The table above shows a crosstab of two Pick One questions. It shows that people aged 65 or more are more likely to have said they Like the brand (Pepsi Light) than the overall percentage (13%).
Although this intuitive understanding is essentially correct regarding how to interpret the data, it is not, at a technical level, because the 24 people in the 65 or more category are included in the total sample of 105. Thus, if we compare the 24 with the 105, we would be double-counting - to use the more formal statistical language, we would violate the assumption of independent samples.
Method
The only way that people 65 or older can be different from the total is if they are different from the people who are not 65 or older. To test whether that is true or not, you need to collapse all the categories other than Like and 65 or more.
- Combine all categories other than 65 or more into a single category
- Combine all categories other than Like into a single category.
The test used to highlight the 43% on this table is precisely the same test used in the table above (i.e., when creating the larger table. Q automatically creates many smaller tests in the background and uses these to compute significance. However, 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).
Next
How to Compare Columns Across Spans
How to Compare Significant Differences Between Columns
How to Compare Previous Periods Using an Unequal Categorical Variable
How to Compare Results to the Previous Time Period
How to Compare Two Waves of a Tracker in Different Data Files