Applied Longitudinal Data Analysis Modeling Change and Event Occurrence

ISBN-10: 0195152964
ISBN-13: 9780195152968
Edition: 2003
List price: $89.00 Buy it from $81.97
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Description: Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. In addition to these natural changes, targeted interventions may cause  More...

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Book details

List price: $89.00
Copyright year: 2003
Publisher: Oxford University Press, Incorporated
Publication date: 3/27/2003
Binding: Hardcover
Pages: 672
Size: 9.29" wide x 6.18" long x 1.50" tall
Weight: 2.596
Language: English

Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. In addition to these natural changes, targeted interventions may cause change: cholesterol levels may decline as a result of a new medication, exam grades may rise following completion of a coaching class. By measuring and charting changes like these - both naturalistic and experimentally induced - researchers uncover the temporal nature of development. The investigation of change has fascinated empirical researchers for generations, and to do it well, they must have longitudinal data. Applied Longitudinal Data Analysis is a much-needed professional book that will instruct readers in the many new methodologies now at their disposal to make the best use of longitudinal data, including both individual growth modelling and survival analysis. Throughout the chapters, the authors employ many cases and examples from a variety of disciplines, covering multilevel models, curvilinear and discontinuous change, in addition to discrete-time hazard models, continuous-time event occurrence, and Cox regression models. Applied Longitudinal Data Analysis is a unique contribution to the literature on research methods and will be useful to a wide range of behavioural and social science researchers.

Judith D. Singeris an Associate Professor at the Graduate School of Education, Harvard University.

John B. Willett is an Associate Professor at the Graduate School of Education, Harvard University.

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
Time-Varying Predictors
Recentering the Effect of TIME
Modeling Discontinuous and Nonlinear Change
Discontinuous Individual Change
Using Transformations to Model Nonlinear Individual Change
Representing Individual Change Using a Polynomial Function of TIME
Truly Nonlinear Trajectories
Examining the Multilevel Model's Error Covariance Structure
The "Standard" Specification of the Multilevel Model for Change
Using the Composite Model to Understand Assumptions about the Error Covariance Matrix
Postulating an Alternative Error Covariance Structure
Modeling Change Using Covariance Structure Analysis
The General Covariance Structure Model
The Basics of Latent Growth Modeling
Cross-Domain Analysis of Change
Extensions of Latent Growth Modeling
A Framework for Investigating Event Occurrence
Should You Conduct a Survival Analysis? The "Whether" and "When" Test
Framing a Research Question About Event Occurrence
Censoring: How Complete Are the Data on Event Occurrence?
Describing Discrete-Time Event Occurrence Data
The Life Table
A Framework for Characterizing the Distribution of Discrete-Time Event Occurrence Data
Developing Intuition About Hazard Functions, Survivor Functions, and Median Lifetimes
Quantifying the Effects of Sampling Variation
A Simple and Useful Strategy for Constructing the Life Table
Fitting Basic Discrete-Time Hazard Models
Toward a Statistical Model for Discrete-Time Hazard
A Formal Representation of the Population Discrete-Time Hazard Model
Fitting a Discrete-Time Hazard Model to Data
Interpreting Parameter Estimates
Displaying Fitted Hazard and Survivor Functions
Comparing Models Using Deviance Statistics and Information Criteria
Statistical Inference Using Asymptotic Standard Errors
Extending the Discrete-Time Hazard Model
Alternative Specifications for the "Main Effect of TIME"
Using the Complementary Log-Log Link to Specify a Discrete-Time Hazard Model
Time-Varying Predictors
The Linear Additivity Assumption: Uncovering Violations and Simple Solutions
The Proportionality Assumption: Uncovering Violations and Simple Solutions
The No Unobserved Heterogeneity Assumption: No Simple Solution
Residual Analysis
Describing Continuous-Time Event Occurrence Data
A Framework for Characterizing the Distribution of Continuous-Time Event Data
Grouped Methods for Estimating Continuous-Time Survivor and Hazard Functions
The Kaplan-Meier Method of Estimating the Continuous-Time Survivor Function
The Cumulative Hazard Function
Kernel-Smoothed Estimates of the Hazard Function
Developing an Intuition about Continuous-Time Survivor, Cumulative Hazard, and Kernel-Smoothed Hazard Functions
Fitting Cox Regression Models
Toward a Statistical Model for Continuous-Time Hazard
Fitting the Cox Regression Model to Data
Interpreting the Results of Fitting the Cox Regression Model to Data
Nonparametric Strategies for Displaying the Results of Model Fitting
Extending the Cox Regression Model
Time-Varying Predictors
Nonproportional Hazards Models via Stratification
Nonproportional Hazards Models via Interactions with Time
Regression Diagnostics
Competing Risks
Late Entry into the Risk Set
Notes
References
Index

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