SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant and accurate code.

Foreword

Introduction



Part I Advanced Statistics

Chapter 1 Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques

T-Test
ANCOVA
MANOVA
Factor Analysis and Unobserved Variables in SPSS
AMOS
Revisiting Factor Analysis and a General Orientation to AMOS
The General Model


Chapter 2 Monte Carlo Simulation and IBM SPSS Bootstrapping

Monte Carlo Simulation
Monte Carlo Simulation in IBM SPSS Statistics
Creating an SPSS Model File
IBM SPSS Bootstrapping
Proportions
Bootstrap Mean
Bootstrap and Linear Regression


Chapter 3 Regression with Categorical Outcome Variables

Regression Approaches in SPSS
Logistic Regression
Ordinal Regression Theory
Assumptions of Ordinal Regression Models
Ordinal Regression Dialogs
Ordinal Regression Output
Categorical Regression Theory
Assumptions of Categorical Regression Models
Categorical Regression Dialogs
Categorical Regression Output


Chapter 4 Building Hierarchical Linear Models

Overview of Hierarchical Linear Mixed Models
A Two-Level Hierarchical Linear Model Example
Mixed Models...Linear
Mixed Models...Linear (Output)
Mixed Models...Generalized Linear
Mixed Models...Generalized Linear (Output)
Adjusting Model Structure


Part II Data Visualization

Chapter 5 Take Your Data Visualizations to the Next Level

Graphics Options in SPSS Statistics
Understanding the Revolutionary Approach in The Grammar of Graphics
Bar Chart Case Study
Bubble Chart Case Study


Chapter 6 The Code Behind SPSS Graphics: Graphics Production Language

Introducing GPL: Bubble Chart Case Study
GPL Help
Bubble Chart Case Study Part Two
Double Regression Line Case Study
Arrows Case Study
MBTI Bubble Chart Case Study


Chapter 7 Mapping in IBM SPSS Statistics

Creating Maps with the Graph board Template Chooser
Creating a Choropleth of Counts Map
Creating Other Map Types
Creating Maps Using Geographical Coordinates


Chapter 8 Geospatial Analytics

Geospatial Association Rules
Case Study: Crime and 311 Calls
Spatio-Temporal Prediction
Case Study: Predicting Weekly Shootings


Chapter 9 Perceptual Mapping with Correspondence Analysis, GPL and OMS

Starting with Crosstabs
Correspondence Analysis
Multiple Correspondence Analysis
Crosstabulations
Applying OMS and GPL to the MCA Perceptual Map
Chapter 10 Display Complex Relationships with Multidimensional Scaling
Metric and Nonmetric Multidimensional Scaling
Nonmetric Scaling of Psychology Sub Disciplines
Multidimenional Scaling Dialog Options
Multidimensional Scaling Output Interpretation
Subjective Approach to Dimension Interpretation
Statistical Approach to Dimension Interpretation


Part III Predictive Analytics

Chapter 11 SPSS Statistics versus SPSS Modeler: Can I Be a Data Miner Using SPSS Statistics?

What Is Data Mining?
What Is IBM SPSS Modeler?
Can Data Mining Be Done in SPSS Statistics?
Hypothesis Testing, Type I Error and Hold-Out Validation
Significance of the Model and Importance of Each Independent Variable
The Importance of Finding and Modeling Interactions
Classic and Important Data Mining Tasks
Partitioning and Validating
Feature Selection
Balancing
Comparing Results from Multiple Models
Creating Ensembles
Scoring New Records


Chapter 12 IBM SPSS Data Preparation

Identify Unusual Cases
Identify Unusual Cases Dialogs
Identify Unusual Cases Output
Optimal Binning
Optimal Binning Dialogs
Optimal Binning Output


Chapter 13 Model Complex Interactions with IBM SPSS Neural Networks

Why "Neural" Nets?
The Famous Case of Exclusive OR and the Perceptron
What Is a Hidden Layer and Why Is It Needed?
Neural Net Results with the XOR Variables
How the Weights Are Calculated: Error Backpropagation
Creating a Consistent Partition in SPSS Statistics
Comparing Regression to Neural Net with the Bank Salary Case Study
Calculating Mean Absolute Percent Error for Both Models
Classification with Neural Nets Demonstrated with the Titanic Dataset


Chapter 14 Powerful and Intuitive: IBM SPSS Decision Trees

Building a Tree with the CHAID Algorithm
Review of the CHAID Algorithm
Adjusting the CHAID Settings
CRT for Classification
Understanding Why the CRT Algorithm Produces a Different Tree
Missing Data
Changing the CRT Settings
Comparing the Results of All Four Models
Alternative Validation Options
The Scoring Wizard


Chapter 15 Find Patterns and Make Predictions with K Nearest Neighbors

Using KNN to Find "Neighbors"
The Titanic Dataset and KNN Used as a Classifier
The Trade-Offs between Bias and Variance
Comparing Our Models: Decision Trees, Neural Nets and KNN
Building an Ensemble


Part IV Syntax, Data Management and Programmability

Chapter 16 Write More Efficient and Elegant Code with SPSS Syntax Techniques

A Syntax Primer for the Uninitiated
Making the Connection: Menus and the Grammar of Syntax
What Is "Inefficient" Code?
The Case Study
Customer Dataset
Fixing the ZIP Codes
Addressing Case Sensitivity of City Names with UPPER() and LOWER()
Parsing Strings and the Index Function
Aggregate and Restructure
Pasting Variable Names, TO, Recode and Count
DO REPEAT Spend Ratios
Merge
Final Syntax File


Chapter 17 Automate Your Analyses with SPSS Syntax and the Output Management System

Overview of the Output Management System
Running OMS from Menus
Contents
Automatically Writing Selected Categories of Output to Different Formats
Suppressing Output
Working with OMS data
Running OMS from Syntax


Chapter 18 Statistical Extension Commands

What Is an Extension Command?
TURF Analysis--Designing Product Bundles
Large Problems
Quantile Regression--Predicting Airline Delays
Comparing Ordinary Least Squares with Quantile Regression Results
Operational Considerations
Support Vector Machines--Predicting Loan Default
Background
An Example
Operational Issues
Computing Cohen's d Measure of Effect Size for a T-Test


Index