Statistics for the Social Sciences
A General Linear Model Approach
$48.00 ( ) USD
- Author: Russell T. Warne, Utah Valley University
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Written by a quantitative psychologist, this textbook explains complex statistics in accessible language to undergraduates in all branches of the social sciences. Built around the central framework of the General Linear Model (GLM), Statistics for the Social Sciences teaches students how different statistical methods are interrelated to one another. With the GLM as a basis, students with varying levels of background are better equipped to interpret statistics and learn more advanced methods in their later courses. Russell Warne makes statistics relevant to students' varying majors by using fascinating real-life examples from the social sciences. Students who use this book will benefit from clear explanations, warnings against common erroneous beliefs about statistics, and the latest developments in the philosophy, reporting and practice of statistics in the social sciences. The textbook is packed with helpful pedagogical features including learning goals, guided practice and reflection questions.Read more
- Written in an accessible way so students with varying levels of background can easily comprehend the material
- Makes the content relatable and relevant to students by including interesting real-life examples from the social sciences
- Robust pedagogical features include learning goals, guided practice, reflection questions and software guides
04th Jan 2018 by Kyounkin
Dr. Warne is an amazing author! You can tell this book was written with students in mind. It's nice to have a book that is not only affordable, but excellent at taking a complex subject and simplifying it down to a student level without losing nuance.
Review was not posted due to profanity×
- Date Published: December 2017
- format: Adobe eBook Reader
- isbn: 9781108548847
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
List of examples
1. Statistics and models
2. Levels of data
3. Visual models
4. Central tendency and variability
5. Linear transformations and z-scores
6. Probability and CLT
7. NHSST and z-tests
8. One-sample t-tests
9. Paired samples t-tests
10. Unpaired two-sample t-tests
11. Analysis of variance
14. Chi-squared test
15. Advanced methods
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