The Treatment of Missing Values
(ITA)
In statistics, missing values ​​are not all the same. With Statistics for Data Analysis powered by IBM SPSS, we learned in this webinar on December 3, 2025, how to distinguish what can be ignored from what will change the conclusions of your analysis.
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A practical webinar to stop treating missing values ​​as a technical nuisance and start seeing them for what they are: hidden information about your phenomena.
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Starting with a very simple application case, we demonstrated how to perform an analysis on available data using specific algorithms tested by the scientific community to deduce the missing value.
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🔴 The reasons why some values ​​are missing can be ignorable or non-ignorable: together, we saw how to recognize the difference and what to do in each case.
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🔴 If the mechanism generating the missing values ​​is ignorable with respect to the phenomenon you are studying, their presence is neutral for your conclusions (we examined tests, logic, and practical criteria to verify this).
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🔴 If they're not ignorable, missing values ​​aren't neutral, but an integral part of the phenomenon you want to understand, and must be included in the parameterization of your model.
We've shared concrete examples and advanced management strategies.
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