01965nas a2200145 4500008004100000245008700041210006900128260005500197520137700252653003201629653001801661653002401679100001701703856009901720 2017 eng d00aAdapting Linear Models for Optimal Test Design to More Complex Test Specifications0 aAdapting Linear Models for Optimal Test Design to More Complex T aNiigata, JapanbNiigata Seiryo Universityc08/20173 a

Combinatorial optimization (CO) has proven to be a very helpful approach for addressing test assembly issues and for providing solutions. Furthermore, CO has been applied for several test designs, including: (1) for the development of linear test forms; (2) for computerized adaptive testing and; (3) for multistage testing. In his seminal work, van der Linden (2006) laid out the basis for using linear models for simultaneously assembling exams and item pools in a variety of conditions: (1) for single tests and multiple tests; (2) with item sets, etc. However, for some testing programs, the number and complexity of test specifications can grow rapidly. Consequently, the mathematical representation of the test assembly problem goes beyond most approaches reported either in van der Linden’s book or in the majority of other publications related to test assembly. In this presentation, we extend van der Linden’s framework by including the concept of blocks for test specifications. We modify the usual mathematical notation of a test assembly problem by including this concept and we show how it can be applied to various test designs. Finally, we will demonstrate an implementation of this approach in a stand-alone software, called the ATASolver.

Session Video

10aComplex Test Specifications10aLinear Models10aOptimal Test Design1 aMorin, Maxim uhttp://iacat.org/adapting-linear-models-optimal-test-design-more-complex-test-specifications-003058nas a2200325 4500008004100000020004100041245008100082210006900163250001500232260000800247300001100255490000700266520201300273653001502286653001002301653004002311653005702351653003302408653001102441653001102452653001802463653000902481653002802490653001202518653005502530100001502585700001802600700001502618856009902633 2002 eng d a0025-7079 (Print)0025-7079 (Linking)00aMultidimensional adaptive testing for mental health problems in primary care0 aMultidimensional adaptive testing for mental health problems in  a2002/09/10 cSep a812-230 v403 aOBJECTIVES: Efficient and accurate instruments for assessing child psychopathology are increasingly important in clinical practice and research. For example, screening in primary care settings can identify children and adolescents with disorders that may otherwise go undetected. However, primary care offices are notorious for the brevity of visits and screening must not burden patients or staff with long questionnaires. One solution is to shorten assessment instruments, but dropping questions typically makes an instrument less accurate. An alternative is adaptive testing, in which a computer selects the items to be asked of a patient based on the patient's previous responses. This research used a simulation to test a child mental health screen based on this technology. RESEARCH DESIGN: Using half of a large sample of data, a computerized version was developed of the Pediatric Symptom Checklist (PSC), a parental-report psychosocial problem screen. With the unused data, a simulation was conducted to determine whether the Adaptive PSC can reproduce the results of the full PSC with greater efficiency. SUBJECTS: PSCs were completed by parents on 21,150 children seen in a national sample of primary care practices. RESULTS: Four latent psychosocial problem dimensions were identified through factor analysis: internalizing problems, externalizing problems, attention problems, and school problems. A simulated adaptive test measuring these traits asked an average of 11.6 questions per patient, and asked five or fewer questions for 49% of the sample. There was high agreement between the adaptive test and the full (35-item) PSC: only 1.3% of screening decisions were discordant (kappa = 0.93). This agreement was higher than that obtained using a comparable length (12-item) short-form PSC (3.2% of decisions discordant; kappa = 0.84). CONCLUSIONS: Multidimensional adaptive testing may be an accurate and efficient technology for screening for mental health problems in primary care settings.10aAdolescent10aChild10aChild Behavior Disorders/*diagnosis10aChild Health Services/*organization & administration10aFactor Analysis, Statistical10aFemale10aHumans10aLinear Models10aMale10aMass Screening/*methods10aParents10aPrimary Health Care/*organization & administration1 aGardner, W1 aKelleher, K J1 aPajer, K A uhttp://iacat.org/content/multidimensional-adaptive-testing-mental-health-problems-primary-care