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Lessons from implementing a field survey in India

Soumya Gupta is a PhD candidate at Cornell’s Dyson School and a research scholar with the Tata-Cornell Agriculture and Nutrition Initiative (TCi).  This blog first appeared on the Economics That Really Matters blog (www.econthatmatters.com).

My dissertation research (described in greater detail here) focuses on linkages between agriculture and health. I look at how different farming systems influence women’s iron status and empowerment in agriculture. To answer these questions I spent three semesters in the Chandrapur district of Maharashtra
in India collecting primary data on agriculture, food security, dietary
intake, women’s empowerment, anthropometry, and blood samples to assess
iron status. We surveyed a total of 1,920 individuals. Here are some
things I learnt along the way.

Invest time in the details up front

I am struck by what an enormous effort it took to lay the groundwork
at every step of the way.  I’ve detailed a few of those steps below.

Formative research: In this phase of preliminary research we
modified questions and response options to suit the local context and
gathered information on local land use and agricultural practices. Some
of the things we had to decide were:

  • How many households/villages to visit and their selection criteria.
  • Which methods to use, whether qualitative, quantitative, or mixed.

Household selection: The road to a random sample of
households was paved with the pains of generating lists of households
that met our inclusion/exclusion criteria. This entailed identifying:

  • What lists of households are available, from which sources and in what format (hard vs. soft copy).
  • How multiple lists can best be triangulated to suit the selection criteria.
  • How to get a copy of these lists, whether by photocopying, taking
    pictures, using a flash-drive (beware of viruses), or having it emailed.

We took hundreds of pictures of the lists obtained from local
schools, health centers, property and water tax office, and the
agriculture office. After transcribing them from Marathi to English we
eventually generated a final list of households per village that met our
criteria.

Team: We decided on the size and composition of our team
based on sample size of the survey, time anticipated to survey one
household, budget, and average daily travel time. To choose team members
we administered written and computer tests, and an interview, to assess
basic understanding of research methods, level of computer proficiency
of each applicant, and previous field experience, respectively. A
waitlist by designation and gender allowed for a relatively quick
replacement of staff later on. The training
schedule was designed to systematically transition from a traditional
paper-based to a tablet-based interviewing method, and from in-class to
field-based sessions. It also allowed us to test the survey software for
any inconsistencies in questions, response options, and skip-codes.

image

(Checking each batch of blood samples after they are centrifuged in the field. Photo by Soumya Gupta.)

Keep good field notes/documentation

Since my return from India, I have had to return to my field notes
several times, not just for details about the various steps of my survey
design, but also when trying to interpret some of my results. My notes
from the phase of formative research in particular have brought back
contextual references that I otherwise would not have been able to
recall.

From an administrative/accounting point of view, the regular cycle of
funding required us to be obsessive about expenditure receipts, service
contracts, and so on. Incidentally, it’s a good idea to discuss
acceptable alternatives to receipts with your funding source since many
local services (such as photocopying, stationery, and local transport)
often do not provide receipts.

Collaboration is a balancing act

Collaboration is hard—and can be tricky—at many levels, including research design, logistics, and cultural context.

Research design: I was collaborating with nutritionists and
medical doctors while developing the research design for this study. On
the one hand, interacting with these experts forced me to broaden my
understanding of certain methodological approaches. On the other hand,
this collaboration made me repeatedly return to the different
motivations that can influence the scope of a survey. For instance, a
highly detailed questionnaire or information on multiple iron assays
would provide us with a lot of data from a research point of view.
However do we really need all that information and would people actually give us all that information?
The challenge is to figure out how and when to incorporate which
inputs, and say no to the rest, without offending anyone.  In the end a
frank – and respectful – discussion about the research goals and
constraints of time, budget, physical and human resources, together with
support from my dissertation committee, helped us agree on a middle
path.

Logistics: Approaching, discussing, negotiating, and
following-up with service providers of various kinds (electronics,
computer software, pathology laboratory) were, for me, the biggest
source of frustration. Once a contract was signed, the providers would
send in receipts for part of the payment at regular intervals – without
delivering the product or service. I realized, eventually, that all it
took was one email saying ‘your receipt has not been approved’ for
everything to suddenly work like clockwork. She who controls the money
has the power.

Cultural context: In the Indian context two other factors
complicate matters further – gender and hierarchy. When you’re female
and most of your collaborators are men who are older than you (in age or
designation, or both), you will encounter cultural nuances that are
quite challenging. In addition, the social hierarchy determines, quite
often, the pace and/or outcome of various decisions. It helped me to
communicate directly with the higher-ups or make it known that the main
higher-up in our survey was me.

image

(A male enumerator plays along with a little boy as his mother is interviewed for the survey. Photo by Soumya Gupta.)

It’s all about the team

Your dissertation research is like your baby. What makes (or breaks)
the survey is having a key number of people on the team for whom your dissertation research is like their baby
too. My team’s project coordinator and two supervisors were crucial in
maintaining the quality of the survey in ways that went beyond the daily
monitoring of enumerators. They would resist replacement of households
to please the village headman, subtly distract people who were
influencing responses, and in general handle the power dynamics (and
egos) in the villages with patience and tact.

Having 20 local enumerators also made the survey team appear less
like a team of ‘outsiders’ from the respondents’ point of view. Several
of them had worked on surveys previously, and were able to offer insight
during the training as to what sort of questions we might face in the
field and to come up with strategies, as a team, to answer them. The
benefits of this teamwork are best seen in the blood study where we
ended up with a non-response rate of less than 1.5%.

Of course no team can be perfect. So don’t be afraid to replace (read ‘fire’) people.

To conclude, we had our share of flat tires, scorpion bites, extreme
heat, and broken equipment. But we also met exceptionally kind people
who shared their homes, time, and lives. Selfish as field research
sometimes felt, it was an incredibly humbling experience too. Organized
planning, quality control, and building working relationships with
collaborators, enumerators, and community members were key to my
successful survey.