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Wiedmann, Michael; Leach, Ryan C.; Rummel, Nikol; Wiley, Jennifer – Instructional Science: An International Journal of the Learning Sciences, 2012
Schwartz and Martin ("Cogn Instr" 22:129-184, 2004) as well as Kapur ("Instr Sci", this issue, 2012) have found that students can be better prepared to learn about mathematical formulas when they try to invent them in small groups before receiving the canonical formula from a lesson. The purpose of the present research was to investigate how the…
Descriptors: Mathematical Formulas, Intellectual Property, Learning, Multivariate Analysis
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Lutz, J. Gary; Cundari, Leigh A. – Journal of Educational Statistics, 1989
Means of identifying sources of rejection of hypotheses regarding linear multivariate statistical models are discussed. Problems with the use of a global test using Roy's largest root criterion and means of solving them are presented, along with a practical application of the techniques. (TJH)
Descriptors: Hypothesis Testing, Mathematical Formulas, Mathematical Models, Multivariate Analysis
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Haase, Richard F. – Multivariate Behavioral Research, 1991
Computational formulas are developed for recovering measures of strength of association from approximate "F" tests and chi-square tests associated with four multivariate test statistics. The four statistics include Wilke's Lambda; Pillai's Trace "V"; Hotelling's Trace "T"; and Roy's greatest characteristic root…
Descriptors: Chi Square, Estimation (Mathematics), Mathematical Formulas, Mathematical Models
Carlson, James E.; Timm, Neil H. – 1980
This paper presents two extensions of the full-rank multivariate linear model that are particularly useful in multivariate analysis of covariance (MANCOVA) and repeated measurements designs. After a review of the basic full-rank model, an extension is described which allows restrictions of a more general nature. This model is useful in the…
Descriptors: Analysis of Covariance, Data Analysis, Hypothesis Testing, Mathematical Formulas