01512nas a2200229   4500008004100000245008700041210006900128300001200197490000700209520075300216653002100969653001300990653003001003653003401033653001101067653001501078653001501093653001801108100002301126700002701149856010601176       2003                        eng d00aUsing response times to detect aberrant responses in computerized adaptive testing0 aUsing response times to detect aberrant responses in computerize  a251-2650 v683 aA lognormal model for response times is used to check response times for aberrances in examinee behavior on computerized adaptive tests. Both classical procedures and Bayesian posterior predictive checks are presented. For a fixed examinee, responses and response times are independent; checks based on response times offer thus information independent of the results of checks on response patterns. Empirical examples of the use of classical and Bayesian checks for detecting two different types of aberrances in response times are presented. The detection rates for the Bayesian checks outperformed those for the classical checks, but at the cost of higher false-alarm rates. A guideline for the choice between the two types of checks is offered.10aAdaptive Testing10aBehavior10aComputer Assisted Testing10acomputerized adaptive testing10aModels10aperson Fit10aPrediction10aReaction Time1 avan der Linden, WJ1 aKrimpen-Stoop, E M L A  uhttp://iacat.org/content/using-response-times-detect-aberrant-responses-computerized-adaptive-testing02403nas a2200193   4500008004100000245010700041210006900148300000900217490000700226520169400233653003201927653002501959653002001984653001802004653002602022653001402048100002602062856012102088       2000                        eng d00aAn exploratory analysis of item parameters and characteristics that influence item level response time0 aexploratory analysis of item parameters and characteristics that  a18120 v613 aThis research examines the relationship between item level response time and (1) item discrimination, (2) item difficulty, (3) word count, (4) item type, and (5) whether a figure is included in an item. Data are from the Graduate Management Admission Test, which is currently offered only as a computerized adaptive test. Analyses revealed significant differences in response time between the five item types: problem solving, data sufficiency, sentence correction, critical reasoning, and reading comprehension. For this reason, the planned pairwise and complex analyses were run within each item type. Pairwise curvilinear regression analyses explored the relationship between response time and item discrimination, item difficulty, and word count. Item difficulty significantly contributed to the prediction of response time for each item type; two of the relationships were significantly quadratic. Item discrimination significantly contributed to the prediction of response time for only two of the item types; one revealed a quadratic relationship and the other a cubic relationship. Word count had significant linear relationship with response time for all the item types except reading comprehension, for which there was no significant relationship. Multiple regression analyses using word count, item difficulty, and item discrimination predicted between 35.4% and 71.4% of the variability in item response time across item types. The results suggest that response time research should consider the type of item that is being administered and continue to explore curvilinear relationships between response time and its predictor variables. (PsycINFO Database Record (c) 2005 APA )10aItem Analysis (Statistical)10aItem Response Theory10aProblem Solving10aReaction Time10aReading Comprehension10aReasoning1 aSmith, Russell Winsor  uhttp://iacat.org/content/exploratory-analysis-item-parameters-and-characteristics-influence-item-level-response-time