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.
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.
Anyone who works with IBM SPSS Statistics and wants to learn advanced statistical procedures to be able to better answer research questions.
Introduction to advanced statistical analysisGroup variables: Factor Analysis and Principal Components AnalysisGroup similar cases: Cluster AnalysisPredict categorical targets with Nearest Neighbor AnalysisPredict categorical targets with Discriminant AnalysisPredict categorical targets with Logistic RegressionPredict categorical targets with Decision TreesIntroduction to Survival AnalysisIntroduction to Generalized Linear ModelsIntroduction to Linear Mixed Models, Cloud Data and AI, Information Architecture Market, AI Tools SPSS Statistics