LEADER 05727nam 22007454a 450 001 9911020009603321 005 20200520144314.0 010 $a9786610447619 010 $a9781280447617 010 $a1280447613 010 $a9780470036488 010 $a0470036486 010 $a9780470036471 010 $a0470036478 035 $a(CKB)1000000000355769 035 $a(EBL)257031 035 $a(OCoLC)71626407 035 $a(SSID)ssj0000194112 035 $a(PQKBManifestationID)11188457 035 $a(PQKBTitleCode)TC0000194112 035 $a(PQKBWorkID)10231424 035 $a(PQKB)11619141 035 $a(MiAaPQ)EBC257031 035 $a(Perlego)2770879 035 $a(EXLCZ)991000000000355769 100 $a20051219d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLongitudinal data analysis /$fDonald Hedeker, Robert D. Gibbons 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (369 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 08$a9780471420279 311 08$a0471420271 320 $aIncludes bibliographical references (p. 313-334) and index. 327 $aLONGITUDINAL DATA ANALYSIS; CONTENTS; Preface; Acknowledgments; Acronyms; 1 Introduction; 1.1 Advantages of Longitudinal Studies; 1.2 Challenges of Longitudinal Data Analysis; 1.3 Some General Notation; 1.4 Data Layout; 1.5 Analysis Considerations; 1.6 General Approaches; 1.7 The Simplest Longitudinal Analysis; 1.7.1 Change Score Analysis; 1.7.2 Analysis of Covariance of Post-test Scores; 1.7.3 ANCOVA of Change Scores; 1.7.4 Example; 1.8 Summary; 2 ANOVA Approaches to Longitudinal Data; 2.1 Single-Sample Repeated Measures ANOVA; 2.1.1 Design; 2.1.2 Decomposing the Time Effect 327 $a2.1.2.1 Trend Analysis-Orthogonal Polynomial Contrasts2.1.2.2 Change Relative to Baseline-Reference Cell Contrasts; 2.1.2.3 Consecutive Time Comparisons-Profile Contrasts; 2.1.2.4 Contrasting Each Timepoint to the Mean of Subsequent Timepoints-Helmert Contrasts; 2.1.2.5 Contrasting Each Timepoint to the Mean of Others-Deviation Contrasts; 2.1.2.6 Multiple Comparisons; 2.2 Multiple-Sample Repeated Measures ANOVA; 2.2.1 Testing for Group by Time Interaction; 2.2.2 Testing for Subject Effect; 2.2.3 Contrasts for Time Effects; 2.2.3.1 Orthogonal Polynomial Partition of SS 327 $a2.2.4 Compound Symmetry and Sphericity2.2.4.1 Sphericity; 2.3 Illustration; 2.4 Summary; 3 MANOVA Approaches to Longitudinal Data; 3.1 Data Layout for ANOVA versus MANOVA; 3.2 MANOVA for Repeated Measurements; 3.2.1 Growth Curve Analysis-Polynomial Representation; 3.2.2 Extracting Univariate Repeated Measures ANOVA Results; 3.2.3 Multivariate Test of the Time Effect; 3.2.4 Tests of Specific Time Elements; 3.3 MANOVA of Repeated Measures-s Sample Case; 3.3.1 Extracting Univariate Repeated Measures ANOVA Results; 3.3.2 Multivariate Tests; 3.4 Illustration; 3.5 Summary 327 $a4 Mixed-Effects Regression Models for Continuous Outcomes4.1 Introduction; 4.2 A Simple Linear Regression Model; 4.3 Random Intercept MRM; 4.3.1 Incomplete Data Across Time; 4.3.2 Compound Symmetry and Intraclass Correlation; 4.3.3 Inference; 4.3.4 Psychiatric Dataset; 4.3.5 Random Intercept Model Example; 4.4 Random Intercept and Trend MRM; 4.4.1 Random Intercept and Trend Example; 4.4.2 Coding of Time; 4.4.2.1 Example; 4.4.3 Effect of Diagnosis on Time Trends; 4.5 Matrix Formulation; 4.5.1 Fit of Variance-Covariance Matrix; 4.5.2 Model with Time-Varying Covariates 327 $a4.5.2.1 Within and Between-Subjects Effects for Time-Varying Covariates4.5.2.2 Time Interactions with Time-Varying Covariates; 4.6 Estimation; 4.6.1 ML Bias in Estimation of Variance Parameters; 4.7 Summary; 5 Mixed-Effects Polynomial Regression Models; 5.1 Introduction; 5.2 Curvilinear Trend Model; 5.2.1 Curvilinear Trend Example; 5.3 Orthogonal Polynomials; 5.3.1 Model Representations; 5.3.2 Orthogonal Polynomial Trend Example; 5.3.3 Translating Parameters; 5.3.4 Higher-Order Polynomial Models; 5.3.5 Cubic Trend Example; 5.4 Summary; 6 Covariance Pattern Models; 6.1 Introduction 327 $a6.2 Covariance Pattern Models 330 $aLongitudinal data analysis for biomedical and behavioral sciencesThis innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data.Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroo 410 0$aWiley series in probability and statistics. 606 $aLongitudinal method 606 $aMedicine$xResearch$xStatistical methods 606 $aMedical sciences$xResearch$xStatistical methods 606 $aSocial sciences$xResearch$xStatistical methods 615 0$aLongitudinal method. 615 0$aMedicine$xResearch$xStatistical methods. 615 0$aMedical sciences$xResearch$xStatistical methods. 615 0$aSocial sciences$xResearch$xStatistical methods. 676 $a610.72/7 700 $aHedeker$b Donald R.$f1958-$01837379 701 $aGibbons$b Robert D.$f1955-$01837380 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020009603321 996 $aLongitudinal data analysis$94416088 997 $aUNINA