Development research in Bangladesh increasingly recognizes that neither numbers nor narratives alone tell the complete story. Combining quantitative surveys with qualitative inquiry—mixed methods research—provides richer insights for policymakers.

When Numbers Fall Short

Consider studying the adoption of salt-tolerant rice varieties in coastal Bangladesh. A household survey might show that only 30% of farmers have adopted BRRI dhan47 despite its availability. But the survey alone cannot explain why. Is it seed availability? Cost? Taste preferences? Distrust of new varieties?

Qualitative interviews reveal that farmers in Satkhira worry about lower market prices for salt-tolerant varieties, while others lack access to irrigation water needed for optimal yields. These insights reshape how we interpret the quantitative findings.

Designing Mixed Methods Research

There are several integration strategies:

Sequential Explanatory: Conduct a survey first, then use qualitative methods to explain unexpected findings. If data shows low adoption of government programs, follow-up interviews can uncover barriers.

Sequential Exploratory: Start with qualitative work to understand the context, then design a survey informed by those findings. Focus group discussions can identify the right questions to ask.

Concurrent: Collect both types of data simultaneously. This works well when you have separate teams for each component.

Practical Considerations

Mixed methods requires more resources and skills. Some practical tips:

  • Plan integration from the start, not as an afterthought
  • Ensure your team has expertise in both approaches
  • Budget adequate time for qualitative analysis
  • Consider software like NVivo for systematic coding

Example: Qualitative Coding in Practice

# Simple thematic coding approach
import pandas as pd

# Load coded interview data  
interviews = pd.read_csv("coded_interviews.csv")

# Count theme frequencies
theme_counts = interviews['theme'].value_counts()
print(theme_counts)

# Cross-tabulate themes by respondent type
pd.crosstab(interviews['respondent_type'], interviews['theme'])

The most impactful development research in Bangladesh—whether on poverty, health, or climate—often draws on both quantitative rigor and qualitative depth.