## Statistics & Methods Centre - Regression-type models

- Regression-type models, presented here, belong to the class of
*dependence models*, in which there is a single response variable (criterion variable, dependent variable) and one or more predictor variables (independent variables). These models are special cases of the*general linear model*. If the response variable is numerical the models are called multiple regression models, when the response variable is the logarithm of a categorical variable the models are called logistic regression models, and when the response variable is categorical with two or more categories, the models are called discriminant analysis models. When there are only categorical predictors the model is called analysis of variance (see under multivariate statistics).

- Multiple regression
- Modelling a single numerical response variable by several (mostly numerical, but possibly binary) predictor variables.
**Basic reading**- Moore & McCabe, Chapter 11: Multiple regression.
- Field, Chapter 5: Regression.
- Hair et al., Chapter 4: Multiple regression.
- Meyer et al., Chapter 5A: Multiple regression, Chapter 5B: Multiple regression using SPSS.
**Advanced reading**- Later sections of the chapters, including analyses of residuals, influence measures.
**Moderation and mediation (by David Kenny)****Software**- SPSS => Analyze => Regression => Linear
- Annotated output multiple regressionn - UCLA-ATS
**Reporting regression analysis in publications****Reporting - examples in journal articles****Power:**Hair et al. Table 4-7 (p. 195, 6th edition)**Effect size:**Field, pp. 172-174 (2nd edition).

- Logistic regression
**Basic reading**- Moore & McCabe, Chapter 16: Logistic regression.
- Field, Chapter 6: Logistic regression; Effect size: section 6.5.5
- Hair et al., Chapter 5: Multiple discriminant analysis and logistic regression, pp. 335-377.
- Meyer et al., Chapter 6A: Logistic regression, Chapter 6B: Logistic regression using SPSS.
**Advanced reading**- Hosmer, D.W. & Lemeshow, S.
*Applied Logistic Regression(2nd Edition)*. New York: Wiley. **Software**- SPSS => Analyze => Regression => Binary logistic
**Effect size:**- Annotated output logistic regression - UCLA-ATS
- Example with annotated output - UCLA-ATS
**Reporting logistic regression in publications****Reporting - examples in journal articles**: Pallant (2007). Chapter 14 (p. 178).; Field (2005), Section 6.6.**Power:****Effect size:**Field (2005), Section 6.3.2.

- Discriminant analysis
- Linear discriminant analyis has one categorical response variable and several (most numerical) predictors. Linear combinations of the predictors are sought (the discriminat functions), such that the group means differ as much as possible on the discriminant functions together.
**Basic reading**- Field, Chapter 14, sections 14.7 & 14.8
- Hair et al., Chapter 5: Multiple discriminant analysis and logistic regression, pp. 269-354.
- Meyer et al., Chapter 7A: Discriminant function analysis, Chapter 7B: Two-group discriminant function analysis using SPSS.
**Advanced reading**- Huberty, C.J. (1994).
*Applied discriminant analysis*. New York: Wiley **Software**- SPSS => Analyze => Classify => Discriminant
- Annotated output - UCLA-ATS
- Example with annotated output - UCLA-ATS
- Example with annotated output - Garson site
**Reporting discriminant analysis in publications****Reporting - examples in journal articles****Power:****Effect size:**