research methodology

1. Research Design
Training of Trainers:
Module 2 Methodology
Webex, May 2020

2. Research Design
Training of Trainers:
Module 2.1 Methodology
design (Research methods)
Webex, May 2020

3. Session
Contents
1. Overview of research methods
2. Distinction between quantitative
& qualitative research
3. Types & applicability of different
research methods
4. Q&A

4. 1. Research
method
overview

5. Research methodology
The methodology is an outline of the overall data collection and analysis strategy that
will be used to implement the research cycle
The methodology should:
 Be compatible with the preliminary data analysis plan
 Be designed in a way that ensures the intended scope of the research (i.e. objectives and
research questions) can be feasibly achieved to the required quality, given the time,
resources and access available
Designing a methodology involves three key components:
 Selecting the overall research method
 Selecting the appropriate data collection approach(es)
 Designing the sampling strategy
Our focus for today! 

6. Categories of research methods
Research methods are broadly distinguished between the following categories:
Quantitative
 Measure prevalence of
issues, verify hypotheses
and establish causal
relations between
variables
 Large samples,
structured data collection,
and predominantly
deductive analysis
Qualitative
 Explore and discover
themes, develop
theories, rather than verify
hypotheses and measure
occurrences
 Smaller samples, semi-
structured data collection,
inductive analysis
Mixed Methods
 Combines both
qualitative and
quantitative to (1) collect
and analyse both types of
data and (2) use both
approaches in tandem

7. Deductive (quantitative) vs. inductive (qualitative) analysis approach

8. Selecting your research method
What factors to consider when choosing one research method over another?
 Overall applicability to meet research objectives
 Time i.e. key planning and decision-making milestones to inform
 Resources available
 Material resources
 Financial resources
 Human resources
 Access to population of interest

9. 2. Quantitative
vs. Qualitative
research

10. Differences between quantitative & qualitative research
The distinction between quantitative and qualitative research is not always as clear-cut:
 Individual and household surveys
o Commonly associated with quantitative, large sample research
o Could also be used for a qualitative case study
 Key Informant interviews and community discussions
o Commonly associated with qualitative, semi-structured data collection & analysis
o Could also be used for quantitative data collection & analysis where time and resources do not
allow a large, representative sample
 Focus Group Discussions
o Perhaps the most distinctly qualitative research method, always administered using a semi-
structured data collection tool
o Often analysed using content analysis i.e. a somewhat quantitative approach counting the
number of times a theme or set of words appear with the discussion transcripts
o This content analysis can then inform the more in-depth qualitative analysis.

11. Differences between quantitative & qualitative research
Distinction between the two can be made based on the following three criteria:
Quantitative Qualitative
1. Type of data collection Structured, close-ended data
collection tools
Semi-structured (but not
unstructured) data collection tools
2. Type of analysis Measuring prevalence,
quantifying issues, and
primarily involves deductive
analysis
Exploratory, and primarily involves
inductive analysis
3. Type of sampling strategy Can use both probability or
non-probability sampling 
generalisation to the wider
population possible
Non-probability sampling 
generalisation to the wider population
not possible

12. 3. Types &
applicability
of different
research
methods

13. Types of research methods (1)
Category Type of research
methods
Description When to use this method
Quantitative Structured, probability
sampling/ census
Structured, close-ended data
collection;
Quantitative analysis;
Data collected from a census or
through large samples, with
sample size calculated based on
probability theory
To measure prevalence and make
generalizable claims,
To conduct deductive analysis
(relationship tests, verify hypothesis)
To identify key factors that influence a
particular outcome or understand the best
predictors of a specific outcome
Quantitative Structured, non-
probability sampling
Structured, close-ended data
collection;
Quantitative analysis;
Can be small or large sizes; non-
probability sampling
To measure prevalence (indicative only)
but contextual and/ or logistical
constraints do not allow for large,
repressentative samples
To draw indicative inferences from a
sample to a population

14. Types of research methods (2)
Category Type of research
methods
Description When to use this method
Qualitative Semi-structured, non-
probability sampling
Semi-structured data collection;
Qualitative analysis;
Relatively small sample sizes;
non-probability sampling
No measurement of prevalence or
verification of hypothesis needed;
No or limited prior understanding of the
situation to be studied and the specific
variables to be assessed;
To conduct inductive analysis i.e.
explore and develop a theory or pattern
of meaning, based on experiences,
observations and perspectives of the
situation being studied
Mixed
Methods
N/A Combines both qualitative and
quantitative methods, both in
terms of collecting and analyzing
both types of data but also using
both in tandem to enhance the
overall strength of the study
Quantitative or qualitative methods by
themselves inadequate to understand
the research problem;
To use all methods possible to obtain an
in-depth, comprehensive understanding
of the research problem.

15. The most powerful research method?
 Mixed methods research – if time, access, resources allow!
 Common misnomer that quantitative research is the strongest – not always!
 Not all issues need to explained in a quantifiable way
 Some issues are over-simplified if only explored in numeric terms
 In-depth explanation and contextualisation is useful
 Ultimately depends on the research objectives

16. Questions?

17. Research Design
Training of Trainers:
Module 2.2 Methodology design
(Data collection approaches)
Webex, May 2020

18. Session
Contents
1. Unit of measurement
2. Types of data collection approaches
(structured)
3. Types of data collection approaches (semi-
structured)
4. Types of data collection approaches (mixed
methods)
5. Frequently Asked Questions (FAQs)
6. Overview of remote data collection
7. Q&A
8. Task for the week

19. Unit of
measurement

20. What is it?
 The unit that will be used to record,
measure and analyse observations/
information collected
 Examples?
 Individual
 Family
 Household
 Community/ group
 Town/ village
 Facility
 Cow

21. Remember…
 Unit will impact the time, resources needed to collect and analyse information
 Unit will define the depth of information possible and scope of analysis
Depth
of
information
Location level
Household level
Individual level
Community/Group level
Time / Cost / Access

22. Data
collection
approaches:
Structured

23. 1. The structured survey approach
 Information collected through an interview, a discussion, a conversation
 Using structured, close-ended data collection tools
 Collection of quantifiable information
 Cross-sectional or longitudinal
Types of data collection methods?
Household (HH) survey – collecting data at HH level, to understand experiences and characteristics of HHs within population of interest
Individual survey – collecting data at individual level, to help understand situation and characteristics of individuals within the population of
interest  can include some HH level indicators if needed
Key informant interview – collecting data at community. location or group level from a key informant (KIs) i.e. an individual whose informal/
formal position gives him specific knowledge about other people, processes, or events that is more extensive, detailed, or privileged than
other individuals in their group/ community/ location
Group discussion – collecting data at community, location or group level from a group of representatives e.g. KIs

24. 1. The structured survey approach- Applicability
When should you use this approach?
 To measure prevalence  provide a quantifiable, numeric description of
the trends, behaviours, experiences, attitudes or opinions of a population
 To generalize findings to a wider population  probability sample 
statistically representative information
 Need prevalence data, understanding of scale of crisis but probability
sampling not possible  non-probability sample  indicative information
Types of research cycles this approach is commonly used for?
Multi-sector needs assessments
In-depth thematic needs assessments e.g. WASH Cluster needs assessment
Longitudinal studies
Third party monitoring (impact evaluation, outcome monitoring, post-
distribution monitoring, etc.)

25. 2. The structured experimental approach
What is it?
 Similar to survey approach
 But relies on experimental survey design 
control vs. treatment group
Types of data collection methods?
Household (HH) survey – collecting data at HH level, to understand experiences and
characteristics of HHs within population of interest
Individual survey – collecting data at individual level, to help understand situation and
characteristics of individuals within the population of interest  can include some HH level
indicators if needed

26. 2. The structured experimental approach - Applicability
When should you use this approach?
To measure prevalence and evaluate the outcomes or impact of a medium to large-scale
intervention on the population of interest
 Generalize findings to a wider population  probability sample  statistically
representative information
Types of research cycles this approach is commonly used for?
 Outcome monitoring
 Impact evaluations
 Etc.

27. 3. The structured observation approach (Description)
What is it?
 Information collected through observation rather than
conversation
 Using structured, close-ended checklists to collect
quantifiable information
 Looking for specific object, behaviour or event against a
checklist e.g. Household using soap? Damage to health
center? Students participating in classroom?
 Can be used as part of experimental approach
Types of data collection methods?
 Participant observation – researcher participates in
context (e.g. anthropologists)
 Direct observation – researchers observes context (e.g.
psychologists or clinical research)


28. 3. The structured observation approach - Applicability
When should you use this approach?
 Serves similar purpose as survey approach
 Depends on research objectives  observation vs.
conversation?
Types of research cycles this approach is commonly used for?
Could be same as survey approach
Could be same as experimental approach

29. Data
collection
approaches:
Semi-
structured

30. 4. The semi-structured discussion approach
What is it?
 Information collected through detailed, narrative interviews, group discussions
 Using semi-structured (NOT UNSTRUCTURED) data collection tools 
open-ended questions, probes
 Purposefully selected participants
Types of data collection methods?
Individual interview – collecting data at individual level, to help understand situation and characteristics of individuals within the population
of interest  can include some HH level indicators if needed
Key informant interview – collecting data at community. location or group level from a key informant (KIs) i.e. an individual whose informal/
formal position gives him specific knowledge about other people, processes, or events that is more extensive, detailed, or privileged than
other individuals in their group/ community/ location
Group discussion – collecting data at community, location or group level from a group of representatives e.g. Kis
Focus group discussion – bringing together people from similar backgrounds or experiences to discuss a specific topic of interest; data
collected at community, location or group level

31. 4. The semi-structured discussion approach - Applicability
When should you use this approach?
 To gather detailed insights about the
experiences, perspectives of specific population
group or location
 To provide a qualitative description of the
experiences, trends, attitudes or opinions of a
population
Types of research cycles this approach is
commonly used for?
 In-depth assessments where there is limited
prior understanding of a situation e.g. access to
cash among refugees & migrants in Libya
 Participatory mapping exercises (mapping FGDs
or KI interviews)

32. ‘Most Significant Change’ data collection technique
 A very specific type of participatory,
discussion-based data collection
method used for monitoring &
evaluation
 Invites participants (through KI
interviews, individual interviews or
FGDs) to explain the most significant
changes brought about in their lives
by a project over a given period of time,
in key domains of change
 Useful for third party monitoring or
impact evaluation research cycles

33. ‘Most Significant Change’ data collection technique
The stories, anecdotes you collect from beneficiaries/ project partners,
broken down by «domain» of interest
The stories, anecdotes you select to qualitatively analyse change per
«domain», in consultation with project team

34. 5. The semi-structured observation approach
 Similar to structured observation approach
 But two key differences:
Structured observation Semi-structured observation
1. Differences in data
collection methods
Information collected using a
structured set of questions,
usually to identify specific object,
behaviour or event against a
checklist
Information collected based on a
short set of open-ended
questions for observations e.g.
movement patterns of refugees
in and out of camps during a
sustained period of time
2. Differences in purpose Provide a quantifiable, numeric
description of the trends,
behaviours, experiences, etc. of
a population
Gather detailed insights about
the behaviours, experiences of
a specific population group or
location, and to understand, by
observation, how things are
done and what issues exist

35. Data
collection
approaches:
Mixed
Methods

36. 6. Sequential mixed methods data collection
 Method used to sequentially elaborate or expand on the findings of one type of
research method with another
• Identify coping
strategies
Qualitative
• Measure
prevalence of
identified coping
strategies
Quantitative
1. Exploratory sequential approach
• Measure
prevalence of
known coping
strategies
Quantitative
• Understand and
contextualize
observed trends
in prevalence
Qualitative
2. Explanatory sequential approach
• Identify coping
strategies
Qualitative
• Measure
prevalence of
identified coping
strategies
Quantitative
• Understand and
contextualize
observed trends
in prevalence
Qualitative
3. The “ideal” sequential approach

37. 7. Concurrent mixed methods approach
 Method used to merge or converge the findings from different research methods collected at the
same time
 Alternative to sequential approach if time constraints  sequential better practice if time and
resources allow
 Concurrent mixed methods serves two key purposes:

Triangulation strategy
Convergence
of information
collected?
Divergence of
information
collected?
Embedded strategy
Primary
method:
quant
(What?
Where?)
Secondary
method:
qual (How?
Why?)
Key findings &
conclusions

38. Case study data collection technique
 Using a combination of different data
collection methods to zoom in to a
specific issue, area or group
 A component within a research
cycle, not a research cycle by itself
 Useful to collect detailed information
on an event, activity, process, group
e.g. zoom in to one specific type of
intervention in an area within a larger
DFID-funded humanitarian programme

39. Frequently
Asked
Questions
(FAQs)

40. FAQs (1)
 What is the difference between a key informant interview and an individual
interview? Isn’t the key informant also technically an individual?
 The differences lies in the unit of measurement  individual experiences
(individual interview) vs. community/ village/ institution experiences (KI interview)
 For semi-structured data collection, when is it recommended to use FGDs
over KI or individual interviews?
 This depends on two things
 Research objectives and type of information needed e.g. Variety of
opinions and experiences useful? Specific information needed from an
expert? Topics sensitive to discuss in group setting?
 Logistical constraints e.g. Large number of individuals to be reached within
a short timeframe?

41. FAQs (2)
 Is it possible to have two different units of measurement in the same questionnaire?
 Ideally, should be avoided, but there are some exceptions:
 Individual information within a household survey (e.g. child attendance roster)
 Household information within an individual survey (e.g. household size or income indicators)
 Individual information within a village/ community/ location level interview (e.g. KI’s displacement status
and experiences, if KI also part of the affected population)
 Household information within a village/ community/ location level interview (e.g. KI estimates # or % of
households affected by a specific situation in a village)
 What if my population of interest includes minors (i.e. individuals <18 years of age)?
Can I collect data from minors?
 Only if absolutely necessary to meet objectives of the research
 Only if required information cannot be collected from adult respondents e.g. parents or caregivers
 Ideally, only from respondents >15 years
 Only if the required protocols are being followed
 Will de discussed later in this training 

42. Questions?

43. What methods
to use if you
don’t have
access to the
population of
interest?

44. What is remote data collection?
Remote data collection is a means of gathering data without a
physical presence in the data collection location and without
direct, in-person contact with the population of interest
When is it useful?
When it is not possible to conduct in-person visits to the
locations / populations of interest because of reasons such as:
 Disease outbreak (e.g. COVID-19)
 Time or resource constraints (e.g. not enough budget to hire
enumerators to cover all areas for face to face interviews)
 Access constraints due to:
 Security concerns
 COVID-19 travel restrictions
 Physical access barriers such as lack of infrastructure
 Severe weather conditions which limits travel
possibilities, etc.
 Etc.

45. Pros and cons of remote data collection
Pros Cons
Planning efficiency
More time and resource efficient; if
necessary logistics already in place,
could be fairly straightforward to
deploy
Challenging and time consuming to
set up correctly (e.g. identifying
respondents, organizing necessary
logistics, etc.), difficult to apply
stratification in sampling; challenging
to monitor progress
Implementation efficiency
Easier to implement even with
limited time, access and resources
(assuming planning and design is
done robustly)
Higher likelihood of low response
rates; limited means of verifying
responses/ data quality assurance;
more challenging to build trust with the
respondents; difficult to deploy long
or complicated questionnaires
Coverage
Ensures maximum possible
coverage of areas and population of
interest despite access constraints
Difficult to have the “full picture” as it
could introduce potential sampling
biases (e.g. based on phone network
coverage) and results in exclusions/
oversight of certain population
groups or areas

46. Some types of remote data collection methods (1)
1. Phone-based (individual, household, community level)
 Most relevant for: needs assessments, post distribution monitoring (PDMs),
humanitarian situation monitoring (HSM)
 Representative sampling could be possible
2. REACH “Area of Knowledge” methodology (face-to-
face data collection in alternate location)
 Most relevant for: community-level needs assessments or HSM
 Representative sampling not relevant (requires identifying the
respondent most likely to have the required knowledge)
3. Internet-based data collection
 Tools include: social media, web-based surveys, online discussion
platforms, chatbots (WFP mVAM), etc.
 Most relevant for: community-level needs assessments or HSM (KI
interviews or group discussions), PDMs (individual perception surveys)
 Representative sampling could be possible (but extremely difficult to
implement e.g. would need email address database and usually low
response rates)

47. Some types of remote data collection methods (2)
4. Remote sensing
 Only relevant if aim is to gain an understanding based on specific physical
characteristics of an area (e.g. agriculture and vegetation health analysis, shelter
damage assessment, flood impact assessment, etc.)
 Representative sampling or even census could be possible
5. Secondary data review and “expert” consultations
 Most relevant for: needs analysis or HSM
 Only feasible if relevant and «reliable» data sources already exist
6. Paper form submissions
 Only applicable if respondents have no movement restrictions and are able to
send paper forms back through required means
 Logistically difficult, not the most time and resource efficient
 Most relevant for: community-level needs assessments or HSM (KI
interviews), PDMs (individual perception surveys)
 Representative sampling could be possible (but extremely difficult to
implement e.g. would need postal address database and expect very low
response rates)

48.  Post-distribution
monitoring (PDM) of cash
assistance and core relief
items to refugees and
IDPs across Iraq
 Project began in 2016
and remains ongoing
 Data collected through
two call centres: Erbil and
Baghdad
 Household level data
collection, providing at
least a 90% confidence
level and 10% margin of
error at Governorate level
Phone-based data collection example: Iraq UNHCR
Cash Assistance PDM (2017-now)
Project background
 To improve time and cost
efficiency, since most of
the data collected would
not be verifiable by
enumerators in the field
 Access to beneficiary
contact lists ensures time-
efficient data collection
 The project has a wide
geographical spread, so
the call centre allows for
rapid, far reaching data
collection
Why was it remote? What worked well? Challenges?
 A team of enumerators
have been well trained and
dedicated to this
assessment continuously
 Availability of anonymised,
comprehensive beneficiary
lists for sampling purposes
 Remote data collection
helps ensure data privacy
 Typically the call centre
remains functional,
regardless of changing
access constraints
 Building trust among
respondents
 Ensuring respondents
understand the role of this
assessment
 Potential for duplication as
beneficiary lists were at the
individual level while
sampling was at the
household level
 Space constraints within
the call centre during
multiple ongoing
assessments

49.  Humanitarian Situation
Monitoring in ‘hard to
reach areas’ of ‘3 border’
area between Mali, Niger
and Burkina Faso
 Since November 2019
 Remote data collection
through face to face
interviews with KIs who
travel between accessible
and inaccessible areas
 Collect information about
humanitarian situation in
each country / areas with
same tool to allow for
comparability
AoK data collection example: 3-border HSM in Sahel
(December 2019- now)
Project background
 To gather information
about areas where
humanitarian access is low
or unreliable
 To ensure supply of
information about these
areas is regular and not
contingent on access,
allowing for trends
monitoring
 Less resource intensive –
good compromise to
gather indicative data in
complement to existing,
more robust data collection
systems
Why was it remote?
 Once knowledge of
population movements
within a region is clear,
easy to set up data
collection to ‘capture’
information about
different areas
 Ability to cover data
across a vast territory
from a handful of static
bases.
 Ability to monitor trends
on situation in hard to
reach areas and to
compare and contrast
between severity levels.
What worked well?
 Reliability is not high and
ability to verify validity of
data collected is low – it’s
indicative only
 KIs reporting on overall
situation at settlement
level can hide inequalities
 While it is less
challenging finding KIs
from relevant geographic
areas, it can be difficult to
find a balance of KI
profiles (men, women,
age groups, vulnerable
groups etc), impacting
comparative analysis.
Challenges?

50. Now available: SOPs for Data Collection during COVID 

51. Questions?

52. Next session?

53. Task for the
week

54. Instructions
Take the research objectives & preliminary analysis plan you formulated last week and briefly
determine:
• Which overall research method would be most appropriate and why?
• Which data collection approach(es) would be most appropriate and why?
• It is up to you to decide whether you want to assume face-to-face data collection is
possible/ remote data collection is necessary in your scenario 
• Don’t go into sampling just yet, we will come back to that next week
• Is there likely to be any sensitive information collected? Is this suitable to the data collection
approach being discussed?
• What additional information do you need to make final decisions on the approaches?
We can discuss how this goes next week!