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Nimon, Kim; Henson, Robin K.; Gates, Michael S. – Multivariate Behavioral Research, 2010
In the face of multicollinearity, researchers face challenges interpreting canonical correlation analysis (CCA) results. Although standardized function and structure coefficients provide insight into the canonical variates produced, they fall short when researchers want to fully report canonical effects. This article revisits the interpretation of…
Descriptors: Multivariate Analysis, Data Analysis, Data Interpretation, Computer Software
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Voelkle, Manuel C. – Multivariate Behavioral Research, 2008
The simultaneous estimation of autoregressive (simplex) structures and latent trajectories, so called ALT (autoregressive latent trajectory) models, is becoming an increasingly popular approach to the analysis of change. Although historically autoregressive (AR) and latent growth curve (LGC) models have been developed quite independently from each…
Descriptors: Simulation, Computation, Pattern Recognition, Skill Development
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Ruscio, John; Ruscio, Ayelet Meron; Meron, Mati – Multivariate Behavioral Research, 2007
Meehl's taxometric method was developed to distinguish categorical and continuous constructs. However, taxometric output can be difficult to interpret because expected results for realistic data conditions and differing procedural implementations have not been derived analytically or studied through rigorous simulations. By applying bootstrap…
Descriptors: Sampling, Equated Scores, Data Interpretation, Inferences
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Pavur, Robert; Nath, Ravinder – Multivariate Behavioral Research, 1984
Several general correlation patterns are shown which give exact F tests in an analysis of variance (ANOVA) procedure. They are the most general correlation patterns one can assume in a one-way and two-way layout and still have the F tests be valid. (Author/BW)
Descriptors: Analysis of Covariance, Analysis of Variance, Correlation, Data Interpretation
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Williams, John Delane – Multivariate Behavioral Research, 1991
A proposed solution for the age x cohort x period issue in lifespan research uses all data, even with missing cells; can be used for repeated measures designs or designs in which new subjects are measured at each period; and allows assessment of each main effect and two-way interaction. (SLD)
Descriptors: Age, Analysis of Variance, Cohort Analysis, Data Interpretation