Hello and welcome to the tests of association rise initiative. In this first sprint we will cover what chi-square and Cramer’s V tests are, when to perform a Chi-Square test and how to interpret the results in a meaningful way. Chi – square is a test used in statistical analysis which tests to see if there is an association between two categorical variables. A chi-square test is a statistical test used to compare observed results with expected results. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying. We find this out by looking for a significant result or a p (probability) value below 0.05. If the p value is lower than this it can be deemed that it is not down to chance alone and we can infer the findings to the rest of the population, not just the sample.
As Chi-Square uses categorical data, if one of the two is nominal the other, can in theory be nominal or categorical but not scale.
Cramer’s V Strength of Association
If Chi Square produces a significant p value, it tells us there is an association between variables. The Cramer’s V statistical test tells us how strong this association is between two values. Performing this is quite simple however it is the interpretations that are slightly more difficult as there is no rule of thumb like p values. Do not worry about this though! Interpreting and reporting the results of both tests will be covered in detail in future sprints!