<?xml version="1.0" encoding="utf-8"?><!DOCTYPE article  PUBLIC '-//OASIS//DTD DocBook XML V4.4//EN'  'http://www.docbook.org/xml/4.4/docbookx.dtd'><article><articleinfo><title>FAQ/mds/example</title><revhistory><revision><revnumber>5</revnumber><date>2018-06-07 14:27:54</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>4</revnumber><date>2018-06-07 14:23:51</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>3</revnumber><date>2013-03-08 10:17:17</date><authorinitials>localhost</authorinitials><revremark>converted to 1.6 markup</revremark></revision><revision><revnumber>2</revnumber><date>2006-08-15 10:59:02</date><authorinitials>PeterWatson</authorinitials></revision><revision><revnumber>1</revnumber><date>2006-08-15 10:55:41</date><authorinitials>PeterWatson</authorinitials></revision></revhistory></articleinfo><para>Here is an example of input for the SPSS procedure found under analyze:scale: multidimensional scaling.  </para><para>The input are pairs (i,j) of times of one subject to correctly respond to semantic category j immediately after identifying category i. The matrix is asymmetric and contains dissimilarities (the default) as higher RTs suggest more dissimilarity between a pair of stimuli. </para><informaltable><tgroup cols="13"><colspec colname="col_0" colwidth="14*"/><colspec colname="col_1"/><colspec colname="col_2"/><colspec colname="col_3"/><colspec colname="col_4"/><colspec colname="col_5"/><colspec colname="col_6"/><colspec colname="col_7" colwidth="14*"/><colspec colname="col_8" colwidth="14*"/><colspec colname="col_9" colwidth="14*"/><colspec colname="col_10" colwidth="14*"/><colspec colname="col_11" colwidth="14*"/><colspec colname="col_12" colwidth="14*"/><tbody><row rowsep="1"><entry colsep="1" nameend="col_6" namest="col_0" rowsep="1"><para> <emphasis role="strong">stimulus</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> <emphasis role="strong">a</emphasis></para></entry><entry colsep="1" rowsep="1"><para> <emphasis role="strong">b</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> <emphasis role="strong">c</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> <emphasis role="strong">d</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> <emphasis role="strong">e</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> <emphasis role="strong">f</emphasis> </para></entry></row><row rowsep="1"><entry colsep="1" nameend="col_6" namest="col_0" rowsep="1"><para> <emphasis role="strong">a</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> 48 </para></entry><entry colsep="1" rowsep="1"><para> 56 </para></entry><entry colsep="1" rowsep="1"><para> 23 </para></entry><entry colsep="1" rowsep="1"><para> 123 </para></entry><entry colsep="1" rowsep="1"><para> 45 </para></entry><entry colsep="1" rowsep="1"><para> 460 </para></entry></row><row rowsep="1"><entry colsep="1" nameend="col_6" namest="col_0" rowsep="1"><para> <emphasis role="strong">b</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> 78 </para></entry><entry colsep="1" rowsep="1"><para> 84 </para></entry><entry colsep="1" rowsep="1"><para> 79 </para></entry><entry colsep="1" rowsep="1"><para> 210 </para></entry><entry colsep="1" rowsep="1"><para> 39 </para></entry><entry colsep="1" rowsep="1"><para> 25 </para></entry></row><row rowsep="1"><entry colsep="1" nameend="col_6" namest="col_0" rowsep="1"><para> <emphasis role="strong">c</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> 80 </para></entry><entry colsep="1" rowsep="1"><para> 237 </para></entry><entry colsep="1" rowsep="1"><para> 200 </para></entry><entry colsep="1" rowsep="1"><para> 145 </para></entry><entry colsep="1" rowsep="1"><para> 178 </para></entry><entry colsep="1" rowsep="1"><para> 127 </para></entry></row><row rowsep="1"><entry colsep="1" nameend="col_6" namest="col_0" rowsep="1"><para> <emphasis role="strong">d</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> 78 </para></entry><entry colsep="1" rowsep="1"><para> 160 </para></entry><entry colsep="1" rowsep="1"><para> 150 </para></entry><entry colsep="1" rowsep="1"><para> 478 </para></entry><entry colsep="1" rowsep="1"><para> 256 </para></entry><entry colsep="1" rowsep="1"><para> 290 </para></entry></row><row rowsep="1"><entry colsep="1" nameend="col_6" namest="col_0" rowsep="1"><para> <emphasis role="strong">e</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> 65 </para></entry><entry colsep="1" rowsep="1"><para> 250 </para></entry><entry colsep="1" rowsep="1"><para> 67 </para></entry><entry colsep="1" rowsep="1"><para> 312 </para></entry><entry colsep="1" rowsep="1"><para>   4 </para></entry><entry colsep="1" rowsep="1"><para> 345 </para></entry></row><row rowsep="1"><entry colsep="1" nameend="col_6" namest="col_0" rowsep="1"><para> <emphasis role="strong">f</emphasis> </para></entry><entry colsep="1" rowsep="1"><para> 410 </para></entry><entry colsep="1" rowsep="1"><para> 340 </para></entry><entry colsep="1" rowsep="1"><para> 312 </para></entry><entry colsep="1" rowsep="1"><para> 324 </para></entry><entry colsep="1" rowsep="1"><para> 367 </para></entry><entry colsep="1" rowsep="1"><para> 367 </para></entry></row></tbody></tgroup></informaltable><para>The code below, which may also be run from the menu, runs the multidimensional scaling procedure specifying a 6x6 asymmetric matrix of user input for six stimuli and the ordinal level of measurement. </para><screen><![CDATA[ALSCAL
  VARIABLES= a b c d e f
  /SHAPE=ASYMMETRIC
  /LEVEL=ORDINAL
  /CONDITION=MATRIX
  /MODEL=EUCLID
  /CRITERIA=CONVERGE(.001) STRESSMIN(.005) ITER(30) CUTOFF(0) DIMENS(2,2) .]]></screen></article>