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The Statistical Package For Social Sciences (IBM SPSS)


In this course participants will learn the main features of the software including setting up a new data file in IBM SPSS ready for analysis, as well as some techniques of data management, and more advanced statistical procedures that are available in SPSS. The course is designed to provide an intensive training to the latest version of the Statistical Package for the Social Sciences (SPSS), now known as IBM SPSS Statistics. The training combines lecture and hands-on sessions, and involves an analysis of a subset of a large dataset. Participants should have knowledge of basic statistical concepts and should have experience in computer operations using Microsoft Windows. Experience with SPSS is not necessary, although a basic understanding of the purpose and functions of the software is helpful.

Learning Outcomes

By the end of this course, the participant will be able to:

  1. Understand the main features of the software
  2. Manage data in SPSS
  3. Apply SPSS statistical techniques to summarize and describe data
  4. Apply more advanced SPSS statistical procedures to analyze the data
  5. Conduct statistical analysis independently based on the type of outcome and study design
  6. Interpret results and present findings

Course Outline

  1. Introduction
    1. Introduction to SPSS
    2. Data analysis with SPSS: general
    3. aspects, workflow, critical issues
    4. SPSS: general description, functions, menus, commands
    5. SPSS file management
  2. Input and data cleaning
    1. Defining variables
    2. Manual input of data
    3. Automated input of data and file import
  3. Data manipulation
    1. Data transformation
    2. Syntax files and scripts
    3. Output management
  4. Descriptive analysis of data
    1. Frequency distributions
    2. Measures of central tendency
    3. Measures of dispersion
    4. Explore
    5. Crosstabs
  5. Statistical tests
    1. Means
    2. T-test
    3. One-way ANOVA
    4. Chi-square test
    5. Nonparametric tests
    6. Normality tests
  6. Linear regression analysis
  7. Binary logistic regression
  8. Multinomial logistic regression
  9. Survival Analysis (Kaplan-Meier)
  10. Factor Analysis
  11. Multivariate Analysis of Variance

Training Delivery Methods

  1. In-class method
  2. Online method
  • Duration: 30 Learning Hours

Accredited By


Participant who complete the stand alone course will be awarded a Certificate of Course Completion.