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I am doing an online study and have just started looking at the data. I noticed two of my participants have listed ages that they couldn't possibly be (e.g 450 and 220).

I'm wondering what the appropriate way to handle this is?

Age isn't the main variable for the study so should I use imputation so I don't lose two data points? Or should I treat it as a missing data point and use little MCARs test to determine if listwise deletion is appropriate?

mkt
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Riss
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    I'd argue that if you can afford treating it as missing values, then do so. If you can't you can check imputation options, but make sure to clearly state that you did impute these values – deemel Sep 30 '19 at 08:56

1 Answers1

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Since these are clear errors, I think you have a good case for ignoring the existing values and using multiple imputation to fill them back in.

If there was uncertainty (say you found an age of 110 - possible but unlikely in most datasets), this would be a tougher decision.

mkt
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