Advanced Statistical Analysis Using IBM SPSS Statistics (v25) equips researchers, analysts, and data professionals with the skills to perform advanced statistical analysis, build predictive models, test hypotheses, interpret results, and generate reliable insights using IBM SPSS Statistics Version 25.
Course Overview:
This course provides an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Students will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for predicting variables, as well as methods to cluster variables and cases.
Course Objectives:
- Introduction to advanced statistical analysis
- Group variables: Factor Analysis and Principal Components Analysis
- Group similar cases: Cluster Analysis
- Predict categorical targets with Nearest Neighbor Analysis
- Predict categorical targets with Discriminant Analysis
- Predict categorical targets with Logistic Regression
- Predict categorical targets with Decision Trees
- Introduction to Survival Analysis
- Introduction to Generalized Linear Models
- Introduction to Linear Mixed Models
Pre-requisites:
- Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions.
Target Audience:
- 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 Duration:
- 14 hours – 2 days
Course Content:
Introduction to advanced statistical analysis
- Taxonomy of models
- Overview of supervised models
- Overview of models to create natural groupings
Group 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 scores
Group 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 Analysis
Predict categorical targets with Nearest Neighbor Analysis
- Nearest Neighbor Analysis basics
- Key issues in Nearest Neighbor Analysis
- Assess model fit
Predict 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 solution
