Statistical Methods for the Social and Behavioural Sciences

Flora provides a comprehensive survey of the major statistical procedures, along with an innovative model-based approach, teaching researchers how to understand and choose the best model to fit their data.

Informationen zum Autor David B. Flora is a Professor in the Department of Psychology at York University in Toronto, Canada. Dr. Flora has also served as Coordinator of the Quantitative Methods Area in York's graduate program in psychology as Coordinator of York's Statistical Consulting Service. As a quantitative psychologist, Dr. Flora is a coauthor on numerous journal articles focused on quantitative methodology itself as well as on a wide range of articles in which advanced quantitative methods are applied to substantive research topics in psychology. Dr. Flora earned his PhD from the Quantitative Psychology program at the University of North Carolina at Chapel Hill. Although he is now a Canadian, Dr. Flora remains a Tar Heel born and a Tar Heel bred. Klappentext Flora provides a comprehensive survey of the major statistical procedures, along with an innovative model-based approach, teaching researchers how to understand and choose the best model to fit their data. Zusammenfassung Flora provides a comprehensive survey of the major statistical procedures! along with an innovative model-based approach! teaching researchers how to understand and choose the best model to fit their data. Inhaltsverzeichnis 1. Foundations of Statistical Modeling Demonstrated with Simple Regression 2. Multiple Regression with Continuous Predictors 3. Regression with Categorical Predictors 4. Interactions in Multiple Regression: Models for Moderation 5. Using Multiple Regression to Model Mediation and Other Indirect Effects 6. Introduction to Multilevel Modeling 7. Basic Matrix Algebra for Statistical Modeling 8. Exploratory Factor Analysis 9. Structural Equation Modeling I: Path Analysis 10. Structural Equation Modeling II: Latent Variable Models 11. Growth Curve Modeling

Inhalt

1. Foundations of Statistical Modeling Demonstrated with Simple Regression
   
   2. Multiple Regression with Continuous Predictors
   3. Regression with Categorical Predictors
   4. Interactions in Multiple Regression: Models for Moderation
   5. Using Multiple Regression to Model Mediation and Other Indirect Effects
   6. Introduction to Multilevel Modeling
   7. Basic Matrix Algebra for Statistical Modeling
   8. Exploratory Factor Analysis
   9. Structural Equation Modeling I: Path Analysis
   10. Structural Equation Modeling II: Latent Variable Models
   11. Growth Curve Modeling