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Statistics Using R
An Integrative Approach
2nd edition
, New York University, , New York University, , Drew University
Online ISBN: 9781009400145
Online publication date:
07 December 2023
- Textbook
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.
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.
Research Methods and Applied Statistics
A Practical Guide to Conducting Research
, Eastern Connecticut State University
Coming soon
Online ISBN: 9781009361422
Expected online publication date:
October 2025
- Textbook
Students have an almost insurmountable task in understanding statistics in the psychological sciences and applying them to a research study. This textbook tackles this source of stress by guiding students through the research process, start to finish, from writing a proposal and performing the study, to analysing the results and creating a report and presentation. This truly practical textbook explains psychology research methods in a conversational style, with additional material of interest placed in focus boxes alongside, so that students don't lose their way through the steps. Every step is detailed visually with processes paralleled in both SPSS and R, allowing instructors and students to learn both statistical packages or to bridge from one to the other. Students perform hands-on statistical exercises using real data, and both qualitative and mixed-methods research are covered. They learn effective ways to present information visually, and about free tools to collect and analyse data.
Students have an almost insurmountable task in understanding statistics in the psychological sciences and applying them to a research study. This textbook tackles this source of stress by guiding students through the research process, start to finish, from writing a proposal and performing the study, to analysing the results and creating a report and presentation. This truly practical textbook explains psychology research methods in a conversational style, with additional material of interest placed in focus boxes alongside, so that students don't lose their way through the steps. Every step is detailed visually with processes paralleled in both SPSS and R, allowing instructors and students to learn both statistical packages or to bridge from one to the other. Students perform hands-on statistical exercises using real data, and both qualitative and mixed-methods research are covered. They learn effective ways to present information visually, and about free tools to collect and analyse data.
Regression and Other Stories
, Columbia University, New York, , New York University, , Aalto University, Finland
Online ISBN: 9781139161879
Online publication date:
01 December 2020
Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
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