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Data Analysis for the Social Sciences

Data Analysis for the Social Sciences
Integrating Theory and Practice

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January 2018 | 664 pages | SAGE Publications Ltd
Accessible, engaging, and informative, this text 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 readers 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 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.

Readers will learn 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 extensive visuals and a companion website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support learners 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


Chapter 2: Descriptive Statistics
Numerical Scales


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


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


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


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

Douglas Bors

Dr. Douglas Alexander Bors is currently an Associate Professor Emeritus at the University of Toronto, the institution from which he received his Ph.D. For over three decades he has taught courses in statistics at the undergraduate and graduate levels ranging from the introductory level to multivariate statistics and structural equation modelling. He is also the Dean’s Designate for Matter of Academic Integrity. He has served as a reviewer for several scholarly journals and has been a regular reviewer for Personality and Individual Differences. During his career his empirical research focused on problem solving and abstract reasoning. The... More About Author

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