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A correlation is used to provide an indication of the similarity between two constructs. For rated grids, the Pearson product-moment correlation is used, for ranked grids the Spearman rho correlation is used. In general these coefficients are fairly similar irrespective of the nature of the grid data. They have an advantage in that they are independent of the scale of rating, mean rating or standard deviation of ratings and form values that lie from –1.0 (where the constructs are perfectly reversed in their pattern of ratings) through zero, where there is no similarity, to +1.0 where they agree perfectly, although correlations for dichotomous ratings may not have a maximum value of 1.0 (see Carroll, 1961). They differ from distances, in that they only attend to the profile similarity across elements, and do not take account of differences in level (means) or scatter (variances). An old (but readable) discussion of this can be found in Cronbach and Gleser (1953). Correlations form the basis of some measures of cognitive complexity, such as intensity. They should not be routinely calculated for elements since they will change if construct poles are reversed, although this can be overcome in some computer programmes such as GRIDSTAT.

  • Carroll, J.B. (1961) The nature of data, or how to choose a correlation coefficient. Psychometrika, 26, 347-372.
  • Cronbach, L.J., and Gleser, G. (1953) Assessing similarity between profiles. Psychological Bulletin, 50, 456-473.

Richard C. Bell

Establ. 2003
Last update: 15 February 2004