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 | 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 | 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 |

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 |

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.