Efnisyfirlit

• Title Page
• Brief Contents
• Contents
• List of Figures and Tables
• Preface
• 1 Research design
• Levels of variable: nominal, ordinal, interval and ratio (NOIR)
• Independent and dependent variables
• Experimental designs
• Confounding variables
• Quasi-experimental designs
• Within- and between-subjects designs
• Within-subjects/repeated measures designs
• Between-subjects designs
• Causation versus correlation
• How many participants do I need and how do I find them?
• Hypotheses and null hypotheses
• Which analysis should I run?
• Summary
• 2 Data preparation, common assumptions and descriptive statistics
• Entering data into jamovi ready for analysis
• Missing data
• Reverse scoring data and why do you need to do it
• How to reverse score items in jamovi
• Computing total and mean scores
• What are the different types of averages and how do you calculate them?
• What is a standard deviation and why are they important?
• What are descriptives, why do you need them, and which should you choose?
• Common assumptions in statistical analysis
• Summary
• 3 P-values, effect sizes and 95% confidence intervals
• Hypothesis testing
• Be kind – rewind…
• Presumed innocent
• About this statistical evidence…
• Probability values
• The significance of p = .05
• One-tailed versus two-tailed hypotheses
• Inferential statistics
• Degrees of freedom
• Weakness of the probability value
• To be significant, or not to be significant…
• Probability values are dependent on sample size
• Size of the difference?
• Effect sizes!
• Effect sizes are standardised
• Yeah, but can they do it on a cold wet Tuesday night at Stoke City?
• Feeling confident?
• Summary
• Epilogue
• 4 Statistical power
• What is statistical power?
• This is all useful, but what about power?
• Factors affecting power
• Sample size
• Effect size
• Alpha level (significance)
• Can I only calculate power levels?
• When to run a power analysis
• How to run power analyses in jamovi
• Running the analyses
• How to write it up
• Summary
• 5 Reliability and validity
• Background context
• Reliability testing: what is it and how do I test for it in jamovi?
• Validity testing: what is it and how do I test it in jamovi?
• Testing for different types of validity in jamovi
• Summary
• 6 Tests of associations (correlations)
• What are correlations?
• Why do we use correlations?
• Correlation coefficients
• Direction of relationship
• Strength of relationship
• Proportion of variance explained
• Example analysis
• Entering the data in jamovi
• Running the analysis in jamovi – assumptions
• What to ‘click’ in jamovi when running a correlation
• Pearson’s r correlation output
• But my data didn’t meet the assumptions for a Pearson’s correlation
• Non-parametric equivalents: Spearman’s rho and Kendall’s tau
• An example research project
• Partial and semi-partial correlations
• One more thing…
• Correlations in the literature
• Summary
• 7 Categorical variables – tests for differences and associations (Chi Square)
• What is this test for?
• What does a goodness of fit test tell us?
• So why would you use a test of association?
• Assumptions for using chi-square (χ2) tests
• Example studies
• The chi square χ2 goodness of fit analysis
• Analysis steps in jamovi
• Time to analyse – what to click
• Results and interpretation
• Write-up with APA-style citation of statistical evidence
• Setting your own expected values
• Examples of goodness of fit in published research
• Chi square χ2 test of association
• Entering data into jamovi
• Interpreting the χ2 test of association output
• Statistics menu = further options
• Tests
• Comparative measures
• Nominal
• Ordinal
• Writing up the analysis
• Examples of test of association in published research
• Summary
• 8 Comparing two groups (Independent t-tests and Mann-Whitney U)
• What are t-tests?
• Why do we use t-tests?
• Assumptions
• Example analysis: the independent t-test (between-groups design)
• Entering the data in jamovi
• Running the analysis in jamovi
• What to ‘click’ in jamovi when running an independent t-test
• Independent t-test output
• Overall interpretation with APA-style citation of statistical evidence
• Non-parametric equivalent: the Mann-Whitney U test
• How does a Mann-Whitney U test work and how do I run one?
• Interpreting and writing up a Mann-Whitney U test
• Independent t-tests in the literature
• Summary
• 9 Comparing pairs of scores (paired t-tests and Wilcoxon signed ranks)
• What are paired t-tests?
• Why do we use t-tests?
• Assumptions
• Example analysis: the paired t-test (within-subjects design)
• Entering the data in jamovi
• What to ‘click’ in jamovi when running a paired t-test
• Paired t-test output
• Overall interpretation with APA-style citation of statistical evidence
• Non-parametric equivalent: the Wilcoxon test
• How does a Wilcoxon test work and how do I run one?
• Interpreting and writing up a Wilcoxon test
• Paired t-tests in the literature
• Summary
• 10 Comparing multiple means for between-subjects designs (One-way ANOVA and Kruskal-Wallis)
• What is a one-way ANOVA?
• Hang on – where are all these t-tests coming from?
• Multiplicity
• One test to rule them all…
• One-way ANOVA for between-subjects (groups) designs
• Assumptions
• Example analysis: one-way ANOVA (between-subjects)
• Entering the data in jamovi
• Example data in jamovi
• Running the one-way ANOVA for between-subjects designs in jamovi
• The output
• Descriptive statistics
• Hang on … do I run a Fisher’s or Welch’s ANOVA?
• I can see we have a significant result, but walk methrough the figure…
• F-ratio
• Degrees of freedom
• P-value
• Interim conclusion
• What happens after a significant ANOVA?
• Post hoc tests
• Example write-up
• The other way to run a one-way between-subjects ANOVA
• The ANOVA analysis
• Aargh – two rows of numbers!!??!!
• OK – what are these sums of squares?
• Group sum of squares
• Residual sum of squares
• Total sum of squares
• Degrees of freedom
• Mean square
• F-ratio
• P-value
• Effect size η2
• Interim conclusion
• Hang on – what about all those other options I saw?
• Post hoc tests
• Why not use this Cohen’s d?
• Example write-up
• What about the non-parametric equivalent to this one-way ANOVA?
• The Kruskal-Wallis output
• Post hoc pairwise comparisons?
• Example write-up
• Examples of one-way ANOVA in published research
• Summary
• 11 Comparing multiple means for repeated measures designs (One-way ANOVA and Friedman’s ANOVA)
• Hang on – is this ANOVA different from the one-way ANOVA for between-subjects designs?
• Remind me why I would use this test again?
• A classic study to illustrate
• Once more, with feeling…
• One-way ANOVA for repeated measures designs
• The same assumptions?
• Example analysis: one-way repeated measures ANOVA
• Entering the data in jamovi
• The output
• Descriptive statistics
• ANOVA output
• OK – remind me again, what are these sums of squares?
• TotalSS
• Within SubjectSS
• Effect of IVSS (Stroop condition)
• ResidualSS
• Between-subjects effects residualSS
• Degrees of freedom
• Mean square
• F-ratio
• P-value
• Effect size ηp2
• Interim conclusion
• Post hoc tests
• Example write-up
• What about the non-parametric equivalent to this one-way ANOVA?
• Effect size (W)
• Post hoc pairwise comparisons?
• Example write-up
• Examples of one-way repeated measures ANOVA in published research
• Summary
• 12 Factorial ANOVA (assessing effects of multiple independent variables)
• What is a factorial ANOVA? Is this different from the one-way ANOVA I just learned about?
• A quick example of this benefit
• Types of factorial designs
• Factors, levels, and conditions
• Between-subjects factors
• Repeated measures factors
• A mix of both between-subjects and repeated measure factors?
• Examples of designs
• Example analysis: 2*2 between-subjects design
• Running a two-way ANOVA for a 2*2 design
• The analysis options
• Descriptive statistics
• Assumption checks
• Model
• Post hoc testing
• Example write-up
• What about the non-parametric equivalent?
• Example analysis: two-way ANOVA for a (3*2) repeated measures design
• The analysis options
• Descriptive statistics
• Assumption checks
• Model
• Post hoc testing
• Example write-up
• Example analysis: three-way ANOVA for a mixed 2*2*(2) design
• The analysis options
• Descriptive statistics
• Assumption checks
• Model
• Post hoc analysis of Font*Medium interaction
• Example write-up
• Examples of factorial ANOVA in published research
• Summary
• 13 Predicting scores: simple, multiple and hierarchical linear regression
• What is a regression?
• Assumptions
• Example analysis: simple linear regression
• Entering the data in jamovi and running a simple linear regression
• Making sense of the output
• Writing up
• Multiple linear regression
• Assumptions
• Example analysis: multiple linear regression
• Entering the data (and running a multiple regression) in jamovi
• Writing up
• Hierarchical multiple regression
• Writing up
• Examples from the literature
• Summary
• Decision tree practice guide
• Glossary
• References
• Index
• eCopyright