Regression With Social Data – Modeling Continuous and Limited Response Variables

An introduction to the use of regression analysis in the social sciences for graduate-level behavioral science students and practitioners. This work covers techniques that span various levels of measurement for both continuous and limited response variables.

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This work is an accessible introduction to the use of regression analysis in the social sciences. This is the most complete and fully integrated coverage of regression modeling currently available for graduate-level behavioral science students and practitioners. Covering techniques that span the full spectrum of levels of measurement for both continuous and limited response variables, and using examples taken from such disciplines as sociology, psychology, political science, and public health, the author succeeds in demystifying an academically rigorous subject and making it accessible to a wider audience. The content includes coverage of: Logit, probit, scobit, truncated, and censored regressions; Multiple regression with ANOVA and ANCOVA models; Binary and multinomial response models; Poisson, negative binomial, and other regression models for event-count data; and, Survival analysis using multistate, multiepisode, and interval-censored survival models.