Recently, we’ve had several clients ask us about Developmental Evaluation (DE) including what it is and how it’s done. So we’ve pulled together a few main points about DE in this article.
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Bias in evaluation is inevitable. Reflection helps us to identify our bias and when we do, it is necessary to identify sources of bias in our processes, eliminate which bias we can, and acknowledge which bias we cannot.
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Eval Academy has just released a new free template to help you Develop a Logic Model.
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In this article, we discuss different incentives for participation, explore the biases that offering an incentive can introduce, and guide you in deciding whether to offer an incentive, including what form and value that incentive should be.
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Kirkpatrick is probably one of those names/methods you’ve heard about in your evaluation career, but have you ever used it? In this article, we explain how to use the Kirkpatrick model in your evaluation planning, implementation, and reporting.
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This article is aimed at those who are new to environmental scanning and are looking for new ways to support program planning and improvement.
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In this follow-up article, we will walk you through a step-by-step process of how you can develop your own ToC diagram for evaluation purposes.
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This article aims to introduce you to Theory of Change (ToC) and is written for those who may have heard of this tool before but aren’t quite sure as to what it is and how it can be useful for evaluation.
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Data integration is a way of merging these data from different sources through mixed methods. In this article, we discuss how qualitative and quantitative data can be integrated at the study design level, methods, or analysis level.
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For those interested in calculating sample sizes by hand, or getting a better understanding of the math behind many sample size calculators, we outline the formulae used to calculate sample sizes.
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In this article, we briefly define sample sizes, their importance, and how to calculate them (or how to use a tool to calculate them).
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The Likert scale is one of the more commonly used rating scales in surveys. As evaluators, we should know a thing or two about it, and how to navigate some of the decisions involved in using a Likert scale.
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Is consent always needed? How do I obtain consent? In this article, we discuss the principles and considerations involved in obtaining consent for evaluation data collection.
As an evaluator, a big part of our role is to collect data. Sometimes that data comes from administrative databases that require data sharing agreements, but sometimes it comes from people, who need to consent to sharing their information (or data) with you. So let’s talk about what consent is (and isn’t).
It is ideal to have a data dictionary whenever you have quantitative data that will be used and shared by multiple people or groups. Without precise definitions, it is very easy to arrive at different results while using the same dataset. In this article, we focus on how evaluators can (and should) clarify details about the data being used for evaluation. In other words, how and why build an evaluation-specific data dictionary.
Don’t get us wrong, surveys are useful tools and we’re a fan of any survey platform that makes it easier to use the results. But what about when you want to scratch beneath the surface or present a legible graph that will convince the program director or funder that action needs to be taken? This is where the canned survey tools start to falter.
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Recently we used outcome harvesting as part of a developmental evaluation. As with most developmental and participatory techniques, using this method was a bit time intensive, but the results were worth it! Here we share how we used the methodology and what we wished someone had told us before we started.
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Evaluators have several options for transcribing audio from interviews, including voice-to-text software, outsourcing, and doing it ourselves. If you are taking the DIY approach, here are four tips to get you transcribing like a pro!
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Here, I share solutions to two challenges you are likely to come across while entering survey data: 1) coding complex question types and 2) dealing with unclear responses. Addressing these challenges will require some advanced coding that I did not cover in my first article.
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When entering survey data, it is important that it is accurate, easy to analyze, and fast. Use these tips to prevent mistakes from the beginning.
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