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Updated: May 8, 2026
08:04
Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
Published on: December 4, 2013
Measuring change
1a Department of Kinesiology , University of Waterloo.
Journal of Motor Behavior
|August 20, 2013
Summary
This study corroborates previous findings on multicollinearity issues when using analysis of effects (AE), contrasts (CE), and variance (VE) in ANOVA. It also discusses implications for analyzing changes in motor memory performance.
Area of Science:
- Motor learning and performance analysis
- Statistical modeling in behavioral science
Background:
- Multicollinearity poses challenges in statistical analyses, potentially affecting the interpretation of results.
- The application of ANOVA techniques, particularly with specific effect measures, requires careful consideration of variable interdependencies.
Purpose of the Study:
- To provide corroborating evidence for existing statements on multicollinearity in ANOVA.
- To extend the discussion of multicollinearity to the context of motor memory change analysis.
Main Methods:
- Review and corroboration of statistical principles related to ANOVA.
- Conceptual analysis of multicollinearity within the framework of motor memory research.
Main Results:
- Confirmation of multicollinearity issues associated with analysis of effects (AE), contrasts (CE), and variance (VE) in ANOVA.
- Identification of specific considerations for analyzing motor memory changes under multicollinear conditions.
Conclusions:
- The findings reinforce the importance of addressing multicollinearity when employing AE, CE, and VE in ANOVA.
- The study highlights the need for methodological awareness in the statistical analysis of motor memory development and adaptation.
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