Andy Field

Discovering Statistics Using IBM SPSS Statistics

• Broschiertes Buch

Produktbeschreibung

Lecturers/instructors - request a free digital inspection copy here

With a little help from his weird band of characters the Fourth Edition of the award-winning book continues, with its unique blend of humour and collection of bizarre examples, to bring statistics - from first principles to advanced concepts - well and truly to life using IBM SPSS Statistics.

Lecturers: with WebAssign® you can manage and monitor your students' progress quickly and easily online or give them more opportunities to practise!
Ideal for short courses, choose to use WebAssign® alongside the Fourth Edition of Andy Field's textbook to quickly set up courses and schedule assignments (using the 2159 questions available) and track individual performance so you can spot in an instant where more instruction or practice is needed. If not using for fomal assessment, WebAssign® still lets you set questions for your students to practise over and over again. They get instant feedback and also links to the relevant chapter or section in the integral ebook to help them work out the correct solution.

For more information on how to integrate WebAssign® into a forthcoming course or to arrange a class test please contact your local SAGE representative for more details. (Students please note: access to WebAssign® is dependent not only on the purchase of a student access code (ISBN: 9781446273043) but also a username, institution code and password supplied by your course leader/instructor).

SAGE MobileStudy - study where and when you like
Scan any QR code within the book to access revision material on a smartphone or tablet such as Cramming Sam's Study tips, flashcard glossaries, interactive multiple choice questionnaires and more. Click here to take a look (if you're accessing the site from a desktop you'll be taken to the Companion Website instead; look out for the MobileStudy icon to show you which pages are also available on the MobileStudy site).

See how Andy's book is changing the landscape for textbooks through the use of technology!

Support materials for a wide range of disciplines

Education and Sport Sciences lecturer support materials with enhanced ones for Psychology, Business and Management and the Health Sciences on the enhanced Companion Website make the book even more relevant to a wider range of subjects across the social sciences and where statistics is taught to a cross-disciplinary audience.

Other major new updates include:

Now fully compatible with recent IBM SPSS Statistics releases.
Two new characters! Statistical cult leader Oditi provides students with access to video clips via his Lantern to help further understanding of statistical/SPSS concepts, while Confusius helps students to make better sense of statistical terms.
The enhanced Companion Website offers plenty of lecturer and student material to use in conjunction with the textbook. These include PowerPoints and subject-specific testbanks for lecturers as well as answers to the Smart Alex tasks at the end of the each chapter; datafiles for testing problems in SPSS; flashcards of key concepts; self-assessment multiple-choice questi

Produktdetails

• Introducing Statistical Methods Series
• Verlag: Sage Publications
• 4th ed.
• Seitenzahl: 952
• Erscheinungstermin: 28. Mai 2013
• Englisch
• Abmessung: 265mm x 197mm x 32mm
• Gewicht: 1974g
• ISBN-13: 9781446249185
• ISBN-10: 1446249182
• Artikelnr.: 36806629

Inhaltsangabe

Why Is My Evil Lecturer Forcing Me to Learn Statistics?
What Will This Chapter Tell Me?
What The Hell Am I Doing Here? I Don't Belong Here
Initial Observation: Finding Something That Needs Explaining
Generating Theories And Testing Them
Collect Data to Test Your Theory
Analyzing Data
Reporting Data
Everything You Never Wanted to Know about Statistics
What Will This Chapter Tell Me?
Building Statistical Models
Populations And Samples
Statistical Models
Going Beyond The Data
Using Statistical Models To Test Research Questions
Modern Approaches toTheory Testing
Reporting Statistical Models
The IBM SPSS Statistics Environment
What Will This Chapter Tell Me?
Versions Of IBM SPSS Statistics
Windows versus MacOS
Getting Started
The Data Editor
Importing Data
The SPSS Viewer
Exporting SPSS Output
The Syntax Editor
Saving Files
Retrieving A File
Exploring Data with Graphs
What Will This Chapter Tell Me?
The Art Of Presenting Data
The SPSS Chart Builder
Histograms
Boxplots (Box-Whisker Diagrams)
Graphing Means: Bar Charts And Error Bars
Line Charts
Graphing Relationships: The Scatterplot
Editing Graphs
The Best of Bias
What Will This Chapter Tell Me?
What is Bias?
Spotting Bias
Reducing Bias
Non-parametric Models
What Will This Chapter Tell Me?
When to Use Non-parametric Tests
General Procedure on Non-parametric Tests in SPSS
Comparing Teo Independent Conditions: The Wilcox 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
Correlation
What Will This Chapter Tell Me?
Modelling Relationships
Data Entry For Correlation Analysis Using SPSS
Bivariate Correlation
Partial Correlation
Comparing Correlations
Calculating The Effect Size
How To Report Correlation Coefficents
Regression
What Will This Chapter Tell Me?
An Introduction To Regression
Bias in Regression Models?
Regression Using SPSS: One Predictor
Multiple Regression
Regression With Several Predictors Using SPSS
Interpreting Multiple Regression
Comparing Two Means
What Will This Chapter Tell Me?
Looking at Differences
The t- test
Assumptions of the t- test
The Independent t- test Using SPSS
Paired-samples t- test Using SPSS
Between Groups or Repeated Measures
What is I Violate the Test Assumptions
Moderation, Mediation and More Regression
What Will This Chapter Tell Me?
Installing Custom Dialog Boxes in SPSS
Moderation: Interactions in Regression
Mediation
Categorical Predictors in Regression
Comparing Several Means: ANOVA (GLM 1)
What Will This Chapter Tell Me?
The Theory Behind Anova
Assumptions of Anova
Planned Contrasts
Post hoc Procedures
Running One-way Anova in SPSS
Output From One-way Anova
Calculating the Effect Size
Reporting Results From One-way Independent Anova
Analysis of Covariance, ANCOVA (GLM 2)
What Will This Chapter Tell Me?
What Is ANCOVA?
Assumptions And Issues In ANCOVA
Conducting ANCOVA in SPSS
Interpreting the Output From ANCOVA
Testing The Assumption Of Homogeneity Of Regression Slopes
Calculating The Effect Size
Reporting Results
Factorial ANOVA (GLM 3)
What Will This Chapter Tell Me?
Theory Of Factorial ANOVA (Independent Designs)
Assumptions of Factorial ANOVA
Factorial ANOVA using SPSS
Output From Factorial ANOVA
Interpreting Interaction Graphs
Calculating Effect Sizes
Reporting The Results Of Two-Way ANOVA
Repeated-Measures Designs (GLM 4)
What Will This Chapter Tell Me?
Introduction To Repeated Measures Designs
Theory Of One-Way Repeated-Measures ANOVA
Assumptions in 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
Factorial Repeated-Measures Designs
Output For Factorial Repeated-Measures ANOVA
Effect Sizes For Fa

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Chapter 1: Why is my evil lecturer forcing me to learn statistics?
What the hell am I doing here? I don't belong here
The research process
Initial observation: finding something that needs explaining
Generating and testing theories and hypotheses
Collecting data: measurement
Collecting data: research design
Reporting Data
Chapter 2: The SPINE of statistics
What is the SPINE of statistics?
Statistical models
Populations and Samples
P is for parameters
E is for Estimating parameters
S is for standard error
I is for (confidence) Interval
N is for Null hypothesis significance testing, NHST
Reporting significance tests
Chapter 3: The phoenix of statistics
Problems with NHST
NHST as part of wider problems with science
A phoenix from the EMBERS
Sense, and how to use it
Preregistering research and open science
Effect sizes
Bayesian approaches
Reporting effect sizes and Bayes factors
Chapter 4: The IBM SPSS Statistics environment
Versions of IBM SPSS Statistics
Windows, MacOS and Linux
Getting started
The Data Editor
Entering data into IBM SPSS Statistics
Importing Data
The SPSS Viewer
Exporting SPSS Output
The Syntax Editor
Saving files
Opening files
Extending IBM SPSS Statistics
Chapter 5: Exploring data with graphs
The art of presenting data
The SPSS Chart Builder
Histograms
Boxplots (box-whisker diagrams)
Graphing means: bar charts and error bars
Line charts
Graphing relationships: the scatterplot
Editing graphs
Chapter 6: The beast of bias
What is bias?
Outliers
Overview of assumptions
Additivity and Linearity
Normally distributed something or other
Homoscedasticity/Homogeneity of Variance
Independence
Spotting outliers
Spotting normality
Spotting linearity and heteroscedasticity/heterogeneity of variance
Reducing Bias
Chapter 7: Non-parametric models
When to use non-parametric tests
General procedure of non-parametric tests in SPSS
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
Chapter 8: Correlation
Modelling relationships
Data entry for correlation analysis
Bivariate correlation
Partial and semi-partial correlation
Comparing correlations
Calculating the effect size
How to report correlation coefficents
Chapter 9: The Linear Model (Regression)
An Introduction to the linear model (regression)
Bias in linear models?
Generalizing the model
Sample size in regression
Fitting linear models: the general procedure
Using SPSS Statistics to fit a linear model with one predictor
Interpreting a linear model with one predictor
The linear model with two of more predictors (multiple regression)
Using SPSS Statistics to fit a linear model with several predictors
Interpreting a linear model with several predictors
Robust regression
Bayesian regression
Reporting linear models
Chapter 10: Comparing two means
Looking at differences
An example: are invisible people mischievous?
Categorical predictors in the linear model
The t-test
Assumptions of the t-test
Comparing two means: general procedure
Comparing two independent means using SPSS Statistics
Comparing two related means using SPSS Statistics
Reporting comparisons between two means
Between groups or repeated measures?
Chapter 11: Moderation, mediation and multicategory predictors
The PROCESS tool
Moderation: Interactions in the linear model
Mediation
Categorical predictors in regression
Chapter 12: GLM 1: Comparing several independent means
Using a linear model to compare several means
Assumptions when comparing means
Planned contrasts (contrast coding)
Post hoc procedures
Comparing several means using SPSS Statistics
Output from one-way independent ANOVA
Robust comparisons of several means
Bayesian comparison of several means
Calculating the effect size
Reporting results from one-way independent ANOVA
Chapter 13: GLM 2: Comparing means adjusted for other predictors (analysis
of covariance)
What is ANCOVA?
ANCOVA and the general linear model
Assumptions and issues in ANCOVA
Conducting ANCOVA using SPSS Statistics
Interpreting ANCOVA
Testing the assumption of homogeneity of regression slopes
Robust ANCOVA
Bayesian analysis with covariates
Calculating the effect size
Reporting results
Chapter 14: GLM 3: Factorial designs
Factorial designs
Independent factorial designs and the linear model
Model assumptions in factorial designs
Factorial designs using SPSS Statistics
Output from factorial designs
Interpreting interaction graphs
Robust models of factorial designs
Bayesian models of factorial designs
Calculating effect sizes
Reporting the results of two-way ANOVA
Chapter 15: GLM 4: Repeated-measures designs
Introduction to repeated-measures designs
A grubby example
Repeated-measures and the linear model
The ANOVA approach to repeated-measures designs
The F-statistic for repeated-measures designs
Assumptions in repeated-measures designs
One-way repeated-measures designs using SPSS
Output for one-way repeated-measures designs
Robust tests of one-way repeated-measures designs
Effect sizes for one-way repeated-measures designs
Reporting one-way repeated-measures designs
A boozy example: a factorial repeated-measures design
Factorial repeated-measures designs using SPSS Statistics
Interpreting factorial repeated-measures designs
Effect Sizes for factorial repeated-measures designs
Reporting the results from factorial repeated-measures designs
Chapter 16: GLM 5: Mixed designs
Mixed designs
Assumptions in mixed designs
A speed dating example
Mixed designs using SPSS Statistics
Output for mixed factorial designs
Calculating effect sizes
Reporting the results of mixed designs
Chapter 17: Multivariate analysis of variance (MANOVA)
Introducing MANOVA
Introducing matrices
The theory behind MANOVA
MANOVA using SPSS Statistics
Interpreting MANOVA
Reporting results from MANOVA
Following up MANOVA with discriminant analysis
Interpreting discriminant analysis
Reporting results from discriminant analysis
The final interpretation
Chapter 18: Exploratory factor analysis
When to use factor analysis
Factors and Components
Discovering factors
An anxious example
Factor analysis using SPSS statistics
Interpreting factor analysis
How to report factor analysis
Reliability analysis
Reliability analysis using SPSS Statistics
Interpreting Reliability analysis
How to report reliability analysis
Chapter 19: Categorical outcomes: chi-square and loglinear analysis
Analysing categorical data
Associations between two categorical variables
Associations between several categorical variables: loglinear analysis
Assumptions when analysing categorical data
General procedure for analysing categorical outcomes
Doing chi-square using SPSS Statistics
Interpreting the chi-square test
Loglinear analysis using SPSS Statistics
Interpreting loglinear analysis
Reporting the results of loglinear analysis
Chapter 20: Categorical outcomes: logistic regression
What is logistic regression?
Theory of logistic regression
Sources of bias and common problems
Binary logistic regression
Interpreting logistic regression
Reporting logistic regression
Testing assumptions: another example
Predicting several categories: multinomial logistic regression
Chapter 21: Multilevel linear models
Hierarchical data
Theory of multilevel linear models
The multilevel model
Some practical issues
Multilevel modelling using SPSS Statistics
Growth models
How to report a multilevel model
A message from the octopus of inescapable despair
Chapter 22: Epilogue

Rezensionen

I think this is a really good starting point for teaching stats – from assuming students knows nothing about and taking them gradually to a more advanced understanding. The book is – very helpfully- full of interesting examples and engaging style of writing. I like it that the book has several 'levels of difficulty' and engages both with practical stats and theory. The book I believe is targeted at UG students mainly, but some chapters can be recommended to MA students on research methods courses provided that they know nothing about statistics – the book is written in a very accessible manner which means that it can satisfy the need of international students in terms of level of difficulty and language (and business programmes normally have a lot of international students at MA level). The explanations are logically organized and explained in a lucid and clear manner. Little features like 'faces' I believe would make the book more attractive to UG students. I think self-test questions and the tasks at the end of chapter are very helpful, as well as the real world data and (often humorous) examples. My course is MA so I am not adopting this book for a course as a main text, but I may recommend it to students who are completely unfamiliar with statistics. Maria Karepova