PSYC 2200: Elementary Statistics for the Behavioral and Social Sciences

Reviewed by Rebeca Petean, Psychology Instructor, Portland State University on 8/14/25

Comprehensiveness rating: 5 see less

This textbook does a great job walking students through the “big picture” of statistics for the behavioral and social sciences. It starts with the basics—what data look like and how to describe them—then builds step-by-step toward more complex topics like hypothesis testing, t-tests, ANOVAs, correlations, and chi-square. I appreciate that each test has its own dedicated section, which makes it easy for students (and instructors) to find exactly what they need. The coverage feels complete for an undergraduate course, though it might be even stronger with a few more real-world examples drawn from a wider range of social science research.

Content Accuracy rating: 5

From what I’ve reviewed, the content is accurate, clear, and in line with standard statistical practices. The explanations match what’s taught in most social science statistics courses, and the formulas and definitions are correct. The tone is approachable without oversimplifying, which helps students build confidence while still learning the “right way” to do things. I didn’t spot any errors or outdated information.

Relevance/Longevity rating: 5

This is the kind of material that doesn’t go out of style anytime soon. Core statistical concepts—like calculating a mean, running a t-test, or interpreting an ANOVA—are timeless in the behavioral and social sciences. Because of that, the book will stay relevant for years, with any future updates likely just needing fresh examples or datasets. Including APA style is a nice touch too, since it gives students a head start on professional reporting standards they’ll need beyond the classroom.

Clarity rating: 5

The writing is clear and approachable, which is especially important for a subject that can feel intimidating to students. The explanations break down statistical concepts step-by-step, and the use of examples makes it easier to connect abstract ideas to real-world situations. Technical terms are introduced with enough context so that students aren’t left guessing, and the structure of each section helps reinforce understanding before moving on.

Consistency rating: 5

The text uses terminology and notation consistently from start to finish, which really helps prevent confusion. Once a concept is introduced—like “mean difference” or “p-value”—it’s applied in the same way throughout the book. The framework and style stay steady too, so students can focus on learning the material rather than adjusting to new formats or language in each chapter.

Modularity rating: 5

The book is easy to break into smaller chunks for teaching. Each topic stands well on its own, so an instructor can assign readings in different orders or skip sections without losing flow. The logical division into units and subtopics also means students can revisit a single concept—like regression or chi-square—without having to reread unrelated material. That flexibility makes it a great fit for different course structures.

Organization/Structure/Flow rating: 5

The book’s organization feels natural and easy to follow. It starts with foundational concepts before moving into more complex analyses, which helps students build confidence step-by-step. The chapters are well-sequenced, and the transitions between topics are smooth, so it never feels like a sudden jump. This structure works well for both teaching and self-paced learning.

Interface rating: 5

The online format is clean and easy to navigate. Headings, tables, and figures display clearly without distortion, and the layout makes it simple to jump between sections

Grammatical Errors rating: 5

Sentences are well-constructed, punctuation is correct, and the tone balances professionalism with accessibility. The grammar supports the clarity of the content.

Cultural Relevance rating: 5

The text is culturally neutral and free from bias or insensitive language. While the examples are clear and accessible, there’s room to incorporate a broader range of cultural contexts and research scenarios so that students from different backgrounds can see themselves reflected in the material. Even small additions—like datasets from diverse communities or case studies across various cultural settings—could make the book feel even more inclusive.

Overall, this is a well-written, well-structured, and student-friendly statistics textbook. It balances clarity with depth, making complex topics approachable without oversimplifying. With a few more diverse, real-world examples, it could be an even stronger resource for today’s classrooms. I would recommend it to colleagues looking for an open, adaptable, and reliable text for behavioral and social science statistics courses.