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Data Analysis for the Social Sciences

Data Analysis for the Social Sciences
Integrating Theory and Practice

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  
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  

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

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Chapter 2

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