Applied Longitudinal Data Analysis

• Brings together a number of recent advances in methodology, making them accessible to a broad range of researchers
• Shows how to select an appropriate method given a research question
• Will be useful for behavioural and social researchers using quantitative methods to analyse longitudinal data

Readership: Social and developmental psychologists

Judith D. Singer, James Bryant Conant Professor, and John B. Willett, Charles William Eliot Professor, both at Harvard University Graduate School of Education, USA

Part I
1: A framework for investigating change over time
2: Exploring Longitudinal Data on Change
3: Introducing the multilevel model for change
4: Doing data analysis with the multilevel mode for change
5: Treating TIME more flexibly
6: Modelling discontinuous and nonlinear change
7: Examining the multilevel model's error covariance structure
8: Modelling change using covariance structure analysis
Part II
9: A Framework for Investigating Event Occurrence
10: Describing discrete-time event occurrence data
11: Fitting basic Discrete-Time Hazard Models
12: Extending the Discrete-Time Hazard Model
13: Describing Continuous-Time Event Occurrence Data
14: Fitting Cox Regression Models
15: Extending the Cox Regression Model