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Steiner, Peter M.; Kim, Jee-Seon – Society for Research on Educational Effectiveness, 2015
Despite the popularity of propensity score (PS) techniques they are not yet well studied for matching multilevel data where selection into treatment takes place among level-one units within clusters. This paper suggests a PS matching strategy that tries to avoid the disadvantages of within- and across-cluster matching. The idea is to first…
Descriptors: Computation, Outcomes of Treatment, Multivariate Analysis, Probability
Shimada, Atsushi; Mouri, Kousuke; Taniguchi, Yuta; Ogata, Hiroaki; Taniguchi, Rin-ichiro; Konomi, Shin'ichi – International Educational Data Mining Society, 2019
In this paper, we focus on optimizing the assignment of students to courses. The target courses are conducted by different teachers using the same syllabus, course design, and lecture materials. More than 1,300 students are mechanically assigned to one of ten courses taught by different teachers. Therefore, mismatches often occur between students'…
Descriptors: Student Placement, Learning Activities, Learning Analytics, Cognitive Style
Weiss, Michael J.; Lockwood, J. R.; McCaffrey, Daniel F. – Journal of Research on Educational Effectiveness, 2016
In the "individually randomized group treatment" (IRGT) experimental design, individuals are first randomly assigned to a treatment arm or a control arm, but then within each arm, are grouped together (e.g., within classrooms/schools, through shared case managers, in group therapy sessions, through shared doctors, etc.) to receive…
Descriptors: Randomized Controlled Trials, Error of Measurement, Control Groups, Experimental Groups
Dong, Nianbo; Lipsey, Mark – Society for Research on Educational Effectiveness, 2010
This study uses simulation techniques to examine the statistical power of the group- randomized design and the matched-pair (MP) randomized block design under various parameter combinations. Both nearest neighbor matching and random matching are used for the MP design. The power of each design for any parameter combination was calculated from…
Descriptors: Simulation, Statistical Analysis, Cluster Grouping, Mathematical Models
Dorans, Neil J. – 1985
Two reduced rank classification procedures, principal components classification and equal weights classification, are described and compared by a simulation study to the standard classification procedure to determine their feasibilities as alternative classification procedures. A justification for the development of these two reduced rank…
Descriptors: Classification, Cluster Grouping, Research Methodology, Simulation

So, Tak-Shing Harry; Peng, Chao-Ying Joanne – 2002
This study compared the accuracy of predicting two-group membership obtained from K-means clustering with those derived from linear probability modeling, linear discriminant function, and logistic regression under various data properties. Multivariate normally distributed populations were simulated based on combinations of population proportions,…
Descriptors: Cluster Grouping, Group Membership, Prediction, Probability
Kim, Jee-Seon – Journal of Educational Measurement, 2006
Simulation and real data studies are used to investigate the value of modeling multiple-choice distractors on item response theory linking. Using the characteristic curve linking procedure for Bock's (1972) nominal response model presented by Kim and Hanson (2002), all-category linking (i.e., a linking based on all category characteristic curves…
Descriptors: Multiple Choice Tests, Test Items, Item Response Theory, Simulation

Gordon, Michael D. – Journal of the American Society for Information Science, 1991
Discussion of clustering of documents and queries in information retrieval systems focuses on the use of a genetic algorithm to adapt subject descriptions so that documents become more effective in matching relevant queries. Various types of clustering are explained, and simulation experiments used to test the genetic algorithm are described. (27…
Descriptors: Algorithms, Cluster Grouping, Documentation, Information Retrieval
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