NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 6 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Hauser, Carl; Thum, Yeow Meng; He, Wei; Ma, Lingling – Educational and Psychological Measurement, 2015
When conducting item reviews, analysts evaluate an array of statistical and graphical information to assess the fit of a field test (FT) item to an item response theory model. The process can be tedious, particularly when the number of human reviews (HR) to be completed is large. Furthermore, such a process leads to decisions that are susceptible…
Descriptors: Test Items, Item Response Theory, Research Methodology, Decision Making
Peer reviewed Peer reviewed
Direct linkDirect link
Li, Ying; Rupp, Andre A. – Educational and Psychological Measurement, 2011
This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…
Descriptors: Test Length, Item Response Theory, Statistical Analysis, Error Patterns
Peer reviewed Peer reviewed
Direct linkDirect link
Holden, Jocelyn E.; Kelley, Ken – Educational and Psychological Measurement, 2010
Classification procedures are common and useful in behavioral, educational, social, and managerial research. Supervised classification techniques such as discriminant function analysis assume training data are perfectly classified when estimating parameters or classifying. In contrast, unsupervised classification techniques such as finite mixture…
Descriptors: Discriminant Analysis, Classification, Computation, Behavioral Science Research
Peer reviewed Peer reviewed
McQuitty, Louis L. – Educational and Psychological Measurement, 1983
Iterative Intercolumnar Correlation Classification (IICC) computes the correlation coefficients for the entries of every column of a matrix with those of every other column of the matrix. Iteration increases the size and validity of the object indices, reduces error in the indices, and increases homogeneity amongst them. (Author/BW)
Descriptors: Classification, Cluster Analysis, Correlation, Error Patterns
Peer reviewed Peer reviewed
Carter, Walter H., Jr. – Educational and Psychological Measurement, 1971
Descriptors: Classification, Error Patterns, Grading, Guessing (Tests)
Peer reviewed Peer reviewed
Boruch, Robert F. – Educational and Psychological Measurement, 1972
A listing of the program is available from the author at Northwestern University. (MB)
Descriptors: Classification, Computer Programs, Data Processing, Error Patterns