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Exploratory data structure comparisons: three new visual tools based on principal component analysis*
- Publication Year :
- 2020
- Publisher :
- Taylor & Francis, 2020.
-
Abstract
- Datasets are sometimes divided into distinct subsets, e.g. due to multi-center sampling, or to variations in instruments, questionnaire item ordering or mode of administration, and the data analyst then needs to assess whether a joint analysis is meaningful. The Principal Component Analysis-based Data Structure Comparisons (PCADSC) tools are three new non-parametric, visual diagnostic tools for investigating differences in structure for two subsets of a dataset through covariance matrix comparisons by use of principal component analysis. The PCADCS tools are demonstrated in a data example using European Social Survey data on psychological well-being in three countries, Denmark, Sweden, and Bulgaria. The data structures are found to be different in Denmark and Bulgaria, and thus a comparison of for example mean psychological well-being scores is not meaningful. However, when comparing Denmark and Sweden, very similar data structures, and thus comparable concepts of well-being, are found. Therefore, inter-country comparisons are warranted for these countries.
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....89b2b42a6256c42b86d27b3c5665a3cf
- Full Text :
- https://doi.org/10.6084/m9.figshare.12382094.v1