Creating a dummy variable to indicate a missing predictor value is a valid way to handle missing values

Question

BlackTom AI · Accepted Answer

Consider the statement: 'Creating a dummy variable to indicate a missing predictor value is a valid way to handle missing values.'

Option True: This approach is widely used as part of a broader missing-data strategy. A dummy indicator (for example, a binary column that is 1 when the predictor is missing and 0 otherwise) preserves information about the fact that a value was missing, which can be predictive in itself. In many modeling frameworks, especially linear models and tree-based methods, including such an indicator allows the model to learn whether missingness is associated with the outcome. It is often paired with imputation for the missing values themselves, so the model has both a filled-in estimate and a signal about missingness. This makes the approach a standard, valid technique in missing data handling.

Option False: If one focuses only on imputing or only on dropping cases, or if the missingness mechanism is complex (for example, not missing at random in a way that the missingness itself carries information that the model should learn from), relying solely on a single dummy indicator without appropriate imputation or model checks can lead to biased estimates or poor predictive performance. However, this does not inherently render the method invalid; it merely cautions that it should be used as part of a careful strategy rather than in isolation.

In summary, incorporating a missingness indicator is a recognized and valid component of handling missing predictor values, especially when used alongside other imputation or modeling techniques. The effectiveness depends on the data context and the modeling approach, but the core idea is sound and commonly recommended in practice.

QBUS6810 (ND) Week 3 Quiz

Creating a dummy variable to indicate a missing predictor value is a valid way to handle missing values

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