Check out the top 10 articles + top 10 resources of 2023 from Eval Academy!

Unlock the Potential of Power Query in Excel for Effortless Data Preparation! In this article, we demystify the art of transforming raw data into actionable insights. With step-by-step instructions and hands-on examples, you'll master the essential skills to save time, reduce errors, and supercharge your data analysis.

This article walks you step-by-step through the process we use when merging datasets from multiple sources.

Evaluations of any size tend to need to adhere to budgets, whether for financial or human resourcing constraints. There are certain pitfalls that can quickly derail your budget. This article will guide you through some of the most common budget pitfalls to help you plan and support you to stay on budget throughout your evaluation.

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.

This article supports evaluators who are new to qualitative data analysis. We start by defining thematic analysis, then give you a 5-step process to complete your own analysis. We end the article by highlighting some common challenges with thematic analysis.

So you've successfully gathered the data you need to evaluate your program. But how do you engage stakeholders and partners to ensure a thorough understanding of the results? A data party could be part of the answer!

The end goal of collecting data is to eventually draw meaningful insights from said data. However, the transition from raw data to meaningful insights is not always a linear path. Data are prone to human-error and this guide will help you correct those errors, as well as provide tips on how to minimize these errors in the future.

As evaluators, we are rarely organizational decision-makers; it is our job to provide those decision-makers with actionable insights. In this article we highlight how you can translate data into meaningful findings, or insights, so you can support decision-makers to drive action within their organizations.

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.

One of the most common things I see when I work data in excel that others have compiled or analyzed is the under-use of Excel’s computational powers. In part, this has to do with setting up your data correctly to all Excel to do the work.

Evaluations are inherently political, which means they are fraught with ethical choices and decisions along the way. There have been many instances throughout my career where I faced an ethical dilemma - here are some things that have helped me silence the devil on my left shoulder and figure out the right thing to do.