Advanced Statistical Analysis Using IBM SPSS Statistics (V25)

Audience

• Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions.

Prerequisites

• Experience with IBM SPSS Statistics (navigation through windows; using dialog boxes)
• Knowledge of statistics, either by on the job experience, intermediate-level statistics oriented courses, or completion of the Statistical Analysis Using IBM SPSS Statistics (V25) course.

Course Topics

• Introduction to advanced statistical analysis
• Taxonomy of models
• Overview of supervised models
• Overview of models to create natural groupingsGroup variables: Factor Analysis and Principal Components Analysis
• Factor Analysis basics
• Principal Components basics
• Assumptions of Factor Analysis
• Key issues in Factor Analysis
• Improve the interpretability
• Use Factor and component scoresGroup similar cases: 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 AnalysisPredict categorical targets with Nearest Neighbor Analysis
• Nearest Neighbor Analysis basics
• Key issues in Nearest Neighbor Analysis
• Assess model fitPredict categorical targets with Discriminant Analysis
• Discriminant Analysis basics
• The Discriminant Analysis model
• Core concepts of Discriminant Analysis
• Classification of cases
• Assumptions of Discriminant Analysis
• Validate the solutionPredict categorical targets with Logistic Regression
• Binary Logistic Regression basics
• The Binary Logistic Regression model
• Multinomial Logistic Regression basics
• Assumptions of Logistic Regression procedures
• Testing hypothesesPredict categorical targets with Decision Trees
• Decision Trees basics
• Validate the solution
• Explore CHAID
• Explore CRT
• Comparing Decision Trees methodsIntroduction to Survival Analysis
• Survival Analysis basics
• Kaplan-Meier Analysis
• Assumptions of Kaplan-Meier Analysis
• Cox Regression
• Assumptions of Cox RegressionIntroduction to Generalized Linear Models
• Generalized Linear Models basics
• Available distributions
• Available link functionsIntroduction to Linear Mixed Models
• Linear Mixed Models basics
• Hierachical Linear Models
• Modeling strategy
• Assumptions of Linear Mixed Models