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Course starting semester Spring Spring Spring Spring Main field of study Computational Social Science. Level Second cycle. Course type Programme course. Examiner Benjamin Jarvis. Course coordinator Benjamin Jarvis. Director of studies or equivalent Karl Wennberg. With the book, Long and Freese provide a suite of commands for model interpretation, hypothesis testing, and model diagnostics.
The new commands that accompany the third edition make it easy to include powers or interactions of covariates in regression models and work seamlessly with models estimated with complex survey data. The authors' new commands greatly simplify the use of margins, in the same way that the marginsplot command harnesses the power of margins for plotting predictions.
The authors discuss how to use margins and their new mchange, mtable, and mgen commands to compute tables and to plot predictions. They also discuss how to use these commands to estimate marginal effects, averaged either over the sample or at fixed values of the regressors. The authors introduce and advocate a variety of new methods that use predictions to interpret the effect of variables in regression models.
The third edition begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fit, and interpretation of this class of models. New to the third edition is an entire chapter about how to interpret regression models using predictions—a chapter that is expanded upon in later chapters that focus on models for binary, ordinal, nominal, and count outcomes.
Long and Freese use many concrete examples in their third edition. All the examples, datasets, and author-written commands are available on the authors' website, so readers can easily replicate the examples with Stata. This book is ideal for students or applied researchers who want to learn how to fit and interpret models for categorical data. After reviewing the linear regression model and introducing maximum likelihood estimation, Long extends the binary logit and probit models, presents multinomial and conditioned logit models and describes models for sample selection bias.
Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables.
The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity.
Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs.
These features help students learn to choose the appropriate models for their purpose. This book presents the econometric analysis of single-equation and simultaneous-equation models in which the jointly dependent variables can be continuous, categorical, or truncated. Despite the traditional emphasis on continuous variables in econometrics, many of the economic variables encountered in practice are categorical those for which a suitable category can be found but where no actual measurement exists or truncated those that can be observed only in certain ranges.
Such variables are involved, for example, in models of occupational choice, choice of tenure in housing, and choice of type of schooling. Models with regulated prices and rationing, and models for program evaluation, also represent areas of application for the techniques presented by the author. Ordinal measures provide a simple and convenient way to distinguish among possible outcomes.
The book provides practical guidance on using ordinal outcome models. The book provides br. Readers will become familiar with applications of ordinary least squares OLS regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models.
The author returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.
Available with Perusall—an eBook that makes it easier to prepare for class Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective.
Learn more. Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics.
Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The companion website www.
An accessible introduction to the use of regression analysis in the social sciences Regression with Social Data: Modeling Continuous and Limited Response Variables represents 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.
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 Survival analysis using multistate, multiepisode, and interval-censored survival models Concepts are reinforced throughout with numerous chapter problems, exercises, and real data sets.
Step-by-step solutions plus an appendix of mathematical tutorials make even complex problems accessible to readers with only moderate math skills. Multiple regression analysis has been widely used by researchers to analyze complex social problems since the s. A specialization in economics, known as econometrics, developed out of a recognition that multiple regression is based upon a large number of assumptions--many of which are commonly violated in specific applications, as well as a variety of corrective measures for estimating regression models in the presence of many of the violations.
Unfortunately, the mathematical sophistication required to understand the econometrics literature started out high and has continued to rise over the years. Seller Inventory n. Seller Inventory Q Book Description Hardcover. Seller Inventory SGE New copy - Usually dispatched within 4 working days.
Seller Inventory B Long, John Scott. This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Synopsis About this title A unified treatment of the most useful models for categorical and limited dependent variables CLDVs is provided in this book. Review : "Regression Models for Categorical and Limited Dependent Variables excels at explaining applications of nonlinear regression models. Buy New Learn more about this copy. Customers who bought this item also bought.
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