SPSS Statistics for Data Analysis and Visualization: Timely, Practical, Reliable
- 1st ed
- New Delhi Wiley 2017
- 490
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 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
9788126569199
Social sciences--Statistical methods--Computer programs