You’ve completed your qualitative data collection and you’re writing up your report. You step back and look at All. The. Text. If only you had some quantitative data to include in a chart, or some numbers to report! In this article, we talk about 3 ways you can quantify your qualitative data.

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.

In this article, we briefly define sample sizes, their importance, and how to calculate them (or how to use a tool to calculate them).

In the evaluation world, scheduling can be a nightmare. This article describes how to use Calendly to schedule interviews in three simple steps.

In this article, we describe what assumptions are in evaluation, explain why you should document assumptions, and describe how to reflect on your assumptions when collecting and analyzing evaluation results. We also provide some practical examples of how to include assumptions in your own evaluations!

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.

A book review of Michael Quinn Patton’s Developmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use (2011).

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 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.

If you work with data in Excel, whether frequently or infrequently, learning the basics of PivotTables will improve your ability to quickly explore and analyze raw data.

Every evaluation is different, but there are a few topics that should be covered in every kick-off meeting.

Recognizing that equitable evaluation is an emerging area of work, this article aims to add to the growing discussion. While it does not include an exhaustive list of issues and strategies, it will help you introduce some changes to your evaluation practice.

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.

What is the scope of an evaluator’s role: should an evaluator make recommendations? Do we tell programs how to improve? Or do we simply share the data with them and let them draw their own conclusions?

A few years ago, we completed an evaluation for a breast cancer clinic. In honour of Breast Cancer Awareness month, we thought we would highlight some excerpts from that report to help inspire your next evaluation report!

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.

Ideally, our evaluation projects would proceed as planned. But as all project managers know, sometimes things change. Actually, most of the time, things change! In some situations, our evaluation approach can be modified to adapt to the changing context, but in others, we have to say no to scope creep.

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.