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Irby, Stefan M.; Phu, Andy L.; Borda, Emily J.; Haskell, Todd R.; Steed, Nicole; Meyer, Zachary – Chemistry Education Research and Practice, 2016
There is much agreement among chemical education researchers that expertise in chemistry depends in part on the ability to coordinate understanding of phenomena on three levels: macroscopic (observable), sub-microscopic (atoms, molecules, and ions) and symbolic (chemical equations, graphs, etc.). We hypothesize this "level-coordination…
Descriptors: Chemistry, Formative Evaluation, Graduate Students, College Students
Page, Stewart; Cramer, Kenneth M.; Page, Laura – Interchange: A Quarterly Review of Education, 2010
We present a data-based perspective concerning recent (e.g., 2008) "Maclean's" magazine rankings of Canadian universities, including cluster analysis of the 2008 data. Canadian universities empirically resemble and relate to each other in a manner different from their formal classification and final rank ordering in the…
Descriptors: Higher Education, Universities, Multivariate Analysis, Foreign Countries

Fok, Lillian Y.; And Others – Journal of Education for Business, 1995
Discusses the nature, power, and limitations of four multivariate techniques: factor analysis, multiple analysis of variance, multiple regression, and multiple discriminant analysis. Shows how decision trees assist in interpreting results. (SK)
Descriptors: Business Administration Education, Data Interpretation, Discriminant Analysis, Factor Analysis
Jarrell, Michele E.; And Others – 1989
Procedures used in Doctor of Education (Ed.D.) dissertations at the University of Alabama (Tuscaloosa) were studied. Focus was on identifying: (1) characteristics of the research designs used; (2) sources of the instruments used to collect data; (3) reports of reliability estimates and evidence of validity of the instruments; and (4) types of…
Descriptors: Comparative Analysis, Data Interpretation, Doctoral Dissertations, Doctoral Programs

Beck, E. M.; Tolnay, Stewart E. – Historical Methods, 1995
Asserts that traditional approaches to multivariate analysis, including standard linear regression techniques, ignore the special character of count data. Explicates three suitable alternatives to standard regression techniques, a simple Poisson regression, a modified Poisson regression, and a negative binomial model. (MJP)
Descriptors: Data Interpretation, Evaluation Criteria, Higher Education, Multivariate Analysis