This book is designed to provide students, teachers, and researchers with a text that includes a full range of statistical methods available to address commonly encountered research problems.
This book is designed to provide students, teachers, and researchers with a text that includes a full range of statistical methods available to address commonly encountered research problems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology, and Professor in the Department of Educational Psychology at Ball State University where he has been since 2003. He received his PhD from the University of South Carolina in 2002. Dr. Finch teaches courses in factor analysis, structural equation modeling, categorical data analysis, regression, multivariate statistics and measurement to graduate students in psychology and education. His research interests are in the areas of multilevel models, latent variable modeling, methods of prediction and classification, and nonparametric multivariate statistics. Holmes is also an Accredited Professional Statistician (PStat ®).
Inhaltsangabe
1. Introduction 2. Theoretical Foundation 3. One Sample Parameter Estimation 4. Comparing Measures of Central Tendency Between Two Independent Groups 5. Comparing Measures of Central Tendency Between More Than Two Independent Groups 6. Factorial Designs 7. Easures Analysis of Variance and Split Plot Designs 8. Correlation 9. Ordinary Least Squares Linear Regression 10. Robust Linear Regression Models 11. Regression for Dichotomous Dependent Variables 12. Advanced Issues in Regression Modeling 13. Multilevel Modeling
1. Introduction 2. Theoretical Foundation 3. One Sample Parameter Estimation 4. Comparing Measures of Central Tendency Between Two Independent Groups 5. Comparing Measures of Central Tendency Between More Than Two Independent Groups 6. Factorial Designs 7. Easures Analysis of Variance and Split Plot Designs 8. Correlation 9. Ordinary Least Squares Linear Regression 10. Robust Linear Regression Models 11. Regression for Dichotomous Dependent Variables 12. Advanced Issues in Regression Modeling 13. Multilevel Modeling
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