Stats with jamovi
Welcome
I Overview
1. Introduction
1.1 Getting help in this class
1.2 Dana, your instructor
1.3 Navigating this website/book
1.4 Technology Tips
1.5 Errors, mistakes, and suggestions
2. Statistics foundations
2.1 Describing our data
Measures of Central Tendency
Measures of Dispersion
Measures of the Shape of the Distribution
2.2 Levels of measurement
Examples of levels of measurement
2.3 Descriptive vs inferential statistics
An example
2.4 Design & Methods Key Terms
Variables
Study design terms
Reliability and validity
Statistical terms
Other terms
II Intro to jamovi
3. Overview of jamovi
3.1 Installing jamovi
3.2 Navigating the jamovi interface
3.3 Additional jamovi videos and resources
4. Describing data
4.1 Data variables
4.2 Describing your data
Describing nominal or ordinal data
Describing continuous data
Describing one variable split by another variable
4.3 Writing up descriptive statistics
5. Visualizing data
5.1 A continuous variable
5.2 A categorical variable
5.3 A continuous variable split by a categorical variable
5.4 Expanding your data visualization
6. Cleaning data
6.1 Data setup
6.2 Compute
6.3 Transform
Recoding
Reverse-scoring
Multiple transformations
6.4 Filter
Row filters
Column filters
III NHST
7. Hypothesis testing
7.1 Example of hypothesis testing
Step 1. Look at the data
Step 2. Check assumptions
Step 3. Perform the test
Step 4. Interpret the results
7.2 Final note about hypothesis testing
8. BEAN
8.1 Effect sizes
Types of effect sizes
Small, medium, and large effect sizes
What makes an effect practically significant?
8.2 Alpha & p-values
p-values
Alpha
Videos and resources
8.3 Power
B(E)A(N): Alpha and power
How alpha and power relate to one another
Video
8.4 Sample size & power analysis
Sample size (N)
BEAN: Power analysis
Power analysis example #1
Play with jpower
Extending our knowledge of power analysis
9. Inferential statistics
9.1 Choosing the correct test
Forward mapping: Choose the correct test
Backwards map: Determine the data you need
9.2 Parametric assumptions
Interval/ratio data
Independent scores
Normal distribution
Homogeneity of variance
Recapping parametric assumptions
9.3 Violated assumptions
Interval/ratio data
Independent data
Normality or homogeneity of variance
10. Writing up results in APA style
IV Inferential Statistics
11. Chi-square
11.1 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
11.2 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
11.3 McNemar’s test
Step 1: Look at the data
Step 2: Check assumptions
Step 3: Perform the test
Step 4: Interpret results
12. t-tests
12.1 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!
12.2 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
13. ANOVA
13.1 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
13.2 Finding Group Differences
Post hoc comparisons
Welch’s F-test post hoc tests
Kruskal-Wallis post hoc tests
Planned Contrasts
Additional practice
13.3 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
13.4 Factorial ANOVA
Independent Factorial ANOVA
Repeated Measures Factorial ANOVA
Mixed Factorial ANOVA
13.5 ANCOVA
Step 1: Look at the data
Step 2: Check Assumptions
Step 3: Perform the test
Step 3: Interpret results
14. Correlation and regression
14.1 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
14.2 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
14.3 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
V Psychometrics
25. Reliability
Appendices
Answers to Your Turn exercises
Statistics with jamovi
25. Reliability
This chapter will eventually discuss reliability testing. Stay tuned!