📈Applied Impact Evaluation Unit 9 – Impact Evaluation in Practice

Key Concepts and Definitions

• Impact evaluation assesses the changes in outcomes that can be attributed to a specific intervention or program
• Counterfactual represents what would have happened in the absence of the intervention
• Selection bias occurs when treatment and comparison groups differ in ways that affect outcomes
• Randomized controlled trials (RCTs) randomly assign participants to treatment and control groups to minimize selection bias
• Difference-in-differences (DID) compares changes in outcomes over time between treatment and comparison groups
  • Assumes that in the absence of the intervention, the difference between the treatment and comparison groups would remain constant over time (parallel trends assumption)
• Propensity score matching (PSM) matches treatment and comparison units based on observable characteristics to create balanced groups
• Instrumental variables (IV) use an exogenous source of variation to estimate causal effects when treatment assignment is not random

Importance of Impact Evaluation

• Determines the effectiveness of interventions in achieving desired outcomes and impacts
• Provides evidence-based insights for policy decisions and resource allocation
• Identifies unintended consequences or spillover effects of interventions
• Contributes to the knowledge base on what works in development and social programs
• Promotes accountability and transparency in the use of public funds
• Informs the design and implementation of future interventions
• Helps optimize the allocation of limited resources to maximize impact
• Encourages continuous learning and improvement in development practice

Types of Impact Evaluation Methods

• Experimental methods involve random assignment of participants to treatment and control groups (RCTs)
• Quasi-experimental methods use non-random assignment but aim to mimic experimental conditions
  • Difference-in-differences (DID) compares changes in outcomes over time between treatment and comparison groups
  • Propensity score matching (PSM) matches treatment and comparison units based on observable characteristics
  • Regression discontinuity design (RDD) exploits a cutoff point that determines treatment assignment
• Non-experimental methods do not involve a comparison group and rely on assumptions to estimate impact
  • Pre-post comparison measures outcomes before and after the intervention for the same group
  • Instrumental variables (IV) use an exogenous source of variation to estimate causal effects
• Mixed methods combine quantitative and qualitative approaches to provide a comprehensive understanding of impact
• The choice of method depends on the research question, data availability, and ethical considerations

Designing an Impact Evaluation

• Clearly define the research question and the intervention's theory of change
• Identify the key outcomes of interest and how they will be measured
• Determine the appropriate impact evaluation method based on the research question and context
• Develop a sampling strategy to ensure representativeness and adequate statistical power
  • Power calculations determine the minimum sample size needed to detect a desired effect size
• Plan for data collection, including the timing, frequency, and instruments used
• Establish a timeline and budget for the evaluation, considering resource constraints
• Engage stakeholders, including program implementers and beneficiaries, in the evaluation design process
• Obtain necessary ethical approvals and ensure informed consent from participants

Data Collection Strategies

• Surveys are commonly used to collect quantitative data on outcomes and participant characteristics
  • Design survey questionnaires to capture relevant information while minimizing respondent burden
  • Train enumerators to ensure consistent and accurate data collection
• Administrative data from program records or government databases can provide valuable information
• Qualitative methods, such as interviews and focus group discussions, offer in-depth insights into participants' experiences and perceptions
• Observations and field visits allow for direct assessment of program implementation and contextual factors
• Technology-based tools, such as mobile surveys and remote sensing, can improve data collection efficiency and accuracy
• Quality control measures, including data validation and audits, ensure the reliability of collected data
• Data management protocols, including data security and privacy, are essential to protect participant information

Statistical Analysis Techniques

• Descriptive statistics summarize key variables and check for data quality
• Regression analysis estimates the relationship between the intervention and outcomes, controlling for other factors
  • Ordinary least squares (OLS) regression is used for continuous outcomes
  • Logistic regression is used for binary outcomes
  • Multilevel models account for clustered data (e.g., students within schools)
• Propensity score matching (PSM) estimates treatment effects by comparing matched treatment and comparison units
• Difference-in-differences (DID) estimates the impact by comparing changes in outcomes over time between treatment and comparison groups
• Instrumental variables (IV) analysis uses an exogenous source of variation to estimate causal effects
• Subgroup analysis examines heterogeneous treatment effects across different participant characteristics
• Sensitivity analysis assesses the robustness of results to alternative specifications or assumptions

Interpreting and Reporting Results

• Interpret results in the context of the research question and the intervention's theory of change
• Assess the statistical significance and practical importance of the estimated impact
• Consider alternative explanations for the findings and discuss limitations of the study
• Present results using clear and accessible language, tailored to the intended audience
• Use visualizations, such as graphs and tables, to effectively communicate key findings
• Report effect sizes and confidence intervals to convey the magnitude and precision of the estimates
• Discuss the implications of the findings for policy and practice, including scalability and generalizability
• Engage stakeholders in the interpretation and dissemination of results to ensure relevance and uptake

Ethical Considerations and Challenges

• Obtain informed consent from participants and ensure their voluntary participation
• Protect participant privacy and confidentiality, especially for vulnerable populations
• Minimize potential harm or risks to participants, and provide appropriate support services if needed
• Ensure equitable selection of participants and avoid discrimination or bias
• Consider the social and cultural context of the intervention and adapt the evaluation accordingly
• Be transparent about the evaluation process and results, and provide feedback to participants and communities
• Manage potential conflicts of interest, such as pressure from funders or implementers to produce favorable results
• Address ethical concerns related to the use of comparison groups, such as withholding potentially beneficial interventions
• Plan for the dissemination and use of evaluation results to promote learning and accountability