Stats with jamovi
Welcome
I Overview
1. Introduction
2. Statistics foundations
Describing Our Data
Levels of measurement
Descriptive vs inferential statistics
An example
Design & Methods Key Terms
Study design terms
Variables
Reliability and validity
Statistical terms
Other terms
II Intro to jamovi
3. Overview of jamovi
Describing data
Data variables
Describing your data
Writing up descriptive statistics
Visualizing data
Expanding your data visualization
Cleaning data
Data setup
Compute (create new variables using some computation)
Transform (revise current variables)
Filters
III NHST
4. Hypothesis testing
Example of hypothesis testing
1. Look at the data
2. Check assumptions
3. Perform the test
4. Interpret the results
Final note
5. BEAN
Effect sizes
Types of effect sizes
Small, medium, and large effect sizes
What makes an effect practically significant?
Alpha & p-values
p-values
Alpha
Video
Power
B(E)A(N): Alpha and power
How alpha and power relate to one another
Video
Sample size & power analysis
Sample size (N)
BEAN: Power analysis
Power analysis example #1
Play with jpower
Extending our knowledge of power analysis
6. Inferential statistics
Choosing the correct test
Forward mapping: Choose the correct test
Backwards map: Determine the data you need
Parametric assumptions
Interval/ratio data
Independent scores
Normal distribution
Homogeneity of variance
Recapping parametric assumptions
Violated assumptions
Interval/ratio data
Independent data
Normality or homogeneity of variance
7. Reliability
8. Writing up results
IV Individual Tests
t-tests
9. One sample t-test
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpret results
Wilcoxon W test
Your turn!
10. Independent t-test
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpret results
Welch’s t-test
Mann-Whitney U test
Additional practice
11. Dependent t-test
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpret results
Wilcoxon rank
Additional practice
Chi-Square
12. Chi-Square Goodness-of-Fit
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpret results
Additional practice
13. Chi-Square Test of Independence
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpret results
Fisher’s exact test
Additional practice
14. McNemar’s Test
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpret results
ANOVA
15. One-way ANOVA
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpret results
Welch’s F-test
Kruskal-Wallis test
Additional practice
16. Finding Group Differences
Post hoc comparisons
Welch’s F-test post hoc tests
Kruskal-Wallis post hoc tests
Planned Contrasts
Additional practice
17. Repeated Measures ANOVA
Step 1: Look at the data
Step 2: Check Assumptions
Step 3: Perform the test
Step 4: Interpret results
Friedman’s test
Additional practice
18. Factorial ANOVA
Independent Factorial ANOVA
Repeated Measures Factorial ANOVA
Mixed Factorial ANOVA
19. ANCOVA
Step 1: Look at the data
Step 2: Check Assumptions
Step 3: Perform the test
Step 3: Interpret results
Correlation and regression
20. Correlation
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpreting results
Comparing strengths of correlations
Additional practice
21. Regression
Step 1: Look at the data
Step 2: Check Assumptions
Step 3: Perform the test
Step 4: Interpret results
Categorical Predictors
Hierarchical regression
Additional practice
22. General Linear Model
Correlation as a regression
Independent t-test as a regression
Dependent t-test as a regression
One-way ANOVA as a regression
Appendices
Answers to Your Turn exercises
Statistics with jamovi
7. Reliability
This chapter will eventually discuss reliability testing. Stay tuned!