Overview

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.

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9781009391009

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Reviews

'Statistics Using Stata is an engaging and accessible introduction to the practice of statistics. Readers will benefit from the book's clear, step-by-step illustrations of statistical techniques and superb sample exercises, which use data and applications often encountered in the real world. Before they know it, users of this text will be confidently using Stata to analyze and interpret their own data.' Sean P. Corcoran, Vanderbilt University, USA 'This textbook has been extremely helpful in introducing students to issues in research design, instrument development, and data analysis. Every topic is explained clearly, with examples and code, making the preparation of lectures and recitation easier for me and my teaching assistants, and allowing students to review the material independently.' Alejandro Ganimian, New York University, USA 'Statistics Using Stata is a great intro stats text that assumes a minimal math background, provides good coverage of foundational topics, and has a lot of examples (code and data) to work through in Stata. I especially like the new chapter on public-use data sources. Great for teachers!' Peter F. Halpin, University of North Carolina, USA

Author Information

Sharon Lawner Weinberg is Professor of Applied Statistics and Psychology at New York University and formerly Vice Provost for Faculty Affairs at New York University, USA. She has taught, over many years in higher education, a broad range of statistics courses at both the undergraduate and graduate level, from introductory to advanced. She is a recipient of the NYU Steinhardt Teaching Excellence Award, the NYU Steinhardt Daniel Griffiths Award for Distinguished Research, and a four-time recipient of the Outstanding Reviewer Award for her work on the Editorial Board of the Educational Researcher, American Educational Research Association's (AERA's) flagship journal, as a reviewer of manuscripts submitted for publication. Her research has been supported by grants from both federal and private agencies, including the IES, NSF, and the Sloan Foundation, and she has published numerous papers on her research. She is the co-editor with NYU colleague, Lisa Stulberg, of Diversity in American Higher Education: Toward a More Comprehensive Approach (Routledge, 2011). She has also published numerous expository papers on methodology as book chapters and journal articles. Sarah Knapp Abramowitz is the John H. Evans Professor and Chair of the Department of Mathematics and Computer Science at Drew University, USA. She earned an A.B. in Mathematics from Cornell University and a Ph.D. in Mathematics Education from New York University, USA. She is an Associate Editor of the Journal of Statistics and Data Science Education and has published expository papers and presented at national conferences on topics related to the teaching of statistics. She is currently teaching an undergraduate course in statistics that uses this text along with a flipped approach, in which students watch instructor-created videos outside of class and spend class time participating in student-centered, activity-based learning. Daphna Harel is Associate Professor of Applied Statistics at New York University, USA. She is known for her innovative pedagogical approach to the teaching of statistics, from the introductory undergraduate to the advanced graduate level. She earned her BSc and PhD from the McGill University Department of Mathematics and Statistics, Canada. Her research has been supported by federal agencies and foundations, such as the National Institutes for Health, the Canadian Institutes for Health Research, and the Spencer Foundation. As a highly productive researcher, she has published numerous peer-reviewed articles across statistics, as well as several domain areas.

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