| Topic |
Sub-topic |
| LESSON 1: EXPLORATORY DATA ANALYSIS |
Exploratory data analysis with SPSS. Descriptive statistics. Graphic examination of the data: Boxplots and Stem-and-Leaf Graphics. Construction and interpretation of tables. Construction and interpretation of tables based on multiple responses. Social data applications. |
| LESSON 2: MULTIVARIATE ANALYSIS TECHNIQUES CLASSIFICATION |
Relevance of multivariate analysis. Definition. Measurement types. Types of variables. Relationship. Description of multivariate analysis techniques. Classification criteria. Types of research problems. |
| LESSON 3: SIMPLE AND MULTIPLE REGRESSION ANALYSIS |
Definition of regression analysis. Least squares criterion. Forecast errors. Predictive ability. Special features of the multiple regression analysis. Variable selection methods. The problem of multicollinearity. The research process: objectives, design, assumptions, extraction, interpretation and validation. |
| LESSON 4: FACTOR AND PRINCIPAL COMPONENT ANALYSIS |
Historical background. Definition. Factor solution. Common and specific variance. Factor Analysis vs. Principal component analysis. Applications and uses. The research process: objectives, design, assumptions, extraction, interpretation and validation. |
| LESSON 5: CORRESPONDENCE ANALYSIS |
Scopes. Definition of correspondence analysis. Research objectives. Limitations of correspondence analysis. The basic structure of the data matrix. Central concepts. Multiple correspondence analysis. The research process: objectives, design, assumptions, extraction, interpretation and validation. |
| LESSON 6: CLUSTER ANALYSIS |
Definition of cluster analysis. Objectives. Procedure. Similarity measures. Clustering methods. Limitations of cluster analysis. The research process: objectives, design, assumptions, extraction, interpretation and validation. |
| LESSON 7: DISCRIMINANT ANALYSIS |
Historical background. Definition of discriminant analysis. Objectives. Multivariate profiles. Discriminating variables. Discriminant functions. Reclassification. The research process: objectives, design, assumptions, extraction, interpretation and validation. |
| LESSON 8: LOGISTIC REGRESSION ANALYSIS |
The logic of logistic regression. Preconditions for logistic regression. Logistic regression assumptions. Extraction and estimation of regression model fit. Interpretation of logistic regression coefficients. Probit analysis. The research process: objectives, design, assumptions, extraction, interpretation and validation. |
Subjects