# Advanced Statistical Analysis using SPSS

Course Number: SPSS-106
Duration: 2 days (13 hours)
Format: Live, hands-on

## SPSS Advanced Statistical Analysis Training Overview

This Advanced Statistical Analysis using SPSS training course teaches attendees more advanced SPSS® regression and analysis techniques. Students learn when and how to use each approach and how to interpret the results.

## Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

In addition, some courses are available as live, instructor-led training from one of our partners.

## Objectives

• Master various analytic techniques in SPSS, including Discriminant, Survival, Cluster, Factor, Loglinear, Multivariate, and more
• Perform Binary and Multinomial logistic regression
• Understand when to apply each approach based on the characteristics of your data and the analytic outcomes you are seeking

## Prerequisites

All students should have taken Introduction to SPSS or have equivalent experience.

## Outline

Introduction and Overview
• Goals of the Course
• Taxonomy of Methods
• General Approach
Discriminant Analysis
• How Does Discriminant Analysis Work?
• The Elements of Discriminant Analysis
• The Discriminant Model
• How Cases are Classified
• Assumptions of Discriminant Analysis
• A Two-Group Discriminant Example
• Checking Variance Assumptions
• How Does Discriminant Analysis Work?
• The Elements of Discriminant Analysis
• The Discriminant Model
• How Cases are Classified
• Assumptions of Discriminant Analysis
• A Two-Group Discriminant Example
• Checking Variance Assumptions
• Running a Discriminant Analysis
• The Discriminant Coefficients
• Classification Statistics 2- 18 Prediction
• The Assumption of Equal Covariance
• Modifying the List of Predictors
• Casewise Statistics and Outliers
• Validating the Discriminant Model
• Stepwise Model Selection
• Three-Group Discriminant Analysis
Binary Logistic Regression
• How Does Logistic Regression Work?
• The Logistic Equation
• The Elements of Logistic Regression
• Assumptions of Logistic Regression
• A First Example of Logistic Regression
• Interpreting Logistic Regression Coefficients
• Making Predictions
• The Accuracy of Prediction
• Estimated Probabilities
• Checking Classifications
• Residual Analysis
• Stepwise Logistic Regression
• Summary
• Comparison to Discriminant Analysis
Multinomial Logistic Regression
• Multinomial Logistic Model
• Assumptions of Multinomial Logistic Regression
• A Multinomial Logistic Analysis: Predicting Credit Risk
• Interpreting Coefficients
• Classification Table
• Making Predictions
• Appendix: Multinomial Logistic with a Two-Category Outcome
Survival Analysis (Kaplan-Meier)
• What is Survival Analysis
• Concepts
• Censoring
• What to Look for in Survival Analysis
• Survival Procedures in SPSS
• An Example: Kaplan-Meier
• Results
• Extensions
Cluster Analysis
• How Does Cluster Analysis Work?
• Types of Data Used for Clustering
• What to Look at When Clustering
• Methods
• Distance and Standardization
• Overall Recommendations
• Hierarchical Cluster Analysis
• Cluster Results
• Obtaining Mean Profiles of Clusters
• Relating Clusters to Other Variables
• Summary of First Cluster Example
• Example II: K-Means Clustering
• Running K-Means Clustering
Factor Analysis
• Uses of Factor Analysis
• What to Look for When Running Factor Analysis
• Principles
• The Idea of a Principal Component
• Factor Analysis Versus Principal Components
• Number of Factors
• Rotation
• Factor Scores & Sample Size
• Methods
• An Example: 1988 Olympic Decathlon Scores
• Looking at Correlations
• Principal Components Analysis with an Orthogonal Rotation
• Principal Axis Factoring with an Oblique Rotation
Loglinear Analysis
• What are Loglinear Models
• Relations Among Loglinear, Logit Models and Logistic Regression
• What to Look for in Loglinear and Logit Analysis
• Assumptions
• Procedures in SPSS that Run Loglinear or Logit Analysis
• Example: Analysis of Location Preference (Model Selection)
• Running the Analysis
• Significance Tests
• Coefficient Interpretation
• Summary
• Logit Analysis with Specific Model (Genlog)
• Results
Multivariate Analysis of Variance
• Why Perform MANOVA
• Assumptions of MANOVA
• What to Look for in MANOVA
• SPSS Version 7 Differences
• An Example: Memory Influences
• Examining the Output
• Post Hoc Tests
• Post Hoc Testing of Means
Repeated measures Analysis of Variance
• Why do a Repeated Measures Study
• The Logic of Repeated Measures
• Assumptions
• Example: One Factor Drug Study
• Examining Results
• Further Analysis
• Planned Comparisons
• Ad Viewing with Pre-Post Brand Ratings
• Examining Results
• Tests of Assumptions
• Profile Plots
• Extensions
Conclusion

## Training Materials

All SSPS training attendees receive an extensive library of SPSS examples to take with them following the training.

## Software Requirements

This class uses SPSS Statistics 25 or newer but is appropriate to SPSS Statistics 19 and newer. Attendees are assumed to have their own copy of the software.

## Related Topics

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