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 T. 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.

lgli/R:\libgen-is\2692000/60dd03fdd5638d50e363f306d7236af2..pdf

nexusstc/Statistics for the Social Sciences/60dd03fdd5638d50e363f306d7236af2.pdf

lgrsnf/1107576970.pdf

zlib/Self-Help, Relationships & Lifestyle/Diet & Nutrition/Russell T. Warne/Statistics for the Social Sciences_5921490.pdf

Warne, Russell T.

United Kingdom and Ireland, United Kingdom

Cambridge, United Kingdom, 2018

lg2776956

producers:
Acrobat Distiller 10.0.0 (Windows); modified using iText® 5.3.2 ©2000-2012 1T3XT BVBA (AGPL-version)

{"isbns":["1107576970","2017023186","9781107576971"]}

Cover
Half-title
Title page
Copyright information
Dedication
Table of contents
Preface
What Makes this Textbook Different
For Students
For Instructors
Last Words
Acknowledgements
List of Examples
1 Statistics and Models
Learning Goals
Why Statistics Matters
Two Branches of Statistics
Models
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
2 Levels of Data
Learning Goals
Defining What to Measure
Levels of Data
So What?
Other Ways to Classify Data
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
3 Visual Models
Learning Goals
Sample Data
Frequency Tables
Histograms
Describing Shapes of Histograms
Number of Peaks
Describing Distribution Shapes: A Caveat
Frequency Polygons
Box Plot
Bar Graphs
Stem-and-Leaf Plots
Line Graphs
Pie Charts
Scatterplots
Selecting the Proper Visual Model
Other Visual Models
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
4 Models of Central Tendency and Variability
Learning Goals
Models of Central Tendency
Models of Variability
Using Models of Central Tendency and Variance Together
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
5 Linear Transformations and z-Scores
Learning Goals
Linear Transformations
z-Scores: A Special Linear Transformation
Linear Transformations and Scales
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
6 Probability and the Central Limit Theorem
Learning Goals
Basic Probability
The Logic of Inferential Statistics and the CLT
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
7 Null Hypothesis Statistical Significance Testing and z-Tests
Learning Goals
Null Hypothesis Statistical Significance Testing
z-Test
Cautions for Using NHSTs
General Linear Model
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
8 One-Sample t-Tests
Learning Goals
Shortcomings of the z-Test – and a Solution
Steps of a One-Sample t-Test
The p-Value in a One-Sample t-Test
Caveats for One-Sample t-Tests
Confidence Intervals (CIs)
Another Use of the One-Sample t-Tests
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
9 Paired-Samples t-Tests
Learning Goals
When to Use the Paired Two-Sample t-Test
Steps of a Paired-Samples t-Test
Finding p
Old Concerns
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
10 Unpaired Two-Sample t-Tests
Learning Goals
Making Group Comparisons
Steps of an Unpaired-Samples t-Test
p and Type I and Type II errors
Assumptions of the Unpaired Two-Sample t-Test
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
11 Analysis of Variance
Learning Goals
Comparing Means from Three or More Groups
Problems with Multiple t-Tests
Solutions to Problems of Multiple Comparisons
ANOVA
p and Type I and Type II Errors
Additional Thoughts on ANOVA
Post Hoc Tests
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
12 Correlation
Learning Goals
Purpose of the Correlation Coefficient
Definition of a Correlation
Calculating Pearson’s r
Interpreting Pearson’s r
Visualizing Correlations
Pearson’s r in the Null Hypothesis Testing Context
Warning: Correlation Is Not Causation
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
13 Regression
Learning Goals
Using Pearson’s r to Make Predictions
The Regression Line
Regression Towards the Mean
Assumptions of Regression and Correlation
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
14 Chi-Squared Test
Learning Goals
Nominal Outcomes
One-Variable Chi-Squared Test
Two-Variable Chi-Squared Test
Information About Odds Ratios
Summary
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
15 Applying Statistics to Research, and Advanced Statistical Methods
Learning Goals
How to Choose a Statistical Method
Multiple Regression and Related Procedures
Nonparametric Statistics
Multivariate Methods
Reflection Questions: Comprehension
Reflection Questions: Application
Further Reading
Appendix A1 z-Table
Appendix A2 t-Table
Appendix A3 F-Table
Appendix A4 Q-table (Tukey’s Post Hoc Test)
Appendix A5 Critical Values for r
Appendix A6 .2-Table
Glossary
Answer Key
References
Name Index
Subject Index

"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."-- Résumé de l'éditeur

"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"--Provided by publisher

Machine generated contents note: Preface; Acknowledgements; 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; 12. Correlation; 13. Regression; 14. Chi-squared test; 15. Advanced methods; Appendices; Glossary; Answer key; References; Index

2020-09-17