[Blueboard] UPDATED ABSTRACT - Lecture on Multiple Correspondence Analysis (MCA)
Editha V. Bagtas
ebagtas at ateneo.edu
Thu Sep 5 13:59:04 PHT 2013
The Ateneo de Manila University
cordially invites you to a talk on
Applications of Multiple Correspondence Analysis
Anthony R. Zosa, MS
School of Science and Engineering
on Monday, September 9, 2013
4:30 - 6:00 pm at SECA 303.
Multiple Correspondence Analysis (MCA), a nonparametric approach to
data reduction, is normally applied to multidimensional scaling of
categorical variables. MCA, designed for nominally scaled variables,
is the counterpart of the metric-based factor and principal component
analyses. Thus, it is suitable for contingency tables and frequency
matrices. MCA uses the centroid principle governed by a squared loss
function. The squared canonical correlations are the eigenvalues
derived from the singular value decomposition (SVD) D-1/2CD-1/2 with
C as the matrix of bivariate marginals. These eigenvalues, in turn,
suggests the relevant dimensions. Hence, SVD is used to
simultaneously approximate all possible two-dimensional subtables of a
multidimensional table. This aggregation technique is used to
determine latent variables from several variables.
Like the principal component analysis, MCA is normally used as a
preliminary analysis of categorical data; thus, it can be used without
any statistical inference. The statistical properties of MCA hinges
on (a) the principle of linearizing its statistics and using the delta
method to approximate the standard errors, and (b) the assumption that
with large samples, the eigenvalues are distributed asymptotically as
eigenvalues of a matrix with independent standard normal in each of
the off diagonal blocks.
A sample data will be used to illustrate how MCA can be applied to
analyze data that are nominally scaled. R, a statistical freeware
will be used to run MCA on the sample data.
MCA is highly applicable to many AMF related projects where banks
require students to find out the determinants of “good” or “bad” loans
from an array of categorical variables.
More information about the Blueboard