UPCOMING WEBINAR: 10 Market Research Predictions Over the Next 4 Years Displayr comes with built-in statistical testing that automatically runs on values in drag and drop tables. On new documents, the default significance testing is Arrows and font colors, which compares each sub-group against the total/NET in a crosstab (also known as exception testing). This is also what is done using Arrows and Font colors testing individually. This article explains how to apply Arrows and Font colors testing and walks through an example of how to interpret the testing that is done against the total/NET.
How it works
The table above shows a crosstab of two Nominal variable sets, with ratings for Diet Pepsi shown in the rows and Age categories shown in the columns. It uses Arrows and Font colors to denote significant results for each cell against its Total. Among other things, it shows that respondents aged 65 or more are more likely to have said they Like the brand (Diet Pepsi) than the overall percentage in the NET column (13%).
Although this intuitive understanding is correct regarding how to interpret the data, the actual test being conducted on a technical level is against the complement because the 24 respondents in the 65 or more category are also included in the total of 105. Thus, if we compare the 24 with the 105, we would double-count respondents - to use the more formal statistical language, we would violate the assumption of independent samples.
The only way respondents 65 or older can be different from the total is if they are different from respondents younger than 65. So in order to test each cell against its total, we must remove that cell's contribution to the total and test it against the rest of the sample that are not in that cell (known as exception testing). The table below shows the actual values being tested. It combines all the other Age categories besides 65 or more and the other Diet Pepsi ratings besides Like, and tests the 24 respondents 65 or more who Like Diet Pepsi against the other 81 respondents in the other age groups combined.
The test used to highlight the 43% on this table is the same test used in the first table above. Displayr automatically creates many smaller tests like this in the background for each cell and uses these to compute significance.
Requirements
- An imported Data Set with raw observation-level data.
- A built-in drag and drop table comparing data by categories
Method
- Select your table on the Page or in the Report tree.
- In the object inspector, select Appearance > Significance > Arrows and Font colors (or simply Arrows or Font colors if desired).
Additional notes
When using Multiple Comparison Corrections, it is possible to get situations where the collapsed table will have a cell marked as significant and the non-collapsed table will not (and vice versa).
Please note that when working with Banners that have missing data, the Total column will be different than the overall NET column, see Banners in our technical reference for more details.
Next
How to Investigate Your Statistical Significance Testing
How to Compare Significant Differences Between Columns
How to Apply Multiple Comparison Correction to Statistical Significance Testing