Sensory Analyses
Chi Squared Test
Purpose Performs a chi-squared test on a contingency table that is derived from a cross-tabulation of counts on two categorical variables in your data set. A chi-squared test is used to determine if there is significant association between the rows ...
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 ...
Reward Management
Mostly companies offers rewards or incentives to increase participation in the consumer research. Reward management allows a user to grant and pay rewards to panellists from an EyeQuestion questionnaire. The user can setup reward criteria in the ...
How to Set up and Monitor Quotas in EyeQuestion
In this article, we will discuss what quotas are, how to set up a simple quota in EyeQuestion, and how to monitor the status of the quota. What is a quota? A quota is a pre-defined target or limit for the number of respondents who can answer a ...