IBM SPSS Statistics: Ordinal Regression, GLM and Hierarchical Modeling (V19) is a one day instructor-led course that provides a practical, application-oriented introduction to some of the advanced statistical methods available in IBM® SPSS® Statistics for data analysts and researchers. Students will review several 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, as well as how to interpret the results. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence and interpret their output.
This advanced course is for:
- Anyone who has worked with IBM SPSS Statistics and wants to become better versed in the more advanced statistical capabilities.
- Anyone who has a solid understanding of statistics and wants to expand their knowledge of appropriate statistical procedures and how to set them up using IBM SPSS Statistics.
- Analysts and Modelers
You should have:
- On the job statistical experience or completion of the Introduction to Statistical Analysis Using IBM SPSS Statistics course and/or Intermediate-level statistics oriented courses.
- Knowledge of basic statistics, including linear regression.
- IBM SPSS Statistics Standard, IBM SPSS Statistics Professional, IBM SPSS Statistics Premium.
Please refer to course overview for description information.
- Explain the basic theory and assumptions of ordinal regression
- Specify an ordinal regression analysis and interpret model fit, coefficients and model accuracy
- Describe the key features of the Ordinal Regression procedure
Generalized Linear Models
- Explain the use of the exponential family of distributions and a link function and how these differential a generalized linear model from a general linear model
- Specify a Generalized Linear Model analysis and interpret the resulting output
- Check model assumptions and predictions
Linear Mixed Models
- Explain the general principles of a linear mixed model approach to data analysis
- Specify a Linear Mixed Model analysis and interpret the resulting output, building successive models of greater complexity