Our team is evaluating India’s National Rural Livelihoods Mission (NRLM)—one of the world’s largest poverty alleviation programs. The study spans nine states of the country: Uttar Pradesh, Bihar, Rajasthan, Chhattisgarh, Maharashtra, Madhya Pradesh, West Bengal, Jharkhand, and Odisha. The survey, recently completed, covered over 25,000 households and 7,000 institutions, focusing on socio-economic indicators, institution-level data, and women’s empowerment. Managing such a vast operation required more than just structure and strategy. It demanded constant troubleshooting, adaptability, and resilience. The pre-data collection stage—where tools are tested and teams are trained—is the most crucial and determines the success of the subsequent legs of the survey.
Building a strong digital backbone
Integrating logic and validation checks
CAPI (Computer-Assisted Personal Interviewing) tools streamline large-scale data collection by automating questionnaire flows and reducing human errors. We worked to ensure this system incorporated proper logic and validation checks to work effectively. In early testing, we discovered issues where specific skip patterns were misaligned, causing confusion among enumerators. We revisited our coding and refined these patterns, ensuring the tool guided interviewers correctly without skipping essential sections.
Additionally, real-time validation checks, such as rejecting illogical responses, helped maintain data accuracy.
Testing and troubleshooting
Given the survey scale and the various modules, thoroughly testing the CAPI system was one of our most significant learning experiences. Initially, a few logic failures and auto-fill errors surfaced, which had the potential to derail interviews. We identified and corrected these issues through extensive testing (in-house and in-field simulations) before launching full-scale data collection.
Audio audits, integrated into CAPI, also helped us monitor interview quality. However, excessive audio file sizes slowed down data uploads. To address this, we adjusted the frequency of recordings based on the data collection stage, optimizing server performance without compromising quality control of the key indicators.
Mitigating enumerator shortcuts
One of the challenges we anticipated was enumerators skipping lengthy sections. An example from our survey was the agriculture section in the household module, which covered multiple aspects of crop production, expenditure, and labor costs. Instead of presenting it as a single block of questions, we restructured it into smaller, more manageable segments. This not only improved data flow but also ensured respondents remained engaged.
Strengthening the field team
Comprehensive training for trainers and enumerators: Survey training was critical to ensure enumerators understood the content and technical aspects. We trained over 30 trainers, leading sessions for over 500 enumerators across multiple states. We realized that theoretical training alone was insufficient. Some enumerators struggled to navigate CAPI efficiently, affecting their speed and accuracy. To address this, we integrated real-world case studies, visual aids, and interactive discussions into training sessions. This helped break down complex sections, making them more digestible for field staff.
Testing for readiness and retention: We conducted assessments at multiple levels. Trainers were assessed before leading their teams, ensuring they were fully prepared. Enumerators underwent daily quizzes and an exit test, allowing us to identify those needing additional support. We gave immediate feedback to the enumerators instead of waiting until the end of training to address knowledge gaps. We conducted same-day test checks and provided feedback the following morning. This helped enumerators correct mistakes quickly and improved their overall retention of concepts.
Field practice and dummy data checks: To bridge the gap between theory and real-world execution, we introduced a full day of field practice, followed by immediate dummy data checks to assess the quality of the dummy data. During one of these exercises, enumerators consistently misinterpreted specific questions. This helped us revise and rephrase questions for better clarity. Additionally, field practice allowed supervisors to monitor enumerator performance in real time, reinforcing proper survey protocols before data collection began.
Feedback mechanisms
Feedback from trainers and enumerators was crucial in refining our tools and processes. During training, enumerators flagged specific questions as confusing or redundant. This feedback helped us make last-minute adjustments, improving question clarity and flow. Our survey identified specific questions that were frequently misunderstood or skipped. Feedback from enumerators and trainers brought these issues to our attention, prompting a review and revision of those questions—addressing confusion and improving the overall data quality.
Ensuring reliable and impactful data
Pre-data collection preparation is imperative for any successful large-scale survey. From refining CAPI systems to training field teams and implementing structured feedback loops, every step is vital in ensuring data quality.
Our experience taught us that challenges will arise no matter how well a survey is planned. However, large-scale data collection can be executed efficiently and effectively by fostering a solution-oriented approach, where problems are identified, addressed, and iterated in real time. Large-scale survey teams can navigate complexities more smoothly when pre-data collection preparation is robust. This leads to reliable and impactful data that informs policy and decision-making.
More information about 3ie's role in evaluating the NRLM program can be found here.
