Classification of Statistical Analyses and Tests by Types of Variables
OUTCOME
CONTINUOUS
CATEGORICAL
Dichotomous
EXPOSURE
CONTINUOUS
Univariate
Polychotomous
Ordinal
Pearson correlation, linear regression
Spearman rank correlation
Multiple linear regression
Logistic regression, discriminant
analysis, Wilcoxon rank sum
Logistic regression,
discriminant analysis
t-test, anova, linear regression
Wilcoxon rank sum
N-way anova, anlaysis of covariance
multiple linear regression
Matched pairs t-test
Wilcoxon rank sum, Wilcoxon signed
rank test
Chi-square, logistic regression
McNemar’s test (univariate)
Conditional logistic regression
(multivariate)
Does not exist*
Discriminant analysis
Nominal regression
Does not exist*
ANOVA, linear regression
Spearman rank correlation (ordinal)
Kruskall-Wallis (nominal)
Chi-square, logistic regression
Spearman rank correlation (ordinal)
Chi-square test for trend
Ordinal regression
Spearman rank correlation
Chi-square,
Discriminant analysis
Nominal regression
Multivariate
n-way ANOVA, multiple regression
Logistic regression
Ordinal regression
Matched
repeated measures ANOVA
multiple linear regression
Conditional logistic regression
Does not exist*
Discriminant analysis
Nominal regression
Does not exist*
Multivariate
CATEGORICAL
Dichotomous
Univariate
Multivariate
Matched
Polychotomous
Ordinal or nominal
Univariate
Mantel-Haenszel, logistic regression
Ordinal regression
Spearman rank correlation
Ordinal regression
Nominal
Wilcoxon rank sum
Chi-sqaure test for trend
Ordinal regression
Ordinal regression
Discriminant analysis
Kruskall-Wallis
Discriminant analysis
Nominal regression
Chi-square
* does not exist: convert the polychotomous responses to dichotomous and do conditional logistic regression.
NOTE: Multiple regression is used here to refer to using the least squares algorithm to do an analysis of variance, analysis of covariance, regression or any
combination. Regression, theoretically, is used for the situation when one continuous variable is regressed on other continuous variables. Analysis of variance is used
for a continuous outcome and one or more categorical exposures; analysis of covariance is used for comparing two or more regression lines such as arises with a
continuous outcome regressed against one ore more continuous exposure (or predictor) variables and one other categorical variable. In practice, the term multiple
regression is applied when referring to any one of these situations. Similarly, other multivariate regression models (logistic, ordinal and nominal regression) can be
used in these “mixed mode” situation. The corresponding non-parametric tests are given in italics.
Prepared by: Nancy E. Mayo, PhD (Revised April 2004)
Prepared by: Nancy E. Mayo, PhD (Revised April 2004)