Excel x axis data points
Homogeneity: The homogeneity of the assessors is displayed. It corresponds to the sum of the pre-processed assessor data weighted by the weights of these assessors. Knowing that STATIS gives more weight to the closest assessors from a global point of view, a much lower weight than the others will mean that the assessor is atypical.Ĭonsensus configuration: The consensus configuration is displayed. The greater the weight, the more the assessor contributed to the consensus. Weight of each assessor: The weights calculated by STATIS are displayed with the associated bar chart. The less space a subject has between its products, the larger its scaling factor.
These scaling factors standardize the use of each subject's sheet. Scaling factor for each assessor: The scaling factors are displayed with the associated bar chart. This matrix is used by STATIS to calculate the weights of the assessors. The closer it is to 1, the stronger the similarity. The RV index is a coefficient of similarity between two assessors included between 0 and 1. RV matrix: The matrix of RV coefficients between all assessors is displayed. These results only concern the STATIS analysis, and are only available if this is the method you have chosen:Įigenvalues and percentages of inertia: The eigenvalues and corresponding chart ( scree plot) are displayed.Ĭonsensus coordinates: Consensus coordinates in the factors space are displayed, with the corresponding charts (depending on the number of factors chosen). Summary statistics: The summary statistics table presents simple statistics for all selected assessors. Results of a projective mapping data analysis in XLSTAT If the percentage is low, a good idea to produce representations on several axis pairs in order to validate the interpretation made on the two first factor axes. If this percentage is high (for example 80%), the representation can be considered reliable. These representations are only reliable if the sum of the variability percentages associated with the axes of the representation space are sufficiently high. We can also use the cumulative variability percentage represented by the factor axes and decide to use only a certain percentage. The number of factors k to be kept corresponds to the first turning point found on the curve. Watch the decreasing curve of eigenvalues.
Two methods are commonly used to determine how many factors must be retained for the interpretation of the results: We can consider that the projection of a product on a plane is reliable if it is far from the center of the graph. The representation of the products in the space of k factors allows you to visually interpret the proximities between the products, by means of precautions. The data of the subjects are merged vertically. Options of a projective mapping data analysis in XLSTAT Structure of the dataĮach row represents a product and the columns are the x-axis and y-axis coordinates for each subject. While both methods have the primary objective of synthesizing information to graphically represent the products, they also allow you to determine relationships between the subjects' answers.
These data can be analyzed with the STATIS method or with Multiple Factor Analysis (MFA). Each subject brings a table with nn rows (one per product) and 2 columns. The data collected are simply the coordinates of the products on the x-axis and y-axis of the sheet of paper. You ask each subject to place products on a sheet of paper. The projective mapping (or Napping) task is one of the so-called "rapid" tests that are becoming increasingly popular in the context of the sensory characterization of products.