One of the many areas in which Correspondence Analysis (CA) is an effective method, concerns ordination problems. For example, CA is a well-known technique for the seriation of archaeological assemblages. A problem with the CA seriation solution, however, is that only a relative ordering of the...

Correspondence analysis (CA) is a popular method that can be used to analyze relationships between categorical variables. It is closely related to several popular multivariate analysis methods such as canonical correlation analysis and principal component analysis. Like principal component...

Two new methods for dealing with missing values in generalized canonical correlation analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component analysis. In the second approach, missing values...

In correspondence analysis, rows and columns of a data matrix are depicted as points in low-dimensional space. The row and column profiles are approximated by minimizing the so-called weighted chi squared distance between the original profiles and their approximations, see or example, Greenacre...

Classical multivariate analysis techniques such as principal components analysis and correspondence analysis use inner products to estimate data values. The results of these techniques may be visualized by representing the row and column points jointly in a biplot where the projection of a row...

In this paper we consider the use of correspondence analysis (CA) of rating data. CA of rating data allows a joint representation of the rated items (e.g. attributes or products) and individuals. However, as the number of individuals increases, the interpretation of the CA map becomes difficult....

A response style denotes a certain mapping of latent preferences to a rating scale that is common among a certain group of individuals. For example, individuals from the same country may assign high ratings to the majority of objects regardless of the specific preferences for the objects. The...

Multidimensional scaling is a statistical technique to visualize dissimilarity data. In multidimensional scaling, objects are represented as points in a usually two dimensional space, such that the distances between the points match the observed dissimilarities as closely as possible. Here, we...

This article is based on a combined dataset of 183,819 port state control (PSC) inspections from various port state control regimes around the world. In theory, there should be no differences in treatments of inspections to determine if a vessel is substandard or not no matter where the...