A tool developed in Matlab for multiple correspondence analysis of fuzzy coded data sets: Application to morphometric skull data

2010 ◽  
Vol 98 (1) ◽  
pp. 66-75 ◽  
Author(s):  
Antonio Pinti ◽  
Fabienne Rambaud ◽  
Jean-Louis Griffon ◽  
Abdelmalik Taleb Ahmed
Keyword(s):  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Data Sets

scholarly journals Using multiple correspondence analysis to identify behaviour patterns associated with overweight and obesity in Vanuatu adults

2019 ◽  
Vol 22 (09) ◽  
pp. 1533-1544 ◽  
Author(s):  
Andrew van Horn ◽  
Charles A Weitz ◽  
Kathryn M Olszowy ◽  
Kelsey N Dancause ◽  
Cheng Sun ◽  
...  
Keyword(s):  
Body Fat ◽  
Correspondence Analysis ◽  
Categorical Data ◽  
Multiple Correspondence Analysis ◽  
Overweight And Obesity ◽  
Data Sets ◽  
Pacific Island ◽  
Binary Responses ◽  
Or Groups ◽  
The Us

AbstractObjectiveThe present study evaluates the use of multiple correspondence analysis (MCA), a type of exploratory factor analysis designed to reduce the dimensionality of large categorical data sets, in identifying behaviours associated with measures of overweight/obesity in Vanuatu, a rapidly modernizing Pacific Island country.DesignStarting with seventy-three true/false questions regarding a variety of behaviours, MCA identified twelve most significantly associated with modernization status and transformed the aggregate binary responses of participants to these twelve questions into a linear scale. Using this scale, individuals were separated into three modernization groups (tertiles) among which measures of body fat were compared and OR for overweight/obesity were computed.SettingVanuatu.ParticipantsNi-Vanuatu adults (n 810) aged 20–85 years.ResultsAmong individuals in the tertile characterized by positive responses to most of or all the twelve modernization questions, weight and measures of body fat and the likelihood that measures of body fat were above the US 75th percentile were significantly greater compared with individuals in the tertiles characterized by mostly or partly negative responses.ConclusionsThe study indicates that MCA can be used to identify individuals or groups at risk for overweight/obesity, based on answers to simply-put questions. MCA therefore may be useful in areas where obtaining detailed information about modernization status is constrained by time, money or manpower.

Performance Improvement in the Pattern Classification of Nominal Data Sets Applying Multiple Correspondence Analysis

2014 ◽  
Vol 670-671 ◽  
pp. 1482-1487
Author(s):  
Rodrigo Clemente Thom de Souza ◽  
Maria Teresinha Arns Steiner ◽  
Leandro dos Santos Coelho
Keyword(s):  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Principal Component ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Data Types ◽  
Nominal Data ◽  
Linear Discriminant ◽  
Whole Process ◽  
Geometric Data Analysis

Classification is a supervised learning problem used to discriminate data instances in different classes. The solution to this problem is obtained through algorithms (classifiers) that look for patterns of relationships between classes in known cases, using these relationships to classify unknown cases. The performance of the classifiers depends substantially of the data types. In order to give proper treatment to nominal data, this paper shows that the application of previous transformations can substantially improve the performance of classifiers, bringing significant benefits to the result of the whole process of Knowledge Discovery in Databases (KDD). This paper uses three different data sets with nominal data and two well-known classifiers: the Linear Discriminant Analysis (LDA), and the Naïve-Bayes (NB). For data transformation, the paper applies an approach called Geometric Data Analysis (GDA). The GDA techniques compared in this paper are the traditional Principal Component Analysis (PCA) and the underexplored Multiple Correspondence Analysis (MCA). The results confirm the capability of the GDA transformation to improve the classification accuracy and attest the superiority of the MCA in comparison with its precursor, the PCA, when applied to nominal data.

Multiple correspondence analysis for “tall” data sets

2009 ◽  
Vol 13 (6) ◽  
pp. 873-885 ◽  
Author(s):  
Angelos Markos ◽  
George Menexes ◽  
Theophilos Papadimitriou
Keyword(s):  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Data Sets

Exploring Historical Canadian Foodborne Outbreak Data Sets for Human Illness Attribution

2009 ◽  
Vol 72 (9) ◽  
pp. 1963-1976 ◽  
Author(s):  
A. RAVEL ◽  
J. GREIG ◽  
C. TINGA ◽  
E. TODD ◽  
G. CAMPBELL ◽  
...  
Keyword(s):  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Data Sets ◽  
Foodborne Outbreak ◽  
Escherichia Coli Infection ◽  
Foodborne Outbreaks ◽  
Human Illness ◽  
Illness Attribution ◽  
Changes Over Time ◽  
Over Time

Human illness attribution has been recently recognized as an important tool to better inform food safety decisions. Analysis of outbreak data sets has been used for that purpose. This study was conducted to explore the usefulness of three comprehensive Canadian foodborne outbreak data sets covering 30 years for estimating food attribution in cases of gastrointestinal illness, providing Canadian food attribution estimates from a historical perspective. Information concerning the microbiological etiology and food vehicles recorded for each outbreak was standardized between the data sets. The agent–food vehicle combinations were described and analyzed for changes over time by using multiple correspondence analysis. Overall, 6,908 foodborne outbreaks were available for three decades (1976 through 2005), but the agent and the food vehicle were identified in only 2,107 of these outbreaks. Differences between the data sets were found in the distribution of the cause, the vehicle, and the location or size of the outbreaks. Multiple correspondence analysis revealed an association between Clostridium botulinum and wild meat and between C. botulinum and seafood. This analysis also highlighted changes in food attribution over time and generated the most up-to-date food attribution values for salmonellosis (29% of cases associated with produce, 15% with poultry, and 15% with meat other than poultry, pork, and beef), campylobacteriosis (56% of cases associated with poultry and 22% with dairy products other than fluid milk), and Escherichia coli infection (37% of cases associated with beef, 23% with cooked multi-ingredient dishes, and 11% with meat other than beef, poultry, and pork). Because of the inherent limitations of this approach, only the main findings should be considered for policy making. The use of other human illness attribution approaches may provide further clarification.

scholarly journals Informed Geoheritage Conservation: Determinant Analysis Based on Bibliometric and Sustainability Indicators Using Ordination Techniques

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 539
Author(s):  
Boglárka Németh ◽  
Károly Németh ◽  
Jon N. Procter
Keyword(s):  
Correspondence Analysis ◽  
Conservation Planning ◽  
Earth Science ◽  
Multiple Correspondence Analysis ◽  
Contributing Factors ◽  
Usual Model ◽  
Development Indicators ◽  
Individual Variables ◽  
Business As Usual ◽  
World Development

Ordination methods are used in ecological multivariate statistics in order to reduce the number of dimensions and arrange individual variables along environmental variables. Geoheritage designation is a new challenge for conservation planning. Quantification of geoheritage to date is used explicitly for site selection, however, it also carries significant potential to be one of the indicators of sustainable development that is delivered through geosystem services. In order to achieve such a dominant position, geoheritage needs to be included in the business as usual model of conservation planning. Questions about the quantification process that have typically been addressed in geoheritage studies can be answered more directly by their relationships to world development indicators. We aim to relate the major informative geoheritage practices to underlying trends of successful geoheritage implementation through statistical analysis of countries with the highest trackable geoheritage interest. Correspondence analysis (CA) was used to obtain information on how certain indicators bundle together. Multiple correspondence analysis (MCA) was used to detect sets of factors to determine positive geoheritage conservation outcomes. The analysis resulted in ordination diagrams that visualize correlations among determinant variables translated to links between socio-economic background and geoheritage conservation outcomes. Indicators derived from geoheritage-related academic activity and world development metrics show a shift from significant Earth science output toward disciplines of strong international agreement such as tourism, sustainability and biodiversity. Identifying contributing factors to conservation-related decisions helps experts to tailor their proposals for required evidence-based quantification reports and reinforce the scientific significance of geoheritage.

scholarly journals Patterns of Italian high school and university students’ attitudes towards physics: an analysis based on semiotic-cultural perspective

2021 ◽  
Author(s):  
Italo Testa ◽  
Raffaele De Luca Picione ◽  
Umberto Scotti di Uccio
Keyword(s):  
High School ◽  
Cluster Analysis ◽  
University Students ◽  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Psychological Theory ◽  
Cultural Perspective ◽  
Undergraduate Level ◽  
Students Attitudes ◽  
And Cluster Analysis

AbstractThe purpose of this study was to analyse Italian high school and university students’ attitudes towards physics using the Semiotic Cultural Psychological Theory (SCPT). In the SCPT framework, attitudes represent how individuals interpret their experience through the mediation of generalized meaning with which they are identified. A view-of-physics questionnaire was used as an instrument to collect data with 1603 high school and university students. Data were analysed through multiple correspondence analysis and cluster analysis. We identified four generalized meanings of physics: (a) interesting and important for society; (b) a quite interesting, but badly taught subject at school and not completely useful for society; (c) difficult to study and irrelevant for society; and (d) a fascinating and protective niche from society. The identified generalized meanings are significantly correlated to the choice to study physics at undergraduate level and to the choice of attending physics-related activities in high school. Implications for research are discussed.

Sign in / Sign up

Export Citation Format

Share Document