used in psychology to determine whether a significant difference or correlation exists (and consequently, whether the null hypothesis should be rejected or retained)
statistical tests
1 of 9
quantitative data can be classified into types or levels of measurement, such as nominal, ordinal and interval
levels of measurement
2 of 9
test for an association (difference or correlation) between two variables or conditions. data should be nominal level using an unrelated (independent) design
chi-square
3 of 9
test for a significant difference between two sets of scores. data should be at least ordinal level using unrelated design (repeated measures)
mann-whitney U test
4 of 9
test for a significant difference between two sets of scores. data should be at least ordinal level using a related design (repeated measures)
wilcoxon
5 of 9
test for correlation when data is at least ordinal level
spearman's rho
6 of 9
parametric test for correlation when data is at interval level
pearson's r
7 of 9
parametric test for difference between two sets of scores. data must be interval with a related design, i.e. repeated measures or matched pairs
related t-test
8 of 9
parametric test for difference between two sets of scores. data must be interval with unrelated design, i.e. independent groups
unrelated t-test
9 of 9
Other cards in this set
Card 2
Front
quantitative data can be classified into types or levels of measurement, such as nominal, ordinal and interval
Back
levels of measurement
Card 3
Front
test for an association (difference or correlation) between two variables or conditions. data should be nominal level using an unrelated (independent) design
Back
Card 4
Front
test for a significant difference between two sets of scores. data should be at least ordinal level using unrelated design (repeated measures)
Back
Card 5
Front
test for a significant difference between two sets of scores. data should be at least ordinal level using a related design (repeated measures)
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