Data Analysis for the Social Sciences

Integrating Theory and Practice
Douglas Bors
Data Analysis for the Social Sciences
January 2018 | 664 pages | Sage UK
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Description

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.

Contents

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

Additional materials

Description

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.

Contents

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

Additional materials

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Data Analysis for the Social Sciences

Integrating Theory and Practice

Douglas Bors
Cancel Print

January 2018 | 664 pages | Sage UK

Format Published Date ISBN Price

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.

Table Of Contents:

  • 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

Recent Product Reviews:

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
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
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

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