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.