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Course Outline
Introduction to IBM SPSS Statistics
Review basic concepts in IBM SPSS Statistics
Identify the steps in the research process
Review basic analyses
Use help
Reading data and defining metadata
Overview of data sources
Read from text files
Read data from Microsoft Excel
Read data from databases
Define variable properties
Selecting cases for analyses
Select cases for analyses
Run analyses for groups
Apply report authoring styles
Transforming variables
Compute variables
Recode values of categorical and scale variables
Create a numeric variable from a string variable
Using functions to transform variables
Use statistical functions
Use logical functions
Use missing value functions
Use conversion functions
Use system variables
Use the Date and Time Wizard
Setting the unit of analysis
Remove duplicate cases
Create aggregate datasets
Restructure datasets
Merging data files
Add cases from one dataset to another
Add variables from one dataset to another
Enrich a dataset with aggregated information
Summarizing individual variables
Define levels of measurement
Summarizing categorical variables
Summarizing scale variables
Describing the relationship between variables
Choose the appropriate procedure
Summarize the relationship between categorical variables
Summarize the relationship between a scale and a categorical variable
Creating presentation-ready tables with Custom Tables
Identify table layouts
Create tables for variables with shared categories
Create tables for multiple response questions
Customising pivot tables
Perform Automated Output Modification
Customize pivot tables
Use table templates
Export pivot tables to other applications
Working with syntax
Use syntax to automate analyses
Create, edit, and run syntax
Shortcuts in the Syntax Editor
Controlling the IBM SPSS Statistics environment
Set options for output
Set options for variables display
Set options for default working folders
Course Outline
Introduction to statistical analysis
Identify the steps in the research process
Principles of statistical analysis
Examining 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
Summarize individual variables
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
Testing on 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
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
Test on the difference 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
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
Pairwise compare column proportions
Measure the strength of the association
Treatment of missing values
Predicting 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
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
Course Outline
Introduction to advanced statistical analysis
Taxonomy of models
Overview of supervised models
Overview of models to create natural groupings
Group variables: Factor Analysis and Principal Components Analysis
Factor Analysis basics
Principal Components basics
Assumptions of Factor Analysis
Key issues in Factor Analysis
Improve the interpretability
Use Factor and component scores
Group similar cases: Cluster Analysis
Cluster Analysis basics
Key issues in Cluster Analysis
K-Means Cluster Analysis
Assumptions of K-Means Cluster Analysis
TwoStep Cluster Analysis
Assumptions of TwoStep Cluster Analysis
Predict categorical targets with Nearest Neighbour Analysis
Nearest Neighbour Analysis basics
Key issues in Nearest Neighbour Analysis
Assess model fit
Predict categorical targets with Discriminant Analysis
Discriminant Analysis basics
The Discriminant Analysis model
Core concepts of Discriminant Analysis
Classification of cases
Assumptions of Discriminant Analysis
Validate the solution
Predict categorical targets with Logistic Regression
Binary Logistic Regression basics
The Binary Logistic Regression model
Multinomial Logistic Regression basics
Assumptions of Logistic Regression procedures
Testing hypotheses
Predict categorical targets with Decision Trees
Decision Trees basics
Validate the solution
Explore CHAID
Explore CRT
Comparing Decision Trees methods
Introduction to Survival Analysis
Survival Analysis basics
Kaplan-Meier Analysis
Assumptions of Kaplan-Meier Analysis
Cox Regression
Assumptions of Cox Regression
Introduction to Linear Mixed Models
Linear Mixed Models basics
Hierarchical Linear Models
Modeling strategy
Assumptions of Linear Mixed Models
Introduction to Generalized Linear Models
Generalized Linear Models basics
Available distributions
Available link functions
Course Outline
The Logic of Survey Analysis
Data checking and data validation
Data transformations: create new variables
Testing for Reliability and Validity
Analyzing Categorical Variables
Analyzing Interval Variables
Reporting Survey Results for Categorical and Scale Data
Clustering Respondents
Multivariate Analysis using Regression Techniques
Special Issues: Missing Data
Special Issues: Complex Samples and Sample Weights
Measuring Change over Time with Surveys
Decision Tree Analysis