Distances Similarity Measures

From the Measure group, select the alternative that corresponds to your type of data (interval or binary); then, from the drop-down list, select one of the measures that corresponds to that type of data. Available measures, by data type, are:

  • Interval data. Pearson correlation or cosine.
  • Binary data. Russell and Rao, simple matching, Jaccard, Dice, Rogers and Tanimoto, Sokal and Sneath 1, Sokal and Sneath 2, Sokal and Sneath 3, Kulczynski 1, Kulczynski 2, Sokal and Sneath 4, Hamann, Lambda, Anderberg's D, Yule's Y, Yule's Q, Ochiai, Sokal and Sneath 5, phi 4-point correlation, or dispersion. (Enter values for Present and Absent to specify which two values are meaningful; Distances will ignore all other values.)

The Transform Values group allows you to standardize data values for either cases or variables before computing proximities. These transformations are not applicable to binary data. Available standardization methods are z scores, range –1 to 1, range 0 to 1, maximum magnitude of 1, mean of 1, and standard deviation of 1.

The Transform Measures group allows you to transform the values generated by the distance measure. They are applied after the distance measure has been computed. Available options are absolute values, change sign, and rescale to 0–1 range.

Choosing a Similarity Measure

This feature requires the Statistics Base option.

  1. From the menus choose:

    Analyze > Correlate > Distances...

  2. With Similarities selected, click Measures.
  3. From the Measure group, select the alternative that corresponds to your type of data.
  4. From the drop-down list, select a measure that corresponds to that type of measure.