Applied Longitudinal Data Analysis: Modeling Change and Event OccurrenceOxford University Press, 2003. 3. 27. - 644페이지 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. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: · Downloadable data sets · Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more · Additional material for data analysis |
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adolescents alcohol assess assumption asymptote average censored change trajectory compute construct covariance matrix Cox model Cox regression model cumulative hazard functions data collection describe deviance statistic dictors discrete-time hazard model error covariance event occurrence examine fitted hazard fixed effects goodness-of-fit grade hazard ratio homoscedasticity hypothesized identical ij ij includes individual change individual growth parameters individual’s initial status interaction interpretation interval Kaplan-Meier estimate level-2 residuals level-2 submodel likelihood likelihood function linear main effect measurement median lifetime methods metric model fitting model for change multilevel model null hypothesis odds outcome panel of figure parameter estimates PEER period person-period data set plots polynomial population postulate predictor values rate of change represent researchers residual variance results of fitting risk scores risk set sample specification standard errors statistical model strategies substantive predictors survival analysis survivor function time-invariant time-varying predictors tion variable variance components waves of data