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| Author | : Larry D. Schroeder |
| Publisher | : SAGE |
| Total Pages | : 100 |
| Release | : 1986-04 |
| Genre | : Social Science |
| ISBN | : 9780803927582 |
Providing beginners with a background to the frequently-used technique of linear regression, this text provides a heuristic explanation of the procedures and terms used in regression analysis and has been written at the most elementary level.
| Author | : Rachel A. Gordon |
| Publisher | : Routledge |
| Total Pages | : 567 |
| Release | : 2015-03-17 |
| Genre | : Social Science |
| ISBN | : 1317607112 |
Provides graduate students in the social sciences with the basic skills they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: •interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. •thorough integration of teaching statistical theory with teaching data processing and analysis. •teaching of Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.
| Author | : Rachel A. Gordon |
| Publisher | : Taylor & Francis |
| Total Pages | : 800 |
| Release | : 2023-11-15 |
| Genre | : Social Science |
| ISBN | : 1000894738 |
For graduate students in the social and health sciences, featuring essential concepts and equations most often needed in scholarly publications. Uses excerpts from the scholarly literature in these fields to introduce new concepts. Uses publicly-available data that are regularly used in social and health science publications to introduce Stata code and illustrate concepts and interpretation. Thoroughly integrates the teaching of statistical theory with teaching data processing and analysis. Offers guidance about planning projects and organizing code for reproducibility Shows how to recognize critiques of the constructions, terminology, and interpretations of statistics. New edition focuses on Stata, with code integrated into the chapters (rather than appendices, as in the first edition) includes Stata’s factor variables and margins commands and Long and Freese’s (2014) spost13 commands, to simplify programming and facilitate interpretation.
| Author | : Lawrence C. Marsh |
| Publisher | : SAGE |
| Total Pages | : 86 |
| Release | : 2001-09-14 |
| Genre | : Social Science |
| ISBN | : 9780761924203 |
Spline Regression Models shows how to use dummy variables to formulate and estimate spline regression models both in situations where the number and location of the spline knots are known in advance, and where estimation is required.
| Author | : Richard A. Berk |
| Publisher | : SAGE |
| Total Pages | : 286 |
| Release | : 2004 |
| Genre | : Mathematics |
| ISBN | : 9780761929048 |
PLEASE UPDATE SAGE INDIA AND SAGE UK ADDRESSES ON IMPRINT PAGE.
| Author | : Richard B. Darlington |
| Publisher | : Guilford Publications |
| Total Pages | : 689 |
| Release | : 2016-08-22 |
| Genre | : Social Science |
| ISBN | : 1462527981 |
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 use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable’s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.
| Author | : Guangqing Chi |
| Publisher | : SAGE Publications |
| Total Pages | : 229 |
| Release | : 2019-03-06 |
| Genre | : Social Science |
| ISBN | : 1544302053 |
Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.
| Author | : Daniel Zelterman |
| Publisher | : Cambridge University Press |
| Total Pages | : 296 |
| Release | : 2022-05-12 |
| Genre | : Medical |
| ISBN | : 1108786545 |
This textbook for students in the health and social sciences covers the basics of linear model methods with a minimum of mathematics, assuming only a pre-calculus background. Numerous examples drawn from the news and current events with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation. Code and datasets are available for download from the book's website at www.cambridge.org/zelterman
| Author | : Rachel A. Gordon |
| Publisher | : Routledge |
| Total Pages | : 634 |
| Release | : 2010 |
| Genre | : Mathematics |
| ISBN | : 0415991544 |
The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature. thorough integration of teaching statistical theory with teaching data processing and analysis. teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set. This book is for a one-semester course. For a two-semester course, see www.routledge.com/books/details/9780415875363/
| Author | : Robert Bickel |
| Publisher | : Guilford Press |
| Total Pages | : 385 |
| Release | : 2007-03-19 |
| Genre | : Psychology |
| ISBN | : 1609181069 |
This book provides a uniquely accessible introduction to multilevel modeling, a powerful tool for analyzing relationships between an individual-level dependent variable, such as student reading achievement, and individual-level and contextual explanatory factors, such as gender and neighborhood quality. Helping readers build on the statistical techniques they already know, Robert Bickel emphasizes the parallels with more familiar regression models, shows how to do multilevel modeling using SPSS, and demonstrates how to interpret the results. He discusses the strengths and limitations of multilevel analysis and explains specific circumstances in which it offers (or does not offer) methodological advantages over more traditional techniques. Over 300 dataset examples from research on educational achievement, income attainment, voting behavior, and other timely issues are presented in numbered procedural steps.
