Articles
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New Template: Stratified Sampling Tool (Single Strata)
Discover our new Stratified Sampling Tool template, designed for researchers and evaluators working with large datasets. Learn how to generate representative samples, ensure fair subgroup representation, and increase precision in your data collection. Includes both single strata and multiple stratum versions with step-by-step instructions.
How to Use World Cafés as an Evaluation Data Collection Method
Discover how World Cafés can enhance your data collection efforts. This article guides you through organizing a World Café, from selecting facilitators to crafting effective questions and managing conversations. Learn how to analyze the data collected and address potential challenges, making World Cafés a powerful tool for engaging participants and generating comprehensive insights in your evaluations.
Survey Design: Unsure about Unsure
Ever been frustrated by a poorly designed survey? From limited response options to confusing questions, we've all encountered surveys that leave us scratching our heads. But fear not! This article unravels the mysteries of survey design, offering invaluable insights into mitigating respondent frustrations and maximizing data quality.
Evaluative Thinking: What does it mean and why does it matter?
Unleash the power of evaluative thinking in our data-driven world! This article explores how evaluative thinking goes beyond data analysis alone; it involves a systematic and reflective approach to understanding the effectiveness and impact of actions, programs, and decisions. Discover why evaluative thinking matters for informed decision-making, accountability, and fostering innovation.
Stratified Random Sampling in Evaluation
Enhance evaluation precision through Stratified Random Sampling—a method that partitions populations into subgroups for nuanced insights. Whether adopting proportionate or disproportionate approaches, this strategy fosters inclusivity and robust representation, enriching the evaluative process, learn about it in this article.
5 tips for ensuring interviewer safety.
In this article, we highlight the importance of ensuring interviewer safety to make the interview experience effective for collecting data and a positive experience for everyone involved!
Questions to get you thinking about your data
Data are only useful when used! They do no good buried in reports, sitting on shelves (or shared drives) hidden away. Data, particularly data from an evaluation, are begging to be discussed, contemplated, and put into action! This article discusses some ways to make sure your data are used.
New Checklist: Information request checklist
Eval Academy just released a new checklist for anyone who’s about to start a new evaluation project. Use this checklist to make sure you’re gathering the information necessary to support your evaluation endeavour. This checklist can act as a support tool for you to make sure you have the context you need when starting an evaluation.
Sampling and Recruitment 101
You’ve got your evaluation plan; you’ve developed your data collection tools and you’re ready to go live with collecting the data you need to answer your evaluation questions. Step 1: Identify your sample. Step 2. Recruitment. But how do you get participants to take part in the data collection process?
Top Tips for Using a Real-Time Interpreter in Interviews and Focus Groups
In this article, we share our top tips for running an interview or focus group using a real-time interpreter & reflect on a recent online focus group I completed that utilized an Arabic interpreter.
New Template: Interview Tracking Log
Eval Academy just released a new template, “Interview Tracking Log.” This template is available as a Word document as well as an Excel file! This Interview Tracking Log can be used by anyone who will be completing interviews to collect qualitative data.
Sampling bias: identifying and avoiding bias in data collection
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.