lines, the data-collection instrument will be referred to as an "instrument," (representing alternative terms such as "test" and "questionnaire") and the term "trait" is used to denote the construct measured by the items (representing alternative terms such as "ability" and "proficiency"). Exceptions are made in rare instances when necessary to remain consistent with the literature. 2This initiative focuses on more accurate and efficient measurement of patient-reported symptoms and aspects of health-related quality of life. A primary goal of PROMIS is to develop instruments based on IRT methodologies in these domains that are publically available for the clinical research community.In IRT, estimates of respondents' traits (θ) are based not only on the responses they provide, but also the characteristics (i.e., parameters) of the items they are administered such as their difficulty -reflected by category boundary parameters (b) -their ability to differentiate among respondents -reflected by slope parameters (a) -and their susceptibility to guessing -reflected by lower asymptote parameters (c).One unidimensional IRT model frequently applied in health-outcomes settings is the graded response model (GRM;Samejima, 1969). The GRM is appropriate for item responses that fall in multiple ordered categories. It predicts the conditional probability of an individual responding in a particular category as a function of an individual's latent trait value and several item parameters.The GRM is considered a "difference model" (Thissen & Steinberg, 1986) or "indirect IRT model" (Embretson & Reise, 2000) because the probabilities are computed in two stages. Following Dodd, De Ayala, and Koch (1995), the probability that individual i will produce a response in category k or higher for item j is first computed as:where θ i is the individual's trait level, a j is the discrimination parameter for item j, and b jk is the category boundary for category k for item j. In the second step, the actual category 3 response probabilities for each response are computed by subtracting the cumulative probabilities from adjacent response categories conditional on θ using:There are a total of K -1 boundary parameters that need to be estimated for an item with K score categories. Figure 1 illustrates the probability of choosing each of the response options offered with an item from PROMIS designed to measure fatigue impact and experience according to the GRM. It shows that a person with a low level of fatigue would have a high probability of indicating that his fatigue made it "not at all hard to carry on a conversation." In ...

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Comparison of CAT Item Selection Criteria for Polytomous Items

Cited by 80 publications

References 27 publications

Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items

Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items

Evaluating and Improving Item Response Theory Models for Cross-National Expert Surveys

Detecting Local Item Dependence in Polytomous Adaptive Data

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