It's more work re-running analyses, but ultimately it is transparent and saves you from trying to argue for either their inclusion or removal (often on grounds that make assumptions). In my experience, if you don't have strong evidence to support removal (e.g., a lot of deviant responding), I have found it beneficial and reviewer-friendly to run analyses with and without the outlier(s) and report whether the results changed at all. The cause of the outlier and the impact on specific analyses will affect what you do with it. However, in my experience, you often can have a reasonable guess, but not know the cause. Some analyses are more robust or sensitive to outliers.Īs Pavel noted above, it is useful to try and determine the cause of outliers. between the project managers skills and the success of the projects in the. The eponymous scale was invented by an American social psychologist, Rensis Likert, in 1932 to measure his subjects. Management and specifically in this master thesis. It is one of the most widely used psychometric measurement tools to scale responses collected via questionnaires and surveys. But, the answer will change depending on what kind of analyses you are running. A Likert scale is a type of rating scale often used for research in social sciences and education. If you want a specific citation about the impact (or lack of impact) of outliers on Likert scales, could you provide more context regarding what the impact would be on and your sample size? For example, here is a paper on the impact of outliers in Likert scales on alpha.
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