Extreme Scores: Effects and Causes

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 (making the data "lean" in one direction, rather than clustered in the middle) or kurtosis (when the distribution is squashed down or has a very high narrow peak) 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 through 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 incorrectly 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 I will post an updated blog index. 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