Lectures Advanced Research Methods and Statistics for Psychology (ARMS)
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Know your Data - ARMS (neuropsychologie)
In this summary you will learn how distinguish parametric tests from non-parametric tests and how to conduct non-parametric tests using SPSS. Click here for more information on parametric tests.
In this table you can see what test to use:
When performing a test, you first have to test your data for normality. You do this by following these steps in SPSS:
Analyze --> descriptive statistics --> explore --> plots --> check histogram and check normality plots |
Then you get two tests of normality. You use the Smirnov when using >2000 participants, otherwise you use the Shapiro. If this test is significant, the assumption of normality is NOT met.
If the assumption of normality of not met, you can still conduct an analysis, but you have to test with non-parametric tests. In the table above you can see what test to use.
Non-parametric vs parametric:
- Pro: non-parametric is robust against the violation of the normality assumption
- Con: less powerful
You can also log-transform. Non-parametric vs. los-transform:
- Pro: non-parametric outcome is easier to interpret than log-transform
- Con: less powerful
Mann-Whitney U-test
Analyze --> nonparametric tests --> legacy dialogs --> 2 independent samples Test variable: enter the dependent variable. Group: enter the grouping variable. Exact --> exact |
Look at the exact sig. (2-tailed) for the significance. If it is significant, calculate the effect size.
Kruskall-Wallis test
Analyze --> nonparametic tests --> independent samples Enter the variables in 'fields'. Then click settings --> customize test --> Kruskall-Wallis --> Stepwise step-down. |
As output you get a decision. You also get a subset table. If the variables are in different subsets, they differ signifantly from eachother. You do not get a seperate p-value for this. When reporting, also report the H and the freedom of degrees.
Wilcoxon Signed-Rak test
Analyze --> nonparametric test --> legacy dialogs --> 2 related samples Enter the variables in the 'test pairs'. Check descriptives under options (if you want these) |
Report at least the Z-value, the p-value and the effect size.
Friedman test
Analyze --> Nonparametric test --> related samples --> enter variables in 'fields' --> settings --> customize --> friedman 2 way --> stepwise step-down. |
How to calculate effect sizes
For an ANOVA, you can just give the partial eta-squared. For others you might have to calculate it yourself using:
Cohen’s d:
|
Pearson’s r:
|
The effect sizes can be interpreted using this table (also report the interpretation!):
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Lectures Advanced Research Methods and Statistics for Psychology (ARMS)
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Lectures Advanced Research Methods and Statistics for Psychology (ARMS)
- Lecture 1: Multiple Linear regression (ARMS, Utrecht University)
- Lecture 2: Moderation & Mediation (ARMS, Utrecht University)
- Seminar 1: Bootstrapping (ARMS, Utrecht University)
- Lecture 3: ANOVA & ANCOVA (ARMS, Utrecht University)
- Lecture 4: Factorial ANOVA & MANOVA (ARMS, Utrecht University)
- Seminar 2: Open Science (ARMS, Utrecht University)
- Lecture 5: Repeated Measures Analysis & Mixed Designs (ARMS, Utrecht University)
- Know your Data - ARMS (neuropsychologie)
- Signal Detection Theory - ARMS (neuropsychology)
- Single Case Studies - ARMS (neuropsychology)
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Lectures Advanced Research Methods and Statistics for Psychology (ARMS)
In this bundle you can find the lecture and seminar notes for the course 'Advanced Research Methods and Statistics for Psychology (ARMS)'. I followed this course on Utrecht University, during the bachelor (neuro)psychology.
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