The Q-sorting technique 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)
- 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 |
20 |
13 |
6 |
4 |
2 |
70 |
-
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.
[Source:
www.mycoted.com]