If data quality is poor or data are missing for multiple domains, what steps should be taken?

• A. Flag as incomplete, seek alternative sources, arrange a follow-up assessment, and document steps taken; avoid assumptions
• B. Impute missing data using the average across domains
• C. Ignore the missing data and proceed with as-is ratings
• D. Create a composite score using only available domains

Answer: (A)

When data quality is poor or data are missing across multiple domains, the goal is to avoid making unsubstantiated judgments and to gather reliable information before rating. Flagging the data as incomplete signals that the current results aren’t trustworthy. Seeking alternative sources—such as additional records, other informants, or more objective measures—helps build a fuller, more accurate picture. Arranging a follow-up assessment gives you another chance to collect the missing information under better conditions or with more time. Documenting every step creates a clear trail so decisions can be understood and reviewed, and so you’re not making assumptions about what you don’t know.

Imputing missing data with an average across domains can introduce unwarranted assumptions and distort the overall rating, since the missing domain may differ in important ways. Ignoring the missing data and proceeding with as-is ratings relies on incomplete information and can bias conclusions. Creating a composite score from only the available domains omits the impact of missing data and can misrepresent the real needs being assessed. The recommended approach maintains accuracy and transparency by actively improving the data before finalizing judgments.