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Data Analysis for Behavioral Sciences and Health Professions
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Data Analysis for Behavioral Sciences and Health Professions
Regression, ANOVA, and the General Linear Model

  • Peter Vik - Pacific University, Forest Grove, OR, USA
Volume: 17
February 2027 | 360 pages | SAGE Publications, Inc
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In Data Analysis for Behavioral Sciences and Health Professions: Regression, ANOVA, and the General Linear Model, Peter Vik compares traditional statistical and regression approaches to the general linear model so students can understand and apply either approach, depending on the situation and application. This new book serves as a core text for a second course statistics in the social and behavioral sciences, providing a bridge between introductory statistics and more advanced data analysis techniques. The book walks students through the GLM approach and provides a refresher on basic issues in statistics, such as null hypothesis testing and sampling. Then, the books moves on to cover ANOVA, ANCOVA, and bivariate techniques in general, alongside their regression counterparts, and then multivariate techniques and multiple regression. The text ends with introductory information on more advanced techniques, such as structural equation models and factor analysis to lead students into a potential third course in statistics. 
 
Preface
 
Part I: Foundations
 
Chapter 1: Data: Collection, Description, and Hypothesis Testing
Getting Started – Key Questions

 
Two Approaches to Statistics

 
Model Comparison

 
Data: Variables and Descriptive Statistics

 
Hypothesis Testing

 
Summary

 
 
Chapter 2: The Model: Association Between Two Variables
Picture the Association

 
Bivariate Regression

 
Correlation Coefficient

 
Summary

 
 
Chapter 3: Model Comparison: Simple Versus a Regression Model
Model Comparison

 
Steps to compare models

 
Model Comparison – Larger Sample

 
Correlation and Regression Analysis: Two Peas in a Pod

 
Summary

 
 
Chapter 4: Comparing Means: Regression, t-Test, and One-Way Analysis of Variance (ANOVA)
Bivariate regression: A Predictor With Only Two Values

 
Comparing means Using the T-test

 
One-Way Analysis of Variance (ANOVA)

 
Summary

 
 
Part II: Expanding the Models
 
Chapter 5: Multiple Regression: Two Continuous Predictors
Regression with Two Predictors

 
Test the components of the model: Isolating the effects of individual predictors

 
Interaction

 
Summary

 
 
Chapter 6: Comparing Means with Two Predictors (Factors)
Multiple regression with two categorical variables

 
Two-way ANOVA

 
Summary

 
Problems

 
 
Chapter 7: Categorical and Continuous Predictors
Statistical Covariation: The “Third Variable”

 
Multiple Regression with a Continuous and a Categorical Predictor

 
Adding a covariate – Analysis of Covariance (ANCOVA): Model Comparison Approach

 
Interaction

 
Summary

 
 
Chapter 8: One-Way ANOVA with Three Groups
Regression using a categorical predictor with three groups

 
One-way ANOVA with three categories

 
Summary

 
 
Chapter 9: Combining Two- and Three-Group Predictors
Multiple Regression with a two-group and a three-group predictor

 
Two-by-Three Analysis of Variance (ANOVA)

 
Summary

 
 
Chapter 10: Repeated Measures
Matched Pairs T-test

 
Repeated Measures ANOVA: Model Comparison Approach

 
Three Repeated Measures

 
Contrast Weights for Three Scores

 
Summary

 
 
Chapter 11: Mixed Models: Group Comparisons and Repeated Measures
Main Effect Between Groups

 
Main Effect Within Groups and the Interaction

 
Three Repeated Measures: A 2 X 3 Mixed Analysis

 
Summary

 
 
Chapter 12: Conceptual Foundation for Advanced Techniques
Third Variable Effects: A Brief Tribute to Baron and Kenny

 
Statistical Mediation

 
Statistical Mediation

 
Structural Path Models

 
Latent Models

 
Latent Structural Models

 
Summary

 
 
References
Key features

KEY FEATURES

  • Unifies statistical techniques through the general linear model. Frames regression, ANOVA, and related approaches within a single conceptual structure (DATA = MODEL + ERROR), helping students see how methods connect rather than learning them in isolation.
  • Balances conceptual understanding with practical application. Emphasizes how statistical models reduce error and improve prediction, giving students a deeper foundation for interpreting results—not just calculating them.
  • Presents traditional and model-based approaches side-by-side. Helps students understand when to use familiar procedures (e.g., t-tests, ANOVA) while recognizing their equivalence to regression-based models.
  • Builds progressively from fundamentals to advanced topics. Moves from core concepts and bivariate regression through multiple regression, ANOVA/ANCOVA, and mixed designs, before introducing advanced techniques like SEM and factor analysis.
  • Applies directly to behavioral sciences and health professions. Uses examples and framing relevant to psychology, counseling, social sciences, and health-related fields, supporting transfer to real research contexts.