01231nas a2200193 4500008004100000245009400041210006900135260001200204520052700216653002600743653000800769653000800777653003100785653003200816653003000848100002000878700001900898856012000917 2011 eng d00aDetecting DIF between Conventional and Computerized Adaptive Testing: A Monte Carlo Study0 aDetecting DIF between Conventional and Computerized Adaptive Tes c10/20113 a

A comparison od two procedures, Modified Robust Z and 95% Credible Interval, were compared in a Monte Carlo study. Both procedures evidenced adequate control of false positive DIF results.

10a95% Credible Interval10aCAT10aDIF10adifferential item function10amodified robust Z statistic10aMonte Carlo methodologies1 aRiley, Barth, B1 aCarle, Adam, C uhttp://www.iacat.org/content/detecting-dif-between-conventional-and-computerized-adaptive-testing-monte-carlo-study02120nas a2200193 4500008004100000245007900041210006900120260001200189520146800201653002801669653000801697653001801705100002001723700001701743700002301760700002301783700002301806856009701829 2011 eng d00aThe Use of Decision Trees for Adaptive Item Selection and Score Estimation0 aUse of Decision Trees for Adaptive Item Selection and Score Esti c10/20113 a

Conducted post-hoc simulations comparing the relative efficiency, and precision of decision trees (using CHAID and CART) vs. IRT-based CAT.

Conclusions

Decision tree methods were more efficient than CAT

But,...

Conclusions

CAT selects items based on two criteria: Item location relative to current estimate of theta, Item discrimination

Decision Trees select items that best discriminate between groups defined by the total score.

CAT is optimal only when trait level is well estimated.
Findings suggest that combining decision tree followed by CAT item selection may be advantageous.

10aadaptive item selection10aCAT10adecision tree1 aRiley, Barth, B1 aFunk, Rodney1 aDennis, Michael, L1 aLennox, Richard, D1 aFinkelman, Matthew uhttp://www.iacat.org/content/use-decision-trees-adaptive-item-selection-and-score-estimation