I have an ever increasing dataset and am starting to look at analysing some of it.
Here is a very simple example:
Hypothesis: People with higher levels of expertise will be able to correctly identify stimulus of high quality (HQ).
Expertise | CORRECT (freq) | WRONG (freq) -----------|----------------|------------- Very Low | 3 | 0 Low | 6 | 3 Middling | 1 | 2 High | 18 | 4 Very High | 10 | 11 -----------|----------------|------------- Total 38 | 20
So (for example) 3 people with "very low" expertise identified the HQ stimulus; 11 "very high" identified LQ stimulus.
Test: Not sure but is a Chi Squared test the right approach for testing the hypothesis and if so can someone please set out a worked example based on this sample? That way I can apply it across a few other very similar data samples looking at other variables (than expertise - I have a few).
I am confused by the comments a little so will try to explain as best I can. Please do ask more.
The experiment: 58 people were assessed for their expertise in a subject and categorised into one of five bins (very low, low, middling, high and very high). They were then asked to evaluate two pieces of stimulus and choose which piece they believed to be correct - one piece was factually correct (high quality HQ), the other had a number of errors (low quality LQ).
At the end we had the results in the table above. Everyone (N=38) in the CORRECT (HQ) column was correct, the rows give the breakdown of those who were correct by their expertise. Obviously 20 people got the evaluation wrong and are in the WRONG (LQ) column.
The hypothesis (above) stands; people with greater expertise will be able to correctly identify the high quality stimulus.
Is that clearer?
What I want Can a test be used with this data to accept/reject this hypothesis? If so what, if not, why and what would be needed data wise?