Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-12T17:09:19.189Z Has data issue: false hasContentIssue false
  • English
  • Français

A Noniterative Method of Joint Correspondence Analysis

Published online by Cambridge University Press:  01 January 2025

Krishna Tateneni and
Michael W. Browne
Krishna Tateneni*
Affiliation:
The Ohio State University
Michael W. Browne
Affiliation:
The Ohio State University
*
Requests for reprints should be sent to Krishna Tateneni, Educational Testing Service, Mailstop 1 l-L, Princeton NJ 08541. E-Mail: ktateneni@ets.org
Get accessRights & Permissions [Opens in a new window]

Abstract

Correspondence analysis leads to a graphical representation of the associations between categories of the row and column variables of a contingency table. Greenacre's (1988) formulation of joint correspondence analysis is a multivariate extension which finds the optimal joint display of contingency tables between all pairs of variables in a set. Greenacre presented a discrepancy function and an alternating least squares algorithm for its minimization. Boik (1996) presented an alternative algorithm, also of the alternating least squares type, for minimizing the same discrepancy function. In this paper, a noniterative procedure, not based on the minimization of any discrepancy function, is described.

Keywords

joint correspondence analysisnoniterative estimation

Information

Type
Original Paper
Information
Psychometrika , Volume 65 , Issue 2 , June 2000, pp. 157 - 165
Copyright
Copyright © 2000 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Albert, A.A. (1944). The minimum rank of a factor matrix. Proceedings of the National Academy of Sciences, 30, 90–95.CrossRefGoogle Scholar
Boik, R.J. (1996). An efficient algorithm for joint correspondence analysis. Psychometrika, 61, 255–269.CrossRefGoogle Scholar
Browne, M.W. (1980). Factor analysis of multiple batteries by maximum likelihood. British Journal of Mathematical and Statistical Psychology, 33, 184–199.CrossRefGoogle Scholar
Browne, M.W., & Tateneni, K. (1997). Noniterative estimation for the multiple battery factor analysis model. Behaviormetrika, 24, 3–18.CrossRefGoogle Scholar
Cudeck, R. (1991). Noniterative factor analysis estimators, with algorithms for subset and instrumental variable selection. Journal of Educational Statistics, 16, 35–52.CrossRefGoogle Scholar
Graybill, F.A. (1983). Matrices with applications in statistics. Belmont, CA: Wadsworth.Google Scholar
Greenacre, M.J. (1988). Correspondence analysis of multivariate categorical data by weighted least squares. Biometrika, 75, 457–467.CrossRefGoogle Scholar
Greenacre, M.J. (1991). Interpreting multiple correspondence analysis. Applied Stochastic Models and Data Analysis, 7, 195–210.CrossRefGoogle Scholar
Greenacre, M.J. (1993). Correspondence analysis in practice. San Diego, CA: Academic Press.Google Scholar
Ihara, M., & Kano, Y. (1986). A new estimator of the uniqueness in factor analysis. Psychometrika, 51, 563–566.CrossRefGoogle Scholar
Kano, Y. (1989). A new estimation procedure using G-inverse matrix in factor analysis. Mathematica Japonica, 34, 43–52.Google Scholar
Kano, Y. (1990). Comparative studies of noniterative estimators based on Ihara and Kano's method in exploratory factor analysis. Communications in Statistics, Part A, 19, 431–444.CrossRefGoogle Scholar
Kano, Y. (1990). Noniterative estimation and the choice of the number of factors in exploratory factor analysis. Psychometrika, 55, 277–291.CrossRefGoogle Scholar