Articles
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New Template: Analyst Quality Assurance Notes, Checklist and Style Guide Template
Streamline your data analysis process with our Analyst Quality Assurance Notes, Checklist, and Style Guide Template! This tool helps you document data-related assumptions, decisions, and analytic steps in one organized file.
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 Link Surveys in Qualtrics with a Participant ID
This is a step-by-step guide for linking your surveys in Qualtrics with a participant ID to assess change over time. Linking surveys together means that you’re connecting the data from all your surveys in a way that lets you track an individual respondent.
Survey Design Part 1: Planning for your survey – A review of Designing Quality Survey Questions (2019) by Robinson and Firth-Leonard
The article discusses the book "Designing Quality Survey Questions" by Robinson and Leonard, offering guidance for beginners in survey design. It highlights understanding survey purpose, tool suitability, respondent characteristics, and cultural context, serving as a useful resource for both new and experienced survey designers. Part 2 will delve into crafting quality survey questions.
New infographic: Data viz decision tree
Choosing the right type of chart to display your data can help to improve clarity among your readers and lessens the likelihood that your findings may be misinterpreted. With so many different types of charts and graphs, it can be tough to know where to start. This infographic is for anyone who wants to clearly display their quantitative data in a meaningful way but isn’t sure how to pick the best chart for the job.
What’s the Difference: Bias versus Confounding?
In every research and evaluation project, it is important to identify and address sources of error that may impact the accuracy of your findings and the relevance of your recommendations. Here, we will look at what bias and confounding are (and are not), the differences between them, and important considerations to take to prepare for and address both in your next evaluation project.
New Tip Sheet: Tips for Conducting Interviews
Eval Academy just released a new tip sheet for Conducting Interviews. If you will be conducting interviews in your evaluation, then this tip sheet is for you! Whether you’re new to the process or have conducted interviews before, this tip sheet provides a good overview and refresher to make your next interview experience a great one.
New Infographic: Qualitative Data Saturation
Eval Academy just released a new infographic about qualitative data saturation. This infographic is for those who collect or who will be collecting qualitative data and are looking to support the validity of their results. It’s a helpful resource for those who are both new and experienced in evaluation!
What you need to know about member checking
While member checking is commonly used in qualitative research, it’s less commonly used in evaluation and we think that should change! In this article, we’ll review what member checking is and why, when, and how you should use it.
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.
A starter pack of presentation tools (4 min read)
Are you looking to elevate your presentation game? In this article, we list some awesome presentation tools along with ideas for how to use them in your evaluation practice.
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.