Exploratory data structure comparisons: three new visual tools based on principal component analysis*
Research output: Contribution to journal › Journal article › Research › peer-review
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.
Original language | English |
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Journal | Journal of Applied Statistics |
Volume | 48 |
Issue number | 9 |
Pages (from-to) | 1675-1695 |
Number of pages | 21 |
ISSN | 0266-4763 |
DOIs | |
Publication status | Published - 2021 |
- covariance matrix, data structure, exploratory data analysis, Principal component analysis
Research areas
Links
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042046/pdf/CJAS_48_1773772.pdf
Final published version
ID: 244320649