Perform MRSCAL in Hamlet II for multidimensional scaling
The previous article explained how to perform non-metric multidimensional scaling (MDS) method using MINISSA. This article explains how to perform metric multidimensional scaling method MRSCAL in Hamlet II that stands for metric scaling.
Procedure to perform MRSCAL in Hamlet II
Like MINISSA, MRSCAL method can be applied in order to derive similar results from analogous non-linear weighting procedure. This method applies a logarithmic transformation to the distances obtained from the matrix of similarities.
It also follows the same steps as MINISSA up to step 2. Thereafter, Hamlet II will present a log transformation upon selecting the metric ‘MRSCAL‘ method. Click on ‘Scale these items’.
A graph or plot will emerge.
Editing the graph
This data file offers the coefficients of monotonicity and alienation. It also shows the iterations run to derive the results. This data file also shows the configurations of the graph plotted on the main output window. Moreover, it allows the user to view and analyze all three clusters used while plotting the graph.
In order to edit the data in the graph, click on ‘Data’ as shown in the figure above. In order to draw and toggle the graph, choose the options in the output window as shown in the figure above.
Click on the “Save Display” option to save the edited plot. Hamlet offers to save the MDS results upon exiting the main output window. Save the results if required for further analysis.
INDSCAL for comparing co-occurrence matrices
This article dealt with the application of the metric multi-dimensional scaling method of MRSCAL for text analysis. It helps in visualizing the relationship taking the matrix standardized joint frequencies as its input. Its’s only advantage over MINISSA is that the graphical presentation is not based on corresponding labelled points. It allows partitioned matrices to be presented in 3-D. However, there are other multi-dimensional methods like INDSCAL (Individual Differences Scaling) to compare the acquired co-occurrence matrices. In the next article, application of INDSCAL will be shown and explored in depth.