Biagio Simonetti

Transvaginal ultrasound cervical length for prediction of spontaneous labour at term: a systematic review and meta‐analysis

The possibility to predict the delivery date is a question frequently raised by pregnant women. However, a clinician has currently little to predict when a woman at term will deliver.

Correspondence Analysis of Cumulative Frequencies Using a Decomposition of Taguchi's Statistic

Taguchis statistic has long been known to be a more appropriate measure of association for ordinal variables than the Pearson chi-squared statistic. Therefore, there is some advantage in using Taguchis statistic for performing correspondence analysis when a two-way contingency table consists of one ordinal categorical variable. This article will explore the development of correspondence analysis using a decomposition of Taguchis statistic.

Cumulative correspondence analysis of ordered categorical data from industrial experiments

Most studies of quality improvement deal with ordered categorical data from industrial experiments. Accounting for the ordering of such data plays an important role in effectively determining the optimal factor level of combination. This paper utilizes the correspondence analysis to develop a procedure to improve the ordered categorical response in a multifactor state system based on Taguchis statistic. Users may find the proposed procedure in this paper to be attractive because we suggest a simple and also popular statistical tool for graphically identifying the really important factors and determining the levels to improve process quality. A case study for optimizing the polysilicon deposition process in a very large-scale integrated circuit is provided to demonstrate the effectiveness of the proposed procedure.

The analysis of dependence for three ways contingency tables with ordinal variables: A case study of patient satisfaction data

For many questionnaires and surveys in the marketing, business, and health disciplines, items often involve ordinal scales (such as the Likert scale and rating scale) that are associated in sometimes complex ways. Techniques such as classical correspondence analysis provide a simple graphical means of describing the nature of the association. However, the procedure does not allow the researcher to specify how one item may be associated with another, nor does the analysis allow for the ordinal structure of the scales to be reflected. This article presents a graphical approach that can help the researcher to study in depth the complex association of the items and reflect the structure of the items. We will demonstrate the applicability of this approach using data collected from a study that involves identifying major factors that influence the level of patient satisfaction in a Neapolitan hospital.

A dimensional reduction method for ordinal three-way contingency table

For the study of association in three-way, and more generally multi-way, contingency tables the literature offers a large number of techniques that can be considered. When there is an asymmetric dependence structure between the variables the Marcotorchino index [Mar84] (as apposed to the Pearson chi-squared statistic) can be used to measure the strength of their association. When the variables have an ordinal structure, this information is often not take into account. In this paper we introduce a partition of the Marcotorchino index for three ordered categorical variables using a special class of orthogonal polynomials. A graphical procedure is also considered to obtain a visual summary of the asymmetrical relationship between the variables.

Some new aspects of taxicab correspondence analysis

Correspondence analysis (CA) and nonsymmetric correspondence analysis are based on generalized singular value decomposition, and, in general, they are not equivalent. Taxicab correspondence analysis (TCA) is a

The role played by job and non-job-related TMT diversity traits on firm performance and strategic change

\hbox {L}_{1}

Fuzzy Correlation and Fuzzy Non-linear Regression Analysis

L1 variant of CA, and it is based on the generalized taxicab singular value decomposition (GTSVD). Our aim is to study the taxicab variant of nonsymmetric correspondence analysis. We find that for diagonal metric matrices GTSVDs of a given data set are equivalent; from which we deduce the equivalence of TCA and taxicab nonsymmetric correspondence analysis. We also attempt to show that TCA stays as close as possible to the original correspondence matrix without calculating a dissimilarity (or similarity) measure between rows or columns. Further, we discuss some new geometric and distance aspects of TCA.