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Lee, Minji K.; Sweeney, Kevin; Melican, Gerald J. – Educational Assessment, 2017
This study investigates the relationships among factor correlations, inter-item correlations, and the reliability estimates of subscores, providing a guideline with respect to psychometric properties of useful subscores. In addition, it compares subscore estimation methods with respect to reliability and distinctness. The subscore estimation…
Descriptors: Scores, Test Construction, Test Reliability, Test Validity
Socha, Alan; DeMars, Christine E. – Educational and Psychological Measurement, 2013
Modeling multidimensional test data with a unidimensional model can result in serious statistical errors, such as bias in item parameter estimates. Many methods exist for assessing the dimensionality of a test. The current study focused on DIMTEST. Using simulated data, the effects of sample size splitting for use with the ATFIND procedure for…
Descriptors: Sample Size, Test Length, Correlation, Test Format
Martin, Andrew J.; Yu, Kai; Papworth, Brad; Ginns, Paul; Collie, Rebecca J. – Journal of Psychoeducational Assessment, 2015
This study explored motivation and engagement among North American (the United States and Canada; n = 1,540), U.K. (n = 1,558), Australian (n = 2,283), and Chinese (n = 3,753) secondary school students. Motivation and engagement were assessed via students' responses to the Motivation and Engagement Scale-High School (MES-HS). Confirmatory factor…
Descriptors: Foreign Countries, Motivation, Learner Engagement, Secondary School Students
Kwon, Kyongboon; Lease, A. Michele – International Journal of Behavioral Development, 2009
In a sample of 473 4th- and 5th-grade students, we examined the interaction between the type of clique to which a child belongs and the clique's level of perceived cohesion on a clique member's social and emotional adjustment. Perceived cohesion of a clique was operationalized as the degree to which the clique was perceived as an entity and was…
Descriptors: Emotional Adjustment, Multidimensional Scaling, Grade 4, Grade 5
Gierl, Mark J.; Leighton, Jacqueline P.; Tan, Xuan – Journal of Educational Measurement, 2006
DETECT, the acronym for Dimensionality Evaluation To Enumerate Contributing Traits, is an innovative and relatively new nonparametric dimensionality assessment procedure used to identify mutually exclusive, dimensionally homogeneous clusters of items using a genetic algorithm ( Zhang & Stout, 1999). Because the clusters of items are mutually…
Descriptors: Program Evaluation, Cluster Grouping, Evaluation Methods, Multivariate Analysis