The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Multiple Correspondence Analysis
Abstract
This chapter describes some common approaches that can be used for simultaneously visualising the association between multiple categorical variables by focusing on the analysis of only three variables. It confines the application of multiple correspondence analysis to the visual summary of the association between three categorical variables, although it can be applied to a much larger sized table without loss of generality. The chapter also describes three coding methods and how they can be used to perform multiple correspondence analysis. The coding methods covered are crisp coding, Burt matrix, and stacking. Multiple correspondence analysis does not truly describe the underlying multivariate association structure between the variables of a multi-way contingency table. Instead, since it involves some form of bivariate transformation of the original contingency table, they are at best a way of visualising the various bivariate association structures that exist.
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Agresti, A. (2002) · Wiley Series in Probability and Statistics · doi:10.1002/0471249688 · Cited by 6,746
Advanced Methods of Marketing Research
Bagozzi, R. P. (1994) · Cited by 1,159
Multiple Correspondence Analysis and Related Methods
(2006) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420011319 · Cited by 1,029
The Multivariate Analysis of Qualitative Data: Interactions among Multiple Classifications
Goodman, L. A. (1970) · Journal of the American Statistical Association · doi:10.1080/01621459.1970.10481076 · Cited by 746
Weller, S., & Romney, A. (1990) · Sage eBooks · doi:10.4135/9781412985048 · Cited by 166
Interpreting multiple correspondence analysis as a multidimensional scaling method
Hoffman, D. L., & de Leeuw, J. (1992) · Marketing Letters · doi:10.1007/bf00994134 · Cited by 139
THE FACTORIAL ANALYSIS OF QUALITATIVE DATA
Burt, C. (1950) · British Journal of Statistical Psychology · doi:10.1111/j.2044-8317.1950.tb00296.x · Cited by 134
Correspondence analysis of multivariate categorical data by weighted least-squares
Greenacre, M. J. (1988) · Biometrika · doi:10.1093/biomet/75.3.457 · Cited by 123
Multiple and multiway correspondence analysis
Beh, E. J., & Lombardo, R. (2019) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1464 · Cited by 27
Familywise decompositions of Pearson’s chi-square statistic in the analysis of contingency tables
Lombardo, R., Takane, Y., & Beh, E. J. (2019) · Advances in Data Analysis and Classification · doi:10.1007/s11634-019-00374-7 · Cited by 17
Cited by
Abdi, H., & Williams, L. J. (2010) · WIREs Computational Statistics · doi:10.1002/wics.101 · Cited by 11,104
A New Model of Social Class? Findings from the BBC’s Great British Class Survey Experiment
Savage, M., Devine, F., Cunningham, N., Taylor, M., Li, Y., Hjellbrekke, J., Le Roux, B., Friedman, S., & Miles, A. (2013) · Sociology · doi:10.1177/0038038513481128 · Cited by 1,303
Koomson, I., Villano, R. A., & Hadley, D. (2020) · Social Indicators Research · doi:10.1007/s11205-019-02263-0 · Cited by 398
Cuccurullo, C., Aria, M., & Sarto, F. (2016) · Scientometrics · doi:10.1007/s11192-016-1948-8 · Cited by 365
A comparative analysis of diaspora policies
Ragazzi, F. (2014) · Political Geography · doi:10.1016/j.polgeo.2013.12.004 · Cited by 314
Metadata
- Venue
- Wiley Series in Probability and Statistics
- Publisher
- Wiley
- Published
- 2021-04-16
- Pages
- 143-161
- ISSN
- 1940-6347 (ISSN-L)
- OpenAlex
- W1510192517
- Cited by
- 493
- References
- 10
- Type
- other
- Language
- en
- Open access
- closed
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