The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Nonlinear principal components analysis: Introduction and application.
Abstract
The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal variables and that it can handle and discover nonlinear relationships between variables. Also, nonlinear PCA can deal with variables at their appropriate measurement level; for example, it can treat Likert-type scales ordinally instead of numerically. Every observed value of a variable can be referred to as a category. While performing PCA, nonlinear PCA converts every category to a numeric value, in accordance with the variable's analysis level, using optimal quantification. The authors discuss how optimal quantification is carried out, what analysis levels are, which decisions have to be made when applying nonlinear PCA, and how the results can be interpreted. The strengths and limitations of the method are discussed. An example applying nonlinear PCA to empirical data using the program CATPCA (J. J. Meulman, W. J. Heiser, & SPSS, 2004) is provided.
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Cites
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
The Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
The SAGE Handbook of Quantitative Methodology for the Social Sciences
Kaplan, D. (2004) · doi:10.4135/9781412986311 · Cited by 1,454
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Self-citationStrand, S. (2014) · Research Papers in Education · doi:10.1080/02671522.2013.767370 · Cited by 252
Impact of the physical environment of psychiatric wards on the use of seclusion
van der Schaaf, P. S., Dusseldorp, E., Keuning, F. M., Janssen, W. A., & Noorthoorn, E. O. (2013) · British Journal of Psychiatry · doi:10.1192/bjp.bp.112.118422 · Cited by 127
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Agasi-Idenburg, S. C., Thong, M. S. Y., Punt, C. J. A., Stuiver, M. M., & Aaronson, N. K. (2017) · Supportive Care in Cancer · doi:10.1007/s00520-016-3451-4 · Cited by 50
From observation to information: data-driven understanding of on farm yield variation
Jiménez, D., Dorado, H., Cock, J., Prager, S. D., Delerce, S., Grillon, A., Andrade Bejarano, M., Benavides, H., & Jarvis, A. (2016) · PLOS ONE · doi:10.1371/journal.pone.0150015 · Cited by 47
Eastwood, J., Jalaludin, B., Kemp, L., Phung, H., Barnett, B., & Tobin, J. (2013) · Brain and Behavior · doi:10.1002/brb3.107 · Cited by 40
“Exploratory data analysis including nonlinear PCA solutions can best be interpreted through graphical visualization (Tukey 1980; Linting et al. 2007).”
BackgroundMethodExecutive functions as mediators of early educational disparities by SES, gender and birth month
Rémeau, M., Schmitt, P., & Borst, G. (2026) · npj Science of Learning · doi:10.1038/s41539-026-00422-w · Cited by 3
Metadata
- Authors
- Linting, M., Meulman, J. J., Groenen, P. J. F., & van der Koojj, A. J.
- Venue
- Psychological Methods
- Published
- 2007
- Volume
- 12
- Issue
- 3
- Pages
- 336-358
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2082185517
- Cited by
- 596
- References
- 3
- FWCI
- 9.01
- Type
- journal article
- Language
- en
- Open access
- closed
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