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

Principal Components and Factor Analysis

The SPSS Statistics FACTOR procedure provides a comprehensive procedure for doing principal components analysis and factor analysis. The underlying computations for these two techniques are similar, which is why SPSS Statistics bundles them in the same procedure. However, they are sufficiently distinct, so you should consider what your research goals are and choose the appropriate method for your goals.

Principal components analysis (PCA) finds weighted combinations of the original variables that account for the total variance in the original variables. The first principal component finds the linear combination of variables that accounts for as much variance as possible. The second principal component finds the linear combination of variables that accounts for as much of the remaining variance as possible, and also has the property that...