Book Image

Data Analysis with IBM SPSS Statistics

By : Ken Stehlik-Barry, Anthony Babinec
Book Image

Data Analysis with IBM SPSS Statistics

By: Ken Stehlik-Barry, Anthony Babinec

Overview of this book

SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data. The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed. By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease.
Table of Contents (17 chapters)
4
Dealing with Missing Data and Outliers
10
Crosstabulation Patterns for Categorical Data

Creating New Data Elements

New fields can be created in SPSS using a variety of different methods. In Chapter 4, Dealing with Outliers and Missing Data, the SAVE subcommand on both the DESCRIPTIVES and REGRESSION commands resulted in the addition of fields to the original dataset. This same chapter contained an example of using a set of IF commands to create new fields that were designed to address specific missing value issues in the data. In this chapter, the commands available in SPSS for creating new fields will be demonstrated in detail.

Deriving new fields is central to the analytic process since this is how subject matter knowledge is incorporated into the predictive modeling. Ratios and differences of specific data elements, for example, can be very useful as predictors but do not typically exist in the source data.

The four most heavily used commands available on the...