Once a Number or Number - Multi question is selected in each of the Blue and Brown Drop-down Menus the resulting table shows correlations.
For example:
The correlation coefficient measures the strength and direction of a linear relationship between two variables on a scatterplot. The value is always between +1 and –1. A correlation of exactly -1 indicates a perfect negative relationship; a correlation of +1 indicates a perfect positive relationship; and a correlation of 0 indicates no linear relationship.
Note: By default, Q uses Pearson's correlation, which is the main method of correlation in use in survey analysis. However, at a technical level, Pearson's correlation assumes that the underlying data is numeric, whereas the other two methods instead make the milder assumption that the values simply reflect relative ordering. Stated differently: if the values that are shown in the Value column represent arbitrary assumptions that you have made, using Kendall tau-b or perhaps Spearman's Correlation, is technically, more appropriate (however, it is rare that this makes much of a difference with survey data).
Options for computing and displaying different kinds of correlations are also available in Create > Correlation.
Additional details
A correlation matrix can be created by selecting the same Number - Multi question in each of the Blue and Brown Drop-down Menus. The formula used for computing the correlation is determined by the Correlations setting in Statistical Assumptions.
The following options are available:
- Correlations = Pearson: Pearson's Product Moment Correlation
- Correlations = Spearman: Spearman’s Correlation
- Correlations = Kendall tau-b: Kendall's Tau-b
Next
How to Change Q's Default Statistical Assumptions When Setting Up Projects