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Thesis
PRINCIPAL COMPONENTS ANALYSIS AND QEEG DATA TRANSFORMATION
University of West Florida
Master of Arts (MA), University of West Florida
2011
Abstract
Principal component analysis (PCA) is a multivariate, statistical technique that can be applied to qEEG data to reduce the number of dependent measures into a smaller set of component variables. The qEEG distributions are often positively skewed and violate assumptions of normality commonly associated with parametric statistics. Accordingly, prior to statistical analyses, normalizing data transformations are often applied to qEEG variables. Unfortunately, researchers who choose to transform qEEG data do so with limited knowledge of the effects the transformations may have on PCA solutions. The current study investigated the effects of several qEEG data transformations on the accuracy of PCA solutions.
Details
- Title
- PRINCIPAL COMPONENTS ANALYSIS AND QEEG DATA TRANSFORMATION
- Resource Type
- Thesis
- Publisher
- University of West Florida
- Format
- text
- Identifiers
- 99380090833506600
- Academic Unit
- Psychology
- Language
- English
- Awarding Institution
- University of West Florida; Master of Arts (MA)
- Theses and Dissertations
- Master of Arts (MA), University of West Florida