Using Mixed Methods in Development Research and

Project Evaluation

Michael Woolcock, DECRGWorld Bank Poverty & Inequality Course

February 9-10, 2011

Primary source material

• Bamberger, Michael, Vijayendra Rao and Michael Woolcock (2010) “Using Mixed Methods in Monitoring and Evaluation: Experiences from International Development”, in Abbas Tashakkori and Charles Teddlie (eds.) Handbook of Mixed Methods (2nd revised edition) Thousand Oaks, CA: Sage Publications

• Barron, Patrick, Rachael Diprose and Michael Woolcock (2011) Contesting Development: Participatory Projects and Local Conflict Dynamics in Indonesia New Haven: Yale University Press

• Woolcock, Michael (2009) ‘Toward a Plurality of Methods in Project Evaluation: A Contextualized Approach to Understanding Impact Trajectories and Efficacy’ Journal of Development Effectiveness 1(1): 1-14

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Ten Reasons to Use Qualitative Approaches in Projects and Evaluation

1. Understanding Political, Social Change• ‘Process’ often as important as ‘product’• Modernization of rules, social relations, meaning systems

2. Examining Dynamics (not just ‘Demographics’) of Group Membership

• How are boundaries defined, determined? How are leaders determined?

3. Accessing Sensitive Issues and Stigmatized/Marginalized Groups

• E.g., conflict and corruption; sex workers

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Ten Reasons to Use Qualitative Approaches in Projects and Evaluation

4. Explaining Context Idiosyncrasies• Beyond “context matters” to understanding how and why,

at different units of analysis• ‘Contexts’ not merely “out there” but “in here”; the Bank

produces legible contexts

5. Unpacking Understandings of Concepts and (‘Fixed’) Categories

• Surveys assume everyone understands questions and categories the same way; do they?

• Qualitative methods can be used to correct and/or complement orthodox surveys

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6. Facilitating Researcher-Respondent Interaction• Enhance two-way flow of information• Cross-checking; providing feedback

7. Exploring Alternative Approaches to Understanding ‘Causality’

• Econometrics: robustness tests on large N datasets; controlling for various contending factors

• History: single/rare event processes• Anthropology: deep knowledge of contexts• Exploring inductive approaches

• Cf. ER doctors, courtroom lawyers, solving jigsaws

Ten Reasons to Use Qualitative Approaches in Projects and Evaluation

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8. Observing ‘Unobservables’• Project impact not just a function of easily measured

factors; unobserved factors—such as motivation, political ties—also important

9. Exploring Characteristics of ‘Outliers’• Not necessarily ‘noise’ or ‘exceptional’; can be high

instructive (cf. illness informs health)

10. Resolving Apparent Anomalies• Nice when inter and intra method results align, but

sometimes they don’t; who/which is ‘right’?

Ten Reasons to Use Qualitative Approaches in Projects and Evaluation

Overview

• Three challenges:– Allocating development resources– Assessing project effectiveness (in general)– Assessing effectiveness of complex ‘social’

projects (in particular)

• Discussion of options, strategies for assessing projects using mixed methods

Three challenges

• How to allocate development resources?• How to assess project effectiveness in general?• How to assess social development projects

(e.g., CDD, ‘Justice for the Poor’) in particular?

1. Allocating development resources

• How to allocate finite resources to projects believed likely to have a positive development impact?

• Allocations made for good and bad reasons, only a part of which is ‘evidence-based’, but most of which is ‘theory-based’, i.e., done because of an implicit (if not explicit) belief that Intervention A will ‘cause’ Impact B in Place C net of Factors D and E for Reasons F and G.– E.g., micro-credit will raise the income of villagers in Flores,

independently of their education and wealth, because it enhances their capacity to respond to shocks (floods, illness) and enables larger-scale investment in productive assets (seeds, fertilizer)

1. Allocating development resources• Imperatives of the prevailing resource allocation

mechanisms (e.g., those of the World Bank) strongly favor one-size-fits-all policy solutions (despite protestations to the contrary!) that deliver predictable, readily-measurable results in a short time frame– Roads, electrification, immunization

• Want project impacts to be independent of context, scale, and time so that ‘successful’ examples (‘best practices’) can be scaled up and replicated

• Projects that diverge from this structure enter the resource allocation game at a distinct disadvantage. But the obligation to demonstrate impact (rightly) remains; just need to enter the fray well armed, empirically and strategically…

Core task• Ask interesting and important questions, then assemble

the best combination of methods to answer it– Not, “What questions can I answer with this data?”– Not, “I don’t have a randomized design, so therefore I can’t

say anything defensible”• Generate data to help projects ‘learn’, in real time

– Be useful– Make ‘M’ as cool as ‘E’

• Help to more carefully identify the conditions under which given interventions ‘work’– Individual methods, per se, are not inherently ‘rigorous’;

they become so to the extent they appropriately match the problems they confront, the constraints they overcome

– Focus on understanding SD as much as determining LATE

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How to assess project effectiveness?

• Need to disentangle the effect of a given intervention over and above other factors occurring simultaneously– Distinguishing between the ‘signal’ and ‘noise’

• Is my job creation program reducing unemployment, or is it just the booming economy?

• Furthermore, an intervention itself may have many components– TTLs are most immediately concerned about which aspect is the

most important, or the binding constraint– (Important as this is, it is not the same thing as assessing impact)

• Need to be able to make defensible causal claims about project efficacy even (especially) when the apparent ‘rigor’ of econometric methods aren’t suitable/available– Thus need to change both the terms and content of debate

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Impact evaluation 101• Core evaluation challenge:

– Disentangling effects of people, place, and project (or policy) from what would have happened otherwise

• i.e., need a counterfactual (but this is rarely observed)• ‘Tin’ standard

– Beneficiary assessments, administrative checks• ‘Silver’

– Double difference: before/after, program/control• ‘Gold’

– Randomized allocation, natural experiments

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Impact evaluation 101• Core evaluation challenge:

– Disentangling effects of people, place, and project (or policy) from what would have happened otherwise

• i.e., need a counterfactual (but this is rarely observed)• ‘Tin’ standard

– Beneficiary assessments, administrative checks• ‘Silver’

– Double difference: before/after, program/control• ‘Gold’

– Randomized allocation, natural experiments• (‘Diamond’?)

– Randomized, triple-blind, placebo-controlled, cross-over• Alchemy?

– Making ‘gold’ with what you have, given prevailing constraints (people, money, time, logistics, politics)…

Making knowledge claims in project evaluation and development research

• Construct validity– How well does my instrument assess the underlying

concepts (‘poverty’, ‘participation’, ‘conflict’, ‘empowerment’)?

• Internal validity– How well have I addressed various sources of bias

(most notably selection effects) influencing the relationship between IV and DV?

• i.e., what is my identification strategy?• External validity

– How well can I extrapolate my findings? If my project works ‘here’, will it also work ‘there’? Will bigger be better?

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We observe an outcome indicator…

Y1 (observedl)

Y0

t=0 Intervention

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…and its value rises after the program

Y1 (observedl)

Y0

t=0 t=1 time Intervention

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However, we need to identify the counterfactual (i.e., what would have

happened otherwise)…

Y1 (observedl)

Y1

* (counterfactual)

Y0

t=0 t=1 time

Intervention

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… since only then can we determine the impact of the

intervention

Y1

Impact = Y1- Y1*

Y1

*

Y0

t=0 t=1 time

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The challenge of assessing SD projects

• You’re a star in development if you devise a “best practice” and a “tool kit”—i.e., a universal, easy-to-administer solution to a common problem

• There are certain problems for which finding such a universal solution is both desirable and possible (e.g., TB, roads for high rainfall environments)…

• But many key problems, such as those pertaining to local governance and law reform (e.g., J4P), inherently require context-specific solutions that are heavily dependent on negotiation and teamwork, not a technology (pills, bridges, seeds)– Not clear that if such a project works ‘here’ that it will also work

‘there’, or that ‘bigger’ will be ‘better’– Assessing such complex projects is enormously difficult

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Why are ‘complex’ interventions so hard to evaluate? A simple example

• You are the inventor of ‘BrightSmile’, a new toothpaste that you are sure makes teeth whiter and reduces cavities without any harmful side effects. How would you ‘prove’ this to public health officials and (say) Colgate?

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Why are ‘complex’ interventions so hard to evaluate? A simple example

• You are the inventor of ‘BrightSmile’, a new toothpaste that you are sure makes teeth whiter and reduces cavities without any harmful side effects. How would you ‘prove’ this to public health officials and (say) Colgate?

• Hopefully (!), you would be able to:– Randomly assign participants to a ‘treatment’ and ‘control’

group (and then have then switch after a certain period); make sure both groups brushed the same way, with the same frequency, using the same amount of paste and the same type of brush; ensure nobody (except an administrator, who did not do the data analysis) knew who was in which group

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Demonstrating ‘impact’ of BrightSmile vs. SD projects

• Enormously difficult—methodologically, logistically and empirically—to formally identify ‘impact’; equally problematic to draw general ‘policy implications’, especially for other countries

• Prototypical “complex” CDD/J4P project:– Open project menu: unconstrained content of intervention– Highly participatory: communities control resources and decision-

making– Decentralized: local providers and communities given high degree of

discretion in implementation– Emphasis on building capabilities and the capacity for collective action– Context-specific; project is (in principle) designed to respond to and

reflect local cultural realities– Project’s impact may be ‘non-additive’ (e.g., stepwise, exponential,

high initially then tapering off…)

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How does CDD/J4P work over time?(or, What is its ‘functional form’? Does it even have one?)

Impa

ct

TimeIm

pact

Time

Impa

ct

Time

Impa

ct

Time

A

C

B

D

CCTs? ‘Governance’?

‘AIDS awareness’? Bridges?

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How does J4P work over time?(or, What is its ‘functional form’? Does it even have one?)

Impa

ct

TimeIm

pact

Time

Impa

ct

Time

Impa

ct

Time

E

G

F

H

Shocks?(‘Impulse responsefunction’)

Unintended consequences?

‘Empowerment’?‘Pest control’?e.g., cane toads

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How does J4P work over time?(or, What is its ‘functional form’? Does it even have one?)

Impa

ct

TimeIm

pact

Time

?

I JUnknown… Unknowable?

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Science, Complexity, and EvaluationPure Science Applied

ScienceHuman Dev (education, health projects)

Social Dev

(e.g., CDD projects)

Theory Predictive precision Cumulative knowledge Subject/object gap

Hi

Mechanisms # Causal pathways # of ‘people-based’ transactions

Few

Outcomes Plausible range Measurement precision

Lo

Many

Wide Narrow

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So, what can we do when…

• Inputs are variables (not constants)?– Facilitation/participation vs. tax cuts (seeds, pills, etc)– Teaching vs. text books– Therapy vs. medicine

• Adapting to context is an explicit, desirable feature?– Each context/project nexus is thus idiosyncratic

• Outcomes are inherently hard to define and measure?– E.g., empowerment, collective action, conflict mediation, social

capital

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Linking Questions, Methodologies, Methods, Data

• Questions should drive choice of methods and measurement tools (not vice versa)

• Social science data is always partial, an imperfect reflection of a more complex underlying reality

• Data can be manipulated for political purposes• Some (very important) things cannot be measured—love,

identity, meaning• “Not everything that can be counted, counts”• “It’s better to be vaguely right than precisely wrong”

“Triangulation”—integrating more abundant, more diverse, and higher-quality evidence

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• Begin with interesting and important questions – “The most important questions of method begin where the

standard techniques do not apply” (C Wright Mills)– Finding answers may require single or multiple methods and

data forms—need to be a good detective– But difficult to do when one has invested many years in

mastering difficult techniques—“Everything looks like a nail when all you have is a hammer”

• Methodologies as the particular combination and sequence of methods used to answer the question(s)

• Methods can be qualitative and/or quantitative• Data can also be qualitative and/or quantitative

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• Qual/quan disputes often stem from…1. Conflating methods and data2. Mismatches between question, methods and data3. Assumptions that different “standards” apply

• Qualitative approaches seen as – inductive, valid, subjective, process (‘how’), generating ideas

• Quantitative approaches seen as – deductive, reliable, objective, effects (‘whether’), testing ideas

Not necessarily…

• Integrating qual and quan approaches to…• Complement strengths, compensate weaknesses• Address problems of missing/inadequate data• Observing the unobservable

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Types of Mixed Methods

• Pure Qualitative: ‘Think quan, act qual’• Parallel: Quan and qual done separately

• Sequential: Quan follows qual

• Iterative: Quan and qual in constant dialogue

• (Pure Quantitative)

qual

quan

qual quan

qual

quan

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Forms and sources of data

• Quantitative (“numbers”)– Household and other surveys (e.g., census, LSMS)– Opinion polls (e.g., Gallup, marketing research)– Data from official files (e.g., membership lists, government reports)– Indexes created from multiple sources (e.g., “governance”)

• Qualitative (“texts”)– Historical records, political reports, letters, legal documents– Media (print, radio, and television)– Open-ended responses to survey questions– Observation (ethnography)– Interviews—key informants, focus groups– Participatory approaches—PRA, etc

• Comparative (“cases”)– ‘Rare’, ‘small-N’ historical events (e.g. wars, economic crises)

Arraying methods by source of variation(Source: Gerring 2004: 343)

Temporal variation

Spatial variation

No Yes

None (1 unit) Impossible Case study I

Within-unit Case study II Case study III

Across-unit Cross-section Panel data

Across- and within-unit

Hierarchical Hierarchical time-series; comparative-historical

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Types of methods of analysis

• Quantitative– Statistical analysis– Hypothesis testing (deductive)

• Qualitative– Emergent themes– Generates propositions (inductive)– Software available: e.g., N6 (reduces ‘small N’ problem)

• Comparative (“cases”)– Differences among otherwise similar cases– Commonalities among otherwise different cases– Common strategy in history; used to try to explain ‘causality’

“The goal is not to show which approach is best, but rather to generate dialogue between ideas and evidence” (Ragin)

Alternative causal claim-making modalities

1. Econometrics• Robustness tests on large N datasets; controlling for various

contending factors

2. History• Single/rare event processes; ‘processing tracing’ of case studies• (QCA; Fuzzy sets – Ragin)

3. Anthropology• Deep knowledge of contexts; (cf. CEOs…)

4. Legal approaches• Civil standard: ‘Preponderance of the evidence’• Criminal standard: ‘Beyond a reasonable doubt’

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Types of Data and Methods

Methods

DataQual Quan

Qual

Quan

Standard SurveySubjective Welfare

EthnographyPRA

Quantitative AnthropologySmall-N Matched Comparisons

Adapted from Hentschel, 1999

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Integrating Qualitative and Quantitative Approaches

1. Parallel Qual/Quan– Teams work separately– Best suited to large (e.g. country level) assessments (GUAPA)– Quantitative

• Large household survey– Qualitative

• In-depth work with selected groups– Data analyzed separately, integrated as part of write-up and

conclusions

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Integrating Qualitative and Quantitative Approaches

2. Sequential Qual/Quan (the ‘classical’ approach)– Qualitative

• Use PRA, focus groups, etc to get a grounded understanding of key issues

– Quantitative• Use this material to design a survey instrument • Use the survey to test hypotheses that emerged from the

qualitative work– Examples

• Survival and mobility in Delhi slums (Jha, Rao and Woolcock, 2007)

• Evaluating Jamaica Social Investment Fund (Rao and Ibanez, 2002)

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Integrating Qualitative and Quantitative Approaches

3. Iterative Qual/Quan (‘Bayesian’ approach)• Ongoing dialogue between Qual and Quan• Qualitative

• As above: used to generate initial hypotheses, establish validity of questions

• Quantitative• Hypotheses tested with household survey• Return to the field; cycle repeats

• Example:• Potters in India (Rao, 2000)

• Initial study of marriage markets lead to study of domestic violence, and another on unit price differentials/inequality

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Other uses for Mixed Methods

1. When existing time and resources prelude doing or using formal survey/census data• Examples: St Lucia and Colombia

2. When it’s unclear what “intervention” might be responsible for observed outcomes

– That is, no clear ex ante hypotheses; working inductively from matched comparison cases

• Examples: – Putnam (1993) on regional governance in Italy– Mahoney (2010) on governance in Central America– Collins (2001) on “good to great” US companies– Varshney (2002) on sources of ethnic violence in India

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Practical examples

1. Poverty in Guatemala (GUAPA)– ‘Parallel’– Quan: expanded LSMS

• first social capital module• large differences by region, gender, income, ethnicity• pervasive elite capture

– Qual: 10 villages (5 different ethnic groups)• perceptions of exclusion, access to services• fear of reprisal, of children being stolen• legacy of shocks (political and natural)• links to LSMS data

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Practical examples

2. Poverty in Delhi slums (Jha, Rao and Woolcock 2007)– ‘Sequential’– Qual: 4 migrant communities

• near, far, recent, long-term– Quan: 800 randomly selected representative households– From survival to mobility

• role of norms (sharing, status) and networks (kinship, politics)• housing, employment transitions• property rights

– Understanding ‘governance’• managing collective action• crucial role of service provision

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Practical examples

3. ‘Justice for the Poor’ Initiative– Origins in Indonesia

• Draws on the approach and findings from large local conflict study

– Integrated qualitative and quantitative approach– Results show importance of understanding

• Rules of the game (meta-rules)• Dynamics of difference (politics of ‘us’-‘them’ relations)• Efficacy of intermediaries (legitimacy, enforceability)

• Extension to Cambodia…– Research on collective disputes (e.g., land), to inform IDA grant

in 2007• …and now into Africa and East Asia

– Sierra Leone, Kenya, Vanuatu, East Timor, PNG…

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J4P: Core Research Design

• Enormous investment in recruiting, training, keeping local field staff

• Training centers on techniques, ethics, data management and analysis

• Where possible, use existing quantitative data sources to (a) complement qualitative work, and (b) help with sampling

• Sampling based on basic comparative method:– Maximum difference between contexts– Focus on outliers (‘exceptions to the rule’)

• Rough rule of thumb: analysis takes three times as long as data collection– Analysis can’t be “outsourced”: research team needs to be

involved at all stages

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Concluding thoughts• The virtues and limits of measurement

– Tension between simplifying versus complicating reality

• Triangulation– Integrating more data, better data, more diverse data as

“substitutes” and “complements”

• Surveys as tool for adaptation and guidance– Not prescription for uniformity or control– One size does not fit all– Encouraging comparability across time and space