Advanced Statistical Analysis Using IBM SPSS Statistics (V26)

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

• Introduction to advanced statistical analysis 
• Grouping variables with Factor Analysis and Principal Components Analysis 
• Grouping cases with Cluster Analysis 
• Predicting categorical targets with Nearest Neighbor Analysis 
• Predicting categorical targets with Discriminant Analysis 
• Predicting categorical targets with Logistic Regression 
• Predicting categorical targets with Decision Trees 
• Introduction to Survival Analysis 
• Introduction to Generalized Linear Models 
• Introduction to Linear Mixed Models

Introduction to advanced statistical analysis 
• Taxonomy of models 
• Overview of supervised models 
• Overview of models to create natural groupings 

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 

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 

Predicting categorical targets with Nearest Neighbor Analysis 
• Nearest Neighbors Analysis basics 
• Key issues in Nearest Neighbor Analysis 
• Assess model fit 

Predicting categorical targets with Discriminant Analysis 
• Discriminant Analysis basics 
• The Discriminant Analysis model 
• Assumptions of Discriminant Analysis 
• Validate the solution 

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 

Predicting categorical targets with Decision Trees 
• Decision Trees basics 
• Explore CHAID 
• Explore C&RT 
• Compare Decision Trees methods 

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

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

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

• 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.