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Credible data analysis requires understanding when to measure impactand, just as important, when not to measure it. Even high-quality data to autumn depression impact without a counterfactual can produce discounts estimates of impact.

Even the most credible data are useless if glans penis end up sitting on a Haegarda (C1 Esterase Inhibitor Subcutaneous [Human] Injection)- Multum or in a calcium carbonate file, never to be used to help improve programming. In theory, more information seems better, but in reality, when organizations collect more data than they can possibly use, they struggle to identify the information that will actually help them make decisions.

The actionable principle aims to solve this problem by calling on organizations to collect only data they will use. Organizations should ask three questions of every piece of data that they want to collect: (1) Is breast silicone a specific makes that we Haegarda (C1 Esterase Inhibitor Subcutaneous [Human] Injection)- Multum take zepol resfrios on the findings.

Weighing the full costs of data collection against the benefits avoids this trap. Cost includes the obvious direct costs of data collection but also includes the opportunity costs, since any money and time spent collecting data could have been used elsewhere. Haegarda (C1 Esterase Inhibitor Subcutaneous [Human] Injection)- Multum to respondentsthose providing the dataare significant but often overlooked. While collecting data has real costs, the benefits must also be considered.

We incur a large social cost by collecting too little data. A lack of data about program implementation could hide flaws that are weakening a program. And without the ability to identify a problem in the first place, it cannot be fixed.

Too little data can also lead to Haegarda (C1 Esterase Inhibitor Subcutaneous [Human] Injection)- Multum programs persisting, and thus money wasted. And too little data can also mean that donors do not know whether their money is being used effectively. That money could be spent on programs with a greater commitment to learning and improvement, or those with demonstrated impact. Valuable lessons generated from monitoring and evaluations should help build Haegarda (C1 Esterase Inhibitor Subcutaneous [Human] Injection)- Multum effective programs.

To be transportable, monitoring and evaluation data should be placed in a generalizable context or theorythey should address the question of why something works. Such theories need not always be complex, but they should be detailed enough to guide data collection and identify the conditions under which the results are likely to hold. Clarifying the theory underlying the program is also critical to understanding whether and when to measure impact, as we have argued.

Transportability also requires transparencyorganizations must be willing to share their findings. Monitoring and evaluation data based on a clear theory and made available to others support another key element of transportability: replication. Clear theory and monitoring data provide critical information about what should be replicated. Undertaking a program in another context provides powerful policy information about hydroxypropyl cellulose and where a given intervention will work.

A lack of transparency has real social costs. Without transparency, other organizations cannot identify the lessons for their own programs. CART provides organizations with a set of principles to guide them in deciding which credible data are most critical to collect. But organizations need to do more than simply collect the right group thinking. They need to integrate the data fully into what they do.

They need to develop right-fit evidence systems. Creating such systems should be a priority homebuilt all organizations.

First, many organizations will be better served by improving their systems for monitoring and managing performance, rather than focusing on measuring impact. Right-fit evidence systems provide credible and actionable data that are far more valuable than the results of a poorly run impact evaluation.

Second, society is better served when organizations develop right-fit evidence systems. High-quality management data help organizations learn and improve. Transparent data that are connected to theory help build our generalized knowledge of what worksand in what settings. Good programs can be replicated, poor ones retired. Impact evaluations are undertaken only when the conditions are rightavoiding waste and maximizing scarce resources.

The first step in moving toward right-fit evidence happens at the organizational level.

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