A not A Analysis

A not A Analysis


Purpose 

Analyse results from the A-not-A test. 

Data Format

  1. Discrimination_AnotA.xls
  2. Data type is binary. 

Background 

The A-not-A discrimination test is a variation of the paired comparison test.  It is an unspecified test with a probability of guessing of 0.5.

The panellist is presented with the first product (A), which is then removed after evaluation.  

A second product is then presented, and the panellist decides whether it is the same as A or not.  As part of the test design multiple products may be compared with A in a monadic protocol. The order of the samples is randomized. 

Options

  1. Treat Sessions/Replicates separately: If the data has been gathered over different sessions, or there are different replicates, these can be analysed separately.
  2. Reference Product: Select which product should be your reference product. This is the first product in the test(A).
  3. Correction:  If yes, apply the continuity correction for 2x2 contingency tables.
  4. Number of Decimals for Values: Required number of decimals for values given in the results.
  5. Number of Decimals for P-Values: Required number of decimals for any p-values given in the results. 

Results and Interpretation

  1. Contingency table: This is a n x 2 contingency table of the responses, where n is the number of products.
    The first row totals the responses when the reference product (A) was tested.
    Subsequent rows total the responses when the other products were tested.

  2. A chi-squared test is then performed on the contingency table. The following values are returned:
    1. Chi-squared value
    2. The Critical Chi-Squared value at a 5% significance
    3. Degrees of freedom
    4. P-value at 5% significance
If the p-value is < 0.05 for the chi-squared test, it can be concluded that the reference product (A) is significantly different to the other products. Note that even if you conclude a difference, this does not imply similarity.
  1. D-prime.  D-prime indicates the size of the difference between the products. The following values are returned:
    1. d-prime
    2. Lower and upper 95% confidence intervals for d-prime
    3. P-value for Fisher’s Exact Test performed on the contingency table. 
Note that if one of the cells in the contingency table is zero, the d-prime cannot be calculated.

Technical Information 

  1. The R package sensR (Rune Christensen and Per B. Brockhoff) is used.
  2. The AnotA function is used to calculate d-prime. 

References

  1. ISO 8588:1987 – Sensory Analysis – Methodology – ‘A’ – ‘not A’ Test
  2. ​Lawless, H.T. and Heymann, H. (2010).  Sensory Evaluation of Food – Principles and Practices.  Springer.

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