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# 0K51BG - Statistical Analysis Using IBM SPSS Statistics v26

## Overview

Duration: 2 days

This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques and how to apply them with confidence, interpret their output, and graphically display the results.

## Objectives

• Introduction to statistical analysis
• Describing individual variables
• Testing hypotheses
• Testing hypotheses on individual variables
• Testing on the relationship between categorical variables
• Testing on the difference between two group means
• Testing on differences between more than two group means
• Testing on the relationship between scale variables
• Predicting a scale variable: Regression
• Introduction to Bayesian statistics
• Overview of multivariate procedures

## Audience

• IBM SPS Statistics users who want to familiarize themselves with the statistical capabilities of IBM SPSS Statistics Base.
• Anyone who wants to refresh their knowledge and statistical experience.

## Content

1. Introduction to statistical analysis

• Identify the steps in the research process
• Principles of statistical analysis

2. Examine individual variables

• Identify measurement levels
• Chart individual variables
• Summarize individual variables
• Examine the normal distribution
• Examine standardized scores

3. Test hypotheses about individual variables

• Identify population parameters and sample statistics
• Examine the distribution of the sample mean
• Determine the sample size
• Test a hypothesis on the population mean
• Construct a confidence interval for the population mean
• Tests on a single variable: One-Sample T Test, Paired-Samples T Test, and Binomial Test

4. Test the relationship between categorical variables

• Chart the relationship between two categorical variables
• Describe the relationship: Compare percentages in Crosstabs
• Test the relationship: The Chi-Square test in Crosstabs
• Assumptions of the Chi-Square test
• Pairwise compare column proportions
• Measure the strength of the association

5. Test on the difference between two group means

• Compare the Independent-Samples T Test to the Paired-Samples T Test
• Chart the relationship between the group variable and scale variable
• Describe the relationship: Compare group means
• Test on the difference between two group means: Independent-Samples T Test
• Assumptions of the Independent-Samples T Test

6. Test on differences between more than two group means

• Describe the relationship: Compare group means
• Test the hypothesis of equal group means: One-Way ANOVA
• Assumptions of One-Way ANOVA
• Identify differences between group means: Post-hoc tests

7. Test the relationship between scale variables

• Chart the relationship between two scale variables
• Describe the relationship: Correlation
• Test on the correlation
• Assumptions for testing on the correlation
• Treatment of missing values

8. Predict a scale variable: Regression

• What is linear regression?
• Explain unstandardized and standardized coefficients
• Assess the fit of the model: R Square
• Examine residuals
• Include 0-1 independent variables
• Include categorical independent variables

9. Introduction to Bayesian statistics

• Bayesian statistics versus classical test theory
• Explain the Bayesian approach
• Evaluate a null hypothesis: Bayes Factor
• Bayesian procedures in IBM SPSS Statistics

10. Overview of multivariate procedures

• Overview of supervised models
• Overview of models to create natural groupings

## Prerequisites

• Experience with IBM SPSS Statistics (version 18 or later), or
• Completion of the IBM SPSS Statistics Essentials course

## Schedule

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