Further data manipulation techniques

Target Audience:
This course is a natural follow-on to the 'Introduction to SPSS' course and is designed for anyone wishing to become more competent with the full range of file and data manipulation options, and generally increase their efficiency with SPSS.

Prerequisites:
You must have a sound working knowledge of SPSS and be familiar with the topics covered on the Introduction to SPSS course. You must also be familiar with variable definition, use of the data dictionary, setting up dates, generating basic exploratory statistics, using the compute and recode procedures and editing and saving output.

Overview:
The course provides detailed training in the use of a wide range of file and data management techniques. The knowledge and competence gained will enable you to suitably manage your data files to achieve the desired data structures. Advice on optimising efficiency in everyday operations is provided and you will gain an understanding of the various options for operating SPSS. Through an understanding of the command syntax, you will be able to efficiently manage and modify your data.

Objectives:
By the end of the course you will have learned to:
• Manage and manipulate numeric data, including multiple response data
• Manage and manipulate dates and non-numeric data
• Manipulate files so as to achieve the desired data structure
• Work efficiently with SPSS syntax and production mode

Duration: 2 days

Course Content:
The following topics will be covered:

Methods of Running SPSS: When, why and how to use them

Introduction to SPSS Command Syntax:
• Accessing the Journal File
• Basic Syntax Structure

Handling Numeric Data:
• Complex If and Compute statements
• Weighting data

Handling Non-Numeric (String) Data: Complex If and compute statements

Handling Dates:
• Defining dates within SPSS
• Date conversion, extraction and calculation

Handling Multiple Response Data:
• Coding and defining multiple response data in SPSS
• Summarising multiple response data

Manipulating Data Files:
• Combining data files by adding cases, adding variables and through a table look-up match; aggregating data files