92013-03-08 10:17:12localhostconverted to 1.6 markup82011-07-06 10:48:08PeterWatson72011-07-05 11:32:11PeterWatson62011-07-05 11:28:43PeterWatson52011-07-05 11:27:01PeterWatson42011-07-05 11:26:46PeterWatson32011-07-05 10:49:54PeterWatson22011-07-05 09:20:02PeterWatson12011-07-05 09:15:39PeterWatson

Two tests which purport to measure the same underlying variable may be highly correlated (form a straight line when plotted against each other) but not agree. Agreement would be indicated by both tests taking the same value with the line x=y representing the best fitting straight line in a scatterplot of the tests graphed against one another. Bland and Altman (1995) suggested checking agreement between a pair of measures by plotting their difference on the y-axis against their sum on the x-axis. They also suggest working out the mean inter-test difference and the confidence interval for this difference (equal to mean difference +/- 1.96 SD of the differences) and adding these to the scatterplot. These statistics can be worked out in SPSS although SPSS will not add the lines representing the limits of the confidence interval or the mean for the inter-test difference to the scatter plot. Further details with illustrations are given here and here. If the scatterplot is random, the mean difference is around zero and the inter-test differences within +/-1.96 of the mean then the tests may be used interchangeably. Reference Bland JM, Altman DG (1995). Comparing methods of measurement: why plotting difference against standard method is misleading. The Lancet 346 1085-1087.