Discovering Statistics Using SPSS
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`The Second Edition of Andy Field's Discovering Statistics Using SPSS is an excellent book and a valuable addition to the teaching of statistics in the behavioral sciences. The title of the book accurately reflects the approach taken. This is not simply a primer on how to use SPSS, but is a very good statistics text using SPSS as a vehicle for illustrating and expanding on the statistical content of the book. At the same time it also serves as a manual for SPSS, and has taught me things that I had not known about the software. One advantage of the text is that it is not tied specifically to th...
`The Second Edition of Andy Field's Discovering Statistics Using SPSS is an excellent book and a valuable addition to the teaching of statistics in the behavioral sciences. The title of the book accurately reflects the approach taken. This is not simply a primer on how to use SPSS, but is a very good statistics text using SPSS as a vehicle for illustrating and expanding on the statistical content of the book. At the same time it also serves as a manual for SPSS, and has taught me things that I had not known about the software. One advantage of the text is that it is not tied specifically to the latest version of SPSS. Although the examples use version 12.0, there are many references to the difference the students will find if they are using an earlier version. A good example of how the text is structured can be seen in Chapters 8 and 9. Chapter 8 provides an introduction to the analysis of variance, and roughly the first 40 pages are devoted to statistical issues. Only after that introduction does the author move to a discussion of using SPSS to run the analyses. Chapter 9 deals with the analysis of covariance, and here the author moves almost immediately to SPSS printout, having laid most of the groundwork in the previous chapter. I find this flexible approach to the blending of content and software to be an effective way of teaching the material. It is impossible to review this book without commenting on Andy's particular style. I enjoyed it immensely and think that it would appeal to both students and their instructors. It is refreshing to see someone who doesn't take himself too seriously' - David C Howell, Professor Emeritus, University of Vermont
`The new edition of Field's textbook confirms its place as the best statistics text for undergraduate social science students. It provides support for those less confident about statistical analysis whilst having sufficient depth that it will still be valuable to more mathematically experienced people. There is a focus throughout on the practical aspects of data analysis and interpretation whilst at the same time emphasizing the importance of rigour and a good understating of theory essential reading' - Dr Ian Walker, Department of Psychology, University of Bath
`Very useful, valuable and interesting. Summing up: Highly recommended' - CHOICE, Current Reviews for Academic Librarians
This new edition of Field's textbook provides students of statistical methods with everything they need to understand, use and report statistics - at every level.
Written in Andy Field's vivid and entertaining style, and furnished with playful examples from everyday student life (among other places), the book forms an accessible gateway into the often intimidating world of statistics and a unique opportunity for students to ground their knowledge of statistics through the use of SPSS. The text is fully compliant with the latest release of SPSS (version 13).
Key updates in the new edition:
- More coverage with completely new material on non-parametric statistics, loglinear analysis, effect sizes and how to report statistical analysis.
- Even more student-friendly features, including a glossary of key statistical terms and exercises at the end of chapters for students to work through, with datasets and answers to chapter exercises on the accompanying CD-ROM.
- A larger and more easy-to-reference format: notation in each section identifies the intended level of study while the new 2-colour text design enhances the features in the book and, together with the larger format, provides extra clarity throughout.
Andy Field is a Senior Lecturer in Psychology at The University of Sussex where his success in making statistics accessible was recognized with a teaching award in 2001. He is also winner of the British Psychological Society Award for Excellence in the Teaching of Psychology.
Review quote:
... Field makes concerted efforts to provide statistical tools in a very lighthearted fashion by throwing in humor, with great success... Very useful, valuable, and interesting. Highly recommended. D.V. Chopra(Choice Magazine, 20060201)
... Aimed at a wide student audience, the objectives are to explain statistics theory and practice in a clear, engaging, user-friendly way, and the book does this well. For the purist, some of the phrasing might be loose and the theory light, but the book works well as an introductory text for its intended audience. Andrew Millard, Research Officer of Greater Glasgo NHS(20060216)
Table of contents:
EVERYTHING YOU WANTED TO KNOW ABOUT STATISTICS (WELL, SORT OF)
Building Statistical Models
Populations and Samples
Simple Statistical Models
Frequency Distributions
Is My Sample Representative of the Population?
Linear Models
How Can We Tell If Our Model Represents the Real World?
THE SPSS ENVIRONMENT
Versions of SPSS
Getting Started
The Data Editor
The Output Viewer
The Syntax Window
Saving Files
Retrieving a File
EXPLORING DATA
Parametric Data
Graphing and Screening Data
Exploring Groups of Data
Testing Whether a Distribution is Normal
Testing for Homogeneity of Variance
Graphing Means
CORRELATION
How Do We Measure Relationships?
Data Entry for Correlation Analysis Using SPSS
Graphing Relationships
The Scatterplot
Bivariate Correlation
Partial Correlation
How To Report Correlation Coefficients
REGRESSION
An Introduction to Regression
Doing Simple Regression on SPSS
Interpreting a Simple Regression
Multiple Regression
The Basics
How Accurate Is My Regression Model?
How To Do Multiple Regression Using SPSS
Interpreting Multiple Regression
How To Report Multiple Regression
Categorical Predictors and Multiple Regression
LOGISTIC REGRESSION
Background to Logistic Regression
What Are the Principles behind Logistic Regression?
Running the Analysis
A Research Example
Interpreting Logistic Regression
How To Report Logistic Regression
Another Example
Testing for Multicollinearity
Things That Can Go Wrong
COMPARING TWO MEANS
Revision of Experimental Research
Inputting Data and Displaying Means with Error Bar Charts
Testing Differences between Means
The t-Test
The Dependent t-Test
The Independent t-Test
Between Groups or Repeated Measures?
The t-Test as a General Linear Model
What If Our Data Are Not Normally Distributed?
COMPARING SEVERAL MEANS: ANOVA (GLM 1)
The Theory behind ANOVA
Running One-Way ANOVA on SPSS
Output from One-Way ANOVA
Calculating the Effect Size
Reporting Results from One-Way Independent ANOVA
Violations of Assumptions in One-Way Independent ANOVA
ANALYSIS OF COVARIANCE, ANCOVA (GLM 2)
What Is ANCOVA?
Conducting ANCOVA on SPSS
Interpreting the Output from ANCOVA
ANCOVA Run as a Multiple Regression
Additional Assumptions in ANCOVA
Calculating the Effect Size
Reporting Results
FACTORIAL ANOVA (GLM 3)
Theory of Factorial ANOVA (Between Groups)
Factorial ANOVA Using SPSS
Output from Factorial ANOVA
Interpreting Interaction Graphs
Calculating Effect Sizes
Reporting the Results of Two-Way ANOVA
Factorial ANOVA as Regression
REPEATED-MEASURES DESIGNS (GLM 4)
Introduction to Repeated-Measures Designs
Theory of One-Way Repeated-Measures ANOVA
One-Way Repeated-Measures ANOVA Using SPSS
Output for One-Way Repeated-Measures ANOVA
Effect Sizes for Repeated-Measures ANOVA
Reporting One-Way Repeated-Measures ANOVA
Repeated-Measures with Several Independent Variables
Output for Factorial Repeated-Measures ANOVA
Effect Sizes for Factorial Repeated-Measures ANOVA
Reporting the Results from Factorial Repeated-Measures ANOVA
MIXED DESIGN ANOVA (GLM 5)
Mixed ANOVA on SPSS
Output for Mixed Factorial ANOVA
Main Analysis
Calculating Effect Sizes
Reporting the Results of Mixed ANOVA
NON-PARAMETRIC TESTS
Comparing Two Independent Conditions
The Wilcoxon Rank-Sum Test and Mann-Whitney Test
Comparing Two Related Conditions
The Wilcoxon Signed-Rank Test
Differences between Several Independent Groups
The Kruskal-Wallis Test
Differences between Several Related Groups
Friedman's ANOVA
MULTIVARIATE ANALYSIS OF VARIANCE (MANOVA)
Introduction
Similarities and Differences to ANOVA
Theory of MANOVA
Assumptions of MANOVA
MANOVA on SPSS
Output from MANOVA
Following Up MANOVA with Discriminant Analysis
Output from the Discriminant Analysis
EXPLORATORY FACTOR ANALYSIS
Factors
Discovering Factors
Research Example
Running the Analysis
Interpreting Output from SPSS
Reliability Analysis
CATEGORICAL DATA
Theory of Analyzing Categorical Data
Assumptions of the Chi-Square Test
Doing Chi-Square on SPSS
Several Categorical Variables
Log Linear Analysis
Assumptions in Loglinear Analysis
Loglinear Analysis Using SPSS
Output from Loglinear Analysis
Following Up Loglinear Analysis
Effect Sizes in Loglinear Analysis
Reporting the Results of Loglinear Analysis
`The new edition of Field's textbook confirms its place as the best statistics text for undergraduate social science students. It provides support for those less confident about statistical analysis whilst having sufficient depth that it will still be valuable to more mathematically experienced people. There is a focus throughout on the practical aspects of data analysis and interpretation whilst at the same time emphasizing the importance of rigour and a good understating of theory essential reading' - Dr Ian Walker, Department of Psychology, University of Bath
`Very useful, valuable and interesting. Summing up: Highly recommended' - CHOICE, Current Reviews for Academic Librarians
This new edition of Field's textbook provides students of statistical methods with everything they need to understand, use and report statistics - at every level.
Written in Andy Field's vivid and entertaining style, and furnished with playful examples from everyday student life (among other places), the book forms an accessible gateway into the often intimidating world of statistics and a unique opportunity for students to ground their knowledge of statistics through the use of SPSS. The text is fully compliant with the latest release of SPSS (version 13).
Key updates in the new edition:
- More coverage with completely new material on non-parametric statistics, loglinear analysis, effect sizes and how to report statistical analysis.
- Even more student-friendly features, including a glossary of key statistical terms and exercises at the end of chapters for students to work through, with datasets and answers to chapter exercises on the accompanying CD-ROM.
- A larger and more easy-to-reference format: notation in each section identifies the intended level of study while the new 2-colour text design enhances the features in the book and, together with the larger format, provides extra clarity throughout.
Andy Field is a Senior Lecturer in Psychology at The University of Sussex where his success in making statistics accessible was recognized with a teaching award in 2001. He is also winner of the British Psychological Society Award for Excellence in the Teaching of Psychology.
Review quote:
... Field makes concerted efforts to provide statistical tools in a very lighthearted fashion by throwing in humor, with great success... Very useful, valuable, and interesting. Highly recommended. D.V. Chopra(Choice Magazine, 20060201)
... Aimed at a wide student audience, the objectives are to explain statistics theory and practice in a clear, engaging, user-friendly way, and the book does this well. For the purist, some of the phrasing might be loose and the theory light, but the book works well as an introductory text for its intended audience. Andrew Millard, Research Officer of Greater Glasgo NHS(20060216)
Table of contents:
EVERYTHING YOU WANTED TO KNOW ABOUT STATISTICS (WELL, SORT OF)
Building Statistical Models
Populations and Samples
Simple Statistical Models
Frequency Distributions
Is My Sample Representative of the Population?
Linear Models
How Can We Tell If Our Model Represents the Real World?
THE SPSS ENVIRONMENT
Versions of SPSS
Getting Started
The Data Editor
The Output Viewer
The Syntax Window
Saving Files
Retrieving a File
EXPLORING DATA
Parametric Data
Graphing and Screening Data
Exploring Groups of Data
Testing Whether a Distribution is Normal
Testing for Homogeneity of Variance
Graphing Means
CORRELATION
How Do We Measure Relationships?
Data Entry for Correlation Analysis Using SPSS
Graphing Relationships
The Scatterplot
Bivariate Correlation
Partial Correlation
How To Report Correlation Coefficients
REGRESSION
An Introduction to Regression
Doing Simple Regression on SPSS
Interpreting a Simple Regression
Multiple Regression
The Basics
How Accurate Is My Regression Model?
How To Do Multiple Regression Using SPSS
Interpreting Multiple Regression
How To Report Multiple Regression
Categorical Predictors and Multiple Regression
LOGISTIC REGRESSION
Background to Logistic Regression
What Are the Principles behind Logistic Regression?
Running the Analysis
A Research Example
Interpreting Logistic Regression
How To Report Logistic Regression
Another Example
Testing for Multicollinearity
Things That Can Go Wrong
COMPARING TWO MEANS
Revision of Experimental Research
Inputting Data and Displaying Means with Error Bar Charts
Testing Differences between Means
The t-Test
The Dependent t-Test
The Independent t-Test
Between Groups or Repeated Measures?
The t-Test as a General Linear Model
What If Our Data Are Not Normally Distributed?
COMPARING SEVERAL MEANS: ANOVA (GLM 1)
The Theory behind ANOVA
Running One-Way ANOVA on SPSS
Output from One-Way ANOVA
Calculating the Effect Size
Reporting Results from One-Way Independent ANOVA
Violations of Assumptions in One-Way Independent ANOVA
ANALYSIS OF COVARIANCE, ANCOVA (GLM 2)
What Is ANCOVA?
Conducting ANCOVA on SPSS
Interpreting the Output from ANCOVA
ANCOVA Run as a Multiple Regression
Additional Assumptions in ANCOVA
Calculating the Effect Size
Reporting Results
FACTORIAL ANOVA (GLM 3)
Theory of Factorial ANOVA (Between Groups)
Factorial ANOVA Using SPSS
Output from Factorial ANOVA
Interpreting Interaction Graphs
Calculating Effect Sizes
Reporting the Results of Two-Way ANOVA
Factorial ANOVA as Regression
REPEATED-MEASURES DESIGNS (GLM 4)
Introduction to Repeated-Measures Designs
Theory of One-Way Repeated-Measures ANOVA
One-Way Repeated-Measures ANOVA Using SPSS
Output for One-Way Repeated-Measures ANOVA
Effect Sizes for Repeated-Measures ANOVA
Reporting One-Way Repeated-Measures ANOVA
Repeated-Measures with Several Independent Variables
Output for Factorial Repeated-Measures ANOVA
Effect Sizes for Factorial Repeated-Measures ANOVA
Reporting the Results from Factorial Repeated-Measures ANOVA
MIXED DESIGN ANOVA (GLM 5)
Mixed ANOVA on SPSS
Output for Mixed Factorial ANOVA
Main Analysis
Calculating Effect Sizes
Reporting the Results of Mixed ANOVA
NON-PARAMETRIC TESTS
Comparing Two Independent Conditions
The Wilcoxon Rank-Sum Test and Mann-Whitney Test
Comparing Two Related Conditions
The Wilcoxon Signed-Rank Test
Differences between Several Independent Groups
The Kruskal-Wallis Test
Differences between Several Related Groups
Friedman's ANOVA
MULTIVARIATE ANALYSIS OF VARIANCE (MANOVA)
Introduction
Similarities and Differences to ANOVA
Theory of MANOVA
Assumptions of MANOVA
MANOVA on SPSS
Output from MANOVA
Following Up MANOVA with Discriminant Analysis
Output from the Discriminant Analysis
EXPLORATORY FACTOR ANALYSIS
Factors
Discovering Factors
Research Example
Running the Analysis
Interpreting Output from SPSS
Reliability Analysis
CATEGORICAL DATA
Theory of Analyzing Categorical Data
Assumptions of the Chi-Square Test
Doing Chi-Square on SPSS
Several Categorical Variables
Log Linear Analysis
Assumptions in Loglinear Analysis
Loglinear Analysis Using SPSS
Output from Loglinear Analysis
Following Up Loglinear Analysis
Effect Sizes in Loglinear Analysis
Reporting the Results of Loglinear Analysis