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Idioma: Inglés
Publicado por Guilford Publications, New York, 2022
ISBN 10: 1462549861 ISBN 13: 9781462549863
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Añadir al carritoHardcover. Condición: new. Hardcover. The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website () includes data sets and analysis examples from the book, up-to-date software information, and other resources. New to This Edition *Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables. *New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics. *Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples. *Most of the book is entirely new. Revised edition of the author's Applied missing data analysis, c2010. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Publicado por Guilford Press 2022-09-22, 2022
ISBN 10: 1462549861 ISBN 13: 9781462549863
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Añadir al carritoCondición: New. 2022. 2nd Edition. Hardcover. . . . . .
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Publicado por Guilford Publications, New York, 2022
ISBN 10: 1462549861 ISBN 13: 9781462549863
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Añadir al carritoHardcover. Condición: new. Hardcover. The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website () includes data sets and analysis examples from the book, up-to-date software information, and other resources. New to This Edition *Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables. *New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics. *Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples. *Most of the book is entirely new. Revised edition of the author's Applied missing data analysis, c2010. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Añadir al carritoCondición: New. 2022. 2nd Edition. Hardcover. . . . . . Books ship from the US and Ireland.
Librería: moluna, Greven, Alemania
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Añadir al carritoCondición: New. Craig K. Enders, PhD, is Professor and Area Chair in Quantitative Psychology in the Department of Psychology at the University of California, Los Angeles. His primary research focus is on analytic issues related to missing data analyses, and he le.
Idioma: Inglés
Publicado por Guilford Publications Aug 2022, 2022
ISBN 10: 1462549861 ISBN 13: 9781462549863
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 103,87
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Añadir al carritoBuch. Condición: Neu. Neuware - The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation, Bayesian estimation, and multiple imputation. The companion website includes datasets and analysis examples from the book, plus other resources.
Librería: preigu, Osnabrück, Alemania
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Añadir al carritoBuch. Condición: Neu. Applied Missing Data Analysis, Second Edition | Craig K. Enders | Buch | Einband - fest (Hardcover) | Englisch | 2022 | Guilford Publications | EAN 9781462549863 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Idioma: Inglés
Publicado por Guilford Publications, New York, 2022
ISBN 10: 1462549861 ISBN 13: 9781462549863
Librería: AussieBookSeller, Truganina, VIC, Australia
EUR 155,78
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Añadir al carritoHardcover. Condición: new. Hardcover. The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website () includes data sets and analysis examples from the book, up-to-date software information, and other resources. New to This Edition *Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables. *New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics. *Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples. *Most of the book is entirely new. Revised edition of the author's Applied missing data analysis, c2010. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Librería: Majestic Books, Hounslow, Reino Unido
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