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SPSS Statistics for Data Analysis and Visualization

Book Description

Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization

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.

IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results.

  • Conduct a more efficient and accurate analysis
  • Display complex relationships and create better visualizations
  • Model complex interactions and master predictive analytics
  • Integrate R and Python with SPSS Statistics for more efficient, more powerful code

These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.

Table of Contents

  1. Foreword
  2. Introduction
    1. The Audience for This Book
    2. How This Book Is Organized
    3. How to Use This Book
    4. The Themes of the Book
    5. Understanding the SPSS Bundles and the SPSS Modules
    6. The New SPSS Subscription Bundles
    7. What’s New in SPSS 23 and 24?
  3. Part I: Advanced Statistics
    1. Chapter 1: Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques
      1. T-Test
      2. Factor Analysis and Unobserved Variables in SPSS
      3. AMOS
    2. Chapter 2: Monte Carlo Simulation and IBM SPSS Bootstrapping
      1. Monte Carlo Simulation
      2. Monte Carlo Simulation in IBM SPSS Statistics
      3. Creating an SPSS Model File
      4. IBM SPSS Bootstrapping
    3. Chapter 3: Regression with Categorical Outcome Variables
      1. Regression Approaches in SPSS
      2. Logistic Regression
      3. Ordinal Regression Theory
      4. Ordinal Regression Dialogs
      5. Ordinal Regression Output
      6. Categorical Regression Theory
      7. Categorical Regression Dialogs
      8. Categorical Regression Output
    4. Chapter 4: Building Hierarchical Linear Models
      1. Overview of Hierarchical Linear Mixed Models
      2. Mixed Models…Linear
      3. Mixed Models…Linear (Output)
      4. Mixed Models…Generalized Linear
      5. Mixed Models…Generalized Linear (Output)
      6. Adjusting Model Structure
  4. Part II: Data Visualization
    1. Chapter 5: Take Your Data Visualizations to the Next Level
      1. Graphics Options in SPSS Statistics
      2. Understanding the Revolutionary Approach in The Grammar of Graphics
      3. Bar Chart Case Study
      4. Bubble Chart Case Study
    2. Chapter 6: The Code Behind SPSS Graphics: Graphics Production Language
      1. Introducing GPL: Bubble Chart Case Study
      2. GPL Help
      3. Bubble Chart Case Study Part Two
      4. Double Regression Line Case Study
      5. Arrows Case Study
      6. MBTI Bubble Chart Case Study
    3. Chapter 7: Mapping in IBM SPSS Statistics
      1. Creating Maps with the Graphboard Template Chooser
    4. Chapter 8: Geospatial Analytics
      1. Geospatial Association Rules
      2. Case Study: Crime and 311 Calls
      3. Spatio-Temporal Prediction
      4. Case Study: Predicting Weekly Shootings
    5. Chapter 9: Perceptual Mapping with Correspondence Analysis, GPL, and OMS
      1. Starting with Crosstabs
      2. Correspondence Analysis
      3. Multiple Correspondence Analysis
      4. Applying OMS and GPL to the MCA Perceptual Map
    6. Chapter 10: Display Complex Relationships with Multidimensional Scaling
      1. Metric and Nonmetric Multidimensional Scaling
      2. Nonmetric Scaling of Psychology Sub-Disciplines
      3. Multidimenional Scaling Dialog Options
      4. Multidimensional Scaling Output Interpretation
      5. Subjective Approach to Dimension Interpretation
      6. Statistical Approach to Dimension Interpretation
  5. Part III: Predictive Analytics
    1. Chapter 11: SPSS Statistics versus SPSS Modeler: Can I Be a Data Miner Using SPSS Statistics?
      1. What Is Data Mining?
      2. What Is IBM SPSS Modeler?
      3. Can Data Mining Be Done in SPSS Statistics?
      4. Hypothesis Testing, Type I Error, and Hold-Out Validation
      5. Significance of the Model and Importance of Each Independent Variable
      6. The Importance of Finding and Modeling Interactions
      7. Classic and Important Data Mining Tasks
    2. Chapter 12: IBM SPSS Data Preparation
      1. Identify Unusual Cases
      2. Optimal Binning
    3. Chapter 13: Model Complex Interactions with IBM SPSS Neural Networks
      1. Why “Neural” Nets?
      2. XOR Example Syntax
      3. Neural Net Results with the XOR Variables
      4. Comparing Regression to Neural Net with the Bank Salary Case Study
    4. Chapter 14: Powerful and Intuitive: IBM SPSS Decision Trees
      1. Building a Tree with the CHAID Algorithm
      2. Review of the CHAID Algorithm
      3. CRT for Classification
      4. The Scoring Wizard
    5. Chapter 15: Find Patterns and Make Predictions with K Nearest Neighbors
      1. Using KNN to Find “Neighbors”
      2. The Titanic Dataset and KNN Used as a Classifier
      3. The Trade-Offs between Bias and Variance
      4. Comparing Our Models: Decision Trees, Neural Nets, and KNN
      5. Building an Ensemble
  6. Part IV: Syntax, Data Management, and Programmability
    1. Chapter 16: Write More Efficient and Elegant Code with SPSS Syntax Techniques
      1. A Syntax Primer for the Uninitiated
      2. The Case Study
    2. Chapter 17: Automate Your Analyses with SPSS Syntax and the Output Management System
      1. Overview of the Output Management System
      2. Running OMS from Menus
      3. Automatically Writing Selected Categories of Output to Different Formats
      4. Suppressing Output
      5. Working with OMS data
      6. Running OMS from Syntax
    3. Chapter 18: Statistical Extension Commands
      1. What Is an Extension Command?
      2. TURF Analysis—Designing Product Bundles
      3. Quantile Regression—Predicting Airline Delays
      4. Comparing Ordinary Least Squares with Quantile Regression Results
      5. Support Vector Machines—Predicting Loan Default
      6. Computing Cohen’s d Measure of Effect Size for a T-Test
  7. EULA