When I faced missing data while working on my NYAS project, I had to get creative to keep things moving. First, I looked at what I already had—sometimes, repurposing existing research or finding patterns in related studies helped fill in some gaps. I also reached out to mentors and experts through the NYAS network, which was a huge help in getting insights and alternative sources. In one case, when I couldn't access a specific dataset, I ran a small survey to collect preliminary information. Open-source databases and published studies also became lifesavers when I needed supporting data. If nothing else worked, I tried estimating trends based on similar research.

When my project stalls due to missing research data, I focus on three things: maximizing what I have, seeking external insights, and finding temporary alternatives. First, I assess whether existing data can be repurposed or used as a proxy. Sometimes, a different dataset can still provide valuable insights. Next, I reach out to industry peers or experts who might have similar data or useful perspectives. Lastly, I consider open-source datasets or even conducting a small-scale study to bridge the gap while I find a permanent solution. Adaptability is key.

When research data is missing, it is crucial to zoom out and ask yourself some questions. 1. State your hypothesis or your question: If you are looking for data it should be defined for what purpose you are looking it for. 2. Brainstorm on proxies to answer the hypothesis/question: Generally, there are various ways to address a question and derive inferences. Identify various ways to do this. 3. Define the paths that are blocked because of missing data: It is crucial to define things that cannot be answered in specificity due to the missing data. 4. Find alternate data sources and get going. If you are looking for truth, do not generate synthetic data. If you are proving under assumptions, simulate the assumptions to create data.

Firstly, we can make reference to our study protocol as what was planned to missing data. It is important to know how much data is missing. Determine if the data is missing due to incomplete questionaires, data entry or analysis stage. At questionaire stage we may need to revisit incomplete questionaires and enter the missing values. Data validation by the research supervisor would help to have all questionaires completed and certified. It is also important to understand how much data is missing. Data may miss at random due to typing error or not at random when a reserach participant decide not to answer. Avoid deleting at all cost as this may affect the sample size. Do a sensitivity test to determine the impact of the missing data.

A stalled project isn’t a dead end - it’s just a detour. When data is missing, I look for creative workarounds: repurposing existing insights, tapping into expert networks, or even running a quick internal study. Adaptability is key to keeping momentum. Ever faced a data roadblock? How did you push through? Let’s share strategies! 🚀

Acudir a las fuentes iniciales y determinar si para obtener nuevos datos es posible realizar otras encuestas, entrevistas o acudir a otras investigaciones y sus avances. Siempre hay una salida

To overcome missing research data, I will first assess the gaps and explore alternative data sources, such as government reports, industry databases, or similar studies. If feasible, I will conduct follow-up surveys or interviews to fill critical gaps. Additionally, I will refine my analysis to maximize existing data, use proxy variables where applicable, and collaborate with stakeholders to access relevant information, ensuring minimal disruption to the project timeline. N.B This is a real world scenario I recently encountered

To overcome the setback, identify alternative data sources or methods to fill the gaps, such as secondary research or expert consultations. Additionally, reassess the project timeline and adjust priorities to accommodate the delay while maintaining progress.

To overcome a stalled project due to missing research data, first, identify the specific gaps and assess their impact on the project. Look for alternative data sources, such as published research, industry reports, or government databases. If primary data is essential, consider reaching out to experts, conducting surveys, or leveraging partnerships to gather information. Additionally, explore data estimation techniques, simulations, or case studies to fill gaps. Maintain flexibility by adjusting the project scope if needed while ensuring the research remains valid and credible.