{"product_id":"intro-correspondence-analysis-9781119041948","title":"Intro Correspondence Analysis","description":"\u003cp\u003e\u003cb\u003eMaster the fundamentals of correspondence analysis with this illuminating resource\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eAn Introduction to Correspondence Analysis\u003c\/i\u003e assists researchers in improving their familiarity with the concepts, terminology, and application of several variants of correspondence analysis. The accomplished academics and authors deliver a comprehensive and insightful treatment of the fundamentals of correspondence analysis, including the statistical and visual aspects of the subject.\u003c\/p\u003e \u003cp\u003eWritten in three parts, the book begins by offering readers a description of two variants of correspondence analysis that can be applied to two-way contingency tables for nominal categories of variables. Part Two shifts the discussion to categories of ordinal variables and demonstrates how the ordered structure of these variables can be incorporated into a correspondence analysis. Part Three describes the analysis of multiple nominal categorical variables, including both multiple correspondence analysis and multi-way correspondence analysis.\u003c\/p\u003e \u003cp\u003eReaders will benefit from explanations of a wide variety of specific topics, for example: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eSimple correspondence analysis, including how to reduce multidimensional space, measuring symmetric associations with the Pearson Ratio, constructing low-dimensional displays, and detecting statistically significant points\u003c\/li\u003e \u003cli\u003eNon-symmetrical correspondence analysis, including quantifying asymmetric associations\u003c\/li\u003e \u003cli\u003eSimple ordinal correspondence analysis, including how to decompose the Pearson Residual for ordinal variables\u003c\/li\u003e \u003cli\u003eMultiple correspondence analysis, including crisp coding and the indicator matrix, the Burt Matrix, and stacking\u003c\/li\u003e \u003cli\u003eMulti-way correspondence analysis, including symmetric multi-way analysis\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003ePerfect for researchers who seek to improve their understanding of key concepts in the graphical analysis of categorical data, \u003ci\u003eAn Introduction to Correspondence Analysis \u003c\/i\u003ewill also assist readers already familiar with correspondence analysis who wish to review the theoretical and foundational underpinnings of crucial concepts.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Beh\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e John Wiley \u0026amp; Sons\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 01\/01\/1900\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 240\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.27lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.61h x 6.69w x 0.56d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781119041948\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eEric J. Beh\u003c\/b\u003e is Professor of Statistics at the School of Mathematical \u0026amp; Physical Sciences at the University of Newcastle, Australia. He has been actively researching in many areas of categorical data analysis including ecological inference, measures of association and categorical models. For the past 25 years his research has focused primarily on the technical, computational and practical development of correspondence analysis. He has over 100 publications and, with Rosaria Lombardo, has authored \u003ci\u003eCorrespondence Analysis: Theory, Methods and New Strategies\u003c\/i\u003e published by Wiley. Together, they have given short courses and workshops around the world on this topic.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eRosaria Lombardo\u003c\/b\u003e is Associate Professor of Statistics at the Department of Economics of the University of Campania \"L. Vanvitelli\", Italy. Her research interests include non-linear multivariate data analysis, quantification theory and, in particular, correspondence analysis and data visualization. Since receiving her PhD in Computational Statistics and Applications at the University of Naples \"Federico II\", she has authored over 100 publications including those in \u003ci\u003eStatistical Science, Psychometrika, Computational Statistics \u0026amp; Data Analysis\u003c\/i\u003e, and the \u003ci\u003eJournal of Statistical Planning and Inference\u003c\/i\u003e.\u003cbr\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons","offers":[{"title":"Hardcover","offer_id":40689467555955,"sku":"9.78112E+12","price":104.08,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/products\/img_2051e284-e88d-4166-b5f9-c7ece26a9fb3.jpg?v=1675265810","url":"https:\/\/bookstorenmore.com\/products\/intro-correspondence-analysis-9781119041948","provider":"Bookstore N More","version":"1.0","type":"link"}