LEADER 05466nam 22006734a 450 001 9910792235603321 005 20230818223418.0 010 $a0-19-803866-6 010 $a1-280-55918-7 010 $a1-4294-0521-X 035 $a(CKB)2560000000299382 035 $a(EBL)272311 035 $a(OCoLC)476010148 035 $a(SSID)ssj0000204120 035 $a(PQKBManifestationID)11181594 035 $a(PQKBTitleCode)TC0000204120 035 $a(PQKBWorkID)10175738 035 $a(PQKB)11395372 035 $a(StDuBDS)EDZ0000062293 035 $a(MiAaPQ)EBC272311 035 $a(Au-PeEL)EBL272311 035 $a(CaPaEBR)ebr10218530 035 $a(CaONFJC)MIL55918 035 $a(OCoLC)71810603 035 $a(EXLCZ)992560000000299382 100 $a20050321d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aModels for intensive longitudinal data /$fedited by Theodore A. Walls and Joseph L. Schafer 210 $aOxford ;$aNew York $cOxford University Press$d2006 215 $a1 online resource (311 pages) 311 0 $a0-19-517344-9 311 0 $a0-19-984705-3 320 $aIncludes bibliographical references and index. 327 $aContents; Contributors; Introduction: Intensive Longitudinal Data; 1 Multilevel Models for Intensive Longitudinal Data; 1.1 Behavioral Scientific Motivations for Collecting Intensive Longitudinal Data; 1.2 Overview of Multilevel Models; 1.3 Applying Multilevel Modeling to Intensive Longitudinal Data; 1.4 Application: Control and Choice in Indian Schoolchildren; 1.5 Summary; 2 Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations; 2.1 What Is GEE Regression?; 2.2 Practical Considerations in the Application of GEE 327 $a2.3 Application: Reanalysis of the Control and Choice Data Using GEE; 3 A Local Linear Estimation Procedure for Functional Multilevel Modeling; 3.1 The Model; 3.2 Practical Considerations; 3.3 Application: Smoking Cessation Study; 3.4 Discussion; 4 Application of Item Response Theory Models for Intensive Longitudinal Data; 4.1 IRT Model; 4.2 Estimation; 4.3 Application: Adolescent Smoking Study; 4.4 Discussion; 5 Fitting Curves with Periodic and Nonperiodic Trends and Their Interactions with Intensive Longitudinal Data; 5.1 Periodic and Nonperiodic Trends; 5.2 The Model 327 $a5.3 Application: Personality Data; 5.4 Discussion; 6 Multilevel Autoregressive Modeling of Interindividual Differences in the Stability of a Process; 6.1 Defining Stability as Regularity in a Time Series; 6.2 Multilevel Models; 6.3 A Multilevel AR(1) Model; 6.4 Application: Daily Alcohol Use; 6.5 Estimating This Model in SAS PROC MIXED; 6.6 Predicting the Individual AR(1) Coefficients; 6.7 Discussion; 7 The State-Space Approach to Modeling Dynamic Processes; 7.1 Gaussian State-Space Models; 7.2 Some Special Cases of State-Space Models; 7.3 Parameter Estimation 327 $a7.4 Application 1: Connectivity Analysis with fMRI Data; 7.5 Application 2: Testing the Induced Demand Hypothesis from Matched Traffic Profiles; 7.6 Conclusions; 8 The Control of Behavioral Input/Output Systems; 8.1 A Typical Input/Output System; 8.2 Modeling System Dynamics; 8.3 Controller Strategies to Meet an Output Target; 8.4 Fitting Dynamic Models to Intensive Longitudinal Data; 9 Dynamical Systems Modeling: An Application to the Regulation of Intimacy and Disclosure in Marriage; 9.1 Self-Regulation and Intrinsic Dynamics; 9.2 Coupled Regulation and Coupled Dynamics 327 $a9.3 Time-Delay Embedding; 9.4 Accounting for Individual Differences in Dynamics; 9.5 Application: Daily Intimacy and Disclosure in Married Couples; 9.6 Discussion; 10 Point Process Models for Event History Data: Applications in Behavioral Science; 10.1 Ecological Momentary Assessment of Smoking; 10.2 Point Process Models; 10.3 Application: An EMA Study of Smoking Data; 10.4 Discussion of Results; 10.5 Multivariate Point Patterns; 11 Emerging Technologies and Next-Generation Intensive Longitudinal Data Collection; 11.1 Intensive Data Collection Systems; 11.2 Statistical Issues for Intensive Longitudinal Measurement 330 $aIntroduction: Intensive Longitudinal Data Theodore A. Walls and Joseph L. Schafer1. Multilevel Models for Intensive Longitudinal Data, Theodore A. Walls, Hyekyung Jung, and Joseph E. Schwartz2. Marginal Modeling of Intensive Longitudinal Data by Generalized Estimating Equations, Joseph L. Schafer3. A Local Linear Estimation Procedure for Functional Multilevel Modeling, Runze Li, Tammy L. Root, and Saul Shiffman4. Application of Item Response Theory Models for Intensive Longitudinal Data, Donald Hedeker, Robin J. Mermelstein, and Brian R. Flay5. Periodic Trends, Non-periodic Trends, and their 606 $aSocial sciences$xResearch$xStatistical methods 606 $aSocial sciences$vLongitudinal studies 606 $aLongitudinal method 615 0$aSocial sciences$xResearch$xStatistical methods. 615 0$aSocial sciences 615 0$aLongitudinal method. 676 $a300/.72/7 701 $aWalls$b Theodore A$01529824 701 $aSchafer$b J. 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