How do you handle and resolve data conflicts and discrepancies in your program evaluation?

Data is only data. Objective, impersonal information collected for the purpose of evaluating a phenomena. Its limitations are often ignored. For example, a complex set of symptoms reduced to a single diagnosis carries high risk of not only error, but misunderstanding without deconstruction of the rationale used.

In program evaluation, handling data conflicts involves a systematic approach. For instance, if there are discrepancies in participant counts between different data sources, I would conduct a detailed reconciliation. This might involve reviewing enrollment records, cross-referencing with attendance logs, and engaging with program staff to identify and resolve discrepancies. Let’s say an education program reports 100 participants, but attendance records only show 80. I would investigate by checking sign-in sheets, communication records, and potentially conducting interviews with program administrators. By aligning these different data sources, I can identify and rectify the discrepancy, ensuring the accuracy of the evaluation process.

Using data analysis tool(s) to determine answer instead of human intervention, so stake holder don't feel personally attact.

Identify the Sources and Types of Data Conflicts and Discrepancies: Source Identification: Use data mapping and lineage to trace data origin and identify all contributing sources. Employ advanced data profiling tools for understanding source characteristics and quality. Types of Conflicts: Categorize conflicts (structural, semantic, temporal) and implement data quality checks. Assess the Impact and Severity of Data Conflicts and Discrepancies: Quantitative Analysis: Develop metrics for quantifying impact and severity, considering factors like volume and criticality. Business Impact Assessment: Collaborate with stakeholders to understand real-world consequences of decision-making, risk matrix, etc

It is always important to start any process from an informed standpoint. Consequently, a fact-finding mission is the recommended first step. A data audit is one of the best approach. It allows for data quality assessment.

Prioritize discrepancies that have the greatest impact. If there is no real effect, a data discrepancy may be annoying but not worth chasing.

This assessment allows you to prioritize what conflicts needs to be addressed due to the time-implication of evaluations. Some discrepancies may not have significant effect on the overall evaluation results and can be explained as a limitation in the evaluation report.

Like I stated in the previous point, data conflicts can become a major obstacle to resource utilization and available time. So it is important to only deal with what is important.

To prioritize and address data conflicts in program evaluation, start by identifying the root cause. Then, establish clear criteria for resolution, involving stakeholders for input. Utilize data validation techniques and tools, and prioritize high-impact discrepancies first. Transparent communication and documented procedures are key. Regular review and adjustment ensure continuous improvement.

Addressing data conflicts in program evaluation involves a systematic approach. For renewable energy projects, cross-validate data sources in subsea wind turbine initiatives. In grid code compliance studies, reconcile conflicting data through collaboration with regulatory bodies. In manufacturing, implement data reconciliation protocols to ensure accuracy. Utilize consistent validation methods, engage stakeholders, and leverage technology for streamlined conflict resolution.

Asking the question, "What is missing?" may add a needed skepticism to conclusions that are far too clean. Evidence-based meta-analyses are reductionist by design. They were developed to offer a short-cut to understanding a single phenomenon. There is always something missing that may be the very issue your own patient is struggling with.