Multiple Correspondence Analysis

By Hervé Abdi, Dominique Valentin. 2006

931 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c00fb44d644fbc0aa1b53df539ed40867eed7f7e

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Citations in the corpus

Pathways of SME internationalization: a bibliometric and systematic review (2019, result): “This is like principal component analysis (Hoffman et al. 1994).”
Cultural tourism: A review of recent research and trends (2018, background): “Redondo-Carretero, Camarero-Izquierdo, Guti errez-Arranz, and Rodríguez-Pinto (2017) illustrate the importance of language tourism to the Spanish city of Valladolid.”
Multiple factor analysis: principal component analysis for multitable and multiblock data sets (2013, background): “To do so, PMFA combines ideas from metric multidimensional scaling (MDS see Refs 101,125–127 , 153 for”
missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016, methodology): “Principal component methods can be useful in such cases, and several methods are available depending on the nature of the data: Principal component analysis (PCA) can be used for continuous data, multiple correspondence analysis (MCA) for categorical data (Gifi 1990; Greenacre and Blasius 2006; Huss”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “In the splitting procedures, the method used Multiple Correspondence Analysis (MCA) (Abdi and Valentin 2007) to implement the initial splitting, and then optimized the global objective function the sum of Chi-square error (SCE) to refine the initial splitting of clusters.”
The structure and evolution of the strategic management field: A content analysis of 26 years of strategic management research (2008, methodology): “In order to see this evolution better, for each of the five periods, the position of each keyword in the map was calculated as the average position of the articles contained in the keyword and published during the period (Bendixen 1995; Hoffman et al. 1994).”
Effect of Financial Inclusion on Poverty and Vulnerability to Poverty: Evidence Using a Multidimensional Measure of Financial Inclusion (2020, neutral): “MCA is a principal component analysis equivalent for categorical data (Abdi and Valentin 2007; Aslan et al. 2017; Stata 2015).”