Descriptive and inferential statistics for the behavioral and neurological sciences are considered. Techniques such as t-tests, factorial analysis of (co)variance, correlation, multiple regression, and nonparametric tests are introduced. Subject provides an introductory overview of some advanced methods such as path analysis, factor analysis, discriminant analysis, and analysis of functional MRI data. Basic issues of research design and methodology intimately associated with data analysis are discussed.
Presents quantitative approaches to theory construction in the context of multiple response variables, with models for both continuous and categorical data. Topics include the statistical basis for causal inference; principles of path analysis; linear structural equation analysis incorporating measurement models; latent class regression; and analysis of panel data with observed and latent variable models. Draws examples from the social sciences, including the status attainment approach to intergenerational mobility, behavior genetics models of disease and environment, consumer satisfaction, functional impairment and disability, and quality of life.
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