Exploratory data structure comparisons : three new visual tools based on principal component analysis
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
| Veröffentlicht in: | Journal of applied statistics. - 1991. - 48(2021), 9 vom: 01., Seite 1675-1695 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | Journal of applied statistics |
| Schlagworte: | Journal Article 62H25 62P15 Principal component analysis covariance matrix data structure exploratory data analysis |
| Zusammenfassung: | © 2020 Informa UK Limited, trading as Taylor & Francis Group. 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 |
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| Beschreibung: | Date Revised 26.08.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0266-4763 |
| DOI: | 10.1080/02664763.2020.1773772 |