All public courses
These public courses are open for anyone to book. If you have any questions please don't hesitate to get in touch.
Statistical analysis of consumer data - Module C1 - Liking scale data - are my products different?
Comparing data distributions, are mean scores detected as different? This course is a live session, but a recording can also be purchased instead. See recorded webinars page for on-demand training.
Hands on Sensory Statistics - 1, 3, 8, 10 October 2024 @ 09:00 (GMT+1/UTC+1)
This course takes place over 4 half days. Each module is 3.5 hours and made up of a mix of talks and practical workshop exercises with solutions and interpretation of output.
Statistical analysis of consumer data - Module C2 - Key drivers analysis & simple modelling - what product attributes most impact consumer acceptance?
Measuring and modelling relationships between variables. This course is a live session, but a recording can also be purchased. Please look in the 'recorded webinars' section of the website
Statistical analysis of consumer data - Module C3 - Visualising relationships between many variables using maps (PCA)
Principal components analysis for visualising product differences over many variables. This course is a live session, but a recording can also be purchased. Please contact us.
Statistical analysis of consumer data - Module C4 - Who wants what? Clustering consumers using their product liking data
Data requirements and pre-processing. The two main techniques, Hierarchical Clustering (AHC) and K-Means Clustering explained and compared.
Statistical analysis of consumer data - Module C5 - Improving/understanding your Cluster solution to make better decisions
Data quality checking for non discriminators, order effects, identifying outliers and non conformers.
Statistical analysis of consumer data - Module C6 - Who likes what? Demographic segmentation of product acceptability
Do different demographics like different products? Are my liking clusters defined by demographics?
Statistical analysis of consumer data - Module C7 - Analysing "Just About Right" (JAR) scales for product optimisation
Do my products differ by JAR scale? Do these diagnostics impact liking?
Statistical analysis of consumer data - Module C8 - Using categorical variables to drive insights using correspondence analysis (CA) mapping
How the technique works, interpretation of maps etc
Statistical analysis of consumer data - Module C9 - Using "Check All That Apply" (CATA) scales to understand consumer product choices
Which CATA questions discriminate between sample? Visualising product difference in the CATA space using correspondence analysis.
Statistical analysis of consumer data - Module C10 - Comparing products using "Rate All That Apply" (RATA) and Proportion data
Comparing product performance using percentage measures and Rate all that apply (RATA)
Statistical analysis of consumer data - Module C11 - Visualising Consumer Liking - Using Mapping to consider different preference patterns
Mapping individual consumers to identify potential clustering, mapping cluster means to visualise cluster differences. Cluster visualisation
Statistical analysis of consumer data - Module C12 - Comparing products using "Rapid" methods: Napping data (Projective Mapping)
MFA and STATIS. Applications to measuring brand effects and analysis of napping data.
Statistical analysis of consumer data - Module C13 - Predicting product performance from existing data using machine learning methods - Partial Least Squares (PLS) regression modelling
Technique to build models to predict one block of correlated data from another. Applications covered include models to predict liking (from sensory/analytic data) or to predict sensory from data.
Statistical analysis of consumer data - Module C14 - Combining data sets to visualise the link between product characteristics & preferences - External preference mapping
A graphical technique to visualise how liking (by individual consumer or on average) varies over a product space and to thus predict “sweet spots” in the sensory space.
Statistical analysis of consumer data - Module C15 - Looking at product differences using "Rapid" consumer methods: Free sorting task data
Design of studies, number of samples/number of assessors, analysis using multidimensional scaling and cluster analysis. Hierarchical sorting to gain more insights.
Statistical analysis of consumer data - Module C01 - Basic stats refresher
This course is a pre-recorded session and is a refresher for analysis of consumer research data
Statistical analysis of consumer data - Module C02 - Considerations when planning your consumer trial
This course is a pre-recorded session and is a refresher for the Statistical analysis of consumer data course series.