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The Process of Statistical Analysis in Psychology

The Process of Statistical Analysis in Psychology

Experience with SAGE edge

September 2017 | 400 pages | SAGE Publications, Inc

This new introductory statistics text from Dawn M. McBride, best-selling author of The Process of Research in Psychology, covers the background and process of statistical analysis, along with how to use essential tools for working with data from the field. Research studies are included throughout from both the perspective of a student conducting their own research study and of someone encountering research in their daily life. McBride helps readers gain the knowledge they need to become better consumers of research and statistics used in everyday decision-making and connects the process of research design with the tools employed in statistical analysis. Instructors and students alike will appreciate the extra opportunities for practice with the accompanying Lab Manual for Statistical Analysis, also written by McBride and her frequent collaborator, J. Cooper Cutting.

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Part I: Why Do We Use Statistics?
Chapter 1: Why Statistics?
A. What can statistics do for me?  
B. Research design and statistics  
Chapter 2: The Starting Place: Data and Distributions
A. Populations and samples  
B. Types of data collected in research in psychology  
C. Frequency distributions  
D. Frequency distributions in Excel  
E. Introduction to SPSS  
Chapter 3: Probability and sampling
A. Concepts of probability  
B. Sampling techniques  
C. Distribution of sample means introduction  
Part II: Descriptive Statistics
Chapter 4: Central Tendency
A. Central tendency in distributions  
B. Mean  
C. Median  
D. Mode  
E. Which measure of central tendency should I use?  
Chapter 5: Variability
A. Variability in distributions  
B. Range and Interquartile Range  
C. Standard deviation  
D. Which measure of variability should I use?  
Chapter 6: Presenting Descriptive Statistics
A. Descriptive statistics in graphs  
B. Descriptive statistics in tables  
C. APA style for graphs and tables  
Part III: Basics of Hypothesis Testing
Chapter 7: The Normal Distribution and z-Scores
A. The z-score transformation  
B. The normal distribution  
Chapter 8: Hypothesis Testing Logic
A. Using the normal distribution to test hypotheses  
B. Logic of hypothesis testing  
C. Types of hypothesis testing errors  
D. Statistical significance  
Part IV: The Nuts and Bolts of Statistical Tests
Chapter 9: The t-distribution
A. The t-distribution – when we don’t know the value of the population standard deviation  
B. One-sample t-test  
C. Using SPSS to conduct a one-sample t-test  
D. Test Assumptions  
Chapter 10: Related/paired samples t-test
A. Samples with related/paired data  
B. Calculating a related/paired samples t-test  
C. Using SPSS to conduct related/paired samples t-test  
D. Test Assumptions  
Chapter 11: Independent samples t-test
A. Independent samples  
B. Calculating independent samples t scores  
C. Using SPSS to Conduct an independent samples t-test  
D. Test Assumptions  
Chapter 12: One-way ANOVA
A. More than two independent samples  
B. Calculating the F score in an ANOVA  
C. Using SPSS to calculate One-way between-subjects ANOVA  
D. Test Assumptions  
Chapter 13: Two-way ANOVA
A. Factorial designs  
B. Calculating two-way F scores  
C. Using SPSS to calculate Two-way between-subjects ANOVA  
D. Test Assumptions  
E. Understanding interactions  
Chapter 14: One-way within-subjects ANOVA
A. Within-subjects designs  
B. Calculating one-way within-subjects F scores  
C. Using SPSS to calculate one-way repeated-measures ANOVA  
D. Test Assumptions  
E. More Complex Within-Subjects Designs  
Chapter 15: Correlation Tests and Simple linear regression
A. Correlation versus Causation  
B. Hypothesis Testing with Pearson r  
C. Using SPSS to conduct a Pearson r test  
D. Regression Analyses  
E. Non-Linear Relationships  
Chapter 16: Chi-square tests
A. Parametric versus non-parametric tests  
B. Observed versus expected frequencies  
C. Calculating Chi-Square by hand  
D. Calculating Chi-Square using SPSS  


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“This is a good text on introductory statistics that uses clear language and is easy for undergraduate students to read and understand. It goes through data analysis as it relates to research design and hypothesis testing step by step, which is a unique contribution of this book.”

Paul S. Foster
Middle Tennessee State University

“I really like the idea of an integrated stats/methods text that could also be used separately. The activities in the lab manual are nicely done and would provide additional practice for the students.”

Courtney McManus
Colby-Sawyer College

“A well thought-out statistics book that hits all the high points that students need without getting bogged down.”

Michael Ray
The College at Brockport, State University of New York
Key features


  • Statistics is approached as a step-by-step process to guide students through using statistics as a tool for understanding data.
  • A focus on conceptual understanding of inferential tests allows students to comprehend the concepts driving the tests, rather than just how to compute the statistics.
  • Scaffolding of concepts helps students understand concepts as they are introduced multiple
  • times throughout the course content.
  • Thinking About Research features provide a brief summary of a published research study with a focus on the data analysis and interpretation, along with critical thinking questions that help students understand how statistics are used in psychological research.
  • An accompanying lab manualby Dawn M. McBride and co-author J. Cooper Cutting provides students additional opportunities for practice to master the material.
  • SAGE coursepacksallows instructors to import high-quality content into their school’s learning management system (LMS) with no access codes.
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ISBN: 9781506325224