<div class="calibre2"><span class="calibre3"><span class="calibre4">Tucker-1 Plots</span></span></div>
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<div class="calibre2"><span class="calibre7"><span class="calibre8">Two types of plots are generated for the Tucker-1 method. The common scores plot, the first
type, displays how the samples relate to each other based on the assessors’ evaluations. In
the correlation loadings plot, the other type, each point displays the attribute of a specific
assessor.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">The more noise an attribute of an assessor contains, the closer the point will appear in the
middle. The more structured information an attribute contains, the closer it will appear to the
outer circle (100 % explained variance for that attribute).</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">In both plots the first two principal components (PC) are shown. The buttons with arrows are
used to change the PC on the x- and y-axes. At most 4 PCs are possible.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">It is possible to highlight either one specific assessor or one specific attribute in the plot.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">In the tree control all assessors and attributes are listed, together with “Common Scores” (on
the top of the control tree). By selecting a specific assessor, all correlation loadings for this
assessor are plotted. Similarly, for a specific attribute.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">By default no Variable standardization of the data takes place. Alternatively, data may be
standardized by checking the respective radio button. </span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><b class="calibre14">Variable standardization</b></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">For the Tucker-1 analysis, data may be standardized the following way:</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">x<sub class="calibre41">new </sub>= ( x<sub class="calibre41">new </sub>- x<sub class="calibre41">new </sub>) / STD(x)</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8">This is also known as autoscaling. By default the value is None.</span></span></div><div class="calibre2"><span class="calibre7"><span class="calibre8"><br class="calibre10"/></span></span></div><div class="calibre2"> </div>
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