The following table shows general guidelines for choosing a statistical analysis. We emphasize that these are general guidelines and should not be construed as hard and fast rules. Usually your data could be analyzed in multiple ways, each of which could yield legitimate answers. The table below covers a number of common analyses and helps you choose among them based on the number of dependent variables (sometimes referred to as outcome variables), the nature of your independent variables (sometimes referred to as predictors). You also want to consider the nature of your dependent variable, namely whether it is an interval variable, ordinal or categorical variable, and whether it is normally distributed (see What is the difference between categorical, ordinal and interval variables? for more information on this). The table then shows one or more statistical tests commonly used given these types of variables (but not necessarily the only type of test that could be used) and links showing how to do such tests using SAS, Stata and SPSS. Show
*Technically, assumptions of normality concern the errors rather than the dependent variable itself. Statistical errors are the deviations of the observed values of the dependent variable from their true or expected values. These errors are unobservable, since we usually do not know the true values, but we can estimate them with residuals, the deviation of the observed values from the model-predicted values. Additionally, many of these models produce estimates that are robust to violation of the assumption of normality, particularly in large samples. This page was adapted from Choosing the Correct Statistic developed by James D. Leeper, Ph.D. We thank Professor Leeper for permission to adapt and distribute this page from our site.
Binomial test
Chi-square goodness of fit
Two independent samples t-test
Wilcoxon-Mann-Whitney test
npar test /m-w = write by female(0 1). Chi-square test
Fisher’s exact test
One-way ANOVA
Kruskal Wallis test
Paired t-test
Wilcoxon signed rank sum test
McNemar test
One-way repeated measures ANOVA
Repeated measures logistic regression
Factorial ANOVA
Friedman test
Ordered logistic regression
Factorial logistic regression
Correlation
Simple linear regression
Non-parametric correlation
Simple logistic regression
Multiple regression
Analysis of covariance
Multiple logistic regression
Discriminant analysis
One-way MANOVA
Multivariate multiple regression
Canonical correlation
Factor analysis
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