Sensory Analyses
Chi Squared Test
Purpose Performs a chi-squared test on a contingency table. The chi-squared test is generally performed on independent products. Options Decide if you want to include the Product in your comparisons. Results Firstly the Counts for each Product on ...
Bar Chart, Line Chart & Spider Plot (with & without Significance)
Purpose These modules are used to visualise product differences and are available for sensory and consumer analysis. They are a key part of exploratory data analysis, presenting a visualisation to aid understanding of how products differ. In EyeOpenR ...
Product Characterisation
Purpose The product characterisation in the suite of sensory analysis can help identify the most and least characterizing attributes for each product, or group of products and demonstrates their relative discriminating ability. Data Format Note that ...
Multiple Factor Analysis (MFA)
Purpose Performs a multiple factor analysis on a data set. This is not ‘Factor Analysis’ in the classic statistical sense, but rather a method for handling multiple groups of variables that are all measured on the same samples, and from this ...
Correlations
Purpose Performs a Pearson Correlation analysis on all the numeric variables in a data set. A square symmetric table of correlation coefficients is produced. Optionally, statistical significance tests may be performed on each coefficient. Data Format ...
T-test
Purpose To make a statistical test for differences between two means. Or in the case that there are more than two samples, make separate difference tests for each possible pair of samples. Data Format The test is quite general and can be applied in ...
Frequency Tables (Categorical Data)
Purpose Produce summary tables and charts of data per attribute and per product if desired. This option is for categorical data, which can be nominal or interval data. Data Format Categorical coffee.xlsx The analysis will ignore data of type ‘text’. ...
Frequency Tables (Continuous Data)
Purpose Produce summary tables and charts of data per attribute and per product if desired. This option is for continuous data e.g. Interval data on a scale from 1 to 100, or 1 to 10. Data Format Profling.xlsx Background A frequency table lists a set ...
Table of Means
Purpose The table of means presents the average score for each product and attribute in sensory analysis and each product and consumer rating (liking, JAR or CATA) in consumer analysis. The means are averaged across assessor, sessions and replicates. ...
Comparison Values
Purpose In sensory and consumer studies the aim is usually to compare products for each attribute tested. This module reports summary statistics for each pair of products in the data and is a useful starting point for understanding how the ...
Descriptive Statistics
Purpose The first stage in understanding the data in a sensory or consumer study is to summarise using descriptive statistics. For each product and attribute (or consumer liking) there will be many scores arising from multiple assessors and ...
ANOVA with Multiple Comparison Tests
Purpose To provide an analysis of variance (ANOVA) test per selected attribute. ANOVA examines sources of variation in the data: this is often used in sensory science to investigate whether variation in attributes is due to products, samples and ...
Canonical Variates Analysis (CVA)
Purpose To analyse sensory data using the multidimensional visualisation technique, Canonical Variates Analysis. Data Format Assessor by Product matrix measured on multiple attributes – there should be at least 3 attributes, and at least 3 products. ...
Correspondence Analysis (CATA and categorical data)
Purpose To visualise and summarise analyse tabular data and to highlight the patterns of association in two way tables. It is widely used for mapping pure qualitative variables – e.g cluster by demographic use. This is an example of typical data that ...
Cochran and McNemar test (CATA)
Purpose Cochran and McNemar tests are used to test for differences between products when the data has been collected through a ‘Check All That Apply’ (CATA) design. Using a CATA method for sensory research means that the responses collected are ...
Principal Component Analysis (PCA)
Purpose To provide a Principal Components Analysis (PCA) of the imported data. PCA is a popular dimensionality reduction method in sensory and consumer science. In non-technical terms, the analysis reduces an initial set of variables to a smaller set ...
Popular Articles
2-AFC, 3-AFC, Duo Trio, Triangle, Tetrad Analysis
Purpose Analyse results from one of the following tests: 2-AFC, 3-AFC, Duo-Trio, Triangle, Tetrad. Data Format Discrimination.xlsx Results of the discrimination test are binary (1 = correct answer, 0 = incorrect answer) Background 2-AFC Test This is ...
EyeQuestion API
What is API? API stands for application programming interface, which is a set of definitions and protocols for building and integrating application software. These interfaces establish a secure and standardized means for different applications to ...
Show and Hide Specific Part(s) of a Questionnaire
When collecting data, the ability to create questionnaires that adapt dynamically to panelist responses or input values is a game-changer. It not only enhances respondent engagement but also ensures that you collect the most relevant and insightful ...
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 ...
Product Characterisation
Purpose The product characterisation in the suite of sensory analysis can help identify the most and least characterizing attributes for each product, or group of products and demonstrates their relative discriminating ability. Data Format Note that ...