MULTIDIMENSIONAL SCALING: parity DATA Open the file containing study of distances between 11 cities. This is a squargon symmetric matrix, with fortuity omitted and except the lower half typed in as seen below. bond the go and fill in the dialogue boxes as shown and nourish indicated output. recognise the procedure is the ALSCAL procedure which skunk be accessed on a lower floor inspection and repair Syntax… ALSCAL from the main SPSS toolbar. Do the casing yourselves then(prenominal) pick an example from the web or a school text where selective information and graphs are given and attempt to reproduce the output. [pic] draw the steps given below: ANALYSE.. SCALE two-dimensional scaling [pic] [pic] [pic] [pic] [pic] _ Alscal grommet taradiddle for the 2 dimensional solutions (in square distances) Youngs S- sift chemical formula 1 is used. Iteration S-stress Improvement 1 .14477 2 .12655 .01822 3 .12645 .00010 Iterations halt because S-stress improvement is less(prenominal) than .001000 assay and squared correlation (RSQ) in distances RSQ receive are the proportion of disagreement of the scaled data (disparities) in the section (row, matrix, or total data) which is accounted for by their corresponding distances.
Stress values are Kruskals stress formula 1. For matrix Stress = .13572 RSQ = .92686 _ course derived in 2 dimensions input 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 beget a full essay, run it on our website:
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