Means over Time

Means over Time

Available from version: 5.3.1

Purpose

The means over time analysis is a helpful tool that can assist you in seeing how a product changes over time. This type of analysis is often useful for quality control, and it consists of a line chart where the mean values of the attributes by product over time are plotted. 

Data format

  1. Means_over_time.xlsx
For performing a means over time analysis, it is important that the dataset used contains more than one session. If you collected the data in EyeQuestion, please make sure that you ran your project in multiple sessions. Each session corresponds to a specific timepoint, for instance session 1 is timepoint 1, session 2 is timepoint two and so on. On the design page in your project, you can click on the Sessions button to add your session information. In here you can, among others, set your session description, the start date and the end date. You will be able later on to use this information to display it on the x-axis of the line chart and table column titles in the analysis results. 

Please keep in mind that when you set a start date and end date in the session information that the panellists will only be able to run the session in between these dates. In case you do not fill in an end date, EyeQuestion will use the last date based on your panellist sessions the moment that you finalize the project.

Note: when importing external data in EyeOpenR, the first five columns on the Data sheet must include the following in the specified order: Assessor, Product, Session, Replica and Sequence. Sensory attributes start from column six (Column F). If there is no replica or sequence information available, you should input a value of “1” in each cell in the column that contains no collected information. The session information can be added on the Sessions sheet. You are free to name your columns, as long as the first column is called Session and matches your data on the Data sheet. See the example dataset for more information. 

Options

  1. Split Results On: This option is by default set to "Product" to ensure that the results contain line chart per product.
  2. Type of Mean: Adjusted’ takes into account missing data or imbalance in design and takes into account the ANOVA model chosen based on the parameters asked below; ‘Arithmetic’ calculates the mean in the data and is recommend for balanced data. ‘Arithmetic’ is the default in the case of no missing data.
  3. Assessor effect: ‘Yes’ to include an assessor effect in the ANOVA model used in the estimation of the adjusted means; This is typically the case with sensory data. 'No' excludes the assessor effect.
  4. Session effect: ‘Yes’ to include a session effect in the ANOVA model used in the estimation of the adjusted means; ‘No’ otherwise.
  5. Replicate effect: ‘Yes’ to include a replicate effect in the ANOVA model used in the estimation of the adjusted means; ‘No’ otherwise.
  6. Interaction:  ‘Yes’ to include a 2-way interaction in the ANOVA model used in the estimation of the adjusted means; ‘No’ otherwise.
  7. Number of Decimals for Values: Required number of decimals for values given in the results.
  8. Anonymise Sessions? Choose to replace the session names. If this setting is disabled, the session names will be displayed as "Product_Session description". If you select an option, for example the end date, then the session name will be replaced by the end date.

Results and Interpretation

The results contain one line chart for each product. The line chart shows the mean values for each attribute over time. The data used in the chart is also visible in the underneath table. This analysis offers a quick and efficient approach to monitor a product's performance over time, particularly when performing quality control.

    • Related Articles

    • Time Intensity Analysis

      Purpose Time Intensity tests are designed to measure the temporal evolution of a single attribute. Results are plotted as time-intensity curves and key parameters of the curves are calculated. Results can be averaged per panellist or per product. ...
    • 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. ...
    • K-means

      Purpose Cluster analysis (of which K-means clustering is a specific type) is used to assign consumers to different groups (clusters) based on their patterns of product liking. This is important when there are strong and different preferences across ...
    • Time Intensity (TI)

      Introduction A Time-Intensity (TI) method is a sensory evaluation technique used to assess the perceived intensity of a sensory attribute over a specific period, typically involving continuous measurements. Panelists are given a time-intensity ...
    • Calculate Panellists Questionnaire Response Time

      Introduction By tracking the time it takes for respondents to complete surveys, we can gather valuable insights that can help us improve our processes. Firstly, response times reveal user engagement levels and identify potential issues with question ...