Read multiple correspondence analysis 163 quantitative applications in the social sciences by Brigitte Le Roux Henry Rouanet Online

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Requiring no prior knowledge of correspondence analysis, this text provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte LeRoux and Henry Rouanet, present thematerial in a practical manner, keeping the needs of researchers foremost in mind. Key Features Readers learn how to construct geometricRequiring no prior knowledge of correspondence analysis, this text provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte LeRoux and Henry Rouanet, present thematerial in a practical manner, keeping the needs of researchers foremost in mind.Key FeaturesReaders learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations.They also learn how to perform structured data analysis and to draw inferential conclusions from MCA.The text uses real examples to help explain concepts.The authors stress the distinctive capacity of MCA to handle full-scale research studies.This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers.Learn more about “The Little Green Book” - QASS Series! Click Here...

Title : multiple correspondence analysis 163 quantitative applications in the social sciences
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ISBN : 20033495
Format Type : Kindle Edition
Number of Pages : 128 Pages
Status : Available For Download
Last checked : 21 Minutes ago!

multiple correspondence analysis 163 quantitative applications in the social sciences Reviews

  • Santiago Ortiz
    2018-09-30 22:48

    This beautiful book also serves the purpose of being a great introduction to cultural analytics through a specific methodology. MCA is versatile, rich in variations and alternative or complementary paths. It basically requires a table of categories (that could be built out of variables that are not categorical, the book explains how). MCA is also quite easy to understand: how it's built and why it makes sense. Yes, the book contains some detailed mathematical definitions and demonstrations, specially regarding the variance and how the method is super-elegant and preserves many of the statistical properties one would wish. I believe mathematical equations can be overlooked without any harm of comprehension (I did it in part, and plan to re-read the book to touch those details when using the method in actual projects). Finally, this is also a book about sociology and specially about part of Pierre Bourdieu's work. Along the book a table based on an actual survey about taste (essential concept in Bordieu's studies) is being used as the example of the different analyses and visualizations.