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Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan – Cogent Education, 2016
Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…
Descriptors: Discriminant Analysis, Factor Analysis, Student Evaluation of Teacher Performance, Instructional Effectiveness
Warner, Laura A.; Chaudhary, Anil Kumar; Rumble, Joy N.; Lamm, Alexa J.; Momol, Esen – Journal of Agricultural Education, 2017
Today's complex issues require technical expertise as well as the application of innovative social science techniques within Extension contexts. Researchers have suggested that a social science approach will play a critical role in water conservation, and people who use home landscape irrigation comprise a critical target audience for agriculture…
Descriptors: Conservation (Environment), Water, Natural Resources, Use Studies
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
Hurley, Rodney G. – Journal of Applied Research in the Community College, 2009
This study used the five benchmarks from the Community College Survey of Student Engagement (CCSSE) to form clusters of colleges within the CCSSE classification of extra-large community colleges (greater than or equal to 15,000 students). Cluster analysis produced a five-cluster solution for the 48 identified extra-large community colleges. Using…
Descriptors: Community Colleges, School Size, Cluster Grouping, Multivariate Analysis

Koslowsky, Meni – Educational and Psychological Measurement, 1979
Recent trends in the analysis of categorical or nominal variables were discussed for univariate, multivariate, and psychometric problems. It was shown that several statistical procedures commonly used with these problems have analogues which can be applied to assessing categorical variables. (Author/CTM)
Descriptors: Classification, Cluster Grouping, Correlation, Discriminant Analysis

Rogers, Gil; Linden, James D. – Educational and Psychological Measurement, 1973
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Discriminant Analysis

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

Ulrich, Dave; McKelvey, Bill – Organization Science, 1990
Tests for and identifies populations within a family of electronics industries. Data include 669 United States and 144 Japanese electronics firms. Demonstrates the relevance of a general organizational classification for explaining how different natural selection processes affect different populations. (75 references) (MLF)
Descriptors: Classification, Cluster Grouping, Demography, Discriminant Analysis
Werch, Chudley; Jobli, Edessa C.; Moore, Michele J.; DiClemente, Carlo C.; Heather, Dore S.; Brown, C. Hendricks – Journal of Child and Adolescent Substance Abuse, 2006
The overall purpose of this study was to explore the alcohol consumption patterns of adolescents by beverage type. A total of 705 primarily 9th grade students were recruited to participate in this study in the spring of 2002. Alcoholic beverage use differed significantly across gender and ethnicity on a number of beverage-specific drinking…
Descriptors: Drinking, Behavior Patterns, Consumer Economics, Adolescent Attitudes
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)
Farrell, William T. – 1975
"Classification: Purposes, Principles, Progress, Prospects" by Robert R. Sokal is reprinted in this document. It summarizes the principles of classification and cluster analysis in a manner which is of specific value to the Marine Corps Office of Manpower Utilization. Following the article is a 184 item bibliography on cluster analysis…
Descriptors: Bibliographies, Classification, Cluster Analysis, Cluster Grouping
Huberty, Carl J – 1982
The issues in the interpretation of discriminant analyses presented are restricted to the typical uses of discriminant analysis by behavioral science researchers. Because behavioral researchers use computer programs packages, the issues discussed deal with information obtainable from the package discriminant analysis programs. The following issues…
Descriptors: Behavioral Science Research, Classification, Cluster Grouping, Computer Programs

Terenzini, Patrick T.; And Others – Research in Higher Education, 1980
A methodology developed as an alternative to conventional institutional classification structures, intended to reduce the limitations of those models, is described. Ways in which the methodology can be used for planning, administrative, and research purposes are discussed, as are the dangers in using "peer groups" for institutional…
Descriptors: Classification, Cluster Analysis, Cluster Grouping, College Planning

Elkins, John – Australian Journal of Education, 1978
Numerical classification techniques were used to explore the conjecture that inconsistent results of many studies of disabled readers could result from samples being composed of subgroups of children with different characteristics. Some five subgroups were identified using ITPA scores from a subsample of 37 poor readers. (Author)
Descriptors: Classification, Cluster Grouping, Discriminant Analysis, Grade 1