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|a 10.1080/02664763.2020.1773772
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|a eng
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1 |
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|a Petersen, Anne Helby
|e verfasserin
|4 aut
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|a Exploratory data structure comparisons
|b three new visual tools based on principal component analysis
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|c 2021
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|a Date Revised 26.08.2024
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Informa UK Limited, trading as Taylor & Francis Group.
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|a 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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|a Principal component analysis
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|a covariance matrix
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|a data structure
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|a exploratory data analysis
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|a Markussen, Bo
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|a Christensen, Karl Bang
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