The A–not-A test, is a method of discrimination testing comprised of at least two samples; at least one sample is a previously identified sample (“A”) and at least one is a test sample. All samples are presented blindly, and the assessor’s task is to assign the label “A” or “not-A” to each of the samples. The following answer choices are available within the A-not-A Test:
- Different, I'm sure of it
- Different, but I'm not sure of it
- I don't know, but guess it's different
- I don't know, but guess it's the same
- The same, but I'm not sure of it
- The same, I'm sure of it
NOTES:
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Test Setup
Create Samples:
- The first sample will be your Reference sample. Input a unique tracking code - e.g. Current.
- Create another sample. This should be the same as the Reference Sample. Match the tracking code for the first sample - Current.
- Create another unique sample. This should be a Test Sample. Give the sample a unique sample code. E.g., Test.
Create Questionnaire:
- Create a Single Sample Questionnaire.
- Attach samples to the questionnaire.
- Add questions to the questionnaire. Be sure to include the A Not A Discrimination question from the Basic Library > Discrimination Questions area.
- Under the question options area, select the Reference Sample. Here we choose "Control". This question will NOT show for the Reference Sample selected.
- Add any additional questions. E.g., Overall Opinion, JAR Scales, etc. Data will be analyzed as in any other Single Sample questionnaire setup. Generally, inserting a new page first will streamline your questionnaire.
Finalize Test and Launch:
- Create your Design Block. The general recommendation is to drag the Reference Sample identified in Step 4 into the "First" area in Design Block.
- Launch your test and collect data.
View Results
- A Not A results will update continuously throughout testing. Percent Correct, Confidence Level, P Value / Alpha, Beta, Power, D', Standard Error, Upper and Lower Confidence Levels, P(c) and P(A) (R-Index).
- Additional questions will update continuously throughout testing as in all Single Sample questionnaires.