So you originally had (say) something like a 0-100 score like a class grade, where a high score meant
knowledge. After you reversed it, a high score meant "error-score". You can either talk about (1) a high correlation
between knowledge and a predictor; or (2) a high negative correlation between errors and the predictor, if
you insist on not-omitting the sign of the prediction in your statement.
One simple solution might be to take one more step in your transformation: reverse the scoring of the
transformed variable by taking one more step. "A better-distributed test score than the usual score was
created as follows. To avoid confusion, it runs from 0 to 10 instead of 0-100. This 0-to-10 score was
computed by subtracting the square-root of the error score from 10, preserving the original notion that a
high score is good."
Most people would probably not bother with that. They would toss in a comment, "You see negative r's
with the test score because the transformed version runs in the opposite direction.
--
Rich Ulrich
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