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Summary of Statistical Tests
Note that not all tests listed in the table are covered in this course. The one's that are covered are linked to the appropriate material in the course.
This table can help you decide when to use which statistic. The first issue is the level of measurement of the data. The next issue is whether we are trying to assess causal relationships, or do we just want to measure the strength and direction of a relationship. If the experimental approach is taken then we need to look very carefully at the characteristics of the sample for all independent variables (IVs).
| Level of Meas- urement |
Sample Characteristics | Corr- ela tion |
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|---|---|---|---|---|---|---|
| 1- Sample |
2-Sample | K-Sample (i.e., >2) |
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| Indep- endent |
Dependent | Indep- endent |
Dependent | |||
| Categorical or Nominal |
x2 or Binomial |
x2 |
Macnamar's x2 |
x2 |
Cochran's Q |
|
| Rank or Ordinal |
Mann- Whitney U |
Wilcoxin Matched Pairs Signed Ranks |
Kruskal Wallis H |
Friedman's ANOVA |
Spear- man's rho |
|
| Parametric (Interval & Ratio) |
z test or t test |
t test between groups |
t test within groups |
1-way ANOVA between groups |
1-way ANOVA (within or repeated measure) |
Pear- son's r |
| Factorial (2-way) ANOVA can handle more than 1 IV |
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