How do you deal with conflicting or contradictory findings from mixed methods data analysis in M&E?

In my opinion, dealing with conflicting findings in mixed methods data analysis in Monitoring and Evaluation involves recognizing the value of diverse insights, employing triangulation, reviewing research methods, exploring contextual factors, seeking expert opinions, integrating findings into a cohesive narrative, and revisiting research questions. Additionally, considering practical implications, maintaining transparency, and adopting an iterative approach is crucial for resolving discrepancies and ensuring the credibility of M&E results. Stakeholder engagement is also important for gaining valuable perspectives on the implications of conflicting findings.

To address conflicting findings, it's crucial to define the purpose and scope of the mixed methods design clearly. Identify the overarching objectives and research questions driving the study. Evaluate how each method contributes to addressing these questions and understand their strengths and limitations. By establishing a comprehensive framework, you can systematically analyze and reconcile conflicting results, ensuring a nuanced interpretation that captures the complexity of the phenomenon under investigation.

Dealing with conflicting or contradictory findings from mixed methods data analysis in Monitoring and Evaluation (M&E) involves a systematic approach to understanding the reasons behind the discrepancies and deriving meaningful insights. Identify areas where findings from different methods converge (agree) and where they diverge (disagree). Convergent findings can strengthen your conclusions, while divergent findings can indicate areas needing further investigation or clarification.

This is crucial for gaining a comprehensive understanding of a topic. Both sources (where data comes from) and methods (how data is collected and analyzed) play essential roles. They can complement each other, enhance validity, provide depth and breadth of insights, and offer different data types. However, they differ in terms of data type, research process, objectivity, sampling, and data presentation. Effective research involves utilizing both sources and methods strategically to address research questions thoroughly.

In M&E, comparing and contrasting findings from diverse sources and methods is crucial. By identifying similarities, differences, and patterns, we gain a comprehensive understanding of the phenomenon. Techniques like triangulation help validate results, while visual tools aid in presenting comparisons effectively. This process fosters robust insights and recommendations, enhancing decision-making in real-world scenarios.

Review the assumptions and limitations of each method used in the study. Understand how these assumptions might contribute to differences in findings. For example, quantitative data might provide general trends while qualitative data could offer nuanced insights that diverge in specific contexts.

exploring reasons for discrepancies is pivotal. It entails delving into data quality, methodological limitations, and contextual factors to unearth insights. By considering biases, perspectives, and broader contexts, we gain a nuanced understanding. Employing supplementary methods like interviews or case studies bolsters validation. This process ensures robust and actionable findings in complex real-world scenarios.

I see this requires a comprehensive approach. It involves assessing data quality, examining assumptions and limitations, addressing perspectives and biases, contextualizing the data, and using additional data collection or analysis methods. By thoroughly investigating these factors, researchers can gain insights into why discrepancies exist and work towards more robust and reliable conclusions. During the previous years, experts suggested many practices that would be beneficial for professional practitioners, such as developing clear manuals for DQCs.

Consult with stakeholders involved in the M&E process to gather their perspectives on the conflicting findings. Stakeholders may provide insights into why certain results emerged and help reconcile discrepancies.

Acknowledging and reporting discrepancies in research, particularly in mixed methods data analysis, is vital for research integrity and credibility. To do this effectively, researchers should honestly and transparently describe the nature of discrepancies, identify potential causes, attribute them to specific data sources and methods, qualify findings in light of the discrepancies, discuss their implications, and recommend further research if necessary. Communicating these discrepancies with stakeholders and reflecting on the researcher's role is also important. Transparently addressing discrepancies enhances the credibility of the research and its relevance in decision-making and policy formulation.

Acknowledging and reporting discrepancies fosters credibility and trust. Utilizing diverse formats and techniques to present findings ensures clarity and comprehension. By elucidating how discrepancies were addressed, stakeholders gain confidence in the reliability and utility of the insights. This approach ensures that M&E outcomes remain robust and actionable, enhancing decision-making in real-world contexts.

Clearly document all findings, including any conflicting or contradictory results. Provide a transparent account of how these findings were reconciled or what steps were taken to address discrepancies.

To achieve this, you need a reflective, iterative, and adaptive approach. Researchers should actively seek to understand the causes of discrepancies, take corrective actions, and use the experience to enhance the quality and rigor of their research. This process ultimately contributes to the advancement of knowledge and the effectiveness of research in addressing complex issues, such as those encountered in Monitoring and Evaluation.

Through reflection and engagement with stakeholders, insights gleaned from discrepancies can inform actionable changes. Utilizing feedback mechanisms and collaborative approaches fosters a culture of continuous learning and adaptation. By implementing recommendations and monitoring outcomes, M&E strategies evolve to better serve the needs of beneficiaries and enhance program effectiveness.

When navigating conflicting or contradictory findings from mixed methods data analysis in M&E, it's crucial to maintain a balanced approach. Firstly, remember the importance of triangulation - integrating findings from different sources to gain a comprehensive understanding. Secondly, stay open-minded and consider alternative interpretations of the data. Lastly, prioritize transparency and document your decision-making process to ensure accountability and facilitate future analysis. By embracing these principles, one can effectively navigate the complexities of mixed methods analysis in M&E with confidence and clarity.

Consider alternative interpretations or hypotheses that could explain the conflicting findings. Brainstorm potential reasons for the discrepancies and assess their plausibility based on available evidence.