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Longitudinal data analysis / Donald Hedeker, Robert D. Gibbons.

By: Hedeker, Donald R, 1958-.
Contributor(s): Gibbons, Robert D, 1955- [(j.a.)].
Series: Wiley series in probability and statistics. Publisher: Hoboken, N.J. : Wiley-Interscience, c2006Description: xx, 337 p. : ill. ; 25 cm.ISBN: 0471420271 (hbk.); 9780471420279 (hbk.).Subject(s): Longitudinal method | Medicine -- Research -- Statistical methods | Medical sciences -- Research -- Statistical methods | Social sciences -- Research -- Statistical methodsDDC classification: 610.727
Contents:
Preface. - Acknowledgments. - Acronyms. - 1. Introduction. 2. ANOVA approaches to longitudinal data. - 3. ANOVA approaches to longitudinal data. - 4. Mixed-effects regression models for continuous outcomes. - 5. Mixed-effects polynomial regression models. - 6. Covariance pattern models. - 7. Mixed regression models with autocorrelated errors. - 8. Generalized estimating equations (GEE) models. - 9. Mixed-effects regression models for binary outcomes. - 10. Mixed-effects regression models for ordinal outcomes. - 11. Mixed-effects regression models for nominal data. - 12. Mixed-effects regression models for counts. - 13. Mixed-effects regression models for three-level data. - 14. Missing data in longitudinal studies. - Bibliography. - Topic index.
Item type Current location Call number Copy number Status Notes Date due Barcode
Graduate Collection Taylor's Library-TU
610.727 HED (Browse shelf) 1 Available SHTEx,60001,03,GR 1000808684

Includes bibliographical references (p. 313-334) and index.

Preface. - Acknowledgments. - Acronyms. - 1. Introduction. 2. ANOVA approaches to longitudinal data. - 3. ANOVA approaches to longitudinal data. - 4. Mixed-effects regression models for continuous outcomes. - 5. Mixed-effects polynomial regression models. - 6. Covariance pattern models. - 7. Mixed regression models with autocorrelated errors. - 8. Generalized estimating equations (GEE) models. - 9. Mixed-effects regression models for binary outcomes. - 10. Mixed-effects regression models for ordinal outcomes. - 11. Mixed-effects regression models for nominal data. - 12. Mixed-effects regression models for counts. - 13. Mixed-effects regression models for three-level data. - 14. Missing data in longitudinal studies. - Bibliography. - Topic index.