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    Multidimensional scaling (MDS) applied directly to dissimilarity measures (D) is slightly superior to transforming them to profile distances (Δ). Both methods yield results comparable to HOMALS, with minimal average differences.

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    Area of Science:

    • Psychology
    • Statistics
    • Data Analysis

    Background:

    • Dissimilarity measures (D) from stimulus sortings can be analyzed using Multidimensional Scaling (MDS).
    • An alternative involves transforming D to profile distances (Δ), a method previously criticized by Drasgow and Jones (1979).

    Purpose of the Study:

    • To compare the efficacy of MDS on direct dissimilarity measures (D) versus profile distances (Δ).
    • To evaluate these methods against Multiple Correspondence Analysis (HOMALS) and MDS on similarity ratings.

    Main Methods:

    • Applied Multidimensional Scaling (MDS) to both direct dissimilarity measures (D) and profile distances (Δ).
    • Utilized Multiple Correspondence Analysis (HOMALS) on raw sorting data.
    • Conducted MDS on pairwise similarity ratings from the same subjects.

    Main Results:

    • Multidimensional Scaling (MDS) on direct dissimilarity measures (D) showed slight superiority over MDS on profile distances (Δ).
    • Results from MDS on profile distances (Δ) were similar to those obtained from the more efficient HOMALS program.
    • Observed average differences between the compared methods were very small.

    Conclusions:

    • MDS on direct dissimilarity measures (D) is a slightly preferable method for analyzing stimulus sorting data.
    • MDS on profile distances (Δ) offers a viable alternative, yielding comparable results to HOMALS.
    • The choice of method has minimal impact on the overall findings in analyzing stimulus dissimilarity data.