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French, Brian F.; Finch, W. Holmes – Educational and Psychological Measurement, 2013
Multilevel data structures are ubiquitous in the assessment of differential item functioning (DIF), particularly in large-scale testing programs. There are a handful of DIF procures for researchers to select from that appropriately account for multilevel data structures. However, little, if any, work has been completed to extend a popular DIF…
Descriptors: Test Bias, Statistical Analysis, Comparative Analysis, Correlation
Ellis, Robert A. – Active Learning in Higher Education, 2016
There is variation in the university student experience of learning. Prior research has shown that factors that shape this include student characteristics, the learning context, student perceptions of that context and approaches to learning and their learning outcomes. In blended contexts, there is a need to identify variables which can explain…
Descriptors: Student Experience, Educational Environment, Inquiry, Higher Education
Zimmer-Gembeck, Melanie J.; Skinner, Ellen A.; Morris, Helen; Thomas, Rae – Journal of Early Adolescence, 2013
The same stressor can evoke different emotions across individuals, and emotions can prompt certain coping responses. Responding to four videotaped interpersonal stressors, adolescents ("N" = 230, the average values of "X"[subscript age] = 10 years) reported their sadness, fear "and" anger, and 12 coping strategies.…
Descriptors: Stress Variables, Coping, Adolescents, Psychological Patterns
Crossley, Scott A.; Roscoe, Rod; McNamara, Danielle S. – Written Communication, 2014
This study identifies multiple profiles of successful essays via a cluster analysis approach using linguistic features reported by a variety of natural language processing tools. The findings from the study indicate that there are four profiles of successful writers for the samples analyzed. These four profiles are linguistically distinct from one…
Descriptors: Essays, Natural Language Processing, Computational Linguistics, Multivariate Analysis
Smith, Russell K. – Research in Higher Education Journal, 2014
A segmentation study is used to partition college students into groups that are more or less likely to adopt tablet technology as a learning tool. Because the college population chosen for study presently relies upon laptop computers as their primary learning device, tablet technology represents a "next step" in technology. Student…
Descriptors: College Students, Cluster Grouping, Student Attitudes, Laptop Computers
Nelson, Larry J.; Padilla-Walker, Laura M.; Christensen, Katherine J.; Evans, Cortney A.; Carroll, Jason S. – Journal of Youth and Adolescence, 2011
The changing nature of the transition to adulthood in western societies, such as the United States, may be extending the length of time parents are engaged in "parenting" activities. However, little is known about different approaches parents take in their interactions with their emerging-adult children. Hence, this study attempted to…
Descriptors: Mothers, Child Rearing, Parent Child Relationship, Multivariate Analysis
Mallett, Shelley; Rosenthal, Doreen; Myers, Paul; Milburn, Norweeta; Rotheram-Borus, Mary Jane – Journal of Adolescence, 2004
In a study exploring the relationship between typology and risk, we investigated the daily routines of a heterogeneous sample of young men and women from two sites who had been homeless for varying periods (N=1289). Cluster analysis yielded four groups--"Partnered", "Socially engaged", "Service connected-harm avoidant", and "Transgressive"--based…
Descriptors: Young Adults, Homeless People, Risk, Multivariate Analysis

Pickett, Lawrence K., Jr. – Criminal Justice and Behavior, 1981
The MMPI results obtained from 245 adolescent males referred to the evaluation unit of a Juvenile Court were submitted to a multivariate classification system. By correlating individual subject profiles with the modal profiles, six membership groups were formed. No relationship was found between group membership and age or race. (Author)
Descriptors: Adolescents, Age, Classification, Cluster Grouping
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
Sirotnik, Kenneth A. – 1979
This report contains accounts of studies, about scales to be used in the "A Study of Schooling" research project, undertaken to derive indices for constructs presumed to be measureable by composites of items. The report is introduced by a discussion on the rationale for selecting the research methodology used and an explanation of the…
Descriptors: Behavioral Science Research, Cluster Analysis, Cluster Grouping, Correlation