This course provides an applicationoriented 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, 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 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.
Prerequisites

Familiarity with basic concepts in statistics, such as measurement levels, mean, and standard deviation.

Familiarity with the windows in IBM SPSS Statistics either by experience with IBM SPSS Statistics (version 18 or later) or completion of the IBM SPSS Statistics Essentials (V25) course.

Familiarity with basic concepts in statistics, such as measurement levels, mean, and standard deviation.

Familiarity with the windows in IBM SPSS Statistics either by experience with IBM SPSS Statistics (version 18 or later) or completion of the IBM SPSS Statistics Essentials (V25) course.

Introduction to statistical analysis

Examine individual variables

Test hypotheses about individual variables

Test the relationship between categorical variables

Test on the difference between two group means

Test on differences between more than two group means

Test the relationship between scale variables

Predict a scale variable: Regression

Introduction to Bayesian statistics

Overview of multivariate procedures
Introduction to statistical analysis
• Identify the steps in the research process
• Principles of statistical analysis
Examine individual variables
• Identify measurement levels
• Chart individual variables
• Summarize individual variables
• Examine the normal distribution
• Examine standardized scores
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: OneSample T Test, PairedSamples T Test, and Binomial Test
Test the relationship between categorical variables
• Chart the relationship between two categorical variables
• Describe the relationship: Compare percentages in Crosstabs
• Test the relationship: The ChiSquare test in Crosstabs
• Assumptions of the ChiSquare test
• Pairwise compare column proportions
• Measure the strength of the association
Test on the difference between two group means
• Compare the IndependentSamples T Test to the PairedSamples 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: IndependentSamples T Test
• Assumptions of the IndependentSamples T Test
Test on differences between more than two group means
• Describe the relationship: Compare group means
• Test the hypothesis of equal group means: OneWay ANOVA
• Assumptions of OneWay ANOVA
• Identify differences between group means: Posthoc tests
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
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 01 independent variables
• Include categorical independent variables
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
Overview of multivariate procedures
• Overview of supervised models
• Overview of models to create natural groupings