Analysis Multivariate Social Science by Bartholomew David (21 results)
Analysis of Multivariate Social Science Data (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
David J. Bartholomew; Fiona Steele; Irini Moustaki; Jane Galbraith
Language: English
Published by Chapman and Hall/CRC (edition 2), 2011
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
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Paperback. Condition: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
Analysis of Multivariate Social Science Data (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
David J. Bartholomew,Irini Moustaki,Jane Galbraith,Fiona Steele
Language: English
Published by Chapman and Hall/CRC, 2011
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
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paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
Language: English
Published by Chapman and Hall/CRC, 2011
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
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Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Analysis of Multivariate Social Science Data
Bartholomew, David J., Galbraith, Jane, Steele, Fiona, Moustaki, Irini
Language: English
Published by CRC Press LLC, 2008
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
Seller: Better World Books Ltd, Dunfermline, United KingdomBetter World Books Ltd
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Condition: Very Good. 2 Edition. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
Language: English
Published by Chapman and Hall/CRC, 2011
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
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Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
Moustaki, Irini; Steele, Fiona; Chen, Yunxiao; Bartholomew, David
- Softcover
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Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
Moustaki, Irini; Steele, Fiona; Chen, Yunxiao; Bartholomew, David
- Softcover
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Analysis of Multivariate Social Science Data, Second Edition (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
David J. Bartholomew; Fiona Steele; Irini Moustaki; Jane Galbraith
Language: English
Published by Routledge, 2008
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
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Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
Moustaki, Irini; Steele, Fiona; Chen, Yunxiao; Bartholomew, David
- Softcover
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Analysis of Multivariate Social Science Data
Irini Moustaki, Fiona Steele, Yunxiao Chen, David Bartholomew
- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Paperback. Condition: New. Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and…statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.FeaturesContains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.Connects topics with terminology and principles of machine learning.Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.Covers models for hierarchical and longitudinal data and their connections to latent variable models.Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website.The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.
Analysis of Multivariate Social Science Data (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
Moustaki, Irini; Steele, Fiona; Chen, Yunxiao; Bartholomew, David
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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More imagesAnalysis of Multivariate Social Science Data
Irini Moustaki, Fiona Steele, Yunxiao Chen, David Bartholomew
- Softcover
Seller: Rarewaves USA, HEBRON, KY, U.S.A.Rarewaves USA
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Paperback. Condition: New. Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and…statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.FeaturesContains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.Connects topics with terminology and principles of machine learning.Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.Covers models for hierarchical and longitudinal data and their connections to latent variable models.Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website.The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.
Bartholomew, D: Analysis of Multivariate Social Science Data
David J. Bartholomew (London School of Economics and Political Science, UK)|Fiona Steele (University of Bristol, UK)|Jane Galbraith|Irini Moustaki (LSE, London, & Athens University Economics and Business, Gre)
Language: English
Published by CRC Press, 2008
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
Seller: moluna, Greven, Germanymoluna
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- Softcover
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More imagesAnalysis of Multivariate Social Science Data
Irini Moustaki, Fiona Steele, Yunxiao Chen, David Bartholomew
- Softcover
Seller: Rarewaves USA United, HEBRON, KY, U.S.A.Rarewaves USA United
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Paperback. Condition: New. Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and…statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.FeaturesContains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.Connects topics with terminology and principles of machine learning.Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.Covers models for hierarchical and longitudinal data and their connections to latent variable models.Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website.The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.
More imagesLanguage: English
Published by Taylor & Francis, 2008
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Analysis of Multivariate Social Science Data | David J. Bartholomew (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2008 | Taylor & Francis | EAN 9781584889601 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de |…Anbieter: preigu.
Analysis of Multivariate Social Science Data
Irini Moustaki, Fiona Steele, Yunxiao Chen, David Bartholomew
- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
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Paperback. Condition: New. Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and…statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.FeaturesContains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.Connects topics with terminology and principles of machine learning.Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.Covers models for hierarchical and longitudinal data and their connections to latent variable models.Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website.The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.
Analysis of Multivariate Social Science Data (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
Moustaki, Irini; Steele, Fiona; Chen, Yunxiao; Bartholomew, David
- Hardcover
Seller: California Books, Miami, FL, U.S.A.California Books
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Language: English
Published by Chapman And Hall/CRC Jun 2008, 2008
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Softcover
- Print on Demand
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Drawing on the authors' varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. W…ith updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models. After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signaling the move from descriptive to inferential methods and from interdependence to dependence. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data. Relying heavily on numerical examples, the authors provide insight into the purpose and working of the methods as well as the interpretation of data. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional exercises, encouraging readers to explore new ground in social science research. Requiring minimal mathematical and statistical knowledge, this book shows how various multivariate methods reveal different aspects of data and thus help answer substantive research questions. 384 pp. Englisch.
Bartholomew, D: Analysis of Multivariate Social Science Data
David J. Bartholomew (London School of Economics and Political Science, UK)|Fiona Steele (University of Bristol, UK)|Jane Galbraith|Irini Moustaki (LSE, London, & Athens University Economics and Business, Gre)
Language: English
Published by CRC Press, 2017
Series: Book 27 of 40 - Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences
- Hardcover
- Print on Demand
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. David J. Bartholomew, Fiona Steele, Fiona Steele, Irini MoustakiDrawing on the authors varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic…understandin.
