R. Alvarez, M. Bécue, J. J. Lanero, and O. Valencia, Results stability in textual analysis: its application to the study of the Spanish investiture speeches, 6 es Journées internationales d'Analyse Statistique des Données Textuelles, pp.1-12, 2002.

R. Alvarez, M. Bécue, and O. Valencia, Etude de la stabilité des valeurs propres de l'AFC d'un tableau lexical au moyen de procédures de rééchantillonnage, 7 es Journées internationales d'Analyse Statistique des Données Textuelles, pp.42-51, 2004.

R. Alvarez, M. Bécue, and O. Valencia, Partial bootstrap in CA: correction of the coordinates. Application to textual data, 8 es Journées internationales d'Analyse Statistique des Données Textuelles, pp.43-53, 2006.

T. W. Anderson, An Introduction to Multivariate Statistical Analysis, 1958.

A. Baccini, Etude comparative des représentations graphiques en analyses factorielles des correspondances simples et multiples, pp.1-64, 1984.

E. J. Beh, Simple Correspondence Analysis of ordinal cross-classifications using orthogonal polynomials, Biom. J, vol.39, issue.5, pp.589-613, 1997.

E. J. Beh, A comparative study of Scores for Correspondence Analysis with ordered categories (1998), Biom. J, vol.40, pp.413-429

E. J. Beh, Partitioning Chi-squared statistics for singly ordered two-way contingency tables, Aust. N. Z. J. Stat, issue.3, pp.327-333, 2001.

E. J. Beh, Simple Correspondence Analysis of Nominal-Ordinal contingency tables, Journal of Applied Mathematics and Decision Sciences, pp.1-17, 2008.

E. J. Beh and R. Lombardo, Correspondence Analysis Theory, Practice and New Strategies, 2014.

P. Bekker and J. De-leeuw, Relations between variants of non-linear Principal Components Analysis, In: Component and Correspondence Analysis -dimension reduction by functional approximation, pp.1-31, 1988.

S. , B. Hammou, and G. Saporta, Sur la normalité asymptotique des valeurs propres en ACM sous l'hypothèse d'indépendance des variables, Revue de Statistique Appliquée, vol.46, issue.3, pp.21-35, 1998.

S. , B. Hammou, and G. Saporta, On the connection between the distribution of eigenvalues in Multiple Correspondence Analysis and Log-Linear models, Proceedings of CARME2003, pp.41-79, 2003.

J. Benzécri, Analyse des Données, vol.2, 1973.

J. Benzécri, Sur l'analyse des tableaux binaires associésà une correspondance multiple, Les cahiers de l'analyse des données, vol.2, pp.55-71, 1977.

J. Benzécri, Sur le calcul des taux d'inertie dans l'analyse d'un questionnaire, addendum et erratumà, Les cahiers de l'analyse des données, vol.4, pp.377-378, 1979.

J. Benzécri, Histoire et préhistoire de l'Analyse Des Données, 1982.

S. Camiz, The Guttman effect: its interpretation and a new redressing method, Data Analysis Bulletin, vol.5, pp.7-34, 2005.

S. Camiz and G. C. Gomes, Joint Correspondence Analysis Versus Multiple Correspondence Analysis: A Solution to an Undetected Problem, In: Classification and Data Mining, Studies in Classification, Data Analysis and Knowledge Organization, pp.11-18

S. Cornu, L. Quénard, I. Cousin, and A. Samouëlian, Experimental approach of lessivage: quantification and mechanisms, Geoderma, vol.213, pp.357-370, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01458017

G. Diana and C. Tommasi, Cross-validation methods in principal component analysis: a comparison, Statistical Methods & Applications, vol.11, pp.71-82, 2002.

§. Dray, On the number of principal components: a test of dimensionality based on measurements of similarity between matrices, Computational Statistics & Data Analysis, vol.52, pp.2228-2237, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00428163

M. J. Greenacre, Correspondence analysis of multivariate categorical data by weighted least squares, Biometrika, vol.75, issue.3, pp.457-467, 1988.

V. Elisseeff, Possibilité du scalogramme dans l'étude des bronzes chinois archaïques, Mathématiques et Sciences Humaines, vol.11, pp.1-10, 1965.

W. Gauschi, On generating orthogonal polynomials, SIAM J. Sci. Stat. Comput, vol.3, issue.3, pp.289-317, 1982.

W. Gauschi, Is the recurrence relation for orthogonal polynomials alwaws stable?, BIT, pp.277-284, 1993.

W. Gauschi, Orthogonal polynomials: applications and computation, Acta Numerica, pp.45-119, 1996.

J. Josse, J. Pagès, and F. Husson, Testing the significance of the RV coefficient, Computational Statistics & Data Analysis, vol.53, pp.82-91, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00360535

J. Josse and F. Husson, Selecting the number of components in principal component analysis using cross-validation approximations, Computational Statistics & Data Analysis, vol.56, pp.1869-1879, 2012.

J. Josse and S. Holmes, Measuring multivariate association and beyond, Statistics Surveys, vol.10, pp.132-167, 2016.

N. E. Karoui and E. Purdom, The bootstrap, covariance matrices and PCA in moderate and high-dimensions, 2016.

P. L. Emerson, Numerical construction of orthogonal polynomials from a general recurrence formula, Biometrics, vol.24, issue.3, pp.695-701, 1968.

J. C. Gower, Fisher's optimal scores and Multiple Correspondence Analysis, Biometrics, vol.46, pp.947-961, 1990.

M. J. Greenacre, Theory and Applications of Correspondence Analysis, 1984.

L. Guttman, The Cornell technique for Scale and Intensity Analysis, Educational and Psychological Measurement, pp.247-279, 1947.

L. Guttman and E. A. Suchman, Intensity and a Zero Point for Attitude Analysis, American Sociological Review, vol.12, issue.1, pp.57-67, 1947.

S. J. Haberman, Log-Linear Models for Frequency Tables with Ordered Classifications, Biometrics, vol.30, issue.4, pp.589-600, 1974.

A. Hendrickse, L. Spreeuwers, and R. Veldhuis, N inth IEEE International Conference on Data Mining, 2009.

A. Hendrikse, R. Veldhuis, and L. Spreeuwers, Smooth eigenvalue correction, EURASIP Journal on Advances in Signal Processing, pp.1-16, 2013.

M. O. Hill and H. G. Gauch, Detrended Correspondence Analysis: an improved ordination technique, vol.42, pp.47-58, 1980.

D. A. Jackson and K. M. Somers, Putting Things in Order: The Ups and Downs of Detrended Correspondence Analysis, The American Naturalist, vol.137, issue.5, pp.704-712, 1991.

R. G. Knox, Effects of detrending and rescaling on correspondence analysis: solution stability and accuracy, vol.83, pp.129-136, 1989.

L. Lebart, A. Morineau, and M. Piron, Statistique exploratoire multidimensionnelle, 1995.

L. Lebart, G. Saporta, and . Chapman, Historical Elements of Correspondence Analysis and Multiple Correspondence Analysis, in: Visualization and Verbalization of Data, 2014.

R. Lombardo and E. Beh, Simple and multiple correspondence analysis for ordinal-scale variables using orthogonal polynomials, Journal of Applied Statistics, vol.37, pp.2101-2116, 2010.

R. Lombardo and J. Meulman, Multiple correspondence analysis via polynomial transformations of ordered categorical variables, Journal of Classification, vol.27, pp.191-216, 2010.

P. Mair and J. De-leeuw, A general framework for Multivariate Analysis with Optimal Scaling: the R package "aspect, Journal of Statistical Software, vol.32, issue.9, pp.1-23, 2010.

C. Manté, Etude par l'Analyse des Données de la mémoire d'un champ météorologique, Thèse de 3ème cycle, 1981.

C. Manté, G. Bernard, P. Bonhomme, and D. Nerini, Application of ordinal correspondence analysis for submerged aquatic vegetation monitoring, Journal of Applied Statistics, vol.40, issue.8, pp.1619-1638, 2013.

G. Michailidis and J. De-leeuw, The Gifi system of Descriptive Multivariate Analysis, vol.13, pp.307-336, 1998.

J. C. Rayner and D. J. Best, Smooth extensions of Pearson's product moment correlation and Spearman's Rho, Statist. Probab. Lett, vol.30, pp.171-177, 1996.

J. C. Rayner and D. J. Best, Analysis of singly ordered two-way contingency tables, Journal of Applied Mathematics and Decision Sciences, pp.83-98, 2000.

P. Sarnacchiaro, A. , and L. D'ambra, CATANOVA for ordinal variables using orthogonal polynomials with different scoring methods, Journal of Applied Statistics, vol.43, pp.2490-2502, 2016.

M. Tenenhaus and F. Young, An analysis and synthesis of Multiple Correspondence Analysis, Optimal Scaling, Dual Scaling, Homogeneity analysis and other methods for quantifying categorical multivariate data, Psychometrika, vol.50, issue.1, pp.91-119, 1985.

C. J. Ter-braak, CANOCO -a FORTRAN Program for Canonical Community Ordination by, Correspondence Analysis, Principal Components Analysis and Redundancy Analysis (Version 2.1), 1987.

, Mathematica, Version 12, 2020.