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Detecting Univariate Outliers

Outliers

Outliers can influence your results, pulling the mean away from the median. Two types of outliers exist: outliers for individual variables, and outliers for the model.

Univariate

To detect outliers on each variable, just produce a boxplot in SPSS (as demonstrated in the video). Outliers will appear at the extremes, and will be labeled, as in the figure below. If you have a really high sample size, then you may want to remove the outliers. If you are working with a smaller dataset, you may want to be less liberal about deleting records. However, this is a trade-off, because outliers will influence small datasets more than large ones. Lastly, outliers do not really exist in Likert-scales. Answering at the extreme (1 or 5) is not really representative outlier behavior.

Another type of outlier is an unengaged respondent. Sometimes respondents will enter ‘3, 3, 3, 3,…’ for every single survey item. This participant was clearly not engaged, and their responses will throw off your results. Other patterns indicative of unengaged respondents are ‘1, 2, 3, 4, 5, 1, 2, …’ or ‘1, 1, 1, 1, 5, 5, 5, 5, 1, 1, …’. There are multiple ways to identify and eliminate these unengaged respondents:

  • Include attention traps that request the respondent to “answer somewhat agree for this item if you are paying attention”. I usually include two of these in opposite directions (i.e., one says somewhat agree and one says somewhat disagree) at about a third and two-thirds of the way through my surveys. I am always astounded at how many I catch this way…
  • See if the participant answered reverse-coded questions in the same direction as normal questions. For example, if they responded strongly agree to both of these items, then they were not paying attention: “I am very hungry”, “I don’t have much appetite right now”.
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