Advanced Statistical Analysis Using IBM SPSS Statistics (V26)

Audience

• IBM SPS Statistics users who want to learn advanced statistical methods to be able to better answer research questions.

Prerequisites

• Experience with IBM SPSS Statistics (version 18 or later)
• Knowledge of statistics, either by on the job experience, intermediate-level statistics oriented courses, or completion of the Statistical Analysis Using IBM SPSS Statistics (V26) course.

Course topics

1. Introduction to advanced statistical analysis

• Taxonomy of models
• Overview of supervised models
• Overview of models to create natural groupings

2. Grouping variables with Factor Analysis and Principal Components Analysis

• Factor Analysis basics
• Principal Components basics
• Assumptions of Factor Analysis
• Key issues in Factor Analysis
• Use Factor and component scores

3. Grouping cases with Cluster Analysis

• Cluster Analysis basics
• Key issues in Cluster Analysis
• K-Means Cluster Analysis
• Assumptions of K-Means Cluster Analysis
• TwoStep Cluster Analysis
• Assumptions of TwoStep Cluster Analysis

4. Predicting categorical targets with Nearest Neighbor Analysis

• Nearest Neighbors Analysis basics
• Key issues in Nearest Neighbor Analysis
• Assess model fit

5. Predicting categorical targets with Discriminant Analysis

• Discriminant Analysis basics
• The Discriminant Analysis model
• Assumptions of Discriminant Analysis
• Validate the solution

6. Predicting categorical targets with Logistic Regression

• Binary Logistic Regression basics
• The Binary Logistic Regression model
• Multinomial Logistic Regression basics
• Assumptions of Logistic Regression procedures
• Test hypotheses
• ROC curves

7. Predicting categorical targets with Decision Trees

• Decision Trees basics
• Explore CHAID
• Explore C&RT
• Compare Decision Trees methods

8. Introduction to Survival Analysis

• Survival Analysis basics
• Kaplan-Meier Analysis
• Assumptions of Kaplan-Meier Analysis
• Cox Regression
• Assumptions of Cox Regression

9. Introduction to Generalized Linear Models

• Generalized Linear Models basics
• Available distributions
• Available link functions

10. Introduction to Linear Mixed Models

• Linear Mixed Models basics
• Hierarchical Linear Models
• Modeling strategy
• Assumptions of Linear Mixed Models