You are in: Caribbean
Change location

You are here

Book > Textbooks > Quantitative Methods
Data Analysis for Behavioral Sciences and Health Professions
Share
Share

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
360 pages | SAGE Publications, Inc
Download flyer
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. 
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.