Q-Sort
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The Q-sorting technique (Stephenson, 1953) helps facilitate the awesome task of ranking or prioritising valuable, complex and partially overlapping items, it reduces information processing demands making it faster and more reliable (ideal for 60-90 items). Less than 40 items, would be best served by alternative methods; beyond 100 items, makes the task tedious and items could possibly pass through unobserved
Example
A Delphi survey produces 70 items that are to be sorted into 9 levels of importance ranging from most (A) to least important (I) 1. Establish the likely distribution of this amount of items over this number of categories; assuming the importance is a roughly normal distribution (bell-shaped curve) for this ‘population’ of items. With standard statistical tables to work out how 70 randomly selected items would be expected to be distributed over nine equal bands of importance Bands A to I would look like this:
A | B | C | D | E | F | G | H | I | Total |
2 | 4 | 6 | 13 | 25 | 13 | 4 | 2 | 1 | 70 |
2. Select items to match this pattern, using the example above, the first 2 ‘most important’ and the 2 ‘least important’ items, should be put in boxes A and I. Followed by choosing the from what remains the 4 ‘most important’ and 4 ‘least important’ items for categories B and H, and so on for C and G, then finally D and F. The remainder goes in category E.