Indicator selection is the process of critically analyzing a suite of potential indicators or performance measures, refining, and selecting a final set for inclusion in the monitoring plan. Before beginning selection, potential indicators should be identified based on a sound Theory of Change. Each indicator should be documented sufficiently to facilitate shared understanding of the indicator including an overview of the data collection methods anticipated. > Download the companion [Indicator Review Template](https://docs.google.com/document/d/1F6vI-XvnUJaxARndOy-qflNBUIqZk6XIIyNAfdBFwQs/template/preview). # Selecting Performance Indicators The goal of indicator selection is to refine the list of potential indicators to the most parsimonious set that will meet information needs considering cost, coverage, data quality, and information value. Individual indicators are reviewed to ensure each will provide information of sufficient quality to inform management decisions and meet other information needs. Indicators are considered together to ensure that, as a whole, the indicators will meet the information needs of the program. The process is summarized below. 1. **Map indicators to the theory of change:** the theory of change provides context for selected indicators, helps communicate the importance of each indicator in the achievement of intermediate results, and can be used to ensure indicators provide information about all aspects of the strategy at key points in time. 2. **Confirm indicators meet needs of users:** information collected through monitoring should meet the needs of specific users for specific uses; each user should have information needs met by either indicator data or other sources of information (e.g., research, learning activities, evaluations). 3. **Review for cost, coverage, data quality and information value:** each indicator and the set of indicators will be reviewed for cost, coverage, data quality and information value. Information value describes the utility of each indicator. 4. **Recommend the final set of indicators**: modify, replace or remove indicators that do not meet program needs and consider alternatives. Add indicators where gaps are identified. The results of this review process should be captured in a brief (3-5 page) memo, highlighting any concerns with cost, coverage, data quality or information value for specific indicators or the set of indicators. After selecting indicators, it is important to plan for data collection, management, analysis, and reporting. Planning this now will ensure that indicators will provide the expected value at the anticipated cost. It can also lead to efficiencies that will reduce the burden of monitoring over time. Complete indicator documentation, such as the Indicator Review Sheet, create data collection templates, and document processes for efficiently storing, analyzing, and communicating monitoring data. **Box 1. Common Pitfalls** These pitfalls are common in the selection and use of indicators. - Selecting indicators that cannot be measured or measured consistently - Specifying precision/accuracy that cannot reasonably be attained - Failing to incentivize desired behaviors (in grantees, partners or beneficiaries) or incentivizing undesirable behaviors - Ignoring marginal costs of data collection relative to the value generated by collecting the data - Incompatibility with program needs and constraints - Making many isolated decisions that cumulatively make the indicators unusable or significantly inaccurate ## Map indicators to the theory of change Confirm that the theory of change or other program design documents includes a clear description of the context the program will work within, a list of the program’s planned interventions, a description of desired outcomes, a series of if-then statements that represent the causal links between strategies and desired outcomes, and assumptions made in the development of the theory of change. List any critical outputs, weak points in the theory of change, key assumptions, and important aspects of the program’s context. These elements should be prioritized for monitoring efforts. Each indicator should have a clearly defined association to the theory of change. Indicators should be distributed across the theory of change in order to inform adaptive management, track progress over time, distinguish failures of implementation and of strategy, and communicate successes. We define three types of indicators based on their relationship to the theory of change: * **Inputs**: measures resources used, actions taken, or funds expended. Typically easy to control and to measure. * **Outputs**: measures the benefit of strategy implementation. Indicates progress towards desired outcomes and are typically the focus of adaptive management. * **Outcomes**: long-term indicators of success that signal the series outcome has been achieved. Often slow to respond, difficult to attribute to actions directly, and more expensive to measure. 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] **Figure 1\. Mapping indicators to a theory of change.** A variety of input, output, and outcome indicators should be employed by the monitoring program. Each strategy (or intervention), intermediate result, or desired outcome does not necessarily need an indicator. No more than three indicators are typically required for any single strategy, intermediate result, or desired outcome. ## Confirm indicators meet needs of users Every monitoring program has a variety of users each with their own needs. If necessary, conduct a stakeholder analysis to list the distinct users or user groups for information produced through monitoring efforts. Identify the needs of each user or user group. This may require outreach to stakeholders. If possible, have users rate the priority of their information needs or rank information needs by priority. Create an information flow map to quickly confirm that indicator data is meeting an information need of the program’s internal and external stakeholders and check that all priority information needs are being met. 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] **Figure 2\. Example simplified information flow map** List information needs in the information needs column and specify which user(s) need the information. Next, consider all of the different potential sources of information, including each potential indicator, in the information sources column. Finally, connect the information sources to the information needs through existing (or proposed) channels available to the program. This ensures not only that information is available, but that it is also accessible to those who need it. Note any needs that are not being met by existing data sources, or where information channels are not available to deliver information required. Also note any indicators that do not connect to a defined need. If the indicator data will not be used, it is not worth collecting. ## Review for cost, coverage, data quality, and information value We use the primary categories of cost, coverage, data quality and information value to ensure thorough and consistent review of indicators. #### Cost The cost of data collection, management, analysis and reporting must be within resources available and fall within a reasonable range relative to total program expenditures. In general, monitoring costs should not exceed 10% of total program expenditures. #### Coverage Coverage describes how well the set of indicators meets the variety of information needs of an organization and its partners, whether the set of indicators can be used to evaluate both strategy failures and implementation failures, and the ability of a set of indicators to provide meaningful information in the short-, medium- and long terms. Indicators that meet multiple information needs are often better than indicators that only meet specific needs, although specific indicators may be critical to ensuring full coverage. #### Data Quality Indicator data must be of sufficient quality to meet the needs of information users. Data quality assessments should be completed in advance of data collection to increase the likelihood that indicators will pass data quality assessments going forward. We recommend all indicators are reviewed against these five data quality criteria: validity, reliability, timeliness, precision, and integrity. Up to three additional criteria may be selected based on program objectives. #### Information Value The value of monitoring (and other MEL activities) is the ability of information gained to increase the probability of making the right decision in light of the consequences of making a wrong decision. To evaluate information value, weigh the priority of each information need (i.e., the consequence of a wrong decision) and the utility of any indicator for meeting the information need (i.e., its ability to reduce uncertainty). The highest priority information needs should be met with indicators of the highest utility; whereas lower priority information needs can be met with lower utility (and assumedly lower cost) indicators. ### How to Assess Cost Categories of monitoring costs can include * **Data collection:** consider both the cost of collecting each piece of data and the cost of travel, analysis, and reporting for data collection partners. * **Data storage and management:** consider how data will be stored and the cost of maintaining databases or other data storage. Data storage and management often scales with the amount of data stored and managed. * **Quality assurance**: consider both formal Data Quality Assessments and the effort required to manage data providers and eliminate errors from data. * **Analysis costs:** consider how raw data will be transformed to actionable information. Is the skill set required available within the organization or will a third party be required? * **Reporting and communication costs:** consider both internal and external reporting and any other forms of communication anticipated. * **Technology costs:** consider software licenses, custom software, cloud storage fees, special data collection apparatus, etc. Technology costs can also be embedded in any of the cost categories above. Consider both the variable and fixed costs of monitoring for each indicator and the set of indicators. Variable costs are incurred per unit of data collection and scale with the size of the monitoring program. Fixed costs generally do not increase with the scale of data collection, however in practice if the monitoring program increases significantly, fixed costs will also increase. Does the program have the resources (expertise, time and money) to meet the anticipated scale of the monitoring program? Will it have necessary resources if the scale of the monitoring program increases substantially? While cost can be difficult to estimate in advance, at least identify concerns related to monitoring costs for specific indicators and consider alternative indicators if cost is a concern. ### How to Assess Coverage Selecting indicators requires carefully balancing the information needs of the program with the burden of monitoring. Selecting the optimal set of indicators will reduce monitoring costs (leaving more resources for interventions) and avoid monitoring fatigue. It can also help to avoid decision makers “over-fitting” their management to interventions that have been over-emphasized in the monitoring program. Using the theory of change and information flow diagram, ensure that the set of indicators (and other sources of information) satisfies all of the following criteria: - [ ] The most important outputs and weak points in the theory of change are monitored - [ ] Each user need is met by an indicator or other source of information - [ ] Information is available to support key decisions in a timely manner - [ ] Decision makers can test the level of inputs required to cause outputs - [ ] Critical assumptions underlying the theory of change will be tested - [ ] Knowledge gaps or learning questions will be addressed - [ ] Important changes to context will be detected - [ ] All reporting requirements are met - [ ] Redundancies are reduced or eliminated Note for each individual indicator which of these criteria are met. For the set of indicators, ensure that all criteria are met by at least one indicator or other information source. ### How to Assess Data Quality The standard data quality criteria are defined below. Use these five criteria and, optionally, up to three additional criteria to review each indicator. Because data are likely not yet available, the data quality assessment must focus on the suitability of the indicator as defined to meet user needs and identify any likely data quality issues that could be expected after data are collected. * **Validity:** indicators clearly and adequately represent the intended result * **Reliability:** indicators reflect stable and consistent data collection processes and analysis methods over time * **Timeliness:** indicator data are available at a useful frequency, should be current, and are timely enough to influence management decision making * **Precision:** indicator data have a sufficient level of detail to permit management decision making (e.g., the margin of error is less than the anticipated change) * **Integrity**: indicator is not at risk of transcription error or data manipulation #### Additional Criteria Select up to three of the following criteria to augment the data quality criteria * **Established**: established indicators benefit from the network effect of other users. They are often less expensive to set up because they have established methods. They can be better studied, less prone to data quality issues, and more credible. They will often allow for sharing or integration with complementary efforts and are better aligned with partners and grantees. They may have historic datasets for comparison. If already established in the organization, indicator data will be consistent with past efforts and allow for longer trend analysis. * **Science-based**: indicators that are documented in the literature are supported by a stronger evidence base. They can better represent critical points in system transitions, especially when the mechanism relating the strategy to the outcome is known. Science-based indicators may also have models (e.g., water quality models) available for scenario analyses. * **Interpretable**: interpretable indicators have better storytelling value and are less prone to lead decision makers to poor decisions. * **Distinct:** in general, indicators that provide redundant information to another indicator are less useful and less cost-effective. Consider whether an indicator is measuring the same thing as another indicator and the likelihood that two indicators will always trend together. * **Appropriately scaled**: consider both the geographic and temporal scale. Scale includes both extent and grain (i.e., the fineness or coarseness of the indicator). The scale of an indicator should be consistent with the scale of the intervention; however, sometimes more fine-scaled indicators can provide better information than broad-scaled indicators. Broader scale indicators are usually less appropriate for fine-scaled interventions. * **Specific**: an indicator with high specificity will only change when the result it is measuring also changes. Direct measures of a key result are the most specific indicator possible. However, when direct measures are not feasible, proxy indicators can be used. Specific indicators have low noise to signal ratios. * **Sensitive**: when a meaningful change to the intermediate result occurs, an indicator with high sensitivity will also change, without significant lag. * **Feasible:** it must be possible to actually measure the indicator, the cost must be reasonable, and the personnel must be available. * **Culturally appropriate:** does your understanding of the indicator reflect the cultural context in which it is measured? * **Ability to detect unexpected findings**: indicators that can not only detect the expected results, but also detect unexpected results are important for detecting changes in context that might affect program performance. #### Rating Tables Use rating tables to define rating criteria and consistently rate indicators. Create rating tables for the five DQA criteria and any additional criteria. An example is provided below for illustration. **Table 1\.** Example rating criteria table | Criterion: Specific | | | :---: | :---- | | **Rating** | **Description** | | **5** | Indicator directly represents the intermediate result. | | **3** | Indicator is a suitable proxy for the intermediate result, with a well-understood relationship to the result described in peer-reviewed literature. | | **1** | Indicator indirectly represents the intermediate result and suffers from high noise to signal ratio. | After rating each indicator, calculate a summary score for each indicator by averaging across all criteria ratings. Very low ratings (individual or aggregate) should be highlighted for review. Ratings of 3 or above are generally considered sufficient. Compare the rated indicators to the information needs they are intended to meet. Where high priority information needs are related to poorly rated indicators, consider potential remedies or alternative indicators that would be better suited. ### How to Determine Information Value The more an indicator can reduce uncertainty, and the more beneficial it is to reduce that uncertainty (given the program’s objectives), the more utility the indicator has to the program. Formal Value of Information (VoI) analyses can be conducted to incorporate costs and more robustly select the optimal set of indicators, however, the expected value of indicators is often not known well enough to facilitate this precision of analysis. Instead, answer the prompts provided below for each indicator (an example is provided to help you consider each prompt). **Table 2\. Information Value Prompts and Example** | Prompt | Definition | Example | | :---- | :---- | :---- | | **Indicator** | Identify the indicator under review | Workshop attendance disaggregated by remote or in-person delivery | | **Expected Use(s)** | Which decisions or other anticipated uses of the indicator will be influenced? | Whether to use remote workshops, in-person workshops, or a combination to promote issue awareness. | | **Existing Uncertainty** | What is the uncertainty faced by the program and the likelihood of making the wrong decision? (Ensure that existing research and other forms of evidence have been fully utilized). | Low internet access and long travel times are both factors in workshop attendance, which of the two drives attendance more is not known. Choosing the wrong balance of remote and in-person workshops could significantly reduce the awareness of our issue and impact our ability to meet long-term objectives. | | **Resolvable Uncertainty** | How will measuring this indicator resolve uncertainty? | Significant differences in attendance between remote and in-person workshops in each community will inform the best workshop delivery platform for that community. | | **Risk/Reward** | If uncertainty is resolved, what are the benefits? If not, what are the consequences? | Workshops are our primary means of raising awareness, which we believe is critical to promoting behavior change. Delivering workshops through the wrong platform would result in lower attendance and reduce the likelihood of achieving our long-term objectives. | Consider the risk/reward associated with each indicator in light of the cost of monitoring and its ratings across the data quality criteria. Does the indicator provide a meaningful reduction in uncertainty that will yield a real benefit (or reduce a real risk) for the program? Don’t forget about the risk of inaction. Is the cost of monitoring commensurate with this benefit? Will the data collected be of sufficient quality to deliver this benefit? While we typically think first of the value of an indicator for adaptive management, keep in mind that organizations may derive value from communicating success measured by an indicator (the reward for demonstrating measurable success may be continued or increased funding for an activity). **Box 2. Formula for the expected value of an indicator** To quantify the expected value of an indicator in terms that can be compared across indicators and/or compared to the cost of monitoring, use the formula below: $\text{Expected Value} = [ P(\text{consequence} | \text{not monitoring}) - P(\text{consequence} | \text{monitoring}) ] \times \text{total cost of consequence}$ The cost of the consequence of not monitoring this indicator must be specified in the same units for all indicators if this equation will be used to compare across indicators. Specifying the cost of the consequence in monetary terms will allow more direct comparison to costs, but is not necessary. ## Recommend a final set of indicators To recommend a final set of indicators, first review each indicator individually to drop, replace or modify poorly performing indicators. Next, consider the set together to ensure priority information needs are met with high quality information at a feasible level of resources. You may divide your review of the set of indicators across major divisions in program focus or intervention type, if applicable. ### Review individual indicators Follow the steps below to review each individual indicator. 1. Confirm the indicator is sufficiently defined to facilitate review 2. Describe the intent of the indicator and its link to the theory of change 3. Map the indicator to information need(s) through the information flow diagram. Note where information channels are insufficient or unavailable to meet needs. 4. For each cost category (collection, management, quality assurance, analysis, reporting, and technology) estimate costs and identify concerns. 5. Identify the information provided by this indicator from the list of criteria provided in *How to Assess Coverage*. Determine if this indicator is redundant to or significantly overlaps other indicators. 6. Rate the indicator for each data quality criterion selected using the rating tables defined for that criterion. Note any concerns. Calculate an average rating across all criteria. 7. Complete the information value table, replacing the example with your response to each prompt for this indicator. Determine if the risk/reward, given the existing and resolvable uncertainty, is worthy of the cost of monitoring this indicator. Rate information value as ‘high’, ‘medium’, or ‘low’. 8. Provide a summary of the review, recommendations to drop, replace, or modify the indicator, and any other relevant information. Consult with the client to review the results of each indicator’s review and determine which changes to make. before reviewing the set of indicators as a whole. Also identify gaps in the theory of change or information needs where indicators should be added. ### Review the set of indicators Now that you have a suite of strong individual indicators, it is time to consider how the set of indicators will combine to meet the program’s information needs. 1. List any critical outputs, weak points in the theory of change, key assumptions, and important aspects of the program’s context. Ensure that at least one indicator is included for those priority information needs. 2. Review all information needs to ensure that each is met by an indicator or another source of information. 3. Create a rough budget for the monitoring program, identifying where costs for individual indicators can be combined and looking for opportunities to realize cost savings through efficiencies of scope. 4. Review the coverage checklist in *How to Assess Coverage* and confirm that each checklist item is covered by at least one indicator or other source of information. 5. Note where high priority information needs will not be met by high quality data or high value information. 6. Provide a summary for the set of indicators, highlighting any concerns, recommending any additional changes to individual indicators, and noting any significant findings from the review. Make any final recommendations to add, drop, replace or modify indicators. Document this process in a brief (3-5 page) memo to capture the learning and justify changes to indicators. For indicators that are dropped or changed significantly, capture the rationale for excluding them in the monitoring plan. Next, be sure to fully document the indicators and plan for data collection, management, analysis, and reporting. # Additional Resources Learning Lab’s module on [Selecting Performance Indicators](https://usaidlearninglab.org/library/selecting-performance-indicators) includes a USAID-focused version of the process for selecting performance indicators, which is compatible with this process and should be reviewed prior to selecting indicators for USAID, as well as a helpful checklist for quickly reviewing indicators.