Different From Control Test Analysis
To analyse the results of a different from control test.
- Different from control.xlsx
- Attribute data type is ‘category’.
Different from control tests can determine:
- If a difference exists between a product vs. reference.
- Quantifies the size of that difference.
The difference can be compared to either a reference product or a reference value. It can be used as a 2-sample test in situations where multiple sample tests don’t work due to fatigue/carry over.
- The assessor evaluates a labelled ‘Reference’ (Ref) sample.
- A blind-coded test sample is then presented: the assessor is asked to rate how different the test sample is vs. Ref. on a scale familiar to the assessor. Typically, a 5pt, 6pt, 7pt or 10pt scale is used. A common option is 0 (Same as Control) to 10 (Most Different from Control).
- More than one trial can be run, if required. The impact of fatigue should be considered.
- Ideally, the Ref. should also be one of the test samples for one trial as a measure of variation in the data.
The results are analysed using ANOVA.
- Treat Sessions/Replicates separately: If the data has been gathered over different sessions, or there are different replicates, these can be analysed separately.
- Type of Reference: Choose whether to use a reference product or reference value.
- Reference Product: If the Type of Reference = Product then select the reference product. Otherwise, this option is disabled.
Products are compared against the mean value of the Reference product.
- Reference value: If the Type of Reference = value then enter the reference value on the scale used in the test. Otherwise, this option is disabled.
Products are compared against the Reference value that a panellist used to indicate that a product is the same as the reference.
- Threshold: Level at which significance is measured or Type I error.
- ANOVA: 1-way or 2-way
1-way fits the model Attribute ~ Product
2-way fits the model Attribute ~ Product + Assessor
- Number of Decimals for Values: Required number of decimals for values given in the results.
- Number of Decimals for P-Values: Required number of decimals for any p-values given in the results.
Results and Interpretation
- Means: This table provides the mean value for each product.
These are the adjusted means calculated using the selected ANOVA model.
- ANOVA: This table shows the ANOVA results for the selected ANOVA (1-way or 2-way). A summary of the ANOVA is given.
The p-values show whether the Product effects and the Assessor effects (for the 2-way model) are significant.
- If a product reference is used: T-tests are performed to compare each product to the reference product. If there is a significant difference (at the set significance level) then the product is listed in the ‘Significantly more than Ref’ column or ‘Significantly less than Ref’ column as appropriate.
- If a reference value is used: T-tests are performed to compare each product to the reference value. If there is a significant difference (at the set significance level) then the product is listed in the ‘Significantly more than Ref’ column or ‘Significantly less than Ref’ column as appropriate.
The key objective of DFC is to establish if each product (treatment) is significantly different from the Reference. Thus, each product is compared to that reference value and the t- test established whether each product is significantly different or not.
If a Blind Control or Reference product has been included in the DFC test, then, in this case each product is compared against the mean value of that Reference Product to take account scoring bias that might occur.
- Meilgaard, M, Civille, C.V., Carr, B.T. (2007). Sensory Evaluation Techniques. 4th Edition. CRC Press.
- Lawless, H.T., Heymann, H. 2010. Sensory Evaluation of Food: Principles and Practices. 2nd Edition. Springer.
Same/Different Test Analysis
Available from version: 22.214.171.124 Purpose The Same Different Test is a discrimination test that is a variation of the paired comparison test. The assessor is presented with two samples and is asked to decide whether these samples are the same or ...
Discrimination Test Settings - Pd and d' Analysis
Purpose Establish the power of a discrimination test given a set sample size or to calculate the sample size required to get a desired power. This can be done specifying the expected difference as a proportion of discriminators (Pd) or as a ...
Purpose To analyse the results of an R-index test. Data Format R_index_rank_withMD.xlsx Background The R-index applies signal detection theory as an alternative approach to discrimination testing. It is often used where there are a large number ...
A not A Analysis
Purpose Analyse results from the A-not-A test. Data Format Discrimination_AnotA.xls 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 ...
Paired Comparison Analysis
Purpose To analyse the results of a paired comparison test. Data Format paired_comparison.xlsx Datatype for the attribute is pairedcomp. Background A paired comparison test is a directional / specified test. It is used as a difference test ...