00542nas a2200169 4500008004500000245006600045210006600111300001000177490000700187653002100194653000800215653000800223653003000231653001300261100002000274856007800294 2023 Engldsh 00aExpanding the Meaning of Adaptive Testing to Enhance Validity0 aExpanding the Meaning of Adaptive Testing to Enhance Validity a22-310 v1010aAdaptive Testing10aCAT10aCBT10atest-taking disengagement10avalidity1 aWise, Steven, L uhttp://mail.iacat.org/expanding-meaning-adaptive-testing-enhance-validity02532nas a2200205 4500008004100000245011900041210006900160260001200229520179400241653000802035653000802043653003402051653003002085653000802115653003102123653001602154653001302170100002002183856012302203 2011 eng d00aFrom Reliability to Validity: Expanding Adaptive Testing Practice to Find the Most Valid Score for Each Test Taker0 aFrom Reliability to Validity Expanding Adaptive Testing Practice c10/20113 a

CAT is an exception to the traditional conception of validity. It is one of the few examples of individualized testing. Item difficulty is tailored to each examinee. The intent, however, is increased efficiency. Focus on reliability (reduced standard error); Equivalence with paper & pencil tests is valued; Validity is enhanced through improved reliability.

How Else Might We Individualize Testing Using CAT?

An ISV-Based View of Validity

Test Event -- An examinee encounters a series of items in a particular context.

CAT Goal: individualize testing to address CIV threats to score validity (i.e., maximize ISV).

Some Research Issues:

10aCAT10aCIV10aconstruct-irrelevant variance10aIndividual Score Validity10aISV10alow test taking motivation10aReliability10avalidity1 aWise, Steven, L uhttp://mail.iacat.org/content/reliability-validity-expanding-adaptive-testing-practice-find-most-valid-score-each-test02413nas a2200289 4500008004100000020002200041245010800063210006900171260004500240300001000285490000700295520141000302653003201712653002501744653002501769653002501794653002601819653001801845653003701863653002101900653001301921100001501934700001401949700001901963700002001982856012102002 2007 eng d a1015-5759 (Print)00aPsychometric properties of an emotional adjustment measure: An application of the graded response model0 aPsychometric properties of an emotional adjustment measure An ap bHogrefe & Huber Publishers GmbH: Germany a39-460 v233 aItem response theory (IRT) provides valuable methods for the analysis of the psychometric properties of a psychological measure. However, IRT has been mainly used for assessing achievements and ability rather than personality factors. This paper presents an application of the IRT to a personality measure. Thus, the psychometric properties of a new emotional adjustment measure that consists of a 28-six graded response items is shown. Classical test theory (CTT) analyses as well as IRT analyses are carried out. Samejima's (1969) graded-response model has been used for estimating item parameters. Results show that the bank of items fulfills model assumptions and fits the data reasonably well, demonstrating the suitability of the IRT models for the description and use of data originating from personality measures. In this sense, the model fulfills the expectations that IRT has undoubted advantages: (1) The invariance of the estimated parameters, (2) the treatment given to the standard error of measurement, and (3) the possibilities offered for the construction of computerized adaptive tests (CAT). The bank of items shows good reliability. It also shows convergent validity compared to the Eysenck Personality Inventory (EPQ-A; Eysenck & Eysenck, 1975) and the Big Five Questionnaire (BFQ; Caprara, Barbaranelli, & Borgogni, 1993). (PsycINFO Database Record (c) 2007 APA, all rights reserved)10acomputerized adaptive tests10aEmotional Adjustment10aItem Response Theory10aPersonality Measures10apersonnel recruitment10aPsychometrics10aSamejima's graded response model10atest reliability10avalidity1 aRubio, V J1 aAguado, D1 aHontangas, P M1 aHernández, J M uhttp://mail.iacat.org/content/psychometric-properties-emotional-adjustment-measure-application-graded-response-model