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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

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.

TOC:

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