SPSS Statistics Essential Training

Overview

Get up and running with SPSS Statistics. Learn how to work with the program to make data visualizations, calculate descriptive statistics, and more.

Syllabus

Introduction

  • Welcome
  • Using the exercise files

1. What Is SPSS?

  • SPSS in context
  • Versions, releases, licenses, and interfaces

2. Getting Started

  • Navigating SPSS
  • Sample datasets
  • Data types, measures, and roles
  • Options and preferences
  • Extending SPSS
  • Saving and running syntax files

3. Data Visualization

  • Visualizing data with Chart Builder
  • Modifying Chart Builder visualizations
  • Visualizing data with Graphboard templates
  • Modifying Graphboard visualizations
  • Using legacy dialogs: Boxplots for multiple variables
  • Creating regression variable plots
  • Comparing subgroups

4. Data Wrangling

  • Importing data
  • Variable labels
  • Value labels
  • Splitting files
  • Selecting cases and subgroups

5. Recoding Data

  • Recoding variables
  • Reversing values with syntax
  • Recoding by ranking cases
  • Creating dummy variables
  • Recoding with Visual Binning
  • Recoding with Optimal Binning
  • Preparing data for modeling
  • Computing scores

6. Exploring Data

  • Computing frequencies
  • Computing descriptives
  • Exploratory data analysis
  • Computing correlations
  • Computing contingency tables
  • Factor analysis and principal component analysis
  • Reliability analysis

7. Clustering and Classification

  • Hierarchical clustering
  • k-means clustering
  • k-nearest neighbors classification
  • Decision tree classification in SPSS
  • Neural networks in SPSS: Multilayer perceptron classification
  • Neural networks in SPSS: Radial basis function classification

8. Analyzing Data

  • Comparing proportions
  • Comparing one mean to a population: One-sample t-test
  • Comparing paired means: Paired-samples t-test
  • Comparing two means: Independent-samples t-test
  • Comparing multiple means: One-way ANOVA
  • Comparing means with two categorical variables: ANOVA

9. Building Predictive Models

  • Computing a linear regression
  • Variable selection
  • Logistic regression
  • Automatic linear modeling

10. Sharing Your Work

  • Exporting charts and tables
  • Web reports
  • Next steps

Conclusion

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