117,543 research outputs found
Visualization of the Significant Explicative Categories using Catanova Method and Non-Symmetrical Correspondence Analysis for Evaluation of Passenger Satisfaction
No full textANalysis Of VAriance (ANOVA) is a method to decompose the total variation of the observations into sum of variations due to different factors and the residual component. When the data are nominal, the usual approach of considering the total variation in response variable as measure of dispersion about the mean is not well defined. Light and Margolin (1971) proposed CATegorical ANalysis Of VAriance (CATANOVA), to analyze the categorical data. Onukogu (1985) extended the CATANOVA method to two-way classified nominal data. The components (sums of squares) are, however, not orthogonal. Singh (1996) developed a CATANOVA procedure that gives orthogonal sums of squares and defined test statistics and their asymptotic null distributions. In order to study which exploratory categories are influential factors for the response variable we propose to apply Non-Symmetrical Correspondence Analysis (D'Ambra and Lauro, 1989) on significant components. Finally, we illustrate the analysis numerically, with a practical example
Evaluation of Passenger Satisfaction using three-way contingence table with ordinal variables
No full textThe aim of this paper is to evaluate the Passenger Satisfaction (PS) starting from quality factors (punctuality, safeness, staff aspect and conduct, modal integration, etc.). Carrying out two or more ways contingence tables, crossing the overall satisfaction (PS) and the quality factors we can study the dependency between the overall satisfaction and quality factors. In particular, the partition of Marcotorchino index for a three-way contingency table with one, two and three ordered categorical variables (Beh E.J., Simonetti B., D'Ambra L., 2007) will allow us to analyze the asymmetric and ordinal structure of the data and to pick up the nonlinear relationship within the data. To complement the survey Ordered Non-Symmetric Correspondence Analysis (ONSCA) will be carried out
Metodi, Modelli e Tecnologie dell'Informazione a Supporto delle Decisioni. Parte prima: metodologie
No full textVisualizing main effects and interaction in multiple non-symmetric correspondence analysis.
No full textNon-symmetric correspondence analysis (NSCA) is a useful technique for analysing a two-way contingency table. Frequently, the predictor variables are more than one; in this paper, we consider two categorical variables as predictor variables and one response variable. Interaction represents the joint effects of predictor variables on the response variable. When interaction is present, the interpretation of the main effects is incomplete or misleading. To separate the main effects and the interaction term, we introduce a method that, starting from the coordinates of multiple NSCA and using a two-way analysis of variance without interaction, allows a better interpretation of the impact of the predictor variable on the response variable. The proposed method has been applied on a well-known three-way contingency table proposed by Bockenholt and Bockenholt in which they cross-classify subjects by person's attitude towards abortion, number of years of education and religion. We analyse the case where the variables education and religion influence a person's attitude towards abortion
Il contributo dell'analisi delle corrispondenze con variabili ordinali nella valutazione dei servizi sanitari
No full textLe metodologie classiche per l’analisi multidimensionale di variabili qualitative (Analisi delle Corrispondenze, AC; Analisi Multipla delle Corrispondenze, non tengono debitamente in conto la natura ordinale del dato. L’obiettivo del presente lavoro è quello di recuperare l’informazione del dato ordinale ed evidenziare un’eventuale trend presente nei dati nonché la significatività delle componenti
L’informazione ordinale é considerata nell’analisi e attraverso i polinomi ortogonali e i momenti individuano le componenti (lineare, quadratico, cubico etc.) associati alle variabili, ed esse permettono una chiara interpretazione dell’eventuale trend, lineare o di ordine superiore, presente nei dati. Viene anche evidenziato che le componenti seguono una distribuzione chi quadrato
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