# Distances

This procedure calculates any of a wide variety of statistics measuring either similarities or dissimilarities (distances), either between pairs of variables or between pairs of cases. These similarity or distance measures can then be used with other procedures, such as factor analysis, cluster analysis, or multidimensional scaling, to help analyze complex datasets.

**Example.** Is it possible to measure similarities between
pairs of automobiles based on certain characteristics, such as engine
size, MPG, and horsepower? By computing similarities between autos,
you can gain a sense of which autos are similar to each other and
which are different from each other. For a more formal analysis, you
might consider applying a hierarchical cluster analysis or multidimensional
scaling to the similarities to explore the underlying structure.

**Statistics.** Dissimilarity (distance) measures for interval
data are Euclidean distance, squared Euclidean distance, Chebychev,
block, Minkowski, or customized; for count data, chi-square or phi-square;
for binary data, Euclidean distance, squared Euclidean distance, size
difference, pattern difference, variance, shape, or Lance and Williams.
Similarity measures for interval data are Pearson correlation or cosine;
for binary data, Russel 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.

To Obtain Distance Matrices

This feature requires the Statistics Base option.

- From the menus choose:
- Select at least one numeric variable to compute distances between cases, or select at least two numeric variables to compute distances between variables.
- Select an alternative in the Compute Distances group to calculate proximities either between cases or between variables.