A Comparison of Pattern Matching Indices
Multivariate Behavioral ResearchPublished 1 April 1991
Edward Guadagnoli, Wayne F. Velicer
Citations108
SJR quartileQ1
SJR score2.71
SNIP2.12
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TL;DR
A common problem in multivariate applications is the comparison of pattern matrices obtained from two independent studies, and an increase in either saturation or sample size resulted in more accurate index values.
Abstract
A common problem in multivariate applications is the comparison of pattern matrices obtained from two independent studies. We compared the performance of four pattern matching indices (the coefficient of congruence [c], the s-statistic [s], Pearson's r [r], and kappa [k]) under a variety of experimental conditions. We constructed population pattern matrices by varying (a) saturation or the size of the loadings, (b) sample size, (c) the number of observed variables, and (d) the number of derived variables. Sample patterns were computer generated and matched, employing each index, to their population pattern. With the exception of r, little difference in matching performance between indices was observed. In general, an increase in either saturation or sample size resulted in more accurate index values.
Keywords
Decision SciencesMathematicsAgricultural and Biological Sciences
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