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Ratings '''drift''' is when user adjusts to some new average over time and that average becomes their new neutral. Sometimes this is caused by user not being to set in the ways they measure the phenomenon and exactly what ratings mean what. This is a problem because it creates false positives for tests and makes comparing between long time periods difficult. It is possible, but not recommended, to look at distribution, especially extremes, and guess at how ratings may have drifted.
Ratings '''drift''' is when user adjusts to some new average over time and that average becomes their new neutral. Sometimes this is caused by user not being to set in the ways they measure the phenomenon and exactly what ratings mean what. This is a problem because it creates false positives for tests and makes comparing between long time periods difficult. It is possible, but not recommended, to look at distribution, especially extremes, and guess at how ratings may have drifted.
'''Anchored Scales'''. User could connect each level of a scale to specific set of physical characteristics, for example how disruptive it was to user's every day work. This technique stops drift, is less ambiguous, and therefore faster too. In addition this means comparisons can be done between different variables that have the same scale.
'''Anchored Scales'''. User could connect each level of a scale to specific set of physical characteristics, for example how disruptive it was to user's every day work. This technique stops drift, is less ambiguous, and therefore faster too. In addition this means comparisons can be done between different variables that have the same scale. Scales must transcend individual as well as the group.<ref>en.wikipedia.org/wiki/Louis_Leon_Thurstone#Contributions_to_measurement</ref>
'''Simplify'''. Consider all the situations that may occur, information that is compressed into, and things that may influence the scale. The more complicated the variable the more ambiguity in it and effort it will require. It may be easier to split one variable into multiple different variables.
'''Simplify'''. Consider all the situations that may occur, information that is compressed into, and things that may influence the scale. The more complicated the variable the more ambiguity in it and effort it will require. It may be easier to split one variable into multiple different variables.