Description
Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data.
In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to:
- Understand and choose the right statistical model to fit your data
- Match substantive theory and statistical models
- Apply statistical procedures hands-on, with example data analyses
- Develop and use graphs to understand data and fit models to data
- Work with statistical modeling principles using any software package
- Learn by applying, with input and output files for R, SAS, SPSS, and Mplus.
Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.
Table of Contents
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
