Number of
Dependent Variables |
Nature of
Independent Variables |
Nature of Dependent
Variable(s) |
Test(s)
|
|
1
|
0 IVs
(1 population) |
interval
& normal
|
one-sample t-test
|
|
ordinal or interval
|
one-sample median
|
|||
categorical
(2 categories) |
binomial test
|
|||
categorical
|
Chi-square goodness-of-fit
|
|||
1 IV with 2 levels
(independent groups) |
interval
& normal
|
2 independent sample t-test
|
||
ordinal or interval
|
||||
Wilcoxon-Mann
Whitney test
|
||||
categorical
|
Chi- square test
|
|||
Fisher's
exact test
|
||||
1 IV with 2 or more levels
(independent groups)
|
interval
& normal
|
one-way ANOVA
|
||
ordinal or interval
|
Kruskal Wallis
|
|||
categorical
|
Chi- square test
|
|||
1 IV with 2 levels
(dependent/matched groups) |
interval
& normal
|
paired t-test
|
||
ordinal or interval
|
Wilcoxon signed ranks test
|
|||
categorical
|
McNemar
|
|||
1 IV with 2 or more levels
(dependent/matched groups) |
interval
& normal
|
one-way repeated measures ANOVA
|
||
ordinal or interval
|
Friedman test
|
|||
categorical
|
repeated measures logistic
regression
|
|||
2 or more IVs
(independent groups) |
interval
& normal
|
factorial ANOVA
|
||
ordinal or interval
|
ordered logistic regression
|
|||
categorical
|
factorial
logistic regression |
|||
1 interval IV
|
interval
& normal
|
correlation
|
||
simple linear regression
|
||||
ordinal or interval
|
non-parametric correlation
|
|||
categorical
|
simple logistic regression
|
|||
1
or more interval IVs and/or
1 or more categorical IVs |
interval
& normal
|
multiple regression
|
||
analysis
of covariance
|
||||
categorical
|
multiple logistic regression
|
|||
discriminant
analysis
|
||||
2 or more
|
1 IV with 2 or more levels
(independent groups) |
interval
& normal
|
one-way
MANOVA
|
|
2 or more
|
2 or more
|
interval
& normal
|
multivariate multiple linear
regression
|
|
2 sets of
2 or more |
0
|
interval
& normal
|
canonical correlation
|
|
2 or more
|
0
|
interval
& normal
|
factor analysis
|
|
Number of
Dependent Variables |
Nature of
Independent Variables |
Nature of Dependent
Variable(s) |
Test(s)
|
http://www.ats.ucla.edu/stat/stata/whatstat/whatstat.htm#1sampt
In statistics, Spearman's rank correlation coefficient or Spearman's rho, named after Charles Spearman and often denoted by the Greek letter (rho) or as , is a nonparametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other.
