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.
Klappentext
Explore advanced techniques that unlock the full capabilities of SPSS
IBM® SPSS Statistics is a complex software package with more than a dozen specialized, high-octane tools. Unlock its most powerful aspects with this comprehensive tutorial. In-depth study of advanced statistical techniques, data visualization tools, predictive analysis, and SPSS programming will enable you to take advantage of the many SPSS features that are often overlooked, making your work more efficient and accurate.
Each chapter introduces an advanced feature, explains when and why to use it, and then walks you step-by-step through executing the technique. You will learn to use advanced regression, work with maps, display complex relationships with multi-dimensional scaling, and take advantage of powerful and intuitive SPSS decision trees. You'll discover how to write more efficient code with advanced SPSS syntax, combine SPSS syntax with Python, master SPSS bootstrapping and Monte Carlo simulation.
Conquering these advanced features helps you conduct more accurate and thorough analysis and create elegant, sophisticated visualizations that tell the whole story. SPSS Statistics for Data Analysis and Visualization will show you how to:
- Tell a more complete story with SPSS Amos
- Build hierarchical linear models with SPSS Advanced Statistics
- Turn crosstabulations into pictures with Correspondence Analysis
- Map your data with advanced geospatial analysis
- Model complex interactions with SPSS Neural Networks
- Harness the power of R and Python in SPSS Statistics
- Automate your analyses with SPSS Syntax and the Output Management System
- Take advantage of SPSS® Data Preparation and SPSS® Bootstrapping, now included in SPSS Subscription Base
Zusammenfassung
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.
Inhalt
Foreword xxiii
Introduction xxvii
Part I Advanced Statistics 1
Chapter 1 Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques 3
T-Test 7
ANCOVA 8
MANOVA 13
Factor Analysis and Unobserved Variables in SPSS 23
AMOS 26
Revisiting Factor Analysis and a General Orientation to AMOS 26
The General Model 29
Chapter 2 Monte Carlo Simulation and IBM SPSS Bootstrapping 43
Monte Carlo Simulation 44
Monte Carlo Simulation in IBM SPSS Statistics 44
Creating an SPSS Model File 45
IBM SPSS Bootstrapping 59
Proportions 63
Bootstrap Mean 66
Bootstrap and Linear Regression 68
Chapter 3 Regression with Categorical Outcome Variables 71
Regression Approaches in SPSS 72
Logistic Regression 73
Ordinal Regression Theory 74
Assumptions of Ordinal Regression Models 77
Ordinal Regression Dialogs 77
Ordinal Regression Output 81
Categorical Regression Theory 86
Assumptions of Categorical Regression Models 87
Categorical Regression Dialogs 87
Categorical Regression Output 93
Chapter 4 Building Hierarchical Linear Models 101
Overview of Hierarchical Linear Mixed Models 102
A Two-Level Hierarchical Linear Model Example 102
Mixed ModelsLinear 104
Mixed ModelsLinear (Output) 113
Mixed ModelsGeneralized Linear 116
Mixed ModelsGeneralized Linear (Output) 120
Adjusting Model Structure 126
Part II Data Visualization 129
Chapter 5 Take Your Data Visualizations to the Next Level 131
Graphics Options in SPSS Statistics 132
Understanding the Revolutionary Approach in The Grammar of Graphics 136
Bar Chart Case Study 138
Bubble Chart Case Study 143
Chapter 6 The Code Behind SPSS Graphics: Graphics Production Language 147
Introducing GPL: Bubble Chart Case Study 147
GPL Help 155
Bubble Chart Case Study Part Two 156
Double Regression Line Case Study 160
Arrows Case Study 163
MBT…