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.
Number of Dependent Variables Nature of Independent Variables Nature of Dependent Variable(s)* Test(s) How to SAS How to Stata How to SPSS How to R 1 0 IVs (1 population) interval & normal one-sample t-test SAS Stata SPSS R ordinal or interval one-sample median SAS Stata SPSS R categorical (2 categories) binomial test SAS Stata SPSS R categorical Chi-square goodness-of-fit SAS Stata SPSS R 1 IV with 2 levels (independent groups) interval & normal 2 independent sample t-test SAS Stata SPSS R ordinal or interval Wilcoxon-Mann Whitney test SAS Stata SPSS R categorical Chi-square test SAS Stata SPSS R Fisher’s exact test SAS Stata SPSS R 1 IV with 2 or more levels (independent groups) interval & normal one-way ANOVA SAS Stata SPSS R ordinal or interval Kruskal Wallis SAS Stata SPSS R categorical Chi-square test SAS Stata SPSS R 1 IV with 2 levels (dependent/matched groups) interval & normal paired t-test SAS Stata SPSS R ordinal or interval Wilcoxon signed ranks test SAS Stata SPSS R categorical McNemar SAS Stata SPSS R 1 IV with 2 or more levels (dependent/matched groups) interval & normal one-way repeated measures ANOVA SAS Stata SPSS R ordinal or interval Friedman test SAS Stata SPSS R categorical (2 categories) repeated measures logistic regression SAS Stata SPSS R 2 or more IVs (independent groups) interval & normal factorial ANOVA SAS Stata SPSS R ordinal or interval ordered logistic regression SAS Stata SPSS R categorical (2 categories) factorial logistic regression SAS Stata SPSS R 1 interval IV interval & normal correlation SAS Stata SPSS R interval & normal simple linear regression SAS Stata SPSS R ordinal or interval non-parametric correlation SAS Stata SPSS R categorical simple logistic regression SAS Stata SPSS R 1 or more interval IVs and/or 1 or more categorical IVs interval & normal multiple regression SAS Stata SPSS R analysis of covariance SAS Stata SPSS R categorical multiple logistic regression SAS Stata SPSS R discriminant analysis SAS Stata SPSS R 2+ 1 IV with 2 or more levels (independent groups) interval & normal one-way MANOVA SAS Stata SPSS R 2+ interval & normal multivariate multiple linear regression SAS Stata SPSS R 0 interval & normal factor analysis SAS Stata SPSS R 2 sets of 2+ 0 interval & normal canonical correlation SAS Stata SPSS R
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*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.
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