Triangulation is a key principle of qualitative data collection that involves collecting
data from multiple sources, sometimes using multiple tools, to identify and reduce
bias. If you do not triangulate qualitative data, you run the risk of biasing or
distorting the data collected, resulting in incorrect or incomplete information.
By
collecting data from multiple sources or with multiple tools, you can identify and
address discrepancies or inconsistencies in the data. Triangulation often leads to
additional questions or clarifications, which you can answer through follow-up
interviews, discussions or exercises.
A mistake common for M&E systems is to rely solely on either observation data or
participant responses. Observation data alone do not provide an explanation of practices or behaviors and often require large assumptions on the part of the M&E
team. Focus group data (an example of participant responses) may not capture
important practices that participants do not see as relevant and may record instead
what participants think data collection teams want to hear.
To triangulate qualitative data, first determine whether the methods selected will
provide sufficient data to allow for comparison and identification of any bias.
Include additional methods if you decide they are necessary for triangulation. Next,
determine whether you have included an adequate number of respondents or
groups to triangulate your data within each method. Triangulation relies largely on
data analysis and the ability of the data analysis team to identify unreliable data and
inconsistencies.
Focus group discussions often generate social norms and the data often do not
capture the true variation of opinions and values that exist in a community. For this
reason, it is advisable also to include key informant interviews or household surveys
to triangulate focus group data.
For evaluation and formal monitoring efforts, conduct two or three qualitative
exercises (e.g., discussions and interviews) to fully represent each perspective of
interest in the survey. Refer to Purposeful Sampling for site selection for evaluation
and formal monitoring. For informal monitoring, sampling procedures are less
rigorous. Staff should collect informal monitoring data during routine field visits
andneed to consider the types of communities and contexts represented (or not
represented) by the data and the potential for bias if no sampling procedure was
followed.
Be sure to include vulnerable or marginalized groups (households or individuals) in
your sample. If you are following the procedures for purposeful sampling, include
vulnerable or marginalized households (or individuals) as comparison groups. If you
are informally monitoring, seek out vulnerable or marginalized households for
discussions, interviews or direct observations.
Once you select your sites, inform the communities ahead of time so community
leaders and community members can plan to be available on the planned day and
time. There is a risk of bias in the data if you do not inform communities in advance.
For example, without warning, all adult members from poor households might be
away working in distant fields when the data collection team arrives, leaving the
team to collect data only from more wealthy households who rely on hired labor to
tend their land or whose lands liecloser to the village. Consult field staff and
community leaders to identify persons with desired characteristics to participate in
qualitative exercises
