@article {113,
	title = {Detection of aberrant item score patterns in computerized adaptive testing: An empirical example using the CUSUM},
	journal = {Personality and Individual Differences},
	volume = {48},
	number = {8},
	year = {2010},
	pages = {921-925},
	abstract = {The scalability of individual trait scores on a computerized adaptive test (CAT) was assessed through investigating the consistency of individual item score patterns. A sample of N = 428 persons completed a personality CAT as part of a career development procedure. To detect inconsistent item score patterns, we used a cumulative sum (CUSUM) procedure. Combined information from the CUSUM, other personality measures, and interviews showed that similar estimated trait values may have a different interpretation.Implications for computer-based assessment are discussed.},
	keywords = {CAT, computerized adaptive testing, CUSUM approach, person Fit},
	isbn = {01918869},
	author = {Egberink, I. J. L. and Meijer, R. R. and Veldkamp, B. P. and Schakel, L. and Smid, N. G.}
}
@article {278,
	title = {Using patterns of summed scores in paper-and-pencil tests and computer-adaptive tests to detect misfitting item score patterns},
	journal = {Journal of Educational Measurement},
	volume = {41},
	number = {2},
	year = {2004},
	pages = {119-136},
	abstract = {Two new methods have been proposed to determine unexpected sum scores on subtests (testlets) both for paper-and-pencil tests and computer adaptive tests. A method based on a conservative bound using the hypergeometric distribution, denoted ρ, was compared with a method where the probability for each score combination was calculated using a highest density region (HDR). Furthermore, these methods were compared with the standardized log-likelihood statistic with and without a correction for the estimated latent trait value (denoted as l-super(*)-sub(z) and l-sub(z), respectively). Data were simulated on the basis of the one-parameter logistic model, and both parametric and nonparametric logistic regression was used to obtain estimates of the latent trait. Results showed that it is important to take the trait level into account when comparing subtest scores. In a nonparametric item response theory (IRT) context, on adapted version of the HDR method was a powerful alterative to ρ. In a parametric IRT context, results showed that l-super(*)-sub(z) had the highest power when the data were simulated conditionally on the estimated latent trait level. (PsycINFO Database Record (c) 2005 APA ) (journal abstract)},
	keywords = {Computer Assisted Testing, Item Response Theory, person Fit, Test Scores},
	author = {Meijer, R. R.}
}
@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 {279,
	title = {Nouveaux d{\'e}veloppements dans le domaine du testing informatis{\'e} [New developments in the area of computerized testing]},
	journal = {Psychologie Fran{\c c}aise},
	volume = {46},
	number = {3},
	year = {2001},
	pages = {221-230},
	abstract = {L{\textquoteright}usage de l{\textquoteright}{\'e}valuation assist{\'e}e par ordinateur s{\textquoteright}est fortement d{\'e}velopp{\'e} depuis la premi{\`e}re formulation de ses principes de base dans les ann{\'e}es soixante et soixante-dix. Cet article offre une introduction aux derniers d{\'e}veloppements dans le domaine de l{\textquoteright}{\'e}valuation assist{\'e}e par ordinateur, en particulier celui du testing adaptative informatis{\'e}e (TAI). L{\textquoteright}estimation de l{\textquoteright}aptitude, la s{\'e}lection des items et le d{\'e}veloppement d{\textquoteright}une base d{\textquoteright}items dans le cas du TAI sont discut{\'e}s. De plus, des exemples d{\textquoteright}utilisations innovantes de l{\textquoteright}ordinateur dans des syst{\`e}mes int{\'e}gr{\'e}s de testing et de testing via Internet sont pr{\'e}sent{\'e}s. L{\textquoteright}article se termine par quelques illustrations de nouvelles applications du testing informatis{\'e} et des suggestions pour des recherches futures.Discusses the latest developments in computerized psychological assessment, with emphasis on computerized adaptive testing (CAT). Ability estimation, item selection, and item pool development in CAT are described. Examples of some innovative approaches to CAT are presented. (PsycINFO Database Record (c) 2005 APA )},
	keywords = {Adaptive Testing, Computer Applications, Computer Assisted, Diagnosis, Psychological Assessment computerized adaptive testing},
	author = {Meijer, R. R. and Gr{\'e}goire, J.}
}
@article {280,
	title = {Computerized Adaptive Testing: Overview and Introduction},
	journal = {Applied Psychological Measurement},
	volume = {23},
	number = {3},
	year = {1999},
	pages = {187-94},
	abstract = {Use of computerized adaptive testing (CAT) has increased substantially since it was first formulated in the 1970s. This paper provides an overview of CAT and introduces the contributions to this Special Issue. The elements of CAT discussed here include item selection procedures, estimation of the latent trait, item exposure, measurement precision, and item bank development. Some topics for future research are also presented. },
	keywords = {computerized adaptive testing},
	author = {Meijer, R. R. and Nering, M. L.}
}
