1: Introduction. 1.1. Overview. 1.2. Objectives. 1.3. Types of Scales. 1.4. Topics Covered. 1.5. Pedagogy. 2: Multivariate Normal Distribution. 2.1. Univariate Normal Distribution. 2.2. Bivariate Normal Distribution. 2.3. Generalization to Multivariate Case. 2.4. Tests about Means. >2.5. Examples. 2.6. Assignment. 2.7. References. 3: Measurement Theory: Reliability and Factor Analysis. 3.1. Notions of Measurement Theory. 3.2. Factor Analysis. 3.3. Conclusion - Procedure for Scale Construction. 3.4. Application Examples. 3.5. Assignment. 3.6. References.4: Multiple Regression with a Single Dependent Variable. 4.1. Statistical Inference: Least Squares and Maximum Likelihood. 4.2. Pooling Issues. 4.3. Examples of Linear Model Estimation with SAS. 4.4. Assignment. 4.5. References. 5: System of Equations. 5.1. Seemingly Unrelated Regression (SUR). 5.2. A System of Simultaneous Equations. 5.3. Simultaneity and Identification. 5.4. Summary. 5.5. Examples Using SAS. 5.6. Assignment. 5.7. References. 6: Categorial Dependent Variables. 6.1. Discriminant Analysis. 6.2. Quantal Choice Models. 6.3. Examples. 6.4. Assignment. 6.5. References. 7: Rank Ordered Data. 7.1. Conjoint Analysis - MONANOVA. 7.2. Ordered Probit. 7.3. Examples. 7.4. Assignment. 7.5. References. 8: Error in Variables - Analysis of Covariance Structure. 8.1. The Impact of Imperfect Measures. 8.2. Analysis of Covariance Structures. 8.3. Examples. 8.4. Assignment. 8.5. References. 9: Analyses of Similarity and Preference Data. 9.1.Proximity Matrices. 9.2. Problem Definition. 9.3. Individual Differences in Similarity Judgements. 9.4. Analysis of Preference Data. 9.5.Examples. 9.6. Assignment. 9.7. References. Appendices. A: Rules in Matrix Algebra. B: Statistical Tables. C: Description of Data Sets.
