Autorentext

Andy Field is Professor of Quantitative Methods at the University of Sussex. He has published widely (100+ research papers, 29 book chapters, and 17 books in various editions) in the areas of child anxiety and psychological methods and statistics. His current research interests focus on barriers to learning mathematics and statistics.

He is internationally known as a statistics educator. He has written several widely used statistics textbooks including Discovering Statistics Using IBM SPSS Statistics (winner of the 2007 British Psychological Society book award), Discovering Statistics Using R, and An Adventure in Statistics (shortlisted for the British Psychological Society book award, 2017; British Book Design and Production Awards, primary, secondary and tertiary education category, 2016; and the Association of Learned & Professional Society Publishers Award for innovation in publishing, 2016), which teaches statistics through a fictional narrative and uses graphic novel elements. He has also written the adventr and discovr packages for the statistics software R that teach statistics and R through interactive tutorials.

His uncontrollable enthusiasm for teaching statistics to psychologists has led to teaching awards from the University of Sussex (2001, 2015, 2016, 2018, 2019), the British Psychological Society (2006) and a prestigious UK National Teaching fellowship (2010).

Hes done the usual academic things: had grants, been on editorial boards, done lots of admin/service but he finds it tedious trying to remember this stuff. None of them matter anyway because in the unlikely event that youve ever heard of him itll be as the Stats book guy. In his spare time, he plays the drums very noisily in a heavy metal band, and walks his cocker spaniel, both of which he finds therapeutic.

Klappentext

Everything a student needs to learn statistics starting from the basics and progressing onto sophisticated statistical modelling. A genuine one-off that uses humour, and the quirks of the everyday, to bring statistics to life and to make it accessible.

Inhalt

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: Data Visualisation
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 and Mediation
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
R…