Read more about Learning Statistics with R: A tutorial for psychology students and other beginners

Learning Statistics with R: A tutorial for psychology students and other beginners

(1 review)

Danielle Navarro, University of New South Wales

Copyright Year: 2018

Publisher: Danielle Navarro

Language: English

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Reviewed by Jessica Salvatore, Associate Professor, Sweet Briar College on 1/10/20

This text, version 0.6, clocks in at over 600 manuscript pages (to date no version has been typeset) -- but the length is worth it to gain great coverage. Navarro covers not only everything you could expect to learn in a two-course sequence of... read more

Table of Contents

I. Background

  • Chapter 1: Why do we learn statistics?
  • Chatper 2: A brief introduction to research design

II. An introduction to R

  • Chapter 3: Getting started with R
  • Chapter 4: Additional R concepts

III. Working with data

  • Chapter 5: Descriptive statistics
  • Chapter 6: Drawing graphs
  • Chapter 7: Pragmatic matters
  • Chapter 8: Basic programming

IV. Statistical theory

  • Prelude
  • Chapter 9: Introduction to probability
  • Chapter 10: Estimating unknown quantities from a sample
  • Chapter 11: Hypothesis testing

V. Statistical tools

  • Chapter 12: Categorical data analysis
  • Chapter 13: Comparing two means
  • Chapter 14: Comparing several means (one-way ANOVA)
  • Chapter 15: Linear regression
  • Chapter 16: Factorial ANOVA

VI. Other topics

  • Chapter 17: Bayesian statistics
  • Chapter 18: Epilogue
  • References

About the Book

Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.

About the Contributors


Danielle Navarro, PhD is a computational cognitive scientist at the University of New South Wales. Her research focuses on human concept learning, reasoning and decision making. She is also interested in language and cultural evolution, cognitive development, and statistical methods in the behavioural sciences