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Park, Sunyoung; Natasha Beretvas, S. – Journal of Experimental Education, 2021
When selecting a multilevel model to fit to a dataset, it is important to choose both a model that best matches characteristics of the data's structure, but also to include the appropriate fixed and random effects parameters. For example, when researchers analyze clustered data (e.g., students nested within schools), the multilevel model can be…
Descriptors: Hierarchical Linear Modeling, Statistical Significance, Multivariate Analysis, Monte Carlo Methods
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Steiner, Peter M.; Kim, Jee-Seon – Society for Research on Educational Effectiveness, 2015
Despite the popularity of propensity score (PS) techniques they are not yet well studied for matching multilevel data where selection into treatment takes place among level-one units within clusters. This paper suggests a PS matching strategy that tries to avoid the disadvantages of within- and across-cluster matching. The idea is to first…
Descriptors: Computation, Outcomes of Treatment, Multivariate Analysis, Probability
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Samuelsen, Karen – Measurement: Interdisciplinary Research and Perspectives, 2012
The notion that there is often no clear distinction between factorial and typological models (von Davier, Naemi, & Roberts, this issue) is sound. As von Davier et al. state, theory often indicates a preference between these models; however the statistical criteria by which these are delineated offer much less clarity. In many ways the procedure…
Descriptors: Models, Statistical Analysis, Classification, Factor Structure
D'Allegro, Mary Lou; Zhou, Kai – Association for Institutional Research, 2013
Peer selection based on the similarity of a couple of institutional parameters, by itself, is insufficient. Several other considerations, including clarity of purpose, alignment of institutional information to that purpose, identification of appropriate statistical procedures, review of preliminary peer sets, and the application of additional…
Descriptors: Private Colleges, Case Studies, Mixed Methods Research, Institutional Characteristics
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Hwang, Heungsun; Dillon, William R. – Multivariate Behavioral Research, 2010
A 2-way clustering approach to multiple correspondence analysis is proposed to account for cluster-level heterogeneity of both respondents and variable categories in multivariate categorical data. Specifically, in the proposed method, multiple correspondence analysis is combined with k-means in a unified framework in which "k"-means is…
Descriptors: Data Analysis, Multivariate Analysis, Classification, Monte Carlo Methods
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Bahr, Peter Riley; Bielby, Rob; House, Emily – New Directions for Institutional Research, 2011
One useful and increasingly popular method of classifying students is known commonly as cluster analysis. The variety of techniques that comprise the cluster analytic family are intended to sort observations (for example, students) within a data set into subsets (clusters) that share similar characteristics and differ in meaningful ways from other…
Descriptors: College Students, Classification, Multivariate Analysis, Community Colleges
Chen, Yu-Fen; Hsiao, Chin-Hui – New Horizons in Education, 2009
Background: Because of the educational reform and decreasing birth rate in Taiwan over the past 20 years, higher technological and vocational Education (TVE) in Taiwan faces a severe student recruitment competition. Dailey (2007) indicates the need to develop marketing strategies in higher education is evident. TVE institutes are beginning to…
Descriptors: Foreign Countries, Student Recruitment, Vocational Education, Competition