Applied longitudinal data analysis : modeling change and event occurrence /
| Main Authors: | , |
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| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Oxford ; New York :
Oxford University Press,
[2003]
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- A Framework for Investigating Change over Time
- When Might You Study Change over Time?
- Distinguishing Between Two Types of Questions about Change
- Three Important Features of a Study of Change
- Exploring Longitudinal Data on Change
- Creating a Longitudinal Data Set
- Descriptive Analysis of Individual Change over Time
- Exploring Differences in Change across People
- Improving the Precision and Reliability of OLS-Estimated Rates of Change: Lessons for Research Design
- Introducing the Multilevel Model for Change
- What Is the Purpose of the Multilevel Model for Change?
- The Level-1 Submodel for Individual Change
- The Level-2 Submodel for Systematic Interindividual Differences in Change
- Fitting the Multilevel Model for Change to Data
- Examining Estimated Fixed Effects
- Examining Estimated Variance Components
- Doing Data Analysis with the Multilevel Model for Change
- Example: Changes in Adolescent Alcohol Use
- The Composite Specification of the Multilevel Model for Change
- Methods of Estimation, Revisited
- First Steps: Fitting Two Unconditional Multilevel Models for Change
- Practical Data Analytic Strategies for Model Building
- Comparing Models Using Deviance Statistics
- Using Wald Statistics to Test Composite Hypotheses About Fixed Effects
- Evaluating the Tenability of a Model's Assumptions
- Model-Based (Empirical Bayes) Estimates of the Individual Growth Parameters
- Treating TIME More Flexibly
- Variably Spaced Measurement Occasions
- Varying Numbers of Measurement Occasions.