Date: Tue, 8 Dec 1998 22:17:30 -0600 Max Martin "SPSSX(r) Discussion" Max Martin Re: Analysis question (longish) text/plain; charset="iso-8859-1"

Apologies for the length of this post.

A client of mine has an EXPLORATORY project where (n=151) principals of elementary, middle, and high schools in three districts (urban, suburban, and rural) indicate their needs for professional development. The Needs are grouped into three categories (sets A, B, and C), eight needs per category. No assumptions are made about the orthogonality of the categories--besides, the categories themselves have a lot of conceptual simlarities, based on a reading of the set definitions and items. Each principal is asked to select the TWO items from each category most similar in orientation to his personal beliefs. She would like to break down the responses into useful comparisons: for example, district, educational level (el, mid, HS), or by gender of recipients, or by age, etc. The data file had a single string variable for each of the three sets. A value of 25 for set_B would be interepeted as that the top two choices for the principal were b2 and b5. To facilitate analysis, I coded each of the three domains into sets of eight binary variables (a1 to a 8, b1 to b8, and c1 to c8, where a value of 1 meant the items had been selected and a value of 0 meant that it was not selected.) I can then cast this into a series of Multiple Response crosstabs and break out the pecentages of principals selecting (marking a 1) particular needs. Of course, no tests of statistical significance are possible in the Multiple Response procedure.

My question is this: is it appropriate to cast these data as preferences, and use MultiDimensional Scaling or Cluster analysis. It seems as if a choice of one of the eight needs in each set expresses a preference of that category over 6 others (1 vs. 0). The other category selected forces a no preference (1 vs 1), while any two non-selected categories also are "no preference (0 vs. 0). Thus a Proximities matrix could be constructed for each of the 3 raw needs sets (see below), using binary Euclidean distances, for example. The resulting Prox. matrix could be cluster analyzed to identify similar groups of cases or items, or an MDS solution could be generated and the arrangement of the needs in N-space (2 or 3D) could be examined. Also, I could use Answer Tree to look at the segmentation patterns inherent in the data. Am I barking up the wrong tree, or is my proposed coding ok for generating preference matrices? Any ideas?

The preference matix would look like:

variable case a1 a2 a3 a4 a5 a6 a7 a8 . . . . . . . . . . . . . . . . 45 1 0 0 0 1 0 0 0 46 0 1 0 1 0 0 0 0 47 0 1 1 0 0 0 0 0 48 0 0 0 0 0 0 1 1 . . . . . . . . . . . . . . . etc.

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