Sarah Knapp Abramowitz – författare

Statistics Using Stata uses a highly accessible and lively writing style to seamlessly integrate the learning of the latest version of Stata (17) with an introduction to applied statistics using real data in the behavioral, social, and health sciences. The text is comprehensive in its content coverage and is suitable at undergraduate and graduate levels. It requires knowledge of basic algebra, but no prior coding experience. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes a .do-file for each chapter, that was used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter.

Statistics Using R introduces the most up-to-date approaches to R programming alongside an introduction to applied statistics using real data in the behavioral, social, and health sciences. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes an online R tutorial for learning the basics of R, as well as two R files for each chapter, one in Base R code and the other in tidyverse R code, that were used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter. This textbook is appropriate for both undergraduate and graduate students in social sciences, applied statistics, and research methods.

Written in a clear and lively tone, Statistics Using IBM SPSS provides a data-centric approach to statistics with integrated SPSS (version 22) commands, ensuring that students gain both a deep conceptual understanding of statistics and practical facility with the leading statistical software package. With one hundred worked examples, the textbook guides students through statistical practice using real data and avoids complicated mathematics. Numerous end-of-chapter exercises allow students to apply and test their understanding of chapter topics, with detailed answers available online. The third edition has been updated throughout and includes a new chapter on research design, new topics (including weighted mean, resampling with the bootstrap, the role of the syntax file in workflow management, and regression to the mean) and new examples and exercises. Student learning is supported by a rich suite of online resources, including answers to end-of-chapter exercises, real data sets, PowerPoint slides, and a test bank.

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.

Building upon the success of the first edition, Statistics Using Stata uses the latest version of Stata to meet the needs of today''s students. Engaging and accessible for students from a variety of mathematical backgrounds, this textbook integrates statistical concepts with the Stata (version 16) software package. It aligns Stata commands with examples based on real data, enabling students to understand statistics in a way that reflects statistical practice. Capitalizing on Stata''s menu-driven ''point and click'' and program syntax interface, the chapters guide students from the comfortable ''point and click'' environment to the beginnings of statistical programming. Its coverage of essential topics gives instructors flexibility in curriculum planning and provides students with more advanced material to prepare for future work. Online resources - including solutions to exercises, PowerPoint slides, and Stata syntax (do-files) for each chapter - allow students to review independently and adapt code to analyze new problems.

Building upon the success of the first edition, Statistics Using Stata uses the latest version of Stata to meet the needs of today''s students. Engaging and accessible for students from a variety of mathematical backgrounds, this textbook integrates statistical concepts with the Stata (version 16) software package. It aligns Stata commands with examples based on real data, enabling students to understand statistics in a way that reflects statistical practice. Capitalizing on Stata''s menu-driven ''point and click'' and program syntax interface, the chapters guide students from the comfortable ''point and click'' environment to the beginnings of statistical programming. Its coverage of essential topics gives instructors flexibility in curriculum planning and provides students with more advanced material to prepare for future work. Online resources - including solutions to exercises, PowerPoint slides, and Stata syntax (do-files) for each chapter - allow students to review independently and adapt code to analyze new problems.

Written in a clear and lively tone, Statistics Using IBM SPSS provides a data-centric approach to statistics with integrated SPSS (version 22) commands, ensuring that students gain both a deep conceptual understanding of statistics and practical facility with the leading statistical software package. With one hundred worked examples, the textbook guides students through statistical practice using real data and avoids complicated mathematics. Numerous end-of-chapter exercises allow students to apply and test their understanding of chapter topics, with detailed answers available online. The third edition has been updated throughout and includes a new chapter on research design, new topics (including weighted mean, resampling with the bootstrap, the role of the syntax file in workflow management, and regression to the mean) and new examples and exercises. Student learning is supported by a rich suite of online resources, including answers to end-of-chapter exercises, real data sets, PowerPoint slides, and a test bank.