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