Multivariate Cluster Analysis
Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as cri...
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| description | Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and discussed; a method for optimizing these functions is also described. The set of procedures is illustrated through application to data from two samples of students, each student with scores on either ten or eleven subtests of a criterion referenced mathematics inventory. The results indicate that the procedures discussed provide a promising means for grouping students to minimize classroom heterogeneity. (Author) |
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The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and discussed; a method for optimizing these functions is also described. The set of procedures is illustrated through application to data from two samples of students, each student with scores on either ten or eleven subtests of a criterion referenced mathematics inventory. The results indicate that the procedures discussed provide a promising means for grouping students to minimize classroom heterogeneity. (Author)</description><language>eng</language><subject>Ability Grouping ; Algorithms ; Cluster Grouping ; Criterion Referenced Tests ; Data Analysis ; Educational Research ; Elementary School Students ; Grades (Scholastic) ; Grouping (Instructional Purposes) ; Homogeneous Grouping ; Mathematics ; Statistical Analysis ; Teacher Effectiveness</subject><creationdate>1971</creationdate><tpages>22</tpages><format>22</format><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,691,781,886</link.rule.ids><linktorsrc>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=ED050143$$EView_record_in_ERIC_Clearinghouse_on_Information_&_Technology$$FView_record_in_$$GERIC_Clearinghouse_on_Information_&_Technology$$Hfree_for_read</linktorsrc><backlink>$$Uhttp://eric.ed.gov/ERICWebPortal/detail?accno=ED050143$$DView record in ERIC$$Hfree_for_read</backlink></links><search><creatorcontrib>McRae, Douglas J</creatorcontrib><creatorcontrib>McGraw-Hill Book Co., Monterey, CA</creatorcontrib><title>Multivariate Cluster Analysis</title><description>Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and discussed; a method for optimizing these functions is also described. The set of procedures is illustrated through application to data from two samples of students, each student with scores on either ten or eleven subtests of a criterion referenced mathematics inventory. The results indicate that the procedures discussed provide a promising means for grouping students to minimize classroom heterogeneity. 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| subjects | Ability Grouping Algorithms Cluster Grouping Criterion Referenced Tests Data Analysis Educational Research Elementary School Students Grades (Scholastic) Grouping (Instructional Purposes) Homogeneous Grouping Mathematics Statistical Analysis Teacher Effectiveness |
| title | Multivariate Cluster Analysis |
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