Discovering Statistics Using IBM SPSS Statistics - Andy Field

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Résumé :
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 g...

Biographie:
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 g...

Sommaire:
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 g...