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Direct linkNandakumar, Ratna; Yu, Feng; Zhang, Yanwei – Applied Psychological Measurement, 2011
DETECT is a nonparametric methodology to identify the dimensional structure underlying test data. The associated DETECT index, "D[subscript max]," denotes the degree of multidimensionality in data. Conditional covariances (CCOV) are the building blocks of this index. In specifying population CCOVs, the latent test composite [theta][subscript TT]…
Descriptors: Nonparametric Statistics, Statistical Analysis, Tests, Data
Download full textZhang, Yanwei Oliver; Yu, Feng; Nandakumar, Ratna – 2003
DETECT is a nonparametric, conditional covariance-based procedure to identify dimensional structure and the degree of multidimensionality of test data. The ability composite or conditional score used to estimate conditional covariance plays a significant role in the performance of DETECT. The number correct score of all items in the test (T) and…
Descriptors: Estimation (Mathematics), Nonparametric Statistics, Scores, Simulation
Peer reviewedNandakumar, Ratna; Yu, Feng – Journal of Educational Measurement, 1996
DIMTEST is a nonparametric statistical test procedure for assessing unidimensionality of binary item response data that uses the T-statistic of W. F. Stout (1987). This study investigates the performance of the T-statistic with respect to different shapes of ability distributions and confirms its nonparametric nature. (SLD)
Descriptors: Ability, Nonparametric Statistics, Statistical Distributions, Validity
Roussos, Louis; Nandakumar, Ratna; Cwikla, Julie – 2000
CATSIB is a differential item functioning (DIF) assessment methodology for computerized adaptive test (CAT) data. Kernel smoothing (KS) is a technique for nonparametric estimation of item response functions. In this study an attempt has been made to develop a more efficient DIF procedure for CAT data, KS-CATSIB, by combining CATSIB with kernel…
Descriptors: Adaptive Testing, Computer Assisted Testing, Item Bias, Item Response Theory
Peer reviewedNandakumar, Ratna; Stout, William – Journal of Educational Statistics, 1993
A detailed investigation is provided of Stout's statistical procedure (the computer program DIMTEST) for testing the hypothesis that an essentially unidimensional latent trait model fits observed binary item response data from a psychological test. Three refinements achieve greater power. The revised approach is validated using real data sets.…
Descriptors: Computer Simulation, Equations (Mathematics), Hypothesis Testing, Item Response Theory
Nandakumar, Ratna; Yu, Feng – 1994
DIMTEST is a statistical test procedure for assessing essential unidimensionality of binary test item responses. The test statistic T used for testing the null hypothesis of essential unidimensionality is a nonparametric statistic. That is, there is no particular parametric distribution assumed for the underlying ability distribution or for the…
Descriptors: Ability, Content Validity, Correlation, Nonparametric Statistics
