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Data Analysis for the Social Sciences
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Data Analysis for the Social Sciences
Integrating Theory and Practice

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January 2018 | 664 pages | SAGE Publications Ltd

'This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.' —Ruth Horry, Psychology, Swansea University 

'This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers.' Barbra Teater, Social Work, College of Staten Island, City University of New York

Accessible, engaging, and informative, this book will help any social science student approach statistics with confidence. 

With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows students not only how to apply newfound knowledge using IBM SPSS Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling through to t-tests, multiple regression and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types and results reliability.

It shows you how to:

  • Describe data with graphs, tables, and numbers
  • Calculate probability and value distributions
  • Test a priori and post hoc hypotheses
  • Conduct Chi-squared tests and observational studies
  • Structure ANOVA, ANCOVA, and factorial designs

Supported by lots of visuals and a website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support students through their statistics journeys.  

 
Part I: The Foundations
 
Chapter 1: Overview
The general framework

 
Recognizing randomness

 
Lies, damn lies, and statistics

 
Testing for randomness

 
Research design and key concepts

 
Paradoxes

 
 
Chapter 2: Descriptive Statistics
Numerical Scales

 
Histograms

 
Measures of Central Tendency: Measurement Data

 
Measures of Spread: Measurement Data

 
What creates Variance?

 
Measures of Central Tendency: Categorical Data

 
Measures of Spread: Categorical Data

 
Unbiased Estimators

 
Practical SPSS Summary

 
 
Chapter 3: Probability
Approaches to probability

 
Frequency histograms and probability

 
The asymptotic trend

 
The terminology of probability

 
The laws of probability

 
Bayes’ Rule

 
Continuous variables and probability

 
The standard normal distribution

 
The standard normal distribution and probability

 
Using the z-tables

 
 
Part II: Basic Research Designs
 
Chapter 4: Categorical data and hypothesis testing
The binomial distribution

 
Hypothesis testing with the binomial distribution

 
Conducting the binomial test with SPSS

 
Null hypothesis testing

 
The x2 goodness-of-fit test

 
The x2 goodness-of-fit test with more than two-categories

 
Conducting the x2 goodness-of-fit test with SPSS

 
Power and the x2 goodness-of-fit test

 
G -test

 
Can a failure to reject indicate support for a model?

 
 
Chapter 5: Testing for a Difference: Two Conditions
Building on the z-score

 
Testing a single sample

 
Independent-samples t-test

 
t-test assumptions

 
Pair-samples t-test

 
Confidence limits and intervals

 
Randomization test and bootstrapping

 
Nonparametric tests

 
 
Chapter 6: Observational studies: Two categorical variables
x2 goodness-of-fit test reviewed

 
x2 test of independence

 
The phi coefficient

 
Necessary assumptions

 
x2 test of independence SPSS example

 
Power, sample size, and the x2 test of independence

 
The third-variable problem

 
Multi-category nominal variables

 
Tests of independence with ordinal variables

 
 
Chapter 7: Observational studies: Two measurement variables
Tests of association for categorical data reviewed

 
The scatterplot

 
Covariance

 
The Pearson-Product Moment Correlation Coefficient

 
Simple regression analysis

 
The Ordinary Least Squares Regression Line (OLS)

 
The assumptions necessary for valid correlation and regression coefficients

 
 
Chapter 8: Testing for a difference: Multiple between-subject conditions (ANOVA)
Reviewing the t-test and the x2 test of independence

 
The logic of ANOVA: Two unbiased estimates of o2

 
ANOVA and the F-test

 
Standardized effect sizes and the F-test

 
Using SPPS to run an ANOVA F-test: Between-subjects design

 
The third-variable problem: Analysis of covariance (ANCOVA)

 
Non-parametric alternatives

 
 
Chapter 9: Testing for a difference: Multiple related-samples
Reviewing the between-subject ANOVA and the t-test

 
The logic of the randomized block design

 
Running a randomized block design with SPSS

 
The logic of the repeated-measures design

 
Running a repeated-measures design with SPSS

 
Non-parametric alternatives

 
 
Chapter 10: Testing for specific differences: Planned and unplanned tests
A priori versus post hoc tests

 
Per-comparison versus family-wise error rates

 
Planned comparisons: A priori test

 
Testing for polynomial trends

 
Unplanned comparisons: Post hoc tests

 
Non-parametric follow-up comparisons

 
 
Part III: Analyzing Complex Designs
 
Chapter 11: Testing for Differences: ANOVA and Factorial Designs
Reviewing the independent-samples ANOVA

 
The logic of factorial designs: Two between-subject independent variables

 
Main and simple effects

 
Two Between-Subject Factorial ANOVA with SPSS

 
Fixed versus random factors

 
Analyzing a mixed-design ANOVA with SPSS

 
Non-parametric alternatives

 
 
Chapter 12: Multiple Regression
Regression revisited

 
Introducing a second predictor

 
A detailed example

 
Issues concerning normality

 
Missing data

 
Testing for linearity and homoscedasticity

 
A multiple regression: The first pass

 
Addressing multicollinearity

 
Interactions

 
What can go wrong?

 
 
Chapter 13: Factor analysis
What is factor analysis?

 
Correlation coefficients revisited

 
The correlation matrix and PCA

 
The component matrix

 
The rotated component matrix

 
A detailed example

 
Choosing a method of rotation

 
Sample size requirements

 
Hierarchical multiple factor analysis

 
The effects of variable selection

 

This book fosters in-depth understanding of the logic underpinning the most common statistical tests within the behavioural sciences. By emphasising the shared ground between these tests, the author provides crucial scaffolding for students as they embark upon their research journey.

Ruth Horry
Psychology, Swansea University

This unique text presents the conceptual underpinnings of statistics as well as the computation and application of statistics to real-life situations--a combination rarely covered in one book. A must-have for students learning statistical techniques and a go-to handbook for experienced researchers. 

Barbra Teater
Professor of Social Work, College of Staten Island, City University of New York

Statistics textbooks are not often known for their engaging writing style, but Douglas Bors’ work is an exception. Humorous, detailed, and clearly-written, the book guides readers through both a conceptual and procedural understanding of statistics essentials. A great resource that I look forward to using in my courses. 

Julie Alonzo
Education, University of Oregon

An engaging textbook that delivers.

Miss Helen Coleman
Library Science, Glyndwr University
December 13, 2017

THE BOOKS FROM SAGE HAVE TREMENDOUSLY HELPED ME ALL THROUGH MY RESEARCH AND ARE STILL HELPFUL, SUCH THAT I CANNOT HELP BUT ADOPT ALL THE BOOKS FROM THEM THAT I HAVE USED. THANKS, SAGE PUBLISHERS.

Mrs Catherine Otene
Faculty of Engineering & Science, Greenwich University
December 7, 2017

Gathering data is the easy part of the empirical research process but often students do not think carefully enough about the analysis of their data before they start to gather it. This book gives clear guidance on the methodology and process of data analysis giving clear and concise approaches to data analysis methods and tools. A very useful addition to the methodological bookshelf.

Mr Paul Hopkins
Faculty of Education (Hull), Hull University
November 11, 2017

Sample Materials & Chapters

Chapter 1

Chapter 2


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