Publishing Nutrition Research: A Review of Multivariate Techniques-Part 3: Data Reduction Methods
This is the ninth in a series of monographs on research design and analysis, and the third in a set of these monographs devoted to multivariate methods. The purpose of this article is to provide an overview of data reduction methods, including principal components analysis, factor analysis, reduced...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier
2014
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| Online Access: | 51243 http://hdl.handle.net/20.500.11937/51292 |
| _version_ | 1848758661912985600 |
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| author | Gleason, P. Boushey, Carol Harris, J. Zoellner, J. |
| author_facet | Gleason, P. Boushey, Carol Harris, J. Zoellner, J. |
| author_sort | Gleason, P. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This is the ninth in a series of monographs on research design and analysis, and the third in a set of these monographs devoted to multivariate methods. The purpose of this article is to provide an overview of data reduction methods, including principal components analysis, factor analysis, reduced rank regression, and cluster analysis. In the field of nutrition, data reduction methods can be used for three general purposes: for descriptive analysis in which large sets of variables are efficiently summarized, to create variables to be used in subsequent analysis and hypothesis testing, and in questionnaire development. The article describes the situations in which these data reduction methods can be most useful, briefly describes how the underlying statistical analyses are performed, and summarizes how the results of these data reduction methods should be interpreted. |
| first_indexed | 2025-11-14T09:47:33Z |
| format | Journal Article |
| id | curtin-20.500.11937-51292 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:47:33Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-512922017-11-24T01:00:22Z Publishing Nutrition Research: A Review of Multivariate Techniques-Part 3: Data Reduction Methods Gleason, P. Boushey, Carol Harris, J. Zoellner, J. This is the ninth in a series of monographs on research design and analysis, and the third in a set of these monographs devoted to multivariate methods. The purpose of this article is to provide an overview of data reduction methods, including principal components analysis, factor analysis, reduced rank regression, and cluster analysis. In the field of nutrition, data reduction methods can be used for three general purposes: for descriptive analysis in which large sets of variables are efficiently summarized, to create variables to be used in subsequent analysis and hypothesis testing, and in questionnaire development. The article describes the situations in which these data reduction methods can be most useful, briefly describes how the underlying statistical analyses are performed, and summarizes how the results of these data reduction methods should be interpreted. 2014 Journal Article http://hdl.handle.net/20.500.11937/51292 10.1016/j.jand.2015.03.011 51243 49929 49876 Elsevier restricted |
| spellingShingle | Gleason, P. Boushey, Carol Harris, J. Zoellner, J. Publishing Nutrition Research: A Review of Multivariate Techniques-Part 3: Data Reduction Methods |
| title | Publishing Nutrition Research: A Review of Multivariate Techniques-Part 3: Data Reduction Methods |
| title_full | Publishing Nutrition Research: A Review of Multivariate Techniques-Part 3: Data Reduction Methods |
| title_fullStr | Publishing Nutrition Research: A Review of Multivariate Techniques-Part 3: Data Reduction Methods |
| title_full_unstemmed | Publishing Nutrition Research: A Review of Multivariate Techniques-Part 3: Data Reduction Methods |
| title_short | Publishing Nutrition Research: A Review of Multivariate Techniques-Part 3: Data Reduction Methods |
| title_sort | publishing nutrition research: a review of multivariate techniques-part 3: data reduction methods |
| url | 51243 51243 51243 http://hdl.handle.net/20.500.11937/51292 |