Planning HPSR studies:
key issues for specific
designs
IHPSR Presentation 7
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Introduction to Health Policy and
Systems Research
Specific study designs
1. Cross-sectional design
2. Case study
3. Action research
Research questions
Purpose(s) Theory
Methods Sampling
strategy
Researchdesign and strategy
1. Cross-sectional design
• Most frequently reported HPSR study design
• Used to explore, describe or explain a
phenomenon at a particular time
– Does not examine change over time
– Does not assess interventions
• Encompasses a range of disciplinary
perspectives and design approaches
Fixed design Flexible design Mixed method design
Discrete Choice
Experiment studies
e.g. Blaauw et al. (2010)
nurses preferences for
policy interventions to
attract them to work in rural
areas across three
countries
Kurowski et al. (2007)
Building explanatory
frameworks about the
role of trust in the
choice of health care
provider among women
of different socio-
economic status in
Thailand (Riewpaiboon
et al., 2005)
Interpretive analysis of
how policy actors’
understandings
influence
implementation of HIV
clinical guidelines in
India (Sheikh & Porter,
2010)
Constructing a model of
the demand and supply
side dimensions of poor
malaria control in
Vietnam (Morrow et al.,
2009)
Examining the coping
strategies used by
households to manage
the costs of inpatient
care in India (Ranson,
Jayaswal & Mills, 2011)
Always have to think about
• What are phenomena of focus?
• How can key concepts/variables of question be
operationalised?
– to be able to measure, see, interpret, e.g. good quality, trust
in provider, motivation
– what role for theory?
• What data sources (e.g. people, documents, records)
and forms (e.g. numerical, talk, text) are relevant?
• What sampling approach is relevant?
• What are most appropriate data collection tools?
– e.g. structured questionnaire or in-depth interview?
– e.g. record audit or discourse analysis of documents?
• What are most appropriate analysis strategies?
– deductive vs. inductive analysis?
– what role for theory?
• What are relevant analysis steps?
– e.g. DCE steps, statistical modeling, stakeholder analysis,
causal loop diagrams
2. Case study
• Why use a case study design in HPSR?
• What is it?
• Key issues in doing case study work
Why use it?
NB Widely, but weakly used in HPSR
• Use to help explore little understood health policy and
systems (HPS) issues
• Use to investigate HPS complexity:
– Multiple contextual factors
– Actors’ perceptions matter, with multiple
interpretations of same phenomena
• Often can only examine small number of ‘cases’
(statistical inference not possible)
What is it?
Case study is a strategy for doing research
which involves an empirical investigation of:
• a particular contemporary phenomenon
• within its real life context
• using multiple sources of evidence Yin, 2009
When planning a case study you
have to think about more than data
collection procedures!
A case study strategy can be
appropriately used to address any
purpose, and as part of a range of study
types
What’s your purpose?
Exploratory Descriptive Explanatory Emancipatory
Identifying categories,
labelling them &
identifying underlying
principles
Identifying movement
through time
Unravelling complex
causality, esp. relationship
between deliberate
behaviour of several actors
& agencies
Action
research
• Can be cross-sectional, retrospective, prospective
• Can be frame for mixed method studies
• Can be element of evaluation work
Examples
Paper Purpose Question
Mutemwa,
2006
Exploratory/De
scriptive
WHAT information forms
are used in decision-
making?
Rolfe et al.,
2008
Exploratory/De
scriptive
(?Explanatory)
WHAT influences the
development of private
midwifery practices?
Lee et al.,
1998
Explanatory WHY do some countries
have effective family
planning programmes
and others do not?
When to use case study rather
than fixed design?Exploratory & Descriptive
work:
• When do not already
know enough about issue
to be able to develop
structured tool or
approach to
measure/assess
• Mutemwa, 2006
• Rolfe et al., 2008
Explanatory work:
• When complexity of issue
makes
measurement/assessmen
t difficult – and need to
ask how and why
questions
• Russell & Gilson, 2006
Explanatory
• Household case studies, followed over
time
– Revealed the complex and dynamic nature
of economic burdens of illness, and
associated coping strategies
– Allowed a picture of the interconnected
factors mediating the impact of illness on
livelihood outcomes
Case study work should either ‘test’ or
generate ‘theory’
But remember, theory comes in various
forms!
Exploratory & Descriptive
work:
• may involve building
theory: linked to
classification and
identifying underlying
principles
Mutemwa, 2006
Rolfe et al. 2008
Explanatory work:
• use theory to analyse
movement through time
and understand causality
• but be open/flexible even
when have some initial
ideas seek to ‘test’
Rolfe et al. 2008
Lee et al. 1998
Sampling: it is always important to identify
and select cases carefully
Defining feature of
case study work
• Focuses on a single case or small set of
cases
– Robson, 2002:
• use the word ‘site’ as a reminder that every
case is situated in a specific social and physical
setting
• so need to contextualise the case to
understand experience (unlike much fixed
design research)
Many possible cases
Case study types
single multiple
holistic single policy multiple policies
embedded single policy +
multiple districts +
multiple facilities
multiple policies +
multiple districts +
multiple facilities
Case selection
Exploratory Explanatory Single case
• Aim to find as many
different types as
possible, so
describe lots of
cases in limited
detail (to allow
classification,
taxonomy)
• Select to allow
theory testing
through comparison
& contrast, e.g. max
variability/ extremes
• ‘The one next door’
or
extreme/revealing
case
• Use theory to
explain how & why
something happens
by looking in detail
at inter-
relationships and
inner workings of
case
Case selection practicalities
• Think before start – be aware of what you are doing
• When possible, especially for explanatory work:
• develop ideas & ‘theory’ first
• purposively select ‘challenging’ cases to allow ideas & theory to be
tested
• Operationalise key concepts to allow case selection e.g. well/poorly
performing; success/failure
• May need to gather data through e.g. initial rapid appraisal to allow
appropriate case selection
• May choose all cases initially or adapt selection strategy during data
collection to allow theory testing & development (Flexible study design!)
• Test theory/assumptions in analysis AND explore unexpected
Pay particular attention to your analysis
strategy in case study work
In single cases
• Allison, 1971: The 1962 Cuban missile crisis
Three competing but complementary theoretical
explanations considered
– ability of each to explain actual course of events
compared
– explanations drawn out
– lessons intended to be ‘generalisable’ to conduct
of foreign affairs and other complex government
actions
Allison, G (1971). Essence of Decision: Explaining the Cuban
Missile Crisis, Boston, Little Brown.
Analysing multiple cases
• Analyse each case separately (don’t pool data)
• Test assumptions (‘theory’) on case by case basis –
repeating test of theory as you analyse
• Replication in analysis underpins development of
trustworthy ‘generalisable’ claims
NB case selection: apparently similar cases;
theoretically different cases (literal and theoretical
replication), see Rolfe et al., 2008
Develop theory/ideas
Select cases
Design data collection protocol
Conduct 1st case study
Conduct 2nd case study
Conduct remaining case studies
Write individual case report
Modify theory
Develop policy implications
Write individual case report
Write individual case reports
Analyse iteratively across cases
Write cross-case report
Define & design Prepare, collect & analyse
Analyse & conclude
Yin, 2009
Analytic/theoretical
generalisation
• Develop ‘theoretical insights’ (generalisable
claims) by:
• building or testing theory and/or
• comparative analysis across multiple cases
• These insights are universal enough to have
relevance in other settings
3. Participatory Action
Research (PAR)
• Multiple varieties: PRA, action research,
collaborative inquiry, experiential research,
endogenous research, community-driven
development etc etc.
• Emerges from a ‘critical’ social science tradition
“… a good explanation is one that will ultimately
lead to transformation and change in the world”
Babbie & Mouton, 2001
• Key words in PAR: participation, engagement,
collaboration, power
• Emphasis on collaboration between researcher and
subject – the latter becoming involved in the
research process as an equal partner
“People’s role in setting the agendas, participating in
the data gathering and analysis, and controlling the
use of outcomes … shared ownership of the research
enterprise”
Babbie & Mouton, 2001
PAR and theory
• The difference is not method (tools), but the
underlying theory/ethos – of an engaged
researcher with an emphasis on social
relevance, empowerment, and (political)
emancipation
• In PAR, the approach IS the theory
• (Research as action, research as intervention)
PAR and method
• Cycles of observation, critical
reflection and action (but not
always)
• Lots of different ‘tools’ – e.g.
participatory mapping, diagrams,
spidergrams, coffee-corners, etc…
• The theory and the cycle is the keyKemmis & McTaggart, 2005
The real trick:
‘meaningful/genuine’ participation
• Chambers 1994 (classic) … ‘participation’ was hi-jacked
(from its emancipatory roots)
– cosmetic (to make a project appear good … a mask
for top-down)
– co-optation (to mobilise labour and reduce costs …
they participate in our project)
– empowerment process (enables local communities
to do their own analysis, take command … we
participate in their project)
The real trick: ‘meaningful/genuine’ participation (continued)
• Traps and common problems in PAR (Chambers, 1994):
– who participates?
– rushing; self-sustaining myths (ventriloquism)
– routines and ruts (manuals)
– cosmetics (words change not behaviour)
Nature of data?
• Generated ‘from below’
• Context-specific (but generalisable across?)
• Mainly qualitative, but some numbers possible
• The problem when you ‘hand over the stick’
• How do you judge ‘success’ or ‘impact’ of PAR?
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Introduction to Health Policy and Systems Research,
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CHEPSAA (Consortium for Health Policy & Systems
Analysis in Africa) 2014, www.hpsa-africa.org
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This document is an output from a project funded by the European Commission (EC) FP7-Africa (Grant no.
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The CHEPSAA partners
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