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Cox, Kyle; Kelcey, Benjamin – American Journal of Evaluation, 2023
Analysis of the differential treatment effects across targeted subgroups and contexts is a critical objective in many evaluations because it delineates for whom and under what conditions particular programs, therapies or treatments are effective. Unfortunately, it is unclear how to plan efficient and effective evaluations that include these…
Descriptors: Statistical Analysis, Research Design, Cluster Grouping, Sample Size
Gasco, Javier; Villarroel, Jose Domingo; Zuazagoitia, Dani – International Education Studies, 2014
The teaching and learning of mathematics cannot be understood without considering the resolution of word problems. These kinds of problems not only connect mathematical concepts with language (and therefore with reality) but also promote the learning related to other scientific areas. In primary school, problems are solved by using basic…
Descriptors: Word Problems (Mathematics), Problem Solving, Secondary School Mathematics, Mathematical Formulas
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
Hedges, Larry V. – Journal of Educational and Behavioral Statistics, 2007
A common mistake in analysis of cluster randomized trials is to ignore the effect of clustering and analyze the data as if each treatment group were a simple random sample. This typically leads to an overstatement of the precision of results and anticonservative conclusions about precision and statistical significance of treatment effects. This…
Descriptors: Statistical Significance, Computation, Cluster Grouping, Statistics
Dunn-Rankin, Peter; And Others – 1981
Measuring object similarity using the method of free clustering is gaining in popularity. Instructions are usually simple and since no structure is imposed on the subject's selection, response bias is reduced. More importantly, measures of object similarity derived from the judges' clustering can be adequately analyzed by the methods of…
Descriptors: Cluster Analysis, Cluster Grouping, Computer Oriented Programs, Mathematical Formulas

Thom, James A.; Zobel, Justin – Journal of the American Society for Information Science, 1992
Discusses models for the distribution of words in text and proposes a new model based on clustering that can be used to estimate the probability that a document contains a particular word as well as the number of distinct words in a document. Zipf's law and the Poisson approximation are also discussed. (18 references) (LRW)
Descriptors: Cluster Grouping, Mathematical Formulas, Models, Probability

van Rijsbergen, C. J.; And Others – Information Processing and Management, 1981
Describes the use of relevance feedback to select additional search terms and discusses the extraction of these terms from a maximum spanning tree connecting all terms in the index term vocabulary; retrieval effectiveness for different spanning trees is shown to be similar. Eight references are included. (Author/BK)
Descriptors: Cluster Grouping, Feedback, Information Retrieval, Mathematical Formulas

Radecki, Tadeusz – Journal of the American Society for Information Science, 1982
Proposes a means for determining the similarity between search request formulations in online information retrieval systems, and discusses the use of similarity measures for clustering search formulations and document files in such systems. Experimental results using the proposed methods are presented in three tables. A reference list is provided.…
Descriptors: Cluster Grouping, Information Retrieval, Mathematical Formulas, Methods

Johnson, Andrew; Fotouhi, Farshad – Information Systems, 1996
Discussion of hypermedia systems focuses on a comparison of two types of adaptive algorithm (genetic algorithm and neural network) in clustering hypermedia documents. These clusters allow the user to index into the nodes to find needed information more quickly, since clustering is "personalized" based on the user's paths rather than…
Descriptors: Algorithms, Cluster Grouping, Comparative Analysis, Electronic Text

Ottaviani, J. S. – Journal of the American Society for Information Science, 1994
Discusses precision and recall in information science and proposes a new model based on fractal geometry for clusters of relevant documents. Search strategies for retrieving a group of relevant documents are reviewed; fractal sets and chaotic processes are described; and the new model is explained. (Contains 43 references.) (LRW)
Descriptors: Chaos Theory, Cluster Grouping, Fractals, Information Retrieval
Ross, Arun; Owen, Charles B.; Vailaya, Aditya – 2000
This paper focuses on clustering a World Wide Web site (i.e., the 1998 World Cup Soccer site) into groups of documents that are predictive of future user accesses. Two approaches were developed and tested. The first approach uses semantic information inherent in the documents to facilitate the clustering process. User access history is then used…
Descriptors: Access to Information, Cluster Analysis, Cluster Grouping, Information Retrieval

Can, Fazli – Information Processing and Management, 1994
Discussion of relevancy in information retrieval systems focuses on an analysis of the efficiency of various cluster-based retrieval (CBR) strategies. A method for combining CBR and inverted index search is proposed that is cost effective in terms of time efficiency; and results of experiments are reported. (Contains 32 references.) (LRW)
Descriptors: Algorithms, Cluster Grouping, Comparative Analysis, Cost Effectiveness

Radecki, Tadeusz – Information Processing and Management, 1985
Reports research results into a methodology for determining similarity between queries characterized by Boolean search request formulations and discusses similarity measures for Boolean combinations of index terms. Rationale behind these measures is outlined, and conditions ensuring their equivalence are identified. Results of an experiment…
Descriptors: Cluster Grouping, Correlation, Indexing, Information Retrieval

Larson, Ray R. – Journal of the American Society for Information Science, 1992
Presents the results of research into the automatic selection of Library of Congress Classification numbers based on the titles and subject headings in MARC records from a test database at the University of California at Berkeley Library School library. Classification clustering and matching techniques are described. (44 references) (LRW)
Descriptors: Academic Libraries, Bibliographic Databases, Bibliographic Records, Classification