| Topic |
Sub-topic |
| LESSON 1: ANALYTICAL TECHNIQUES AS A REFLECTION OF THE RESEARCH PROBLEM |
Types of problems and research situations. Examples. |
| LESSON 2. BASIC PROCEDURES IN SPSS |
SPSS windows: Data Editor window. Viewer window. Editor of pivot tables window. Graph Editor window. Text editor window. Syntax Editor window. SPSS for Windows menus: (1) common menus: File, Edit, View, Analyze, Graphics, Utilities, Window, Help; (2) Specific Menus Data Editor: Data Transform; (3) Specific pivot tables editor menus: Insert, Pivot, Format; (4) Specific Menus Graph Editor: Gallery, Design, Series, Format, Graphics; (5) specific text editor Menu: Insert; (6) Specific Menus syntax editor: Run. Toolbars SPSS for Windows: Open, Save, Print, Recover, Undo, Redo, Go to graphic, Go to Case, Variables, Search, Insert cases, Insert variables, Split File. Weight cases. Select cases. Value Labels. Use sets. SPSS Status bar for Windows. SPSS Options for Windows. Preparing data for analysis. Organization of data for analysis. Using a word processor to enter data. Creating a command file to read data. Data online. Using SPSS Data Editor. Save/archive data in SPSS. Using SPSS results into other applications. |
| LESSON 3: 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 4: 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 5: 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 6: 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 7: 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 8: 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 9: 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 10: 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