Jenine K. Harris. Statistics With R: Solving Problems Using Real-World Data. 2020. Thousand Oaks, California: Sage. Pages 733, ISBN-13: 978-1506388151; ISBN-10: 1506388159

This book uses innovative pedagogical principles to support the learning of foundational competencies in statistical theory and methods, with first-year statistics students as primary audience. I believe that the book is also a perfect resource for professionals who need to freshen up data analysis statistical skills in careers where data-driven decisions are essential. The carefully sequenced 11 chapters span 733 pages and use open-source data analysis software R, real-world problems, and real data. The book covers data analysis techniques students will need in their careers from data preparation, descriptive statistics, and data visualization to multinomial and ordered logistic regression.

While enabling foundational statistical competencies, the major contribution of this book is its unique features. First, by beginning each chapter with a story, this book engages the reader and helps address statistics anxiety through a storytelling approach. Second, this book involves problem-based learning with topics that students will be able to relate with and may find intriguing and controversial, including marijuana legalization; representation of women in data science careers; global access to clean water; voter registration; opioids policies; and cancer screening for transgender patients. Third, the book incorporates diversity through citing underrepresented voices in statistics and including example problems focused on diversity and inclusion with respect to race, ethnicity, sexual orientation, gender, and subject matter. The book particularly promotes gender equality in STEM by using a narrative of 3 women characters who are experts and learners of R and statistics.

The fourth and fifth features are the unique software and data used. The use of real-world data, which are often messy and may fail some statistical assumptions, makes the stereotypically boring subject of statistics more engaging and realistic for the learner. The book also uses a social justice approach by choosing an open-source software. R is truly an equalizer and enabler because of its zero-cost availability to students and low-resource businesses and organizations and the ability to do everything that expensive software can do. I find it truly appealing because, in my career, I had to switch from one analysis software to another because as I switched my places of work, the software I used either did not have the desired functionality or was too expensive for low-resource organizations. Sixth, the book underscores the importance of reproducible research and teaches ways to ensure reproducibility. Finally, the example problems solved use publicly available data for no cost or low cost. This approach supports many institutions aiming to reduce the cost burden on students.

The engaging and practical approach of this book is a breath of fresh air for learners because statistics anxiety persists in students, particularly those in social and behavioral sciences. Such anxiety partially persists because instructors in social and behavioral sciences rarely find textbooks that bring together statistical knowledge and programming, software that is affordable or free, real-world data, and representation of diverse people doing statistics. This book is a welcomed deviation from statistical tradition that emphasizes technical statistical computations and derivations often detached from real-world applications of those statistical techniques. I believe that this book will have additional appeal for instructors who cherish diversity and inclusion. The book is also valuable for professionals wanting to brush up on statistical concepts or learn R, which are essential skills for data scientists in this era of Big Data, open-source software, and Data Liberation wherein advances in IT and computer science have enabled data generation and sharing at an unprecedented rate. The publisher (Sage) has provided supplementary resources including R syntax and videos to enrich learning. I highly recommend this book to instructors for courses covering foundational competencies in statistics and data analysis.

-Gulzar H. Shah, PhD, MStat, MS

Professor and Department Chair

Jiann-Ping Hsu College of Public Health

Georgia Southern University, Savannah