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.

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Table of Contents (17 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Installing and Configuring SPSS
Installing and Configuring SPSS
The SPSS installation utility
Licensing SPSS
Launching and using SPSS
Setting parameters within the SPSS software
Executing a basic SPSS session
Summary
2
Accessing and Organizing Data
Accessing and Organizing Data
Accessing and organizing data overview
Reading Excel files
Reading delimited text data files
Saving IBM SPSS Statistics files
Reading IBM SPSS Statistics files
Demo - first look at the data - frequencies
Variable properties
Summary
3
Statistics for Individual Data Elements
Statistics for Individual Data Elements
Getting the sample data
Descriptive statistics for numeric fields
Discovering coding issues using frequencies
Explore procedure
Summary
4
Dealing with Missing Data and Outliers
Dealing with Missing Data and Outliers
Outliers
Missing data
Summary
5
Visually Exploring the Data
Visually Exploring the Data
Graphs available in SPSS procedures
Summary
6
Sampling, Subsetting, and Weighting
Sampling, Subsetting, and Weighting
Select cases dialog box
Selecting a random sample of cases
Split File
Weighting
Summary
7
Creating New Data Elements
Creating New Data Elements
Transforming fields in SPSS
The RECODE command
The COMPUTE command
The IF command
The DO IF/ELSE IF command
General points regarding SPSS transformation commands
Summary
8
Adding and Matching Files
Adding and Matching Files
SPSS Statistics commands to merge files
Example of one-to-many merge - Northwind database
One-to-one merge - two data subsets from GSS2016
Example of combining cases using ADD FILES
Summary
9
Aggregating and Restructuring Data
Aggregating and Restructuring Data
Using aggregation to add fields to a file
Aggregating up one level
Second level aggregation
Matching the aggregated file back to find specific records
Restructuring rows to columns
Summary
10
Crosstabulation Patterns for Categorical Data
Crosstabulation Patterns for Categorical Data
Percentages in crosstabs
Summary
11
Comparing Means and ANOVA
Comparing Means and ANOVA
SPSS procedures for comparing Means
Post hoc comparisons
Summary
12
Correlations
Correlations
Pearson correlations
Listwise versus pairwise missing values
Pivoting table editing to enhance correlation matrices
Visualizing correlations with scatterplots
Rank order correlations
Partial correlations
Summary
13
Linear Regression
Linear Regression
Assumptions of the classical linear regression model
Example - motor trend car data
Multiple regression - Model-building strategies
Summary
14
Principal Components and Factor Analysis
Principal Components and Factor Analysis
Choosing between principal components analysis and factor analysis
PCA example - violent crimes
Factor analysis - abilities
Summary
15
Clustering
Clustering
Overview of cluster analysis
Overview of SPSS Statistics cluster analysis procedures
Hierarchical cluster analysis example
K-means cluster analysis example
Twostep cluster analysis example
Summary
16
Discriminant Analysis
Discriminant Analysis
Descriptive discriminant analysis
Predictive discriminant analysis
Assumptions underlying discriminant analysis
Example data
Statistical and graphical summary of the data
Discriminant analysis setup - key decisions
Examining the results
Scoring new observations
Summary

Twostep cluster analysis example

For this example, we return to the USA states violent crime data example. Recall that TWOSTEP CLUSTER offers an automatic method for selecting the number of clusters, as well as a Likelihood distance measure. We will run it to show some of the visuals in the model viewer output.

The approach here is to:

  1. First run TWOSTEP CLUSTER in automatic mode to identify a tentative number of clusters.
  2. Then run TWOSTEP CLUSTER again with a specified number of clusters.

Here is the SPSS code for the first run:

TWOSTEP CLUSTER
 /CONTINUOUS VARIABLES=MurderR RRapeR RobberyR AssaultR  BurglaryR LarcenyR VehicleTheftR
 /DISTANCE Likelihood
 /NUMCLUSTERS AUTO 15 BIC
 /HANDLENOISE 0
 /MEMALLOCATE 64
 /CRITERIA INITHRESHOLD(0) MXBRANCH(8) MXLEVEL(3)
 /VIEWMODEL DISPLAY=YES
 /PRINT IC COUNT SUMMARY.

Here are comments on the SPSS code:

  • In a step not shown, the variable names...
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