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

Companion Website


December 2017 | 736 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  
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
Key features
KEY FEATURES:
  • Includes a math diagnostics test to help readers understand their current strengths and weaknesses
  • Introduces statistics at a gradual, student-friendly pace designed to build confidence
  • Highly visual and designed to ensure key points are not lost in overwhelming amounts of text
  • Supported by interactive demonstrations, simulations, and author videos
  • To counter student fear of statistics it starts from the very basics - including a maths diagnostics test to help students understand their current maths strengths and weaknesses
    It introduces statistics at a gradual, student-friendly pace designed to build confidence and makes no assumptions of prior knowledge.
    Highly visual (over 600 figures and SPSS screenshots and boxed definitions and clarifying explanations of tricky concepts) - designed to make sure key points don’t get lost in overwhelming amounts of text.
    Supported by interactive demonstrations, simulations and author videos so students can take advantage of a variety of ways to learn and digest the important information.
    Focusing on real-world situations, this book demonstrates not only step-by-step instruction about how to approach statistical tests and use SPSS software, but also illustrates how and why these types of tests are relevant to the reality outside of the classroom.
    Provides students with the ability to click through tests and apply their knowledge to different research scenarios.
     
    To counter student fear of statistics it starts from the very basics - including a maths diagnostics test to help students understand their current maths strengths and weaknesses
  • It introduces statistics at a gradual, student-friendly pace designed to build confidence and makes no assumptions of prior knowledge.
  • Highly visual (over 600 figures and SPSS screenshots and boxed definitions and clarifying explanations of tricky concepts) - designed to make sure key points don’t get lost in overwhelming amounts of text.
  • Supported by interactive demonstrations, simulations and author videos so students can take advantage of a variety of ways to learn and digest the important information.
  • Focusing on real-world situations, this book demonstrates not only step-by-step instruction about how to approach statistical tests and use SPSS software, but also illustrates how and why these types of tests are relevant to the reality outside of the classroom.
  • Provides students with the ability to click through tests and apply their knowledge to different research scenarios

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