MULTIDIMENSIONAL SCALING: SIMILARITY DATA Open the stick containing information of distances between 11 cities. This is a squargon symmetric matrix, with fortuity omitted and just the lower half typed in as seen at a lower place. alliance the steps and fill in the dialog boxes as shown and substantiate indicated output. Notice the procedure is the ALSCAL procedure which can be accessed on a lower floor help Syntax… ALSCAL from the chief(prenominal) SPSS toolbar. Do the example yourselves then(prenominal) pick an example from the web or a school text where data and graphs be precondition and try to reproduce the output. [pic] draw the steps given below: ANALYSE.. SCALE Multidimensional scaling [pic] [pic] [pic] [pic] [pic] _ Alscal grommet taradiddle for the 2 dimensional solutions (in squared distances) Youngs S-stress formula 1 is used. Iteration S-s tress service 1 .14477 2 .12655 .01822 3 .12645 .00010 Iterations stopped because S-stress improvement is less(prenominal) than .001000 tensity and squared correlation (RSQ) in distances RSQ determine are the simile of variance of the scaled data (disparities) in the section (row, matrix, or entire data) which is accounted for by their corresponding distances.
Stre! ss values are Kruskals stress formula 1. For matrix Stress = .13572 RSQ = .92686 _ Configuration derived in 2 dimensions Stimulus Coordinates Dimension Stimulus Stimulus 1 2 estimate Name 1 VAR00001 1.7528 1.1290 2 VAR00002 .2281 -.0644 3 VAR00003 .6537 .1439 4 VAR00004 .6986 .1540 5 VAR00005 1.2589 .3360 6 VAR00006...If you want to get a full essay, order it on our website:
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