05557nam 22007094a 450 991046514160332120200520144314.00-19-803866-61-280-55918-71-4294-0521-X(CKB)2560000000299382(EBL)272311(OCoLC)476010148(SSID)ssj0000204120(PQKBManifestationID)11181594(PQKBTitleCode)TC0000204120(PQKBWorkID)10175738(PQKB)11395372(StDuBDS)EDZ0000062293(MiAaPQ)EBC272311(Au-PeEL)EBL272311(CaPaEBR)ebr10218530(CaONFJC)MIL55918(OCoLC)71810603(EXLCZ)99256000000029938220050321d2006 uy 0engur|n|---|||||txtccrModels for intensive longitudinal data[electronic resource] /edited by Theodore A. Walls and Joseph L. SchaferOxford ;New York Oxford University Press20061 online resource (311 p.)Description based upon print version of record.0-19-517344-9 0-19-984705-3 Includes bibliographical references and index.Contents; 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 GEE2.3 Application: Reanalysis of the Control and Choice Data Using GEE3 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 Model5.3 Application: Personality Data5.4 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 Estimation7.4 Application 1: Connectivity Analysis with fMRI Data7.5 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 Dynamics9.3 Time-Delay Embedding9.4 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 Systems11.2 Statistical Issues for Intensive Longitudinal MeasurementIntroduction: 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 Social sciencesResearchStatistical methodsSocial sciencesLongitudinal studiesLongitudinal methodElectronic books.Social sciencesResearchStatistical methods.Social sciencesLongitudinal method.300/.72/7Walls Theodore A977011Schafer J. L(Joseph L.)117400MiAaPQMiAaPQMiAaPQBOOK9910465141603321Models for intensive longitudinal data2225726UNINA