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Comparison of NOHARM and DETECT in Item Cluster Recovery: Counting Dimensions and Allocating Items
Author(s) -
Finch Holmes,
Habing Brian
Publication year - 2005
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/j.1745-3984.2005.00008
Subject(s) - statistics , goodness of fit , curse of dimensionality , cluster analysis , item response theory , correlation , skewness , item analysis , computer science , multidimensional scaling , mathematics , artificial intelligence , psychology , psychometrics , geometry
This study examines the performance of a new method for assessing and characterizing dimensionality in test data using the NOHARM model, and comparing it with DETECT. Dimensionality assessment is carried out using two goodness‐of‐fit statistics that are compared to reference χ 2 distributions. A Monte Carlo study is used with item parameters based on a statewide basic skills assessment and the SAT. Other factors that are varied include the correlation among the latent traits, the number of items, the number of subjects, skewness of the latent traits, and the presence or absence of guessing. The performance of the two procedures is judged by the accuracy in determining the number of underlying dimensions, and the degree to which items are correctly clustered together. Results indicate that the new, NOHARM‐based method appears to perform comparably to DETECT in terms of simultaneously finding the correct number of dimensions and clustering items correctly. NOHARM is generally better able to determine the number of underlying dimensions, but less able to group items together, than DETECT. When errors in item cluster assignment are made, DETECT is more likely to incorrectly separate items while NOHARM more often incorrectly groups them together.