Designing your study: Linking gender and nutrition through qual and quant

methods

Agnes R. Quisumbing

IFPRI/A4NH

Presentation at the A4NH Methods Gender-Nutrition Methods Workshop, December 6-7, 2013, Nairobi

Overview

• There are many research methods to use for linking gender, agriculture, and nutrition

• Need to remember the right tool for the right question, but most importantly, need to have an appropriate study design

• An ideal study design to understand gender, agriculture, health, nutrition linkages must be able to link and integrate: – Qualitative and quantitative methods– Social science and nutrition data

Why should the design take linkages into account from the start?

Qual and quant methods

• Demonstrated gains from various research programs from using mixed methods work (CPRC, CAPRi, IFPRI intrahousehold, GAAP, etc.)

• Quant work allows you to measure impacts, qual work enables you to understand why

• Both are important

Social science and nutrition

• Social norms underlying decisionmaking processes often determine the allocation of resources toward health and nutrition

• Among these are norms and practices surrounding gender

What to take into account in designing your studyQual and quant

• Integrated and iterative qual and quant

• Qual and quant researchers work together to define research questions, analyze and interpret results

• Qual study as diagnostic, help frame the questions

• Use quant sample to define “frame” of qual study—then can link qual study responses to quant data

• Quant sample typically uses larger n, qual study can use small to medium n, but be purposive

Social sciences and nutrition

• Have a “theory of behavior” that links behavior to nutrition outcomes

• Collect data on determinants (gender, resources, etc.) and on outcomes (nutrition, health, education, etc.)

• Collect data on the same individuals (same households) for whom you are collecting nutrition outcomes

Food-Based Approaches to Reducing Micronutrient Malnutrition

Intervention: Dissemination of improved agricultural technologies (vegetables and fish)

Nutrition Objective: Improve micronutrient status of producer population

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Evaluating the long-term impact of agricultural technologies in Bangladesh

Panel data set based on 957 households surveyed in 1996/7 and 2006/7

3 technologies/implementation modalities:

1. improved vegetables for homestead production, disseminated through women’s groups (Saturia)

2. fishpond technology through women’s groups (Jessore)

3. fish pond technology targeted to individuals (Mymensingh)

Compare “early adopters” to “late adopters”

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Survey Design in 1996/7

IN EACH SITE Type of NGO village

HH type

“A” technology

had been

introduced

“B” technology

had not yet been

introduced

NGO member

adopters

A (n=110/site)

NGO member,

likely adopters

B (n=110/site)

Non-NGO members,

general population

C1 (n=55/site) C2 (n=55/site)

-4 round panel 1996/1997

-Coverage of 3 major agric. seasons

-3 sites, 47 villages, 955 HHs

What has happened after 10 years?

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Data collection efforts over the years• 1996-97: 4-round quantitative household survey

• Qualitative work on gender conducted between rounds 3 and 4 (Naved 2000)

• 2001: Qualitative work and further quantitative analysis to look at impact of new technologies on poverty, empowerment, vulnerability in 2000 (Hallman, Lewis, Begum 2007)

• 2006-2007: Qual-quant chronic poverty study– Focus groups (25% of sample villages)

– Quant household survey (all respondents and new households formed from original)

– Life histories (based on poverty transition category; poverty status computed from quant survey)

• 2010: Follow up on impacts of food price crisis

Information collected at household and individual levels in 1996-97 and 2006-7 rounds

Household

• Per capita expenditures (food, nonfood consumption)

• Household assets and landholdings

• Household income, by source

• Detailed production module

Individual

• HH roster information (age, sex, education, relationship to hh head)

• Schooling, labor and employment

• Land and assets

• Individual food consumption, 24-hour recall (and then converted to nutrient equivalents), all individuals

• Hemoglobin (via Hemocue), all children and women up to age 65

• Height, weight for all hh members

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Note that dietary diversity modules were relatively “new” at the time of the baseline survey, but DD scores can be calculated from the food consumption module at household or Individual levels