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202 Mullany and Lay
Up to this stage, the association between the user R-scores and the absolute
KAI score differences have only been qualitatively demonstrated. Since this
association tested significant at p = 0.005, a level well below the assumed
permissible maximum of 0.050, an attempt to quantify the result was made. It was
found that the sample statistic In (k/r), where k is the absolute analyst/user KAI
difference and r the R-score, is approximately normally distributed with mean -
0.20503 and standard deviation 0.82417. By assuming these results for the
population of analyst-user dyad, Table 6 could be constructed. This enables
approximate forecasts for user resistance, given the analyst and user KAI score
differences.
For the sake of comparison, the raw and relative frequencies of the ratios r/
k for the 34 systems researched, which fell into each confidence interval, are given
as well. It will be noticed that the relative frequency of the systems in each
confidence interval agrees approximately with the corresponding confidence level.
This provides some heuristic evidence that the method described above for
estimating the ratio r /k is valid.
At first sight, the confidence intervals given in Table 6 appear somewhat large,
particularly for the higher confidences. However, forecasts based on the lower
confidences may certainly be used for decision-making purposes. Additionally,
Table 6: Confidence Intervals for Ration R/K
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