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Yiran Chen – Research in Higher Education, 2025
The "k"-means clustering method, while widely embraced in college student typology research, is often misunderstood and misapplied. Many researchers regard "k"-means as a near-universal solution for uncovering homogeneous student groups, believing its success hinges primarily on the selection of an appropriate "k."…
Descriptors: College Students, Classification, Educational Research, Research Methodology
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McQuitty, Louis L.; Koch, Valerie L. – Educational and Psychological Measurement, 1975
Develops and illustrates a method for clustering hierarchically the interrelationships between many persons, as represented in a matrix of a thousand by a thousand. (RC)
Descriptors: Classification, Cluster Grouping, Matrices, Measurement Techniques
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Mcquitty, Louis L.; Frary, Jewel M. – Educational and Psychological Measurement, 1971
Discussion of a method of classification which attempts to use the particular set of indices of association which produce the most reliable and valid solution. (PR)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Criteria
Hartman, E. Alan; And Others – 1987
Any attempt to describe every job position and the activities contained in it would yield a confusing mass of information. Consequently, industrial psychologists have generated methods for classifying occupational positions into a smaller number of jobs or job families. Based on prior research it has been concluded that different kinds of job…
Descriptors: Classification, Cluster Grouping, Higher Education, Job Analysis
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Krippendorff, Klaus – 1977
Clustering techniques seek to group together objects or variables that share some observed qualities or, alternatively, to partition a set of objects or variables into mutually exclusive classes whose boundaries reflect differences in the observed qualities of their members. This paper reviews the general principles underlying clustering…
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Communication (Thought Transfer)
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Breckenridge, James N. – Multivariate Behavioral Research, 1989
A Monte Carlo study evaluated the effectiveness of three rules of classifying objects into clusters: nearest neighbor classification; nearest centroid assignment; and quadratic discriminant analysis. Results suggest that the nearest neighbor rule is a useful tool for assessing the validity of the clustering procedure of J. H. Ward (1963). (SLD)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Discriminant Analysis
Huberty, Carl J; Smith, Janet C. – 1982
Predictive discriminant analysis involves a technique used in multivariate classification, i.e., in predicting membership in well-defined groups for units on which multiple measures are available. The validation (assessment) of group membership predictions pertains to two problems: estimating true proportions of correct classifications (i.e., hit…
Descriptors: Classification, Cluster Grouping, Discriminant Analysis, Estimation (Mathematics)
Pearlman, Kenneth – 1978
This report reviews the personnel literature on the development of job families to illustrate and provide examples of varied approaches to the taxonomic issues of objective, content, and method in job family construction. This booklet first examines the definition of job families and then briefly discusses the potential utility of job families.…
Descriptors: Citations (References), Classification, Cluster Grouping, Group Structure