They’re not just customers, employees, market researchers, or event attendees—people across the world use SurveyMonkey to give feedback on anything you can imagine. We ask just a tiny fraction of those people for their opinion on important issues, and get unprecedented access to a sample of the U.S. population.
That access lets us poll the American public for their views on important current events, while our team of expert survey scientists make sure the sampling of individual units matches the U.S. population at large.
How does SurveyMonkey get its data? We’ll take you through it, step by step.
1 Over 2 million people take surveys on SurveyMonkey’s platform each day.
2 A random selection of those people are invited to participate in a survey.
3 After they’ve taken the survey, we filter out people who didn’t complete it (nonresponses).
4 Our survey scientists carefully adjust the data so that it’s representative of the sample population.
5 What does that mean? When groups in our sample don’t exactly match the larger population, we use advanced statistical inferences to balance them.
6 Now we start looking at the results. We aggregate and compile responses to provide an easy-to-understand snapshot of what people are thinking.
7 The large scale of our sample allows us to pinpoint views that others cannot, giving us an inside look on public opinion and experiences.
Our team of survey methodologists and pollsters stand behind our data because of three core principles:
Scale and Diversity: During the millions of survey conversations we have each day, we talk to people from a broad range of demographic groups—doctors under 30, construction workers in Maine or Asian American retirees. We have respondents from every:
Known Sampling: Unlike some, we don’t take personal information from our respondents—we ask for it. We collect demographic information on all our respondents, which provides important context for our results. It also allows for more sophisticated weighting of our data, making it even more accurate.
Our SurveyMonkey research team runs surveys every day on politics, sports, current events, the media, and whatever else piques our curiosity. We surveyed more than 1 million voters over the course of 2016, and we haven’t really slowed down since—though we have modified our methodology slightly. This page is a resource for anyone interested in our current sample design, questionnaire, weighting methodology, and data availability—read on!
*If you’re interested in our 2016 Election Tracking methodology you can always visit this link.
Over 2 million people take user-generated surveys on the SurveyMonkey platform each day. We select a random sample of these respondents to take part in our research surveys. After completing their initial survey, they see a “thank-you” page inviting them to take an additional survey—those are our research surveys that we conduct in partnership with media outlets and other organizations.
Our sampling process is similar to the way polling has traditionally been done, but updated for the internet age. Instead of randomly drawing respondents from a list of phone numbers, we randomly draw from our diverse base of daily survey takers. We ask our respondents how old they are, whether they are registered to vote, what state they live in, and so on… just as phone polls do.
Our survey-takers come from all 50 states—urban, suburban, rural, and everything in between. Because SurveyMonkey is an online platform, all respondents must have internet access in order to complete our surveys. However, this is becoming less of a limitation as internet penetration increases and as more respondents complete surveys on their cell phones or mobile devices.
SurveyMonkey’s research surveys are in the field continuously; we have respondents completing surveys 24 hours a day, 7 days a week. All surveys are written in English, though we occasionally translate into Spanish as well. We always ask respondents for information on their sex, race/ethnicity, age, state, and education level so that we can use this data to weight our results to be nationally representative.
We routinely include questions on party identification, presidential approval, and respondents’ most important issue so that we can track changes to these questions regularly. If multiple surveys are running at once, we aggregate responses to these questions. There is no risk that any preceding questions on different surveys will influence respondents’ answers because these questions are always asked first.
We have several weighting schemes that we can choose to deploy depending on the sample size and the population of interest for each survey. For each of the weighting schemes outlined below, we use the Census Bureau’s 2015 American Community Survey (ACS) to generate estimates that reflect the most up-to-date demographic composition of the US in terms of age, race, sex, education, and geography. We require all respondents to answer the survey questions used to weight these parameters in each of our surveys.
Surveys that use probability-based designs can calculate and report a margin of error estimate for each statistic they produce. You’ll often see language such as “this poll has a margin of error of +/-3.5 percentage points,” which means that if the difference between two estimates is within the margin of error, we can’t tell with confidence which one is greater.
SurveyMonkey research surveys do not have a probability-based design, because there is no well-defined sampling frame of respondents to SurveyMonkey surveys. Therefore, to avoid confusion, we do not report a margin of error term. Instead, we utilize a “modeled error estimate” which is calculated using a bootstrap confidence interval. According to the American Association for Public Opinion Research (AAPOR), this method is a best practice for non-probability surveys, as it “approximates the variance of a survey estimator by the variability of that estimator computed from a series of subsamples taken from the survey data set.”
Here’s an example of our typical methodology summary:
Here, the modeled error estimate of plus or minus 3.5 percentage points has the same interpretation as the margin of error example above. In every blog post or report, we’ll always include the dates during which the survey was in the field, the total number of respondents, a brief description of our weighting methodology, and the modeled error estimate for the survey.
Note: Because we are an online survey organization, people often assume that our research surveys are administered to a non-probability panel. This is incorrect. While SurveyMonkey does maintain a panel of respondents to make available to customers, we seldom employ this panel for respondent recruitment. Our methodology statement will always indicate the way we obtained our sample of respondents and weighted our results.
If you’d like to keep up with our ongoing insights, here are a few ways to do that:
Trump approval: Every Friday we publish a week’s worth of data on President Trump’s approval rating. View the archive here.
Consumer confidence: We publish an index of consumer confidence based on questions about individuals’ current financial health and their expectations for the future. View the archive here.
Small business confidence: Every quarter, in partnership with CNBC, we ask small business owners about the current small business environment and their expectations for the future. View the archive here.
Partners: We have more partnerships now than ever before. Check out recent results published by NBC News, Axios, FiveThirtyEight, The New York Times, ESPN, Vanity Fair’s Hive & theSkimm, OZY and CNBC.
SurveyMonkey Audience is a separate tool with a different method for recruiting respondents. In Audience, respondents take surveys in exchange for donations to charity and customers can pay to hear their opinions. The polling method described on this page isn’t available for purchase.
SurveyMonkey employs a team of survey methodologists—scientists who study surveys, polling, public opinion, and data collection. They know exactly how to structure surveys, ask questions, and analyze data in order to get precise results.