About this Course

Introduction to Statistical Analysis Using IBM SPSS Statistics is a two day instructor-led classroom course that 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, the assumptions made by each method, how to set up the analysis, 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. Students will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, interpret their output, and graphically display the results.

Audience Profile

This basic course is intended 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
• Anyone with limited or no statistical background
• Anyone who wants to refresh their knowledge and statistical experience that were gained many years ago

Prerequisites

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 Outline

1. Introduction to Statistical Analysis

• Explain the basic steps of the research process
• Explain differences between populations and samples
• Explain differences between experimental and non-experimental research designs
• Explain differences between independent and dependent variables

2. Understanding Data Distributions - Theory

• Describe the levels of measurement used in IBM SPSS Statistics
• Use measures of central tendency and dispersion
• Use normal distributions and z-scores

3. Data Distributions for Categorical Variables

• Use the options in the Frequencies procedure
• Interpret the results of the Frequencies procedure

4. Data Distributions for Scale Variables

• Use the options in the Frequencies, Descriptives, and Explore procedures
• Interpret the results of the Frequencies, Descriptives, and Explore procedures

5. Making Inferences about Populations from Samples

• Explain the influence of sample size
• Explain the nature of probability
• Explain hypothesis testing
• Explain different types of statistical errors and power
• Explain differences between statistical and practical importance

6. Relationships Between Categorical Variables

• Use the options in the Crosstabs procedure
• Request appropriate statistics for a crosstabulation
• Interpret cell counts and percents in a crosstabulation
• Use the Chi-Square test, interpret its results, and check its assumptions
• Use the Chart Builder to visualize a crosstabulation
• Use additional syntax-only Crosstabs features

7. The Independent- Samples T Test

• Check the assumptions of the Independent-Samples T Test
• Use the Independent-Samples T Test to test the difference in means
• Know how to interpret the results of a Independent-Samples T Test
• Use the Chart Builder to create an error bar graph to display mean differences

8. The Paired-Samples T Test

• Use the Paired-Samples T Test procedure
• Interpret the results of a Paired-Samples T Test

9. One-Way ANOVA

• Use the options in the One-Way ANOVA procedure
• Check the assumptions for One-Way ANOVA
• Interpret the results of a One-Way ANOVA analysis
• Use the Chart Builder to create an error bar to graph mean differences

10. Bivariate Plots and Correlations for Scale Variables

• Visually assess the relationship between two scale variables with scatterplots, using the Chart Builder procedure
• Explain the Pearson correlation coefficient and its assumptions
• Interpret a Pearson correlation coefficient
• Explain the options of the Bivariate Correlations procedure

11. Regression Analysis

• Explain linear regression and its assumptions
• Explain the options of the Linear Regression procedure
• Interpret the results of the Linear Regression procedure
• Use Automatic Linear Models to perform regression

12. Nonparametric Tests

• Describe when non-parametric tests should and can be used
• Describe the options in the Nonparametric Tests procedure dialog box and tabs
• Interpret the results of several types of nonparametric tests