Fiori d'Italia : inni patriottici : Vallecrosia (Porto Maurizio) 25 Maggio 1910 / Giuseppe Musso

• Record Nr.: UNIBAS000037158
• Autore: Musso, Giuseppe
• Pubbl/distr/stampa: Torino : Muletti Giovanni, [s.d.]
• Descrizione fisica: 64 p. ; 19 cm.
• Disciplina: 782.421 599
• Soggetti: Canti patriottici
• Lingua di pubblicazione: Italiano
• Formato: Materiale a stampa
• Livello bibliografico: Monografia

Models for intensive longitudinal data / / edited by Theodore A. Walls and Joseph L. Schafer

• Record Nr.: UNINA9910792235603321
• Pubbl/distr/stampa: Oxford ; ; New York, : Oxford University Press, 2006
• ISBN: 0-19-803866-6; 1-280-55918-7; 1-4294-0521-X
• Descrizione fisica: 1 online resource (311 pages)
• Altri autori (Persone): WallsTheodore A; SchaferJ. L (Joseph L.)
• Disciplina: 300/.72/7
• Soggetti: Social sciences - Research - Statistical methods; Social sciences; Longitudinal method
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 GEE; 2.3 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; 5.3 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; 7.4 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; 9.3 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
• Sommario/riassunto: Introduction: 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