@article {195,
	title = {Test construction for cognitive diagnosis},
	journal = {Applied Psychological Measurement},
	volume = {29},
	number = {4},
	year = {2005},
	pages = {262-277},
	abstract = {Although cognitive diagnostic models (CDMs) can be useful in the analysis and interpretation of existing tests, little has been developed to specify how one might construct a good test using aspects of the CDMs. This article discusses the derivation of a general CDM index based on Kullback-Leibler information that will serve as a measure of how informative an item is for the classification of examinees. The effectiveness of the index is examined for items calibrated using the deterministic input noisy "and" gate model (DINA) and the reparameterized unified model (RUM) by implementing a simple heuristic to construct a test from an item bank. When compared to randomly constructed tests from the same item bank, the heuristic shows significant improvement in classification rates. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {(Measurement), Cognitive Assessment, Item Analysis (Statistical), Profiles, Test Construction, Test Interpretation, Test Items},
	author = {Henson, R. K. and Douglas, J.}
}
@article {275,
	title = {A Bayesian method for the detection of item preknowledge in computerized adaptive testing},
	journal = {Applied Psychological Measurement},
	volume = {27},
	number = {2},
	year = {2003},
	pages = {121-137},
	abstract = {With the increased use of continuous testing in computerized adaptive testing, new concerns about test security have evolved, such as how to ensure that items in an item pool are safeguarded from theft. In this article, procedures to detect test takers using item preknowledge are explored. When test takers use item preknowledge, their item responses deviate from the underlying item response theory (IRT) model, and estimated abilities may be inflated. This deviation may be detected through the use of person-fit indices. A Bayesian posterior log odds ratio index is proposed for detecting the use of item preknowledge. In this approach to person fit, the estimated probability that each test taker has preknowledge of items is updated after each item response. These probabilities are based on the IRT parameters, a model specifying the probability that each item has been memorized, and the test taker{\textquoteright}s item responses. Simulations based on an operational computerized adaptive test (CAT) pool are used to demonstrate the use of the odds ratio index. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Adaptive Testing, Cheating, Computer Assisted Testing, Individual Differences computerized adaptive testing, Item, Item Analysis (Statistical), Mathematical Modeling, Response Theory},
	author = {McLeod, L. and Lewis, C. and Thissen, D.}
}
@article {63,
	title = {A comparative study of item exposure control methods in computerized adaptive testing},
	journal = {Journal of Educational Measurement},
	volume = {40},
	number = {1},
	year = {2003},
	pages = {71-103},
	abstract = {This study compared the properties of five methods of item exposure control within the purview of estimating examinees{\textquoteright} abilities in a computerized adaptive testing (CAT) context. Each exposure control algorithm was incorporated into the item selection procedure and the adaptive testing progressed based on the CAT design established for this study. The merits and shortcomings of these strategies were considered under different item pool sizes and different desired maximum exposure rates and were evaluated in light of the observed maximum exposure rates, the test overlap rates, and the conditional standard errors of measurement. Each method had its advantages and disadvantages, but no one possessed all of the desired characteristics. There was a clear and logical trade-off between item exposure control and measurement precision. The M. L. Stocking and C. Lewis conditional multinomial procedure and, to a slightly lesser extent, the T. Davey and C. G. Parshall method seemed to be the most promising considering all of the factors that this study addressed. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Adaptive Testing, Computer Assisted Testing, Educational, Item Analysis (Statistical), Measurement, Strategies computerized adaptive testing},
	author = {Chang, S-W. and Ansley, T. N.}
}
@article {277,
	title = {Outlier detection in high-stakes certification testing},
	journal = {Journal of Educational Measurement},
	volume = {39},
	number = {3},
	year = {2002},
	pages = {219-233},
	abstract = {Discusses recent developments of person-fit analysis in computerized adaptive testing (CAT). Methods from statistical process control are presented that have been proposed to classify an item score pattern as fitting or misfitting the underlying item response theory model in CAT Most person-fit research in CAT is restricted to simulated data. In this study, empirical data from a certification test were used. Alternatives are discussed to generate norms so that bounds can be determined to classify an item score pattern as fitting or misfitting. Using bounds determined from a sample of a high-stakes certification test, the empirical analysis showed that different types of misfit can be distinguished Further applications using statistical process control methods to detect misfitting item score patterns are discussed. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Adaptive Testing, computerized adaptive testing, Educational Measurement, Goodness of Fit, Item Analysis (Statistical), Item Response Theory, person Fit, Statistical Estimation, Statistical Power, Test Scores},
	author = {Meijer, R. R.}
}
@article {364,
	title = {An exploratory analysis of item parameters and characteristics that influence item level response time},
	journal = {Dissertation Abstracts International Section A: Humanities and Social Sciences},
	volume = {61},
	number = {5-A},
	year = {2000},
	pages = {1812},
	abstract = {This 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 )},
	keywords = {Item Analysis (Statistical), Item Response Theory, Problem Solving, Reaction Time, Reading Comprehension, Reasoning},
	author = {Smith, Russell Winsor}
}
