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Temporary Summer Internship: Computer Adaptive Tests

at ACT in Iowa City, IA

Position Objective:



Computer adaptive tests (CAT) are able to measure the examinee’s ability more accurately with fewer items than linear tests, but suffer from low exposure rates for many items, even when exposure control algorithms are applied. Multistage testing (MST) compromises a linear test in which all examinees receive the same items and a computer adaptive test (CAT) in which items are adaptively selected to the abilities of examinees. In addition, an MST can allow examinees to review the items that were previously answered. However, properly constructed modules of a multi-stage test can result in nearly as good measurement of examinee abilities as a CAT. An MST typically begins with a medium level module on the first stage, and a difficult and easy module on the second stage, in which the examinee branches to a module depending on the ability estimated from the first stage. The modules on the second stage can either be constructed prior to the beginning of the MST, or can be constructed “on the fly” based on the estimate of the examinee’s ability.



In this project, modules in each stage are dynamically assembled depending on the interim ability estimates of examinees and one of the best modules is selected for administration. For the evaluation, ability estimate and item usage are compared for several testing conditions: linear, CAT, pre-constructed MST, MST on the fly. Additional conditions (e.g., length of modules, number of stages) may be investigated in which the modules themselves are more adaptively selected. The summer intern is responsible for running calibration software and computing measures of accuracy of examinee ability estimates.



Typical work-related activities include:

Literature review
Applying computer software
Some computer programming
Data analysis
Weekly reporting of results
Final research report on the study
Presentation at the end of the internship



MINIMUM QUALIFICATIONS



EDUCATION:

Candidate with graduate level work towards a PhD in related area required, preferably in Psychometrics, Statistics, or Educational Measurement.
Candidate should have completed at least one research project involving data analysis.



EXPERIENCE, KNOWLEDGE, SKILLS, AND ABILITIES:

Candidate should be able to demonstrate experience in mathematical skills; applied statistics; psychometric knowledge
Knowledge of a computer programming language (e.g., SAS or R is preferred); knowledge of adaptive testing
Familiarity with psychometric software such as BILOG and PARSCALE is desired


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Published at 11-20-2015
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