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

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