Extreme scores can cause serious problems for statistical
analyses. They generally increase error variance and reduce the power of
statistical tests by altering the skew or kurtosis of a variable. This can be a
problem with multivariate analyses. The more error variance in your analyses,
the less likely you are to find a statistically significant result when you
should find one (increasing the probability of a Type II error).
Extreme scores also bias estimates such as the mean and SD.
Since extreme scores bias your results, you may be more likely to draw
incorrect conclusions, and your results will not be replicable and
generalizable.
Extreme scores can result from a number of factors. It is
possible that the extreme score is correct- an example is although the average
American male is around 5' 10" there are males that are 7' tall and some
that are 4 foot tall. These are legitimate scores even though they are extreme.
Another cause of an extreme score is data entry error,
someone that was actually 5' 6" tall may be incorrectly entered as 6'
5". So the first step is to always double check that extreme scores were
entered correctly. A third cause may be that participants purposefully report
incorrect scores. It can also happen that a participant accidently reports an
incorrect score. Thus, an extreme score that has been entered correctly may
need to be removed.
*The info in today's post comes from Osborne (2013).
Osborn,
J. W. (2013). Best practices in data
cleaning. DC: Sage.
Next time we will consider health issues and the
dissertation. Do you have an issue or a question that you would like me to
discuss in a future post? Would you like to be a guest writer? Send me your
ideas! leann.stadtlander@waldenu.edu
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