Applied Regression Models in the Social Sciences

Rezensionen / Stimmen

'This textbook on applied regression by Poston, Conde, and Field is well suited for teaching graduate students in all of the social science fields, but is especially suited for demography, population studies, and public health. It is an excellent contribution to the teaching and practice of regression analysis in the twenty-first century that also can serve as a useful reference book for practitioners.' David Swanson, University of California, Riverside 'This textbook successfully combines theoretical statistical concepts and empirical research examples of applied regression models, from the basic to advanced ones. The best part is the detailed interpretations of the results of statistical analyses. Readers can truly understand the meanings and usages of these statistical numbers.' Lang-Wen Huang, Soochow University, Taipei 'Applied Regression Models in the Social Sciences excels in its focus on the application and interpretation of various regression models and its inclusion of commands in Stata and R. Its sequencing and topical coverage set it apart from others in that the reader is guided through the entire research process with a multitude of examples. This will be a valuable resource to faculty, students, and applied researchers alike.' Ginny Garcia-Alexander, University of Texas at San Antonio 'This textbook fills a critical void in the market for statistical tutorials by bridging the gap between elementary and advanced statistics and providing real-world examples in both Stata and R, allowing students to develop proficiency in the statistical software environments in highest demand. It strikes the perfect balance, neither oversimplifying nor overwhelming with complex mathematics, making it the ideal companion for graduate students seeking a solid foundation in the skills needed to generate social science findings with advanced insights. From correlation analysis to multi-level modeling, this comprehensive and versatile book covers a wide range of regression techniques, equipping learners with a diverse toolkit for trustworthy data analysis and allowing them to transition from the classroom to the laboratory with confidence. The authors' expertise shines through in this clear, comprehensive, and engaging book that is destined to be an indispensable resource.' Stephanie Bohon, University of Tennessee