# Introduction

This is a very constrained, simple table to help students decide what type of statistical modeling is appropriate for their research question and data set.

Table 1: Which Model Do I Use?
Comparing Dependent (outcome) Variable Independent (explanatory) variable Parametric Test (normally distributed data) Non-parametric test (ordinal or skewed data)
Single Comparison Tests
Averages of two independent groups Scale Nominal (Binary) Independent t-test Mann-Whitney test/ Wilcoxon rank sum
Averages of 3+ independent groups Scale Nominal One-way ANOVA Kruskal-Wallis test
The average difference between paired (matched) samples e.g. test scores before and after a class Scale Time or Condition variable Paired t-test Wilcoxon signed rank test
The 3+ measurements on the same subject Scale Time or Condition variable Repeated measures ANOVA Friedman test
Association Tests
Relationship between 2 continuous variables Scale Scale Pearson’s Correlation Coefficient Spearman’s Correlation Coefficient
What is value of DV when value of IV changes? Scale Any Simple Linear Regression Transform the data
What is value of DV when value of IV changes? Nominal (Binary) Any Logistic regression NA
What is value of DV when value of IV changes? Nominal (Count) Any Poisson Regression NA
What is value of DV when value of IV changes? Nominal (Count, overdispersed) Any Negative Binomial Regression NA
Assessing the relationship between two categorical variables Categorical Categorical NA Chi-squared test
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