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Models for intensive longitudinal data / / edited by Theodore A. Walls and Joseph L. Schafer
| Models for intensive longitudinal data / / edited by Theodore A. Walls and Joseph L. Schafer |
| Autore | Walls Theodore A |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Oxford University Press, 2006 |
| Descrizione fisica | 1 online resource (311 pages) |
| Disciplina | 300/.72/7 |
| Altri autori (Persone) |
WallsTheodore A
SchaferJ. L (Joseph L.) |
| Soggetto topico |
Social sciences - Research - Statistical methods
Social sciences Longitudinal method |
| ISBN |
0-19-029163-X
0-19-803866-6 1-280-55918-7 1-4294-0521-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9911137361503321 |
| Walls Theodore A | ||
| Oxford University Press, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Models for intensive longitudinal data [[electronic resource] /] / edited by Theodore A. Walls and Joseph L. Schafer
| Models for intensive longitudinal data [[electronic resource] /] / edited by Theodore A. Walls and Joseph L. Schafer |
| Pubbl/distr/stampa | Oxford ; ; New York, : Oxford University Press, 2006 |
| Descrizione fisica | 1 online resource (311 p.) |
| Disciplina | 300/.72/7 |
| Altri autori (Persone) |
WallsTheodore A
SchaferJ. L (Joseph L.) |
| Soggetto topico |
Social sciences - Research - Statistical methods
Social sciences Longitudinal method |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-19-803866-6
1-280-55918-7 1-4294-0521-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 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 Model 5.3 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 Estimation 7.4 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 Dynamics 9.3 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 Systems 11.2 Statistical Issues for Intensive Longitudinal Measurement |
| Record Nr. | UNINA-9910465141603321 |
| Oxford ; ; New York, : Oxford University Press, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Models for intensive longitudinal data / / edited by Theodore A. Walls and Joseph L. Schafer
| Models for intensive longitudinal data / / edited by Theodore A. Walls and Joseph L. Schafer |
| Pubbl/distr/stampa | Oxford ; ; New York, : Oxford University Press, 2006 |
| Descrizione fisica | 1 online resource (311 pages) |
| Disciplina | 300/.72/7 |
| Altri autori (Persone) |
WallsTheodore A
SchaferJ. L (Joseph L.) |
| Soggetto topico |
Social sciences - Research - Statistical methods
Social sciences Longitudinal method |
| ISBN |
0-19-803866-6
1-280-55918-7 1-4294-0521-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910792235603321 |
| Oxford ; ; New York, : Oxford University Press, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Models for intensive longitudinal data / / edited by Theodore A. Walls and Joseph L. Schafer
| Models for intensive longitudinal data / / edited by Theodore A. Walls and Joseph L. Schafer |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Oxford ; ; New York, : Oxford University Press, 2006 |
| Descrizione fisica | 1 online resource (311 pages) |
| Disciplina | 300/.72/7 |
| Altri autori (Persone) |
WallsTheodore A
SchaferJ. L (Joseph L.) |
| Soggetto topico |
Social sciences - Research - Statistical methods
Social sciences Longitudinal method |
| ISBN |
0-19-029163-X
0-19-803866-6 1-280-55918-7 1-4294-0521-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9911091233203321 |
| Oxford ; ; New York, : Oxford University Press, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||