Course Overview

The focus of this course is an introduction to the statistical component of IBM SPSS Statistics Base V.19. This is an application-oriented course and the approach is practical. You’ll take a look at several statistical techniques and discuss situations in which you would use each technique, the assumptions made by each method, how to set up the analysis using IBM SPSS Statistics as well as how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing underlying relationships.
You 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, and graphically display the results using IBM SPSS Statistics. This course uses the IBM SPSS Statistics Base features.

Who Should Attend

This basic course is for anyone who has worked with IBM SPSS Statistics and wants to become better versed in the basic statistical capabilities of IBM SPSS Statistics Base. This course targets those with limited or no statistical background. The course is also an appropriate refresher for those whose main statistical experience was gained many years ago.


You should have:
  • General computer literacy.
  • Completion of the ‘Introduction to IBM SPSS Statistics’ and/or ‘Data Management and Manipulation with IBM SPSS Statistics’ courses
  • or experience with IBM SPSS Statistics (Version 15 or later) including familiarity with opening, defining, and saving data files and manipulating and saving output.

Course Content

  • Introduction to Statistical Analysis
  • Principles of Research Design and Process
  • Understanding Data Distributions – Theory
  • Data Distributions for Categorical Data – Practice
  • Data Distributions for Continuous Data – Practice
  • Making Inferences about Populations from Samples
  • Relationships between Categorical Variables: Crosstabs, Chi-square, and Charts
  • Independent Samples: T Test: Mean difference between two independent groups
  • Paired Samples T Test: Mean difference between related samples
  • One-way ANOVA: Mean differences between Multiple Groups
  • Bivariate plots and correlations
  • Introduction to Regression
  • Introduction to Nonparametric Tests