Research Methodology

Steve Richter • May 27, 2020

COVID-19 Non-Profit Sector Survey

The announcement of the novel coronavirus pandemic in North America in March 2020 and the economic and social upheaval that has followed, signals an era of change in the non-profit world. To gain a firmer understanding of the impact of COVID-19, AgentsC decided to design and administer an anonymous, online survey to the Canadian non-profit industry. 

The survey was intended to shed light on how organisations are dealing with the effects of the pandemic in their day-to-day operations and fundraising. 

Our main methodology for this survey was the creation and distribution of a 23 question online survey using the Google Survey platform. AgentsC used both a non-random and random sampling method for administering the survey. The non-random portion consisted of contacting non-profit professionals in our networks. However, a significant part of our engagement strategy was done via advertising on social media (LinkedIn, Twitter and Facebook). In this way, the survey also received a random sample of participants.

The analytic strategy for the survey data used basic descriptive statistics. This meant analysing and comparing the percentage of participants who responded a certain way to different questions. This technique allows for the survey data to be analysed thoroughly, while ensuring the accessibility of the results to a wider public that may not have a mastery in statistical analysis.

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This survey was also particularly interested in how demographic indicators (age, race, disability status, geography, gender expression, position occupied within organisation) played a role in how organisations were coping under COVID-19.



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We present two types of tables in our report: single and multi-variable. By a single-variable table, we mean that the table displays responses from a single question on the survey (see Graph 3). By a multi-variable table, we mean that the table includes responses from two questions (see Graph 1). 



Multi-variable tables break down responses according to both questions asked. For example, data on age group combined with expectations that fundraising goals were going to be met. The responses for age group were: 20-29, 30-39, 40-49, 50-59, 60-69, 70-79 and 80-89, whilst the responses for fundraising goals were yes or no. Because this is a multi-variable graph, we present the responses to the fundraising goal question across each age group. This technique allows us to see if there is a relationship between these two questions. 



AgentsC was particularly interested in how demographic indicators (age, race, disability status, geography, gender expression, and position occupied within an organisation) played a role in how organisations were coping under COVID-19. Further reports focusing on the disaggregation of our data will be released during the month of March 2020

All images produced by United Nations COVID-19 Responses