Michael Bamberger and Jim Rugh

Michael Bamberger and Jim Rugh
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: RealWorld Evaluation - Steps 1, 2 & 3

Workshop Objectives 1. 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; RealWorld Evaluation - Steps 1, 2 & 3

Workshop Objectives Identifying and assessing various design options that could be used in a particular evaluation setting; Some tips on developing logic models; Ways to reconstruct baseline data; How to account for what would have happened without the project’s interventions: alternative counterfactuals; The advantages of using mixed-methods designs; Considerations for more holistic approaches to impact evaluation. RealWorld Evaluation - Steps 1, 2 & 3

Workshop Objectives Note: 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. RealWorld Evaluation - Steps 1, 2 & 3

Workshop agenda: morning
8:00-8:30 Introduction + Brief overview of the RealWorld Evaluation approach 8:30-9:00 Basic evaluation design scenarios Jim 29-48 9:00-9:30 Small group discussion: personal introductions, including RWE-type constraints you have faced in your own practice 9:30-9:50 break 9:50-10:20 Logic models; reconstructing baselines Jim 10:20-10:40 Mixed Method evaluations Michael 10:40-11:00 Alternative counterfactuals Jim 11:00-12:00 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

Workshop agenda: afternoon
1:00-1:45 Impact Evaluations Michael , Jim 1:45-2:30 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 2:50-3:00 Wrap-up and evaluation of workshop by participants

OVERVIEW OF THE RWE APPROACH
RealWorld Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints OVERVIEW OF THE RWE APPROACH

Typical RealWorld Evaluation Scenario
Evaluator(s) not brought in until near end of project For 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

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)

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

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.

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)

2nd edition published January 2012
RealWorld Evaluation Bamberger Rugh Mabry RealWorld Evaluation Working Under Budget, Time, Data, and Political Constraints EDITION RealWorld Evaluation 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 Michael Bamberger Jim Rugh Linda Mabry 2 EDITI ON 2nd edition published January 2012

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

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.

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.

The Steps of the RealWorld Evaluation Approach
Step 1: Planning and scoping the evaluation Step 2: Addressing budget constraints Step 3: Addressing time constraints Step 4: Addressing data constraints Step 5: Addressing political constraints Step 6: Assessing and addressing the strengths and weaknesses of the evaluation design Step 7: Helping clients use the evaluation See page 5 of the Condensed Summary for more details

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 RealWorld Evaluation - Steps 1, 2 & 3

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? RealWorld Evaluation - Steps 1, 2 & 3

Understanding client information needs
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! RealWorld Evaluation - Steps 1, 2 & 3

Who asked for the evaluation? (Who are the key stakeholders)?
Still part of scoping: Other questions to answer as you customize an evaluation Terms of Reference (ToR): Who asked for the evaluation? (Who are the key stakeholders)? What are the key questions to be answered? Will this be mostly a developmental, formative or summative evaluation? Will there be a next phase, or other projects designed based on the findings of this evaluation? 21 RealWorld Evaluation - Steps 1, 2 & 3

Other questions to answer as you customize an evaluation ToR:
What decisions will be made in response to the findings of this evaluation? What is the appropriate level of rigor? What is the scope / scale of the evaluation / evaluand (thing to be evaluated)? How much time will be needed / available? What financial resources are needed / available? 22 RealWorld Evaluation - Steps 1, 2 & 3

Other questions to answer as you customize an evaluation ToR:
Should the evaluation rely mainly on quantitative or qualitative methods? Should participatory methods be used? Can / should there be a household survey? Who should be interviewed? Who should be involved in planning / implementing the evaluation? What are the most appropriate forms of communicating the findings to different stakeholder audiences? 23 RealWorld Evaluation - Steps 1, 2 & 3

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 24 RealWorld Evaluation - Steps 1, 2 & 3

Different levels of rigor
depends on source of evidence; level of confidence; use of information Objective, high precision – but requiring more time & expense Quick & cheap – but subjective, sloppy Level 5: A very thorough research project is undertaken to conduct in-depth analysis of situation; P= +/- 1% Book published! 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 3: A rapid survey is conducted on a convenient sample of participants; P= +/- 10% Decision maker reads 10-page summary of report Some are now calling this the “Rugh rigor scale.” Its purpose is simply to get us to think in terms of levels of rigor appropriate for the degree of precision required. Remember, the highest level of rigor is neither feasible nor necessary in most RealWorld Evaluation situations. At the low end decisions are based on “efficient” (quick and cheap) sources of information. Unfortunately these typically are based on rather subjective, sloppy sources. At the high end are systems that provide much more objective, precise information; but they take more time and resources to collect and process. Note that levels of rigor apply to qualitative as well as quantitative methods -- how well the evidence/data is collected and analyzed, in addition to sample design and size. They also call for triangulation, using a diversity of methods. What’s important is to determine what level of informational precision is required to inform a particular set of decisions, and thus what level of rigor is appropriate for collecting it. If the consequences of a decision are not significant, it would probably not be justified to spend a whole lot of time and resources collecting the information. On the other hand, if the decision will affect the lives of thousands of people and involve hundreds of thousands of dollars, it had better be based on very reliable information. 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 1: A few people are asked their perspectives about project; P= +/- 40% Decision made in a few minutes 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 25 RealWorld Evaluation - Steps 1, 2 & 3

CONDUCTING AN EVALUATION IS LIKE LAYING A PIPELINE
Randomized sample selection Reliability & validity of indicators Questionnaire quality Depth of analysis Reporting & utilization Quality of data collection Consider the various elements involved in conducting a survey. When we consider rigor we need to consider the level of rigor involved in all of those elements. QUALITY OF INFORMATION GENERATED BY AN EVALUATION DEPENDS UPON LEVEL OF RIGOR OF ALL COMPONENTS

Relevance & validity of indicators Randomized sample selection
Questionnaire quality Depth of analysis Reporting & utilization Quality of data collection The quality of information/data obtained from a survey or other evaluation method depends on all of the components. The “flow” or quality will be limited to the “constraints” or level of rigor of the weakest component. AMOUNT OF “FLOW” (QUALITY) OF INFORMATION IS LIMITED TO THE SMALLEST COMPONENT OF THE SURVEY “PIPELINE”

Same level of rigor Determining appropriate levels of precision for
events in a life-of-project evaluation plan High rigor Low rigor 2 3 4 Same level of rigor Final evaluation Baseline study Mid-term evaluation Here we look a way to lay out plans for evaluation events during the life of a project, considering what level of rigor will be required for each. The initial diagnostic assessment should be rigorous enough to reveal what issues need to be addressed in the target community, but one should not spend a disproportionate amount of the budget on it. The baseline, as we will soon see, should be done with the same methodology and level of rigor as will be expected for the final evaluation. During the life of the project there may be other evaluation events, such as annual self-evaluations, a mid-term evaluation, or a special study to examine some aspect not considered earlier (e.g. gender equity). The appropriate level of rigor (and budget) for each of these needs to be determined, relative to other evaluation events. It is commonly agreed that the final evaluation should be quite rigorous and precise, to assess what was achieved by the project. However, if the baseline (the measure of how things were before the project started) was not done in a comparable way, it is difficult to prove what difference the project made. Special Study Needs assessment Annual self-evaluation Time during project life cycle 28 RealWorld Evaluation - Steps 1, 2 & 3

RealWorld Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints
EVALUATION DESIGNS RealWorld Evaluation - Steps 1, 2 & 3

Some of the purposes for program evaluation
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 RealWorld Evaluation - Steps 1, 2 & 3

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. RealWorld Evaluation - Steps 1, 2 & 3

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. RealWorld Evaluation - Steps 1, 2 & 3

An introduction to various evaluation designs
Illustrating the need for quasi-experimental longitudinal time series evaluation design Project participants scale of major impact indicator Here’s a very short course on Evaluation (research) Design. Let’s assume a project’s Final Goal has a quantifiable indicator. (Think of an example. The enrollment rate of girls in schools might be one.) Higher is better. Evaluation Design #1: Only end-of-project evaluation. Indicator appears to be high, but even if measured very precisely, it begs the question: compared to what? Evaluation Design #2: Pre-test + post-test (baseline + evaluation). Now we see that the indicator went from low to high. Impressive. Did the project have impact? A good evaluator would ask if the change is attributable to the project’s interventions or could it be due to general trends? Evaluation Design #3: Post-test with control (comparison). In this scenario a “comparable” community was found and the indicator measured there during the evaluation. But the evaluator wonders if we purposely chose a poor comparison group. Evaluation Design #4: Quasi-Experimental Design. Pretty convincing that the project’s participants did better than the comparison group. But it only involves two snapshots before and two after. Evaluation Design #5: Longitudinal monitoring (at least small sample of proxy outcome indicator). Gives a more detailed view of trends during the life of the project. Looks good, but evaluator should still ask about sustainability. Evaluation Design #6: Quasi-Experimental Longitudinal Design with Ex-Post Evaluation some time after project completion. Now we see that the project’s participants were spoiled (by some form of subsidy) and in the long-run the “comparison” community did better. Comparison group baseline end of project evaluation post project evaluation 33 RealWorld Evaluation - Steps 1, 2 & 3

… one at a time, beginning with the most rigorous design.
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. 34 RealWorld Evaluation - Steps 1, 2 & 3

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 Note: the 7 RWE evaluation designs are laid out on page 8 of the Condensed Overview of the RealWorld Evaluation book 35 RealWorld Evaluation - Steps 1, 2 & 3

Design #1: Longitudinal Quasi-experimental
P1 X P2 X P3 P4 C C2 C3 C4 Project participants Comparison group baseline midterm end of project evaluation post project evaluation 36 RealWorld Evaluation - Steps 1, 2 & 3

Design #2: Quasi-experimental (pre+post, with comparison)
P1 X P2 C C2 Project participants Notice how much less information is available as various components of evaluation design are removed. Comparison group baseline end of project evaluation 37 RealWorld Evaluation - Steps 1, 2 & 3

Design #2+: Randomized Control Trial
P1 X P2 C C2 Project participants Research subjects randomly assigned either to project or control group. Even if there is an initial random selection of who should participate in the project and who should be held as “control” (the essential yet controversial element of Randomized Control Trials), notice how much information is missing without longitudinal data and ex-post evaluation. Control group baseline end of project evaluation 38 RealWorld Evaluation - Steps 1, 2 & 3

Design #3: Truncated Longitudinal
X P1 X P2 C1 C2 Project participants In this scenario there was no baseline conducted at the beginning of the project, but there was a midterm (e.g. year 2.5 of a 5-year project) survey when relevant data was measured. This helps, but the remaining time is so short it will be hard to create much change. Comparison group midterm end of project evaluation 39 RealWorld Evaluation - Steps 1, 2 & 3

Design #4: Pre+post of project; post-only comparison
P X P2 C Project participants In this design no baseline data was collected on the comparison group, though data was collected on a comparison group at the endline. Requires seeking adequate information to indicate what the conditions of the comparison group were at the time the project started. Look for relevant secondary data, key informants, use of recall methods, etc. Comparison group baseline end of project evaluation 40 RealWorld Evaluation - Steps 1, 2 & 3

Design #5: Post-test only of project and comparison
X P C Project participants No baseline. Begs the question on what the conditions were for both the project’s participants and the comparison group at the time the project started. Look for relevant secondary data, key informants, recall, etc. to fill in the missing information. Comparison group end of project evaluation 41 RealWorld Evaluation - Steps 1, 2 & 3

Design #6: Pre+post of project; no comparison
P X P2 Project participants Pre-test and post-test of the project participants measures change in the indicator, but this design provides no information on the counterfactual -- what would have happened without the project. Needs to be supplemented by information from other sources on what changes occurred in the general population or in comparable communities during the life of the project. baseline end of project evaluation 42 RealWorld Evaluation - Steps 1, 2 & 3

Design #7: Post-test only of project participants
X P Project participants 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? Status of high-level outcome indicator only measured at end of project, only of project beneficiaries. Obviously the weakest evaluation design – yet by far the most common scenario in the real world (at least in international development projects). Even if the indicator in question is measured very precisely (e.g. with a very rigorous survey, or exhaustive qualitative methods) there was no direct measurement of what change occurred during the life of the project, nor any form of counterfactual. Very important to use complementary methods to obtain other information. end of project evaluation 43 RealWorld Evaluation - Steps 1, 2 & 3

See Table 2.2 on page 8 of Condensed Overview of RWE
Design T1 (baseline) X (intervention) T2 (midterm) (intervention, cont.) T3 (endline) T4 (ex-post) 1 P1 C1 P2 C2 P3 C3 P4 C4 2 3 4 5 6 7 This table summarizes these 7 evaluation designs.

“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?”

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

Some potentially strong NEDs
Interrupted time series Single case evaluation designs Longitudinal designs Mixed method case study designs Analysis of causality through program theory models Concept mapping Contribution Analysis

Any questions? 48

TIME FOR SMALL GROUP DISCUSSION
49

How did you cope with them?
Self-introductions What constraints of these types have you faced in your evaluation practice? How did you cope with them? 50

RealWorld Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints
LOGIC MODELS RealWorld Evaluation - Steps 1, 2 & 3

Defining the program theory model
All 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. RealWorld Evaluation - Steps 1, 2 & 3

Defining the program theory model
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 RealWorld Evaluation - Steps 1, 2 & 3

Critical logic chain hypothesis for a Gender-Inclusive Micro-Credit Program
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. Typically project designers begin with their planned activities and outputs, then predict what outcomes and impact will come about due to those project interventions. RealWorld Evaluation - Steps 1, 2 & 3

What does it take to measure indicators at each level?
Impact :Population-based survey (baseline, endline evaluation) Outcome: Change in behavior of participants (can be surveyed annually) The quality of each level in the cause-effect hierarchy is measured at the next higher level. Here we consider what it takes to measure indicators at each level. Inputs are commonly measured by the financial accounting system. (We’re pretty good at this form of accountability.) Activities are frequently reported by field staff. (Too frequently -- typically there is far more recorded than is really needed for management decision-making.) At least in their annual reports, project staff should document what Outputs the project achieved. There are basically two types of Effect (Outcome) measurement: A) One involves following up project participants to see how many are practicing what they were taught. B) To measure the proportion of the community practicing these techniques (behaviors) requires a population-based survey. Project impact goals commonly refer to the percentage of the target population that will be benefited. This calls for a population-based survey. That requires extra effort and resources; is not likely to be done every year, but should be done at least at baseline and final project evaluation. Program impact evaluations that assess the higher (and multi-sectoral) impact of a series of projects require even greater resources and sophistication. These should probably not be attempted at time intervals of less than 10 years. Output: Measured and reported by project staff (annually) Activities: On-going (monitoring of interventions) Inputs: On-going (financial accounts)

PROBLEM Consequences Consequences Consequences PRIMARYCAUSE 1
Ideally project design should begin by key stakeholders (including intended beneficiaries) going through a process of identifying the main problem they want to address – the change they want to bring about. Then identifying primary causes of that problem, secondary causes, tertiary causes (here illustrated with three at each level, though there could be fewer or more). There can, of course, be higher-level consequences; but the project should identify a major problem it will address that can reasonably be expected to be achieved during the project’s lifetime. Secondary cause 2.1 Secondary cause 2.3 Secondary cause 2.2 Tertiary cause 2.2.1 Tertiary cause 2.2.2 Tertiary cause 2.2.3

DESIRED IMPACT Consequences Consequences Consequences OUTCOME 1
The problem tree is then converted to a “Solution Tree” with the high-level change (in human conditions) identified as the desired Impact Goal, Outcome Objectives needed to achieve that Impact, project Outputs needed to achieve each Outcome Objective, etc. At the lower level are the actual interventions the project intends to undertake (e.g. training). Note that good design practice begins at the top, then works down. OUTPUT 2.1 OUTPUT 2.3 OUTPUT 2.2 Intervention 2.2.1 Intervention 2.2.2 Intervention 2.2.3

Reduction in poverty Schools built Women empowered
Economic opportunities for women Women in leadership roles Young women educated We will illustrate this form of logic model with an education project – more specifically one that is focused on building schools. (Typically beginning at the bottom.) The designers need to recognize that more than classrooms are needed in order to achieve the outcome of increased enrollment of girls. And there need to be even other external assumptions fulfilled if higher outcomes and impact are to be achieved, such as girls completing quality education, leading to their long-term empowerment, and even (a higher desired consequence) that this will all lead to a reduction of poverty in these households and communities. Improved educational policies Curriculum improved Female enrollment rates increase Parents persuaded to send girls to school School system hires and pays teachers Schools built

the same target population. Program Goal: Young women educated
To have synergy and achieve impact all of these need to address the same target population. Program Goal: Young women educated Advocacy Project Goal: Improved educational policies enacted Teacher Education Project Goal: Improve quality of curriculum Construction Project Goal: More classrooms built Here is an illustration of what a Program looks like: Two or more projects working in the same area, addressing the needs and rights of the same target population, collaborate together to provide the synergy needed to achieve higher level (program) impact. As in this example, each project has a Outcome Objective at a level that can be achieved and measured during the life of the project. Though our own agency may choose to directly implement one or more of the projects, it may be more appropriate to assist one or more partners to implement complementary projects. If one assumes that someone else will take care of some aspect (e.g. Government Ministry of Education improving educational policies), it is important that we monitor that assumption. For the hypothesis developed from the problem analysis (in this example) is that all three of these causes must be addressed in order for the Program Impact to be achieved (young women completing quality education.) ASSUMPTION (that others will do this) OUR project PARTNER will do this Program goal at impact level

One form of Program Theory (Logic) Model
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 Typically project logic models are linear. This is one illustration of a more holistic model, where key external conditions are considered, since the success of the project is obviously dependent on those conditions. And if those external conditions change (negatively or positively) during the life of the project, the project’s own plans may need to be flexible and change accordingly. Design Inputs Implementation Process Outputs Outcomes Impacts Sustainability Note: The orange boxes are included in conventional Program Theory Models. The addition of the blue boxes provides the recommended more complete analysis.

There are many creative ways to depict logic models
There are many creative ways to depict logic models. Here is one developed by the Asia Foundation for a Trafficking In People (TIP) program in Nepal. It includes three Components (sub-projects), with a variety of types of interventions. The challenge is to make it compressible enough to show the “big picture logic” of the program without becoming so complex that it becomes confusing. Another challenge is to know what aspects of a logic model like this should be tested by an evaluation.

Education Intervention Logic
Global Impacts Output Clusters Outcomes Specific Impact Intermediate Impacts Better Allocation of Educational Resources Improved Family Planning & Health Awareness Institutional Management Quality of Education Increased Affordability of Education Economic Growth Skills and Learning Enhancement Curricula & Teaching Materials Poverty Reduction Here is another example of a more comprehensive logic model, this one by OECE/DAC, with a multi-sectoral national set of programs aimed at having impact on the MDGs (Millennium Development Goals). Question: if you were designing an impact evaluation, what aspects of such a program would you focus on? Improved Participation in Society MDG 2 MDG 1 Teacher Recruitment & Training Equitable Access to Education Social Development MDG 2 Health MDG 3 Greater Income Opportunities Education Facilities Optimal Employment Source: OECD/DAC Network on Development Evaluation

Expanding the results chain for multi-donor, multi-component program
Increased rural H/H income Increased political participation Improved education performance Improved health Impacts Increased production Access to off-farm employment Increased school enrolment Intermediate outcomes Increased use of health services Many programs are much more complex than the simple cause-effect hierarchy depicted in typical project logframes. Although a more comprehensive picture (such as the one on this slide) is more realistic, especially at broader geographical levels where multiple agencies are involved, assessing attribution of each gets rather challenging. Ideally we can identify plausible contributions each actor makes to the achievement of higher level outcomes and ultimate impact. Outputs Credit for small farmers Health services Rural roads Schools Inputs Donor Government Other donors Attribution gets very difficult! Consider plausible contributions each makes.

Contribution Analysis*
Develop a program logic (theory of change) Identify existing evidence on the causal links between interventions and possible effects. Identify gaps in information. Assess alternative explanations of possible effects. Assess the possible contributions of different factors to effects. * John Mayne et al in EES’ Evaluation journal, Vol. 18, No. 3, July 2012

Contribution Analysis*
Create a performance story on the contribution of the intervention to changes in impact variables. Seek out additional evidence to improve the program’s performance story. Revise and strengthen the performance story. * John Mayne et al in EES’ Evaluation journal, Vol. 18, No. 3, July 2012

Where there was no baseline
RealWorld Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints Where there was no baseline

Ways to reconstruct baseline conditions
Secondary data Project records Recall Key informants Participatory methods What experience have you had with any of these methods?

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

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?

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

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

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

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

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

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

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

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.

Mixed-method (MM) evaluations
RealWorld Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints Mixed-method (MM) evaluations

The Main Messages No single evaluation approach can fully address the complexities of development evaluations MM combines the breadth of quantitative (QUANT) evaluation methods with the depth of qualitative (QUAL) methods MM is an integrated approach to evaluation with specific tools and techniques for each stage of the evaluation cycle 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

This explains the growing interest in mixed methods evaluations
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

Why mixed methods? No single evaluation method can fully explain how development programs operate in the real world Programs operate in complex and changing environments Interventions are affected by historical, cultural, political, economic and other contextual factors Different methodologies are needed to measure different contextual factors, processes and outcomes. Even “simple” interventions often involve complex processes of organizational and behavioral change Programs change depending on how different sectors of the target population respond

The benefits of a mixed methods approach
QUALITATIVE depth QUANTITATIVE breadth + 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

Four decisions for designing a mixed methods evaluation
At which stages of the evaluation are mixed methods used? Is the design sequential or concurrent? Which approach is dominant? Is the design single or multi-level?

Decision 1: At which stages of the evaluation are mixed methods used?
QUANT QUAL Mixed 1. Formulation of hypotheses 2. Sample design 3. Evaluation design 4. Data collection and recording 5. Triangulation 6. 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

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

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

Comparison communities
A concurrent MM design: Triangulating QUANT and QUAL estimates of household income in project and comparison areas QUANT and QUAL data collection methods are used at the same time Project communities Triangulation of estimates from the 3 sources – to obtain the most reliable estimate of household income QUANT household surveys QUANT/QUAL Observation of household possessions and construction quality QUAL: Focus groups Comparison communities

Decision 3: which approach is dominant?
QUAL oriented studies gradually incorporating more QUANT focus QUANT QUAL E F G D A B C QUANT oriented studies gradually incorporating more QUAL focus A = completely QUANT design B = dominant QUANT with some QUAL elements C = QUANT oriented design giving equal weight to both approaches D = Study designed as balanced MM E = QUAL oriented design giving equal weight to both approaches. F = dominant QUAL design with some QUANT elements G = completely QUAL design

A QUANT dominant evaluation design
QUAL E F G D A B C 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]

A QUAL dominant evaluation design
QUANT QUAL E F G D A B C Example A 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.

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

C. Using mixed methods to strengthen each stage of the evaluation
1. Hypothesis formulation 2. Sample design 3. Evaluation design 4. Data collection 5. Triangulation 6. Data analysis and interpretation

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

Comparing DEDUCTIVE and INDUCTIVE hypotheses
Mainly 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

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

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

Stage 4. Strengthening data collection
Integrating survey and QUAL data collection Commonly used mixed method data collection methods for strengthening QUANT evaluations Focus groups Observation Secondary data Case studies Reconstructing baseline data Interviewing difficult-to-reach groups Collecting information on sensitive topics Attention to contextual clues

TRIANGULATION PROCESS
Stage 5. Validating findings through triangulation Possible return to field QUANT data collection Household survey data collected on income and expenditures 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 QUANT data analysis: Calculating mean, frequency distributions and standard deviation of income and expenditures QUAL Data analysis Review of interview and observation notes, Analysis using constant comparative method

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

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

Quantitative data collection methods Strengths and weaknesses
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

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

Determining Counterfactuals
RealWorld Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints Determining Counterfactuals

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

The Counterfactual What change would have occurred in the relevant condition of the target population if there had been no intervention by this project?

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?

Comparing the project with two possible comparison groups
Project group. 50% increase 750 50% higher incomes of those involved in the project looks impressive, if others’ incomes stayed stagnant. But if the comparison group (people living in comparable communities) also enjoy higher incomes, the net impact of the project on incomes was actually zero. Scenario 2. 50% increase in comparison group income. No evidence of project impact 500 Scenario 1. No increase in comparison group income. Potential evidence of project impact 250 2004 2009

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)

Randomized Control Trials
IE Designs: Experimental Designs Randomized Control Trials 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)

A graphical illustration of an ‘ideal’ counterfactual using pre-project trend line then RCT
Intervention Subjects randomly assigned either to … IMPACT Impact Primary Outcome Treatment group Control group Counterfactual Trend line Time

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.

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

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

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]

What experiences have you had with identifying counterfactual data?

Enough of our presentations: it’s time for you (THE RealWorld PEOPLE
Enough of our presentations: it’s time for you (THE RealWorld PEOPLE!) to get involved yourselves.

Time for small-group work
Time for small-group work. Read your case studies and begin your discussions.

Small group case study work
Some of you are playing the role of evaluation consultants, others are clients commissioning the evaluation. Decide what your group will propose to do to address the given constraints/ challenges. Prepare to negotiate the ToR with the other group (later this afternoon).

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.

More holistic approaches to Impact Evaluation
RealWorld Evaluation Designing Evaluations under Budget, Time, Data and Political Constraints More holistic approaches to Impact Evaluation

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 2006 2009 2008 Impact Evaluation for Improving Development – 3ie and AfrEA conference in Cairo March 2009 In recent years there has been an increasing emphasis on impact evaluation in public policy and development. In public policy there has been a focus on evidence-based policy and practice, drawing on approaches developed for evidence-based medicine. In development there have been calls for more rigorous impact evaluation. In both areas there have been debates about what constitutes ‘rigorous evidence’ and ‘scientific approaches’ to impact evaluation. Some people and organizations have argued exclusively for increased use of specific research designs – in particular Randomized Controlled Trials (RCTs). Others have argued that these designs are not always appropriate or feasible, and that we need other approaches to doing more systematic, rigorous and useful impact evaluation. 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

Randomized control trials and strong statistical designs
When might they be appropriate … and when not?

The arguments in favor of RCTs
Selection bias has resulted in wrongly attributing changes to the results of an intervention Selection bias tends to over-estimate program impacts Agencies continue to support their favorite programs – even though they may be producing fewer benefits than claimed A high percentage of evaluations use methodologically unsound methods, but funding agencies are rarely challenged Agencies evaluate themselves and are able to ensure that the findings are never too critical.

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. 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

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

Practical, political and ethical limitations on the use of RCTs
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.

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

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

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

Methodological limitations of RCTs and strong QEDs
Can only evaluate a single component (intervention) of a multi-component program Inflexible design – difficult to adjust design to modifications to program design or to changes in the context in which programs are implemented Cannot control what happens during implementation Regression only estimates the average effect size – difficult to generalize the findings to other contexts.

Methodological limitations of RCTs and strong QEDs
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”

Implications for RWE 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 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

Implications for RWE If programs are designed as experiments, an RCT can be cheaper than many alternative survey designs. 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 The real world is too complex for any single evaluation approach to fully address all of the important questions that must be answered.

So what should be included in a “rigorous impact evaluation”?
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 … 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. RealWorld Evaluation - Steps 1, 2 & 3

So what should be included in a “rigorous impact evaluation”?
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)? RealWorld Evaluation - Steps 1, 2 & 3

Different lenses needed for different situations in the RealWorld
Simple Complicated Complex Following a recipe Sending a rocket to the moon Raising 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.

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 … ?

Conditional cash transfers The use of visual aids in Kenyan schools
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.

Quoted by Patricia Rogers, RMIT University
“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 Quoted by Patricia Rogers, RMIT University

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.

A more comprehensive design
Consequences Consequences Consequences DESIRED IMPACT OUTCOME 1 OUTCOME 2 OUTCOME 3 Unfortunately, too often simplistic RCTs only conduct research on one intervention, and judge its “impact” by attributable change in a fairly near-term outcome, without adequate consideration of other causal chains. A more comprehensive design should factor in an adequate number of causal streams to determine not only which but what combination of interventions by one agency or others, or necessary pre-conditions need to be in place to achieve higher level impact. A more comprehensive design OUTPUT 2.1 OUTPUT 2.3 OUTPUT 2.2 A Simple RCT Intervention 2.2.1 Intervention 2.2.2 Intervention 2.2.3

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)

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% Data summarized from four bi-annual meta-evaluations of evaluation reports from CARE projects in many countries. Colleagues familiar with other agencies report proportions of evaluations with no pre-test + post-test, nor counterfactual, are typically higher than the percentages reported here.

Rigorous impact evaluation should include (but is not limited to):
thorough consultation with and involvement by a variety of stakeholders, articulating a comprehensive logic model that includes relevant external influences, getting agreement on desirable ‘impact level’ goals and indicators, adapting evaluation design as well as data collection and analysis methodologies to respond to the questions being asked, …

7) being sufficiently flexible to account for evolving contexts, …
Rigorous impact evaluation should include (but is not limited to): 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, …

8) using a variety of ways to determine the counterfactual,
Rigorous impact evaluation should include (but is not limited to): 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. …

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

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.”

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.

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.

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!

Thank you! 153

ANY QUESTIONS? 154

Time to negotiate! Time for consultancy teams to meet with clients to negotiate the revised ToRs for the evaluation of the housing project. 155

Review What did you learn about RealWorld Evaluation by getting involved in that case study and role-playing exercise? 156

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

Main workshop messages
Evaluators must be prepared for RealWorld evaluation challenges. There is considerable experience to learn from. A toolkit of practical “RealWorld” evaluation techniques is available (see Never use time and budget constraints as an excuse for sloppy evaluation methodology. A “threats to validity” checklist helps keep you honest by identifying potential weaknesses in your evaluation design and analysis. The evaluation report should disclose what constraints were faced and what was done to address them.

Additional References for IE
DFID “Broadening the range of designs and methods for impact evaluations” Robert Picciotto “Experimententalism and development evaluation: Will the bubble burst?” in Evaluation journal (EES) April 2012: Martin Ravallion “Should the Randomistas Rule?” (Economists’ Voice 2009) William Easterly “Measuring How and Why Aid Works—or Doesn't” Control freaks: Are “randomised evaluations” a better way of doing aid and development policy? The Economist June 12, Series of guidance notes (and webinars) on impact evaluation produced by InterAction (consortium of US-based INGOs): Evaluation (EES journal) Special Issue on Contribution Analysis, Vol. 18, No. 3, July Impact Evaluation In Practice

A few thoughts on evaluating complex development interventions
Unanticipated consequences of development interventions: a blind spot for evaluation theory and practice EXTRA BONUS SECTION [and if time permits] A few thoughts on evaluating complex development interventions Michael Bamberger

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

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

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

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

Real-world evaluation constraints:
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

Results frameworks only monitor intended outcomes
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

Focus groups often only cover people involved in the project
Many evaluation designs make it difficult to identify unanticipated outcomes, cont. 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 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.

Critical events occur after the evaluation has ended
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)

Evaluators often ignore context
Many unanticipated events occur outside the vision field of the evaluation, cont. 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

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

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

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

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

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

“Plan B” Planning for the unexpected
What 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

“Eyes wide open” evaluation Knowing 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

A few thoughts on the evaluation of complex programs
The dimensions of complexity Scope and scale Organizational complexity Contextual factors Behavioral Challenges assessing Attribution Contribution Substitution Some interventions are less complex than claimed

1. Scope and scale Number of components and service levels
Number and types of geographic areas Larger programs tend to have more features and to be implemented differently in different regions

2. Organizational complexity
Programs co-funded by different agencies tend to be more complex Different implementation strategies and management procedures Different components/services Coordination problems Programs with several coordinating and implementing agencies National and regional level programs have more levels of coordination National agencies may lack implementation capacity

3. Contextual factors affecting complexity
Economic setting Political context Administrative and organizational Climatic and ecological Legal and regulatory Historical Socio-cultural

4. Behavioral “Simple” projects may involve complex processes of behavioral change Understanding group behavior Social networks and social communication

5. Attribution, contribution and substitution
Attribution difficult to assess for complex programs Different donors Target groups exposed to other programs Difficult to define a counterfactual Contribution analysis: Conventional CA only focuses on process but not outcomes Substitution Difficult to track donor funds and other inputs Even more difficult to track substitution of donor for government resources

6. Interventions that look “complex” may not be
“complex” interventions may comprise a number of “simple” components Coordination and management may be the only “complex” dimensions Some agencies may find it convenient to claim their programs are too complex to be rigorously evaluated – to avoid critical scrutiny.

STRATEGIES FOR EVALUATING COMPLEX PROGRAMS
Counterfactual designs Attribution analysis Contribution analysis Substitution analysis Estimating impacts The value-added of agency X Theory-based approaches Net increase in resources for a program Mixed method designs strengthening alternative counterfactuals “Unpacking complex programs” Portfolio analysis Reconstructing baseline data Creative use of secondary data Secondary data Triangulation Qualitative approaches Quantitative approaches Rating scales

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