Longitudinal Analysis (Multivariate Applications Series),Used

Longitudinal Analysis provides an accessible, applicationoriented treatment of introductory and advanced linear models for withinperson fluctuation and change. Organized by research design and data type, the text uses indepth examples to provide a complete description of the modelbuilding process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter.Written for nonmathematicallyoriented readers, this text features:A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own dataAn emphasis on how the terminology, interpretation, and estimationof familiar general linear models relates to those of more complex models for longitudinal dataIntegrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers understanding of the overall modelbuilding processSample results sections for each example to provide useful templates for published reportsExamples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own dataThe book opens with the building blocks of longitudinal analysisgeneral ideas, the general linear model for betweenperson analysis, and between and withinperson models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe withinperson fluctuation over time and random effects models for withinperson change. Conditional longitudinal models are presented in section 3, including both timeinvariant and timevarying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, threelevel models for multiple dimensions of withinperson time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for nonnormal outcomes and intensive longitudinal data.Classtested at the University of NebraskaLincoln and in intensive summer workshops, this is an ideal text for graduatelevel courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The books accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.