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. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models.
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除了最新的一些纵向分析方法没有涉及到之外，对一般的纵向数据处理方法都有涉及。结构清晰，举例详细，非常实用，而且在ucla的网站上还有可以下载的data配合教材进行示范操作。
评分: O212.7/S617
评分除了最新的一些纵向分析方法没有涉及到之外，对一般的纵向数据处理方法都有涉及。结构清晰，举例详细，非常实用，而且在ucla的网站上还有可以下载的data配合教材进行示范操作。
评分好看好看，上完一般意义上的HLM简直是自学multilevel growth curve 的最佳课本。
评分I never thought that I would use beautiful to describe a book on statistics. But this book IS beautifully written.
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