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

SPSS procedures for comparing Means

The individual procedures available for conducting mean comparisons can be found under the Compare Means group within the Analyze menu, as shown in the following screenshot:

Starting with Means and progressing through One-Way ANOVA (the summary procedure will not be covered, since it is based on a Python add-on), the statistics available for evaluating mean differences become increasingly sophisticated. In addition, each of the techniques offers specialized statistics suited to the task they are designed to perform.

The Means procedure

The examples in this chapter will use the 33-variable subset of the General Social Survey data from 2016 that was the basis for the examples in Chapter 3...