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1 Promoting a RealWorld and Holistic approach to Impact Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints AEA pre-conference professional development workshop Minneapolis, October 24, 2012 Facilitated by Michael Bamberger and Jim Rugh Note: This PowerPoint presentation and the summary (condensed) chapter of the book are available at: www.RealWorldEvaluation.org

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Promoting a RealWorld and Holistic approach to Impact Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints AEA pre-conference professional development workshop Minneapolis, October 24, 2012. Facilitated by Michael Bamberger and Jim Rugh - PowerPoint PPT Presentation

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Page 1: Facilitated by Michael Bamberger  and Jim Rugh

1

Promoting a RealWorld and Holistic approach to

Impact EvaluationDesigning Evaluations under Budget, Time, Data and Political Constraints

AEA pre-conference professional development workshopMinneapolis, October 24, 2012

Facilitated byMichael Bamberger and Jim Rugh

Note: This PowerPoint presentation and the summary (condensed) chapter of the book are available at:

www.RealWorldEvaluation.org

Page 2: Facilitated by Michael Bamberger  and Jim Rugh

Workshop Objectives1. The basics of the RealWorld Evaluation approach

for addressing common issues and constraints faced by evaluators such as: when the evaluator is not called in until the project is nearly completed and there was no baseline nor comparison group; or where the evaluation must be conducted with inadequate budget and insufficient time; and where there are political pressures and expectations for how the evaluation should be conducted and what the conclusions should say;

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Workshop Objectives2. Identifying and assessing various design options

that could be used in a particular evaluation setting;3. Some tips on developing logic models;4. Ways to reconstruct baseline data; 5. How to account for what would have happened

without the project’s interventions: alternative counterfactuals;

6. The advantages of using mixed-methods designs;7. Considerations for more holistic approaches to

impact evaluation.

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Page 4: Facilitated by Michael Bamberger  and Jim Rugh

Workshop ObjectivesNote: In this workshop we focus on project-level impact evaluations. There are, of course, many other purposes, scopes, evaluands and types of evaluations. Some of these methods may apply to them, but our examples will be based on project impact evaluations, most of them in the context of developing countries.

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Workshop agenda: morning8:00-8:30 Introduction + Brief overview of the RealWorld Evaluation

approach8:30-9:00 Basic evaluation design scenarios Jim 29-489:00-9:30 Small group discussion: personal introductions,

including RWE-type constraints you have faced in your own practice

9:30-9:50 break9:50-10:20 Logic models; reconstructing baselines Jim 51- 7710:20-10:40 Mixed Method evaluations Michael 78-10210:40-11:00 Alternative counterfactuals Jim 103-11511:00-12:00 Small group exercise, Part I: Reading of case studies

then preparing an evaluation design when working under budget, time, data or political constraints

12:00-1:00 lunch

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Workshop agenda: afternoon

1:00-1:45 Impact Evaluations Michael 120-134, Jim 135-1551:45-2:30 Small group exercise, Part II: 'Clients’ and ‘consultants’ re-negotiate the case study evaluation ToR. Note: In addition to becoming familiar with some of the RWE approaches, the case study exercises will also illustrate the different evaluation agendas and perspectives of evaluation consultants, project implementers and funding agencies.

2:30-2:50 Plenary discussion of main learnings from group exercises and the RealWorld Evaluation approach. Jim 156-159

2:50-3:00 Wrap-up and evaluation of workshop by participants

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OVERVIEW OF THE RWE APPROACH

RealWorld Evaluation

Designing Evaluations under Budget, Time,

Data and Political Constraints

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Typical RealWorld Evaluation ScenarioEvaluator(s) not brought in until near end of

projectFor political, technical or budget reasons:

• There was no life-of-project evaluation plan• There was no baseline survey• Project implementers did not collect

adequate data on project participants at the beginning nor during the life of the project

• It is difficult to collect data on comparable control groups

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Reality Check – Real-World Challenges to Evaluation• All too often, project designers do not think

evaluatively – evaluation not designed until the end• There was no baseline – at least not one with data

comparable to evaluation• There was/can be no control/comparison group.• Limited time and resources for evaluation• Clients have prior expectations for what they want

evaluation findings to say• Many stakeholders do not understand evaluation;

distrust the process; or even see it as a threat (dislike of being judged)

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RealWorld Evaluation Quality Control Goals Achieve maximum possible evaluation rigor

within the limitations of a given context Identify and control for methodological

weaknesses in the evaluation design Negotiate with clients trade-offs between

desired rigor and available resources Presentation of findings must acknowledge

methodological weaknesses and how they affect generalization to broader populations

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The Need for the RealWorld Evaluation Approach

As a result of these kinds of constraints, many of the basic principles of rigorous impact evaluation design (comparable pre-test -- post test design, control group, adequate instrument development and testing, random sample selection, control for researcher bias, thorough documentation of the evaluation methodology etc.) are often sacrificed.

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The RealWorld Evaluation Approach

An integrated approach to ensure acceptable standards of methodological rigor while operating under real-world budget, time, data and political constraints.

This is the cover of the cover of the 1se edition of the RealWorld Evaluation book (2006)

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Bamberger Rugh Mabry

RealWorld

Evaluation

RealWorld Evaluation

Working Under Budget, Time, Data, and Political Constraints

Michael Bamberger

Jim Rugh

Linda Mabry

EDITION

2

EDITION

This book addresses the challenges of conducting program evaluations in real-world contexts where evaluators and their clients face budget and time constraints and where critical data may be missing. The book is organized around

a seven-step model developed by the authors, which has been tested and refined in workshops and in practice. Vignettes and case studies—representing evaluations from a variety of geographic regions and sectors—

demonstrate adaptive possibilities for small projects with budgets of a few thousand dollars to large-scale, long-term evaluations of complex programs. The text incorporates quantitative, qualitative, and mixed-method designs and this

Second Edition reflects important developments in the field over the last five years.

New to the Second Edition: ·Adds two new chapters on organizing and managing evaluations, including how to strengthen capacity and

promote the institutionalization of evaluation systems

·Includes a new chapter on the evaluation of complex development interventions, with a number of promising new approaches presented

·Incorporates new material, including on ethical standards, debates over the “best” evaluation designs and how to assess their validity, and the importance of understanding settings

·Expands the discussion of program theory, incorporating theory of change, contextual and process analysis, multi-level logic models, using competing theories, and trajectory analysis

·Provides case studies of each of the 19 evaluation designs, showing how they have been applied in the field

“This book represents a significant achievement. The authors have succeeded in creating a book that can be used in a wide variety of locations and by a large community of evaluation practitioners.”

—Michael D. Niles, Missouri Western State University

“This book is exceptional and unique in the way that it combines foundational knowledge from social sciences with theory and methods that are specific to evaluation.”

—Gary Miron, Western Michigan University

“The book represents a very good and timely contribution worth having on an evaluator’s shelf, especially if you work in the international development arena.”

—Thomaz Chianca, independent evaluation consultant, Rio de Janeiro, Brazil

2 EDITION

RealWorld Evaluation

2nd edition published

January 2012

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The RealWorld Evaluation approach Developed to help evaluation practitioners

and clients• managers, funding agencies and external

consultants Still a work in progress (we continue to learn

more through workshops like this) Originally designed for developing countries,

but equally applicable in industrialized nations

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Most RealWorld Evaluation tools are not new— but promote a holistic, integrated approach

Most of the RealWorld Evaluation data collection and analysis tools will be familiar to experienced evaluators.

What we emphasize is an integrated approach which combines a wide range of tools adapted to produce the best quality evaluation under RealWorld constraints.

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What is Special About the RealWorld Evaluation Approach? There is a series of steps, each with

checklists for identifying constraints and determining how to address them

These steps are summarized on the following slide and in the more detailed flow-chart on page 5 of the Condensed Overview.

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The Steps of the RealWorld Evaluation Approach

Step 1: Planning and scoping the evaluation Step 2: Addressing budget constraintsStep 3: Addressing time constraintsStep 4: Addressing data constraintsStep 5: Addressing political constraintsStep 6: Assessing and addressing the strengths and

weaknesses of the evaluation designStep 7: Helping clients use the evaluation

See page 5 of the Condensed Summary for more details

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Planning and Scoping the Evaluation Understanding client information needs Defining the program theory model Preliminary identification of constraints to

be addressed by the RealWorld Evaluation

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Understanding client information needs

Typical questions clients want answered: Is the project achieving its objectives? Is it having desired impact? Are all sectors of the target population

benefiting? Will the results be sustainable? Which contextual factors determine the

degree of success or failure?

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A full understanding of client information needs can often reduce the types of information collected and the level of detail and rigor necessary.

However, this understanding could also increase the amount of information required!

Understanding client information needs

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Still part of scoping: Other questions to answer as you customize an evaluation Terms of Reference (ToR):

1. Who asked for the evaluation? (Who are the key stakeholders)?

2. What are the key questions to be answered?

3. Will this be mostly a developmental, formative or summative evaluation?

4. Will there be a next phase, or other projects designed based on the findings of this evaluation?

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Page 22: Facilitated by Michael Bamberger  and Jim Rugh

5. What decisions will be made in response to the findings of this evaluation?

6. What is the appropriate level of rigor?7. What is the scope / scale of the

evaluation / evaluand (thing to be evaluated)?

8. How much time will be needed / available?

9. What financial resources are needed / available?

Other questions to answer as you customize an evaluation ToR:

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10. Should the evaluation rely mainly on quantitative or qualitative methods?

11. Should participatory methods be used?12. Can / should there be a household

survey?13. Who should be interviewed?14. Who should be involved in planning /

implementing the evaluation?15. What are the most appropriate forms of

communicating the findings to different stakeholder audiences?

Other questions to answer as you customize an evaluation ToR:

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Page 24: Facilitated by Michael Bamberger  and Jim Rugh

Before we return to the RealWorld steps,

let’s gain a perspective on levels of rigor, and what a

life-of-project evaluation plan could

look like

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Different levels of rigordepends on source of evidence; level of confidence; use of information

Level 0: Decision-maker’s impressions based on anecdotes and sound bytes heard during brief encounters (hallway gossip), mostly intuition; Level of confidence +/- 50%; Decision made in a few seconds

Level 1: A few people are asked their perspectives about project; P= +/- 40% Decision made in a few minutes

Level 3: A rapid survey is conducted on a convenient sample of participants; P= +/- 10% Decision maker reads 10-page summary of report

Level 2: A fairly good mix of people are asked their perspectives about project; P= +/- 25% Decision maker reads at least executive summary of report

Level 4: Good sampling and data collection methods used to gather data that is representative of target population; P= +/- 5% Decision maker reads full report

Level 5: A very thorough research project is undertaken to conduct in-depth analysis of situation; P= +/- 1% Book published!

Objective, high precision – but requiring more time & expense

Quick & cheap – but subjective, sloppy 25

Page 26: Facilitated by Michael Bamberger  and Jim Rugh

Rando

mize

d sa

mpl

e sele

ctio

n

Quest

ionn

aire q

ualit

y

Reliab

ility &

valid

ity o

f ind

icato

rs

Qualit

y of d

ata c

ollec

tion

Depth

of a

nalys

isRep

ortin

g & u

tiliza

tion

QUALITY OF INFORMATION GENERATED BY AN EVALUATION DEPENDS UPON LEVEL OF RIGOR OF ALL COMPONENTS

CONDUCTING AN EVALUATION IS LIKE LAYING A PIPELINE

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Rando

mize

d sa

mpl

e sele

ctio

n

Quest

ionn

aire q

ualit

y

Releva

nce &

valid

ity o

f ind

icato

rs

Qualit

y of d

ata c

ollec

tion

Depth

of a

nalys

isRep

ortin

g & u

tiliza

tion

AMOUNT OF “FLOW” (QUALITY) OF INFORMATION IS LIMITED TO THE SMALLEST COMPONENT OF THE SURVEY “PIPELINE”

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Determining appropriate levels of precision for events in a life-of-project evaluation plan

Annualself-evaluation

Mid-termevaluation

Baselinestudy

Needs assessment

Finalevaluation

Time during project life cycle

Special Study

Same level of rigor

High rigor

Low rigor2

3

4

28

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EVALUATION DESIGNS

RealWorld Evaluation

Designing Evaluations under Budget, Time,

Data and Political Constraints

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Formative: learning and improvement including early identification of possible problems

Knowledge generation: identify cause-effect correlations and generic principles about effectiveness.

Accountability: to demonstrate that resources are used efficiently to attain desired results

Summative judgment: to determine value and future of program

Developmental evaluation: adaptation in complex, emergent and dynamic conditions-- Michael Quinn Patton, Utilization-Focused Evaluation, 4th edition, pages 139-140

Some of the purposes for program evaluation

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Determining appropriate (and feasible) evaluation design

Based on the main purpose for conducting an evaluation, an understanding of client information needs, required level of rigor, and what is possible given the constraints, the evaluator and client need to determine what evaluation design is required and possible under the circumstances.

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Some of the considerations pertaining to evaluation design

1: When evaluation events take place (baseline, midterm, endline)

2. Review different evaluation designs (experimental, quasi-experimental, other)

3. Determine what needs to be done to “fill in the missing pieces” when the “ideal” evaluation design/scenario is not feasible.

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baseline end of project evaluation

Illustrating the need for quasi-experimental longitudinal time series evaluation design

Project participants

Comparison group

post project evaluation

An introduction to various evaluation designs

scale of major impact indicator33

Page 34: Facilitated by Michael Bamberger  and Jim Rugh

OK, let’s stop the action to identify each of the major types of evaluation (research) design

… one at a time, beginning with the most rigorous design.

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Page 35: Facilitated by Michael Bamberger  and Jim Rugh

First of all: the key to the traditional symbols:

X = Intervention (treatment), I.e. what the project does in a community

O = Observation event (e.g. baseline, mid-term evaluation, end-of-project evaluation)

P (top row): Project participants C (bottom row): Comparison (control) group

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Note: the 7 RWE evaluation designs are laid out on page 8 of the Condensed Overview of the RealWorld Evaluation book

Page 36: Facilitated by Michael Bamberger  and Jim Rugh

baseline end of project evaluation

Comparison group

post project evaluation

Design #1: Longitudinal Quasi-experimental

P1 X P2 X P3 P4

C1 C2 C3 C4

Project participants

midterm

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Page 37: Facilitated by Michael Bamberger  and Jim Rugh

baseline end of project evaluation

Comparison group

Design #2: Quasi-experimental (pre+post, with comparison)

P1 X P2

C1 C2

Project participants

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Page 38: Facilitated by Michael Bamberger  and Jim Rugh

baseline end of project evaluation

Control group

Design #2+: Randomized Control Trial

P1 X P2

C1 C2

Project participants

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Research subjects randomly assigned either to project or control group.

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end of project evaluation

Comparison group

Design #3: Truncated Longitudinal

X P1 X P2

C1 C2

Project participants

midterm

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Page 40: Facilitated by Michael Bamberger  and Jim Rugh

baseline end of project evaluation

Comparison group

Design #4: Pre+post of project; post-only comparison

P1 X P2

C

Project participants

40

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end of project evaluation

Comparison group

Design #5: Post-test only of project and comparison

X P

C

Project participants

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baseline end of project evaluation

Design #6: Pre+post of project; no comparison

P1 X P2

Project participants

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end of project evaluation

Design #7: Post-test only of project participants

X P

Project participants

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Need to fill in missing data through other means:• What change occurred during the life of the

project?• What would have happened without the

project (counterfactual)?• How sustainable is that change likely to be?

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Design

T1

(baseline)

X(intervention)

T2

(midterm)

X(intervention,

cont.)

T3

(endline)

T4

(ex-post)

1 P1

C1X

P2

C2X

P3

C3

P4

C4

2 P1

C1X X

P2

C2

3X

P1

C1X

P2

C2

4 P1 X XP2

C2

5X X

P1

C1

6 P1 X X P2

7 X X P1

See Table 2.2 on page 8 of Condensed Overview of RWE

Page 45: Facilitated by Michael Bamberger  and Jim Rugh

“Non-Experimental” Designs [NEDs] NEDs are impact evaluation designs that

do not include a matched comparison group

Outcomes and impacts assessed without a conventional statistical counterfactual to address the question• “what would have been the situation of the

target population if the project had not taken place?”

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Page 46: Facilitated by Michael Bamberger  and Jim Rugh

Situations in which an NED may be the best design option Not possible to define a comparison group When the project involves complex processes of

behavioral change Complex, evolving contexts Outcomes not known in advance Many outcomes are qualitative Projects operate in different local settings When it is important to study implementation Project evolves slowly over a long period of time

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Page 47: Facilitated by Michael Bamberger  and Jim Rugh

Some potentially strong NEDsA. Interrupted time seriesB. Single case evaluation designsC. Longitudinal designsD. Mixed method case study designsE. Analysis of causality through program

theory modelsF. Concept mappingG. Contribution Analysis

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Any questions?48

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TIME FOR SMALL GROUP DISCUSSION 49

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1. Self-introductions

2. What constraints of these types have you

faced in your evaluation practice?

3. How did you cope with them?

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LOGIC MODELS

RealWorld Evaluation

Designing Evaluations under Budget, Time,

Data and Political Constraints

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Defining the program theory modelAll programs are based on a set of assumptions

(hypothesis) about how the project’s interventions should lead to desired outcomes.

Sometimes this is clearly spelled out in project documents.

Sometimes it is only implicit and the evaluator needs to help stakeholders articulate the hypothesis through a logic model.

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Defining and testing critical assumptions are essential (but often ignored) elements of program theory models.

The following is an example of a model to assess the impacts of microcredit on women’s social and economic empowerment

Defining the program theory model

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Sustainability• Structural changes will lead to long-term impacts.

Medium/long-term impacts• Increased women’s economic and social empowerment. • Economic and social welfare of women and their families will

improve. Short-term outcomes

• If women obtain loans they will start income-generating activities.• Women will be able to control the use of loans and reimburse them.

Outputs• If credit is available women will be willing and able to obtain loans

and technical assistance.

Critical logic chain hypothesis for a Gender-Inclusive Micro-Credit Program

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What does it take to measure indicators at each level?

Outcome: Change in behavior of participants (can be surveyed annually)

Output: Measured and reported by project staff (annually)

Activities: On-going (monitoring of interventions)

Inputs: On-going (financial accounts)

Impact :Population-based survey (baseline, endline evaluation)

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PROBLEM

PRIMARY CAUSE 2

PRIMARYCAUSE 1

PRIMARY CAUSE 3

Secondary cause 2.2

Secondary cause 2.3

Secondary cause 2.1

Tertiary cause 2.2.1

Tertiary cause 2.2.2

Tertiary cause 2.2.3

Consequences Consequences Consequences

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DESIRED IMPACT

OUTCOME 2

OUTCOME 1

OUTCOME 3

OUTPUT 2.2 OUTPUT 2.3OUTPUT 2.1

Intervention 2.2.1

Intervention 2.2.2

Intervention 2.2.3

Consequences Consequences Consequences

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Women empowered

Young women educated

Women in leadership roles

Economic opportunities

for women

Female enrollment rates

increase

Curriculum improved

Improved educational

policies

Parents persuaded to send girls to

school

Schools built

School system hires and pays

teachers

Reduction in poverty

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Advocacy Project Goal:

Improved educational

policies enacted

Program Goal: Young women educated

Construction Project Goal:

More classrooms

built

Teacher Education

Project Goal:

Improve quality of

curriculum

Program goal at impact level

ASSUMPTION(that others will do this) PARTNER will do this

OUR project

To have synergy and achieve impact all of these need to address

the same target population.

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Design Inputs ImplementationProcess

Outputs Outcomes Impacts Sustainability

Economic context in which the

project operates

Political context in which the project

operates

Institutional and operational

context

Socio-economic and cultural characteristics of the affected populations

Note: The orange boxes are included in conventional Program Theory Models. The addition of the blue boxes provides the recommended more complete analysis.

One form of Program Theory (Logic) Model

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Education Intervention Logic

Institutional Management

Curricula & Teaching Materials

Teacher Recruitment & Training

Education Facilities

Health

Better Allocation of Educational Resources

Increased Affordability of

Education

Optimal Employment

Skills and Learning

Enhancement

Equitable Access to Education

Quality of Education

Greater Income Opportunities

Economic Growth

Social Development

Poverty Reduction

OutputClusters

Outcomes Specific Impact

IntermediateImpacts

GlobalImpacts

Improved Participation in

Society

Improved Family Planning &

Health Awareness

MDG 3

MDG 2

MDG 1MDG 2

Source: OECD/DAC Network on Development Evaluation

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Inputs

Outputs

Intermediate outcomes

Impacts

Donor Government Other donors

Credit for small farmers

Rural roads

SchoolsHealth services

Increased rural H/H income

Increased production

Increased school enrolment

Increased use of health services

Access to off-farm employment

Improved education performance

Improved health

Increased political participation

Expanding the results chain for multi-donor, multi-component program

Attribution gets very difficult! Consider plausible contributions each makes.

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Contribution Analysis*1. Develop a program logic (theory of change)2. Identify existing evidence on the causal links

between interventions and possible effects. Identify gaps in information.

3. Assess alternative explanations of possible effects. Assess the possible contributions of different factors to effects.

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* John Mayne et al in EES’ Evaluation journal, Vol. 18, No. 3, July 2012

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Contribution Analysis*4. Create a performance story on the

contribution of the intervention to changes in impact variables.

5. Seek out additional evidence to improve the program’s performance story.

6. Revise and strengthen the performance story.

65

* John Mayne et al in EES’ Evaluation journal, Vol. 18, No. 3, July 2012

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Where there was no baseline

RealWorld Evaluation

Designing Evaluations under Budget, Time,

Data and Political Constraints

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Ways to reconstruct baseline conditionsA. Secondary dataB. Project recordsC. RecallD. Key informantsE. Participatory methods

What experience have

you had with any of

these methods?

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Assessing the utility of potential secondary data Reference period Population coverage Inclusion of required indicators Completeness Accuracy Free from bias

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Assessing the reliability of project records Who collected the data and for what

purpose? Were they collected for record-keeping or to

influence policymakers or other groups? Do monitoring data only refer to project

activities or do they also cover changes in outcomes?

Were the data intended exclusively for internal use? For use by a restricted group? Or for public use?

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Limitations of recall

Generally not reliable for precise quantitative data

Sample selection bias Deliberate or unintentional distortion Few empirical studies (except on

expenditure) to help adjust estimates

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Sources of bias in recall Who provides the information Under-estimation of small and routine expenditures “Telescoping” of recall concerning major expenditures Distortion to conform to accepted behavior:

• Intentional or unconscious• Romanticizing the past• Exaggerating (e.g. “We had nothing before this project came!”)

Contextual factors:• Time intervals used in question• Respondents expectations of what interviewer wants to

know Implications for the interview protocol

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Improving the validity of recall Conduct small studies to compare recall

with survey or other findings. Ensure all relevant groups interviewed Triangulation Link recall to important reference events

• Elections• Drought/flood/tsunami/war/displacement• Construction of road, school etc

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Key informants Not just officials and high status people Everyone can be a key informant on their

own situation:• Single mothers• Factory workers• Users of public transport• Sex workers• Street children

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Guidelines for key-informant analysis Triangulation greatly enhances validity

and understanding Include informants with different

experiences and perspectives Understand how each informant fits into

the picture Employ multiple rounds if necessary Carefully manage ethical issues

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PRA and related participatory techniques PRA (Participatory Rapid Appraisal) and PLA

(Participatory Learning and Action) techniques collect data at the group or community [rather than individual] level

Can either seek to identify consensus or identify different perspectives

Risk of bias:• If only certain sectors of the community

participate• If certain people dominate the discussion

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Participatory approaches should be used as much as possible

but even they should be used with appropriate rigor: how many (and which) people’s perspectives contributed to the story? 76

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Summary of issues in baseline reconstruction Variations in reliability of recall Memory distortion Secondary data not easy to use Secondary data incomplete or unreliable Key informants may distort the past

Yet in many cases we need to use one or more of these methods to determine what change occurred during the life of a project.

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Mixed-method (MM) evaluations

RealWorld Evaluation

Designing Evaluations under Budget, Time,

Data and Political Constraints

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The Main Messages1. No single evaluation approach can fully address the

complexities of development evaluations2. MM combines the breadth of quantitative (QUANT)

evaluation methods with the depth of qualitative (QUAL) methods

3. MM is an integrated approach to evaluation with specific tools and techniques for each stage of the evaluation cycle

4. MM are used differently by evaluators with a QUANT orientation and those with a QUAL orientation – and offer distinct benefits for each kind of evaluation

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A. Why mixed methods?No single evaluation

methodology can fully explain how

development programs operate in the real world

This explains the growing interest in mixed methods

evaluations

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Why mixed methods? No single evaluation method can fully explain how development programs operate in the real world

1. Programs operate in complex and changing environments

2. Interventions are affected by historical, cultural, political, economic and other contextual factors

3. Different methodologies are needed to measure different contextual factors, processes and outcomes.

4. Even “simple” interventions often involve complex processes of organizational and behavioral change

5. Programs change depending on how different sectors of the target population respond

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The benefits of a mixed methods approach

QUANTITATIVE breadth

QUALITATIVE depth

How many?How much?

How representative of the total population?

Are changes statistically significant?

• How were changes experienced by individuals?

• What actually happened on the ground?

• The quality of services

+

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Four decisions for designing a mixed methods evaluation

1. At which stages of the evaluation are mixed methods used?

2. Is the design sequential or concurrent?

3. Which approach is dominant?4. Is the design single or multi-level?

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Decision 1: At which stages of the evaluation are mixed methods used?

QUANT QUAL Mixed1. Formulation of hypotheses2. Sample design3. Evaluation design4. Data collection and recording5. Triangulation6. Data analysis and interpretation

Mixed methods can be used at any stage of the evaluation.A fully integrated MM design combines QUANT and QUAL

methods at all stages of the evaluation

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Decision 2: Is the design sequential or concurrent?

Sequential designs:• QUANT and QUAL approaches are used in

sequence Concurrent designs

• QUANT and QUAL approaches are both used at the same time

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Sequential QUAL dominant MM design: Evaluating the adoption of new seed varieties by different types of rural families.

Rapid QUANT household survey in project villages to estimate HH characteristics, ethnicity, agricultural production and seed adoption

QUAL data collection using key informants focus groups, observation, and preparation of case studies on households and farming practices.

QUAL data analysis using within and between-case analysis and constant comparison. Triangulation among different data sources.

quant QUAL QUAL

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A concurrent MM design: Triangulating QUANT and QUAL estimates of household income in project and comparison areas

Project communities

Comparison communities

QUANT household surveys

QUANT/QUAL Observation of household possessions and construction quality

QUAL: Focus groups

Triangulation of estimates from the 3 sources – to obtain the most reliable estimate of household income

QUANT and QUAL data collection methods are used at the same time

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A B CD

E F G

QUAL oriented studies gradually incorporating more QUANT focus

QUANT oriented studies gradually incorporating more QUAL focus

QU

AN

T QU

AL

A = completely QUANT designB = dominant QUANT with some QUAL elementsC = QUANT oriented design giving equal weight to both approachesD = Study designed as balanced MME = QUAL oriented design giving equal weight to both approaches.F = dominant QUAL design with some QUANT elementsG = completely QUAL design

Decision 3: which approach is dominant?

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A B CD

E F G

QU

AN

T

QU

AL

A QUANT dominant evaluation design

Example:A rapid qualitative diagnostic study is conducted to help design a quantitative household survey. The data is analyzed using quantitative analysis techniques [e.g. regression analysis]

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A B CD

E F G

QU

AN

T

QU

AL

A QUAL dominant evaluation design

ExampleA rapid quantitative sample survey is conducted. This is used to develop a typology of rice production systems. Qualitative case studies are selected to represent each type. The data is analyzed and presented using qualitative methods such as narrative descriptions, photographs and social maps.

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A Multi-level mixed methods designThe effects of a school feeding program on school enrolment

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C. Using mixed methods to strengthen each stage of the evaluation

1. Hypothesis formulation

2. Sample design

3. Evaluation design

4. Data collection

6. Data analysis and interpretation

5. Triangulation

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Stage 1. Mixed methods approaches to hypothesis development

Combining deductive (QUANT) and inductive (QUAL) hypotheses

Basing the evaluation framework on a theory of change

Strengthening construct validity by combining different QUANT and QUAL indicators

Contextualizing the evaluation

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Comparing DEDUCTIVE and INDUCTIVE hypotheses

Deductive InductiveMainly used in QUANT research Mainly used in QUAL research

Hypotheses test theories based on prior research

Hypotheses based on observations in the field

Hypotheses defined at start of the evaluation before data collection begins

Hypotheses not defined until data collection begins

Hypotheses normally do not change Hypotheses evolve as data collection progresses

Hypotheses can be tested experimentally

Hypotheses are tested using Theory of Change or logically

Mixed methods hypotheses combine both deductive and inductive

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Stage 2. Mixed method sample designs

Parallel mixed method sampling• Random (QUANT) and purposive (QUAL)

sampling Sequential MM sampling Multi-level MM sampling Strengthening the coverage of the sampling

frame Strengthening the matching of the project

and control groups

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Stage 3. Mixed method evaluation design

Combining experimental and quasi-experimental; designs with QUAL techniques to explore:• Processes and quality of services• Context• Behavioral change

Flexibility to adapt the evaluation to changes in the project design or the project context

In-depth analysis of how the project affects different groups

Creative identification of comparison groups

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Stage 4. Strengthening data collectionA. Integrating survey and QUAL data collectionB. Commonly used mixed method data collection

methods for strengthening QUANT evaluations1. Focus groups2. Observation3. Secondary data4. Case studies

C. Reconstructing baseline dataD. Interviewing difficult-to-reach groupsE. Collecting information on sensitive topicsF. Attention to contextual clues

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QUANT data collection

Household survey data collected on income and expenditures

QUANT data analysis:

Calculating mean, frequency

distributions and standard deviation of

income and expenditures

QUALData analysis

Review of interview and observation notes,

Analysis using constant comparative method

QUAL Data collection.

Sub-sample of household interview families selected. Interviews, key informants and observation. Detailed notes, taped interviews and photos.

TRIANGULATION PROCESS

Findings compared, reconciled and integrated. When different estimates are obtained all of the data is reviewed to understand why differences occur. If necessary teams may return to the field to investigate further

Possible return to field

Stage 5. Validating findings through triangulation

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Different kinds of triangulation Different data collection methods Different interviewers Collecting information at different times Different locations and contexts

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Stage 6. Mixed method data analysis and interpretation

Parallel MM data analysis Conversion MM data analysis

• Converting QUAL data into QUANT indicators and vice versa]

Sequential MM data analysis Multi-level MM data analysis Generalizing findings and recommendations

to other potential program settings

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Quantitative data collection methodsStrengths and weaknesses

Strengths Generalization statistically representative Estimate magnitude and

distribution of impacts Clear documentation of

methods Standardized approach Statistical control of bias and

external factors

Weaknesses Surveys cannot capture many

types of information Do not work for difficult to reach

groups No analysis of context Survey situation may alienate Long delay in obtaining results Reducing data to easy-to-

measure indicators loses perspectives on complex information

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Using Qualitative methods to improve the Evaluation design and results

Use recall to reconstruct the pre-test situation Interview key informants to identify other changes in the

community or in gender relations Conduct interviews or focus groups with women and

men to • assess the effect of loans on gender relations within the

household, such as changes in control of resources and decision-making

• identify other important results or unintended consequences: • increase in women’s work load, • increase in incidence of gender-based or domestic violence

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Determining Counterfactuals

RealWorld Evaluation

Designing Evaluations under Budget, Time,

Data and Political Constraints

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Attribution and counterfactuals

How do we know if the observed changes in the project participants or communities • income, health, attitudes, school attendance.

etc

are due to the implementation of the project • credit, water supply, transport vouchers, school

construction, etc

or to other unrelated factors?• changes in the economy, demographic

movements, other development programs, etc

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The Counterfactual What change would have occurred in

the relevant condition of the target population if there had been no intervention by this project?

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Where is the counterfactual?

After families had been living in a new housing project for 3 years, a study found average household income had increased by an 50%

Does this show that housing is an effective way to raise income?

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Comparing the project with two possible comparison groups

2004 2009

250

500

750

I n c o m e

Project group. 50% increase

Scenario 2. 50% increase in comparison group income. No evidence of project impact

Scenario 1. No increase in comparison group income.

Potential evidence of project impact

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Control group and comparison group

Control group = randomized allocation of subjects to project and non-treatment group

Comparison group = separate procedure for sampling project and non-treatment groups that are as similar as possible in all aspects except the treatment (intervention)

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Eligible individuals, communities, schools etc are randomly assigned to either:

• The project group (that receives the services) or• The control group (that does not have access

to the project services)

109

IE Designs: Experimental Designs

Randomized Control Trials

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A graphical illustration of an ‘ideal’counterfactual using pre-project trend line then RCT

110Time

Prim

ary

Out

com

e

Impact

Counterfactual

Intervention

Trend line

Subjects randomly assigned either to …

Treatment group

Control group

IMPACT

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There are other methods for assessing the counterfactual Reliable secondary data that depicts

relevant trends in the population Longitudinal monitoring data (if it includes

non-reached population) Qualitative methods to obtain perspectives

of key informants, participants, neighbors, etc.

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Ways to reconstruct comparison groups Judgmental matching of communities When there is phased introduction of

project services beneficiaries entering in later phases can be used as “pipeline” comparison groups

Internal controls when different subjects receive different combinations and levels of services

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Using propensity scores and other methods to strengthen comparison groups Propensity score matching Rapid assessment studies can compare

characteristics of project and comparison groups using:• Observation• Key informants• Focus groups• Secondary data• Aerial photos and GIS data

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Issues in reconstructing comparison groups Project areas often selected purposively and

difficult to match Differences between project and comparison

groups - difficult to assess whether outcomes were due to project interventions or to these initial differences

Lack of good data to select comparison groups

Contamination (good ideas tend to spread!) Econometric methods cannot fully adjust for

initial differences between the groups [unobservables]

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What experiences have you had with identifying counterfactual data?

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Enough of our presentations: it’s time

for you (THE RealWorld PEOPLE!)

to get involved yourselves.

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Time for small-group work. Read your case studies

and begin your discussions.

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1. Some of you are playing the role of evaluation consultants, others are clients commissioning the evaluation.

2. Decide what your group will propose to do to address the given constraints/ challenges.

3. Prepare to negotiate the ToR with the other group (later this afternoon).

Small group case study work

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The purpose of this exercise to gain some practical ‘feel’ for applying what we’ve learned about RealWorld Evaluation.

Group A (consultants) Evaluation team to consider how they will propose a revised evaluation design and plan that reduces the budget by 25% to 50%, and yet meets the needs of both clients (City Housing Department and the international donor).

Group B (clients) will also review the initial proposal in light of what they have learned about RealWorld Evaluation, and prepare to re-negotiate the plans with the consultancy group. Note: there are two types of clients: the Housing Department (project implementers) and the international donor (foundation).

Groups are given 45 minutes to prepare their cases.

Later the Consultants’ group will meet with Clients’ groups to negotiate their proposed revisions in the plans for this evaluation. 45 minutes will be available for those negotiation sessions.

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More holistic approaches to

Impact Evaluation

RealWorld Evaluation

Designing Evaluations under Budget, Time,

Data and Political Constraints

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Some recent developments in impact evaluation in development

J-PAL is best understood as a network of affiliated researchers … united by their use of the randomized trial methodology…

2003

2008

2006

2009

Impact Evaluation for Improving Development –  3ie and AfrEA conference in Cairo March 2009

2012 Stern, E., N. Stame, J. Mayne, K. Forss, R. Davies and B. Befani. Broadening the Range of Designs and Methods for Impact Evaluations DFID Working Paper 38

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Randomized control trials and strong statistical

designs

When might they be appropriate … and when not?

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The arguments in favor of RCTs1. Selection bias has resulted in wrongly attributing

changes to the results of an interventiona. Selection bias tends to over-estimate program impactsb. Agencies continue to support their favorite programs –

even though they may be producing fewer benefits than claimed

2. A high percentage of evaluations use methodologically unsound methods, but funding agencies are rarely challenged

3. Agencies evaluate themselves and are able to ensure that the findings are never too critical.

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4. Isolating defined treatments and testing them in a systematically controlled way is an effective way to improve our understanding of the potential benefits of this specific intervention.

5. It has proved possible to use RCTs in a very wide range of contexts and sectors and in many cases they have produced useful findings at the operational and policy levels

The arguments in favor of RCTs

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6. RCTs provide a reference point against which to assess the methodological rigor of other evaluation designs. A good way to keep evaluators honest.

The arguments in favor of RCTs

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Practical, political and ethical limitations on the use of RCTs1. It is only possible to use RCTs in a

small proportion of evaluations It is often not possible to identify a

comparison group The goal of reaching as many people as

possible does not permit the exclusion of certain people

Programs are often implemented gradually over a period of time and it is not possible to define a specific starting point.

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2. Opposition to the random assignment of beneficiaries Politicians do not wish to lose their control

over who receives benefits Managers (and others) may wish to target

the poorest or most vulnerable sectors and not to give scarce resources to better-off groups

Communities may not accept this approach

Practical, political and ethical limitations on the use of RCTs

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3. Ethical considerations

Withholding needed resources from people who need them

Giving benefits to less needy groups while depriving the most needy

Treating people as guinea pigs in a laboratory

Practical, political and ethical limitations on the use of RCTs

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The ethical arguments are not so clear and one-sided

Using weaker evaluation designs may result in poor programs continuing to be funded – thus depriving people of potentially higher benefits from new programs that could use these funds

Resources are frequently limited so that some allocation criteria is required – what are the alternatives to randomization?

Rigorous evaluations can improve program performance

Practical, political and ethical limitations on the use of RCTs

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Methodological limitations of RCTs and strong QEDs1. Can only evaluate a single component

(intervention) of a multi-component program2. Inflexible design – difficult to adjust design to

modifications to program design or to changes in the context in which programs are implemented

3. Cannot control what happens during implementation

4. Regression only estimates the average effect size – difficult to generalize the findings to other contexts.

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5. Simple RCTs to not assess the implementation process and when intended impacts are not found, the analysis cannot distinguish between “design failure” and “implementation failure”

Methodological limitations of RCTs and strong QEDs

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Implications for RWE1. RCTs are a potentially useful component of

some kinds of evaluation – but should almost never be used in isolation An RCT is strongest when part of a mixed methods

design

2. Define whether a program is intended: as an experiment to rigorously assess promising new

interventions that might be replicated on a large scale OR

To provide services to as many people as possible

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3. If programs are designed as experiments, an RCT can be cheaper than many alternative survey designs.

4. Always ask the question of how non-experimental and weak QEDs can address the very serious issues of selection bias and the over-estimation of project impacts. Never automatically reject RCTs without assessing the

important reasons why these methods were developed But never accept RCTs (or any other evaluation

method) as the best or only approach

Implications for RWE

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5. The real world is too complex for any single evaluation approach to fully address all of the important questions that must be answered.

Implications for RWE

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1. Direct cause-effect relationship between one output (or a very limited number of outputs) and an outcome that can be measured by the end of the research project? Pretty clear attribution.

… OR …

2. Changes in higher-level indicators of sustainable improvement in the quality of life of people, e.g. the MDGs (Millennium Development Goals)? More significant but much more difficult to assess direct attribution.

So what should be included in a “rigorous impact evaluation”?

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OECD-DAC (2002: 24) defines impact as “the positive and negative, primary and secondary long-term effects produced by a development intervention, directly or indirectly, intended or unintended. These effects can be economic, sociocultural, institutional, environmental, technological or of other types”.

Does it mention or imply direct attribution? Or point to the need for counterfactuals or Randomized Control Trials (RCTs)?

So what should be included in a “rigorous impact evaluation”?

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Different lenses needed for different situations in the RealWorld

Simple Complicated ComplexFollowing a recipe Sending a rocket to the

moonRaising a child

Recipes are tested to assure easy replication

Sending one rocket to the moon increases assurance that the next will also be a success

Raising one child provides experience but is no guarantee of success with the next

The best recipes give good results every time

There is a high degree of certainty of outcome

Uncertainty of outcome remains

Sources: Westley et al (2006) and Stacey (2007), cited in Patton 2008; also presented by Patricia Rodgers at Cairo impact conference 2009.

137

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Examples of cause-effect correlations that are generally accepted

• Vaccinating young children with a standard set of vaccinations at prescribed ages leads to reduction of childhood diseases (means of verification involves viewing children’s health charts, not just total quantity of vaccines delivered to clinic)

• Other examples … ?

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But look at examples of what kinds of interventions have been “rigorously tested” using RCTs

• Conditional cash transfers• The use of visual aids in Kenyan schools• Deworming children (as if that’s all that’s needed

to enable them to get a good education)

• Note that this kind of research is based on the quest for Silver Bullets – simple, most cost-effective solutions to complex problems.

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Quoted by Patricia Rogers, RMIT University 140

“Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.“

J. W. Tukey (1962, page 13), "The future of data analysis". Annals of Mathematical Statistics 33(1), pp. 1-67.

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Is that what we call “scientific method”?

There is much more to impact, to rigor, and to “the scientific method” than RCTs. Serious impact evaluations require a more holistic approach.

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DESIRED IMPACT

OUTCOME 2

OUTCOME 1

OUTCOME 3

OUTPUT 2.2 OUTPUT 2.3OUTPUT 2.1

Intervention 2.2.1

Intervention 2.2.2

Intervention 2.2.3

Consequences Consequences Consequences

A Simple RCT

A more comprehensive design

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The limited use of strong evaluation designs In the RealWorld (at least of international

development programs) we estimate that:

• fewer than 5%-10% of project impact evaluations use a strong experimental or even quasi-experimental designs

• significantly less than 5% of impact evaluations use randomized control trials (‘pure’ experimental design)

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What kinds of evaluation designs are actually used in the real world of international development? Findings from meta-evaluations of 336 evaluation reports of an INGO.

Post-test only 59%

Before-and-after 25%

With-and-without 15%

Other counterfactual

1%

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1) thorough consultation with and involvement by a variety of stakeholders,

2) articulating a comprehensive logic model that includes relevant external influences,

3) getting agreement on desirable ‘impact level’ goals and indicators,

4) adapting evaluation design as well as data collection and analysis methodologies to respond to the questions being asked, …

Rigorous impact evaluation should include (but is not limited to):

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5) adequately monitoring and documenting the process throughout the life of the program being evaluated, 6) using an appropriate combination of methods to triangulate evidence being collected, 7) being sufficiently flexible to account for evolving contexts, …

Rigorous impact evaluation should include (but is not limited to):

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8) using a variety of ways to determine the counterfactual, 9) estimating the potential sustainability of whatever changes have been observed, 10) communicating the findings to different audiences in useful ways, 11) etc. …

Rigorous impact evaluation should include (but is not limited to):

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The point is that the list of what’s required for ‘rigorous’ impact evaluation goes way beyond initial randomization into treatment and ‘control’ groups.

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We must be careful that in using the “Gold Standard”

we do not violate the “Golden Rule”:“Judge not that you not be judged!”

In other words:“Evaluate others as you would have

them evaluate you.”

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Caution: Too often what is called Impact Evaluation is based on a “we will examine and judge you” paradigm. When we want our own programs evaluated we prefer a more holistic approach.

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How much more helpful it is when the approach to evaluation is more like holding up a mirror to help people reflect on their own reality: facilitated self-evaluation.

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The bottom line is defined by this question:

Are our programs making plausible contributions towards positive impact on the quality of life of our intended beneficiaries? Let’s not forget them!

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Thank you!

153

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154ANY QUESTIONS? 154

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Time for consultancy teams to meet with

clients to negotiate the revised ToRs for the

evaluation of the housing project.

155

Time to negotiate!

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What did you learn about RealWorld Evaluation by getting involved in that

case study and role-playing exercise?

156

Review

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Next steps: What are some of the practical realities of applying RWE approaches in our

own work?

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Main workshop messages1. Evaluators must be prepared for RealWorld

evaluation challenges.2. There is considerable experience to learn from.3. A toolkit of practical “RealWorld” evaluation

techniques is available (see www.RealWorldEvaluation.org). 4. Never use time and budget constraints as an excuse

for sloppy evaluation methodology.5. A “threats to validity” checklist helps keep you

honest by identifying potential weaknesses in your evaluation design and analysis.

6. The evaluation report should disclose what constraints were faced and what was done to address them.

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Additional References for IE DFID “Broadening the range of designs and methods for impact evaluations”

http://www.oecd.org/dataoecd/0/16/50399683.pdf Robert Picciotto “Experimententalism and development evaluation: Will the

bubble burst?” in Evaluation journal (EES) April 2012: http://evi.sagepub.com/ Martin Ravallion “Should the Randomistas Rule?” (Economists’ Voice 2009)

http://ideas.repec.org/a/bpj/evoice/v6y2009i2n6.html William Easterly “Measuring How and Why Aid Works—or Doesn't”

http://aidwatchers.com/2011/05/controlled-experiments-and-uncontrollable-humans/

Control freaks: Are “randomised evaluations” a better way of doing aid and development policy? The Economist June 12, 2008 http://www.economist.com/node/11535592

Series of guidance notes (and webinars) on impact evaluation produced by InterAction (consortium of US-based INGOs):http://www.interaction.org/impact-evaluation-notes

Evaluation (EES journal) Special Issue on Contribution Analysis, Vol. 18, No. 3, July 2012. www.evi.sagepub.com

Impact Evaluation In Practice www.worldbank.org/ieinpractice

159

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Unanticipated consequences of development interventions:

a blind spot for evaluation theory and practice

[and if time permits]

A few thoughts on evaluating complex development interventions

Michael Bamberger

EXTRA

BONUS

SECTION

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1. Development programs almost never work out as planned

The outcomes of most programs are unpredictable• Programs propose simple, linear solutions to complex

problems • Even when programs go smoothly and are implemented as

planned – human behavior is difficult to predict Programs operate in complex systems with constantly

changing economic, political, security, natural environmental and socio-cultural environments

Program planners have much less control than they often think

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The scope of unanticipated outcomes increases as we move from programs to complex development interventions - • such as Country Programs or multi-donor

collaborative initiatives

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2. So why are evaluators still surprised when programs have unanticipated outcomes? Unanticipated outcomes still catch

planners and evaluators off-guard even though experience has shown that very few programs work out exactly as planned

Many unanticipated outcomes, including serious negative consequences, are not even detected by many evaluations

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Some serious negative outcomes not captured by the evaluation A food-for-work program in Central America:

• Many women were forbidden by their husbands from participating in the project

• Some women were seriously beaten by their husbands for attending meetings

A slum upgrading project in South East Asia:• Prior to the official start of the project many slum dwellers were

forced (sometimes at gunpoint) to sell their houses at a very low price to people with political contacts

A road construction project in East Africa:• Prior to the first supervision mission for a road construction project

the government destroyed a number of houses to avoid paying compensation to the families as specified in the loan agreement

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3. Why are evaluations often unable to detect these unanticipated outcomes? Funders only want evaluators to look at

whether their programs achieve their intended outcomes/ goals• Don’t rock the boat

Real-world evaluation constraints:• Budget, time, data constraints• Political and organizational constraints

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Many evaluation designs make it difficult to identify unanticipated outcomes Many theories of change and logic models only

explain and assess how a program is expected to achieve its objectives• They say very little about other things that might happen

Results frameworks only monitor intended outcomes

Conventional quantitative evaluation designs only assess whether observed changes can be attributed to the program intervention

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Structured surveys collect information relevant to program objectives but rarely provide an opportunity for respondents to talk about other things on their mind

Difficult to find/interview vulnerable groups and those who do not benefit

Focus groups often only cover people involved in the project

Many evaluation designs make it difficult to identify unanticipated outcomes, cont.

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Many development evaluations focus too much on the internal concerns and procedures of their own agency• And do not focus sufficiently on the national

development context and the concerns of national stakeholders.

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Many unanticipated events occur outside the vision field of the evaluation Key events occur before the evaluation begins (e.g.

people are forced to sell land or houses at bargain prices)

Events occur when the evaluator is not there (at night, during the rainy season, in the privacy of the household)

Critical events occur after the evaluation has ended Many quantitative evaluations have no contact with

the program between the two applications of the survey instruments (pre-test and post-test)

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Evaluators often ignore context Budget constraints mean that the

poorest and most vulnerable groups who live far from the village center, often cannot be interviewed

Many unanticipated events occur outside the vision field of the evaluation, cont.

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4. Why should we worry? Unanticipated events:

• Seriously reduce the efficiency and effectiveness of programs

• Important lessons are not learned• The vulnerable and voiceless tend to be the

most affected• Some groups or communities can be

significantly worse off as a result of development interventions

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5. What can be done?This section draws on and expands the framework presented in:

Jonathan Morell “Evaluation in the Face of Uncertainty: Anticipating Surprise and Responding to the Inevitable”Guilford 2010

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What can be done? Understanding the nature of the unexpected

Types of surprise• Surprises we anticipate and address • Surprises that could be planned for and detected• Surprises that cannot be anticipated

The probability and magnitude of surprise is related to:• The level of innovation in the program [uncharted territory]• The nature of the intervention• Fidelity to the design protocol• Robustness of the program in different settings• Level of understanding of the program context• Time between the intervention and the estimation of outcomes

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What can be done?Ways to foresee and address unanticipated outcomes

Use of theory-based evaluation• With focus on mechanisms, linkages and testing

assumptions Systematic use of past experience

• Participatory consultations with affected populations Break the evaluation down into shorter

segments Adapt forecasting methods used by planners Broader definition of the role of monitoring

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What can be done?Agile evaluation Always have a “Plan B” [see next slide] Flexibility in terms of data collection Flexible designs that can be rapidly adapted to

changing circumstances Dynamic monitoring Unpacking the program into separate, but closely

linked, components• Each of which is easier to evaluate

Constant review of the program theory and the underlying assumptions

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“Plan B” Planning for the unexpectedWhat to do when: the program begins before the baseline

study has been conducted? the comparison groups vanish? the program is restructured? Essential data is not available?

• It may be intentionally withheld

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“Eyes wide open” evaluationKnowing what is really happening on the ground Find out about critical events that took place before

the evaluation/ program officially began Develop diverse sources inside and outside the

program to make sure you know what everyone else in the country knows

Check who you are getting information from and who you are not meeting• Know who participates in focus groups

Develop creative ways to get information on, and about, non-beneficiaries and groups who are worse off

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A few thoughts on the evaluation of complex programsThe dimensions of complexity1. Scope and scale2. Organizational complexity3. Contextual factors4. Behavioral5. Challenges assessing

• Attribution• Contribution• Substitution

6. Some interventions are less complex than claimed

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1. Scope and scalea. Number of components and service

levelsb. Number and types of geographic areasc. Larger programs tend to have more

features and to be implemented differently in different regions

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2. Organizational complexitya. Programs co-funded by different agencies tend to be

more complex• Different implementation strategies and management procedures• Different components/services• Coordination problems

b. Programs with several coordinating and implementing agencies

c. National and regional level programs have more levels of coordination

d. National agencies may lack implementation capacity

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3. Contextual factors affecting complexitya. Economic settingb. Political contextc. Administrative and organizationald. Climatic and ecologicale. Legal and regulatoryf. Historicalg. Socio-cultural

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4. Behaviorala. “Simple” projects may involve complex

processes of behavioral changeb. Understanding group behaviorc. Social networks and social

communication

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5. Attribution, contribution and substitutiona. Attribution difficult to assess for complex programs

• Different donors• Target groups exposed to other programs• Difficult to define a counterfactual

b. Contribution analysis:• Conventional CA only focuses on process but not outcomes

c. Substitution• Difficult to track donor funds and other inputs• Even more difficult to track substitution of donor for

government resources

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6. Interventions that look “complex” may not bea. “complex” interventions may comprise a

number of “simple” components• Coordination and management may be the

only “complex” dimensions

b. Some agencies may find it convenient to claim their programs are too complex to be rigorously evaluated – to avoid critical scrutiny.

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Theory-based

approaches

strengthening alternative counterfactuals

• “Unpacking complex programs”

• Portfolio analysis• Reconstructing baseline

data• Creative use of secondary

data• Secondary data• Triangulation

Qualitative approaches

Quantitative approaches

STRATEGIES FOR EVALUATING COMPLEX PROGRAMS

Rating scales

Counterfactual designs• Attribution analysis

• Contribution analysis• Substitution analysis

Mixed method designs

Estimating impacts

The value-added of agency X

Net increase in resources

for a program