How To Determine Outliers In Spss
One Exception is an observation that is unusually far away from other values in the data set. Outliers can be a problem because they can affect the results of an analysis. This tutorial explains how to identify and handle outliers in SPSS.
How to identify outliers in SPSS
Suppose we have the following data set showing the annual income (in thousands) of 15 individuals:
One way to determine if outliers occur is to create a box plot for the data set. To do so, click Analysis tab, then Descriptive statisticsafterward Discover:
In the new window that pops up, drag the variable earnings = earnings in the box labeled List of Dependencies. Then click Statistics and make sure the box next to it Percent checked. Then click Continue. Then click ALRIGHT.
When you click ALRIGHTa box will appear:
Read more: how to create a flag banner in photoshop If there are no circles or asterisks on either end of the box, this is an indication that there are no outliers. the following ranges:
- 3rd quadrant + 1.5 * interquartile range
- First quadrant – 1.5 * interquartile range
We can calculate the interquartile range by taking the difference between the 75th and 25th percentiles in the labeled row Tukey’s hinge in the output:
For this data set, the interquartile range is 82 – 36 = forty six. Therefore, any value outside of the following ranges will be considered an outlier value:
- 82 + 1.5 * 46 = 151
- 36 – 1.5 * 46 = -33
Obviously income cannot be negative, so the lower bound in this example is not useful. However, any income above 151 will be considered an exception. For example, let’s say the largest value in our dataset is 152. Here is the box plot for this dataset:
The circle is an indication that there is an outlier in the data. The number 15 indicates which observations in the data set are outliers. PSS also treats any data value as an outlier. extreme exception if it falls outside the following ranges:
- 3rd quadrant + 3 * interquartile range
- First quadrant – 3 * interquartile range
Therefore, any value outside the following ranges would be considered an extreme outlier value in this example:
- 82 + 3 * 46 = 220
- 36 – 3 * 46 = -102
Read more: How to get sea pickles in minecraft For example, let’s say the largest value in our dataset is 221. Here is the box diagram for this dataset:
Asterisk
is an indication that there is a large outlier in the data. The number 15 indicates which observations in the data set are extreme outliers.
How to handle exception elementsIf there is an outlier in your data, you have several options:1. Make sure that the exception value is not the result of data entry errors.Sometimes an individual just needs to enter the wrong data value when recording data. If there is an exception value, first verify that the value was entered correctly and that it is not an error.2. Remove extraneous factors.If the value is a true outlier, you may choose to remove it if it has a significant impact on your overall analysis. Just be sure to mention in your final report or analysis that you have eliminated an outlier. 3.Assign a new value to the exception
If the outlier turns out to be a data entry error, you can decide to assign a new value to it, such as the mean or the mean of the data set.
Additional ResourcesIf you are working with multiple variables at the same time, you may want to use the Mahalanobis distance to detect different variables. Read more: how to get wirt’s diablo 3 chân
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