title: A sampling and classification item selection approach with content balancing.
abstract: Existing automated test assembly methods typically employ constrained combinatorial optimization. Constructing forms sequentially based on an optimization approach usually results in unparallel forms and requires heuristic modifications. Methods based on a random search approach have the major advantage of producing parallel forms sequentially without further adjustment. This study incorporated a flexible content-balancing element into the statistical perspective item selection method of the cell-only method (Chen et al. in Educational and Psychological Measurement, 72(6), 933-953, 2012). The new method was compared with a sequential interitem distance weighted deviation model (IID WDM) (Swanson & Stocking in Applied Psychological Measurement, 17(2), 151-166, 1993), a simultaneous IID WDM, and a big-shadow-test mixed integer programming (BST MIP) method to construct multiple parallel forms based on matching a reference form item-by-item. The results showed that the cell-only method with content balancing and the sequential and simultaneous versions of IID WDM yielded results comparable to those obtained using the BST MIP method. The cell-only method with content balancing is computationally less intensive than the sequential and simultaneous versions of IID WDM.
authors.affiliation: Management Science, National Chiao Tung University, Hsinchu City, Taiwan, 30010, Republic of China, paulachen@g2.nctu.edu.tw.
authors.forename: Pei-Hua
authors.initials: PH
authors.lastname: Chen
mesh_terms: D004521:Educational Measurement / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D008962:Models, Theoretical; D018579:Patient Selection; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods*