PINDIS in Hamlet II for multidimensional scaling

By Avishek Majumder & Priya Chetty on October 25, 2018

The previous article showed steps to apply Individual Difference Scaling (INDSCAL). This article explains how to apply multidimensional scaling with Procrustean Individual Differences Scaling (PINDIS) in Hamlet II. It is similar to INDSCAL and focuses on centroid configuration of the points derived from INDSCAL group space. PINDIS method applies a series of variations by increasing flexibility in each iteration. The aim is to maximize the optimization. It provides better specifics when compared to the INDSCAL results. In simpler words, PINDIS technique helps in exploring the relationship between the different text sources in depth with more details.

In order to perform PINDIS analysis, ‘Select’ procedure is used to gather a set of multidimensional configurations for the comparison. The output derived from PINDIS analysis can be exhibited in multiple graphics display. The primary output obtained from PINDIS is “centroid configuration”. It is extracted by exposing the subject configurations. It illustrates each of the texts comparisons to a series of allowed changing iterations while keeping the original order of distances between the words or categories.

Steps to apply PINDIS using ‘Select’

In order to apply PINDIS in Hamlet II, follow the below steps.

• Go to the Tool Bar.
• Select ‘MDS’.
• Click on ‘MDS configurations to compare with PINDIS’.

The below figure will appear.

Click on one of the MINISSA files to select as an input for the analysis. Then click on “Full view” (box 2 of the figure above). It represents the displayed configuration in three-dimensional space to help analyze the plottings.

Click on “Select Configuration” (box 3 of the figure above).

A small window will open (box 4 of the figure above). Enter a new name to save the file (in this case “sample”). If the file already exists, browse and select it.

Then click on “Open”. The figure below will appear. Click on “Yes”.

The prompt below will appear. Select the number of dimensions to scale the input data for displaying. Then click on “Yes”.

The window as shown in the figure below will appear. Click on “Fit data to this configuration” to continue the analysis.

If you receive a warning box stating “The viewpoint is not at the origin! ZOOM out to continue…”. Keep clicking on “OK” till it zooms out automatically in the background and optimizes the shape accordingly. The graphical representation will eventually appear in an output window.

Once satisfied with all the edits and drawings on the original displayed graph, click on “Save Display” to save the edited output.

Exit the window and save the files for future references.

Interpretations of PINDIS three-dimensional graph

The resulted three-dimensional graph will look like the displayed output in the above figure. Some basic drawing functions are available on the toolbar to draw freely, create line or ellipse, write texts, and erase anywhere on the displayed output. Functions like “Left”, “Right”, “Down” and “Up” are also available to change the orientation of the default displayed graph. “In” and “Out” functions are provided above to zoom in and zoom out the plot so as to focus on the main areas while representing the model. If one wants to restore the graph back to its original form, click on the “Refresh” button provided on the toolbar. Another tool “Line drawing” helps the user to change the colour of the lines drawn on this window.

Editing a data file in Hamlet II

Hamlet also allows the user to edit or view the data file by simply clicking on “Data” on the top of the toolbar and selecting “View or Edit data”.  In order to adjust the size of the labels presented on the graph, use the “Labels” function. To highlight a specific set of labels, click on the endpoints of the particular labels. “Grid” option helps to toggle the grid lines displayed in the graph for a cleaner view.

This article was about the application of PINDIS method of multidimensional scaling to explore the relationship between the texts using centroids based on the optimized weights of each text. However, this article explained only one ‘Select’ method of performing PINDIS. The next article will further explain the use of PINDIS analysis in depth with illustrations.

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