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Survey Limitations

The COVID-19 Trends and Impact Survey (CTIS) gathers large amounts of detailed data; however, it is not perfect, and its design means it is subject to several crucial limitations. Anyone using the data to make policy decisions or answer research questions should be aware of these limitations. Given these limitations, we recommend using the data to:

  • Track changes over time, such as to monitor sudden increases in reported symptoms or changes in reported vaccination attitudes.
  • Make comparisons across space, such as to identify regions with much higher or lower values.
  • Make comparisons between groups, such as between occupational or age groups, keeping in mind any sample limitations that might affect these comparisons.
  • Augment data collected from other sources, such as more rigorously controlled surveys with high response rates.

We do not recommend using CTIS data to

  • Make point estimates of population quantities (such as the exact percentage of people who meet a certain criterion) without reference to other data sources. Because of sampling, weighting, and response biases, such estimates can be biased, and standard confidence intervals and hypothesis tests will be misleading.
  • Analyze very small or localized demographic subgroups. Due to the response behavior issues discussed below, there is measurement error in the demographic data. Very small demographic groups may disproportionately include respondents who pick their demographics at random or attempt to disrupt the survey in other ways, even if those respondents are rare overall.

The sections below explain these limitations in more detail.

Table of contents

  1. The Sample
  2. Weighting
  3. Response Behavior
  4. Missing Data

The Sample

Facebook takes a random sample of active adult users every day and invites them to complete the survey. (“Adult” means the user has indicated they are least 18 years old in their profile.) Taking the survey is voluntary, and only 1-2% of those users who are invited actually take the survey. This leaves opportunities for sampling bias, if the sample is construed to represent the US adult population:

  1. Sampling frame. The sample is random and maintains similar user characteristics each day, but it is drawn from adult Facebook active users who use one of the languages the survey is translated into: English [American and British], Spanish [Spain and Latin American], French, Brazilian Portuguese, Vietnamese, and simplified Chinese. This is not the United States population as a whole. While most American adults use Facebook and the available languages are more comprehensive than for many public health surveys, “most” is not the same as “all”, and some demographic groups may be poorly represented in the Facebook sample.
  2. Non-response bias. Only a small fraction of invited users choose to take the survey when they are invited. If their decision on whether to take the survey is random, this is not a problem. However, their decision to take the survey may be correlated with other factors—such as their level of concern about COVID-19 or their trust of academic researchers. If that is the case, the sample will disproportionately contain people with certain attitudes and beliefs.

Facebook calculates survey weights (see below) that are intended to help correct for these issues. The weights adjust the age and gender distribution of the respondents to match Census data, and adjust for non-response by using a model for the probability of any user to click on the survey link. However, if that non-response model is not perfect (for example, non-response varies with respondent attributes not included in the model), or if the Facebook population differs from the US population on more features than just age and gender, the weights will not account for all sampling biases. For example, analyses of weighted survey data shows demographics relatively similar to the US population, with slightly higher levels of education and a smaller proportion of non-white respondents; however, comparisons of self-reported vaccination rates of survey respondents with CDC US population benchmarks indicate that CTIS respondents are more likely to be vaccinated than the general population.

We do, however, expect that any sampling biases will remain relatively consistent over time, allowing us to make reliable comparisons over time (such as noting an increase or decrease in vaccination rates or vaccine intent) even if the point estimates are consistently biased. This is a common issue with self-reported data; for example, surveys on illegal drug use expect under-reporting (as they ask about an illegal activity) but are commonly used to make comparisons between groups or over time.

Also, Facebook’s sampling process allows users to be invited to the survey repeatedly. A user will only be reinvited at least thirty days after their previous invitation. Because respondents are anonymous and we do not receive any unique identifiers, responses from the same user are not linked in any way. Analysts must be aware that when working with responses submitted more than a month apart, some responses may be from the same users.

Weighting

It is important to read the weights documentation to understand how Facebook calculates survey weights and what they account for. There are some key limitations:

  1. Because we do not receive Facebook profile data and Facebook does not receive survey response data, the weights are based only on attributes in Facebook profiles, not on demographics reported in response to survey questions. For example, if a respondent’s Facebook profile says they are 35 years old and live in Delaware, but on the survey they respond that they are 45 years old and live in Maryland, the weight will be calculated based on the profile information and reflect the Delaware location. This causes measurement error in the weights.
  2. Similarly, the non-response model used by Facebook only uses information available to Facebook, such as profile information. As discussed above, if this model is not perfect, for example if factors not included in the model affect non-response, the weights will not fully account for this non-response bias.
  3. Facebook only invites users who it believes reside in the 50 states or Washington, DC. (Puerto Rico is sampled separately as part of the international version of the survey.) If Facebook believes a user qualifies, but the user then replies that they live in Puerto Rico or another US territory, their weight will be incorrect. Starting in September 2021, these responses are not included in any microdata.

Response Behavior

Survey scientists have long known that humans do not always provide complete and truthful responses to questions about their attributes, beliefs, and behaviors. There are two primary reasons CTIS responses may be suspect.

First is social desirability bias. As with all self-report measurements, survey respondents may give responses consistent with what they believe is socially desirable, because they feel pressured to fit social norms. For example, if someone lives in an area where masks are widely used and seen as essential, they may report that they wear their mask most or all of the time when in public, even if they don’t. While this effect is likely smaller on an anonymous online survey than in an in-person interview, it could still be present.

The second problem is deliberate trolling. While intentional mis-reporting is always a possibility when users provide self-report data, it is a particular concern for a large, online survey on a controversial topic offered through a large social media platform. It appears that the vast majority of CTIS respondents complete the survey in good faith; however, we occasionally receive emails from survey respondents gloating that they have deliberately provided false responses to the survey, usually because they believe the COVID-19 pandemic is a conspiracy or that scientists are suppressing key information.

We have also observed problematic behavior in a specific subset of respondents. While less than 1% of respondents opt to self-describe their own gender, a large percentage of respondents who do choose that option provide a description that is actually a protest against the question or the survey; for example, making trans-phobic comments or reporting their gender identification as “Attack Helicopter”. Additionally, these respondents disproportionately select specific demographic groups, such as having a PhD, being over age 75, and being Hispanic, all at rates far exceeding their overall presence in the US population, suggesting that people who want to disrupt the survey also pick on specific groups to troll.

(Note that if a respondent is invited once but completes the survey multiple times, or shares their unique link with friends to take it, only the first response is counted; this limits the impact of deliberate trolling. If the respondent is sampled and invited again later, they receive a new unique link.)

For overall estimates, trolling is not expected to impact results in a meaningful way. However, given the concentration of trolls in small demographic groups, users interested in comparisons of small demographic groups should examine a sample of the raw data. For example, if you are interested in responses from Hispanic adults over age 65, examine the other demographic variables for this group of respondents to ensure they appear to match what you would expect and do not appear influenced by respondents who give deliberately strange answers.

Importantly, weights cannot correct for trolling behavior. Users can either note any concerns they have when reporting for small groups, or they may choose to analyze the data without a suspect group. We are continuing to evaluate trolling and will provide updates if new patterns appear.

Missing Data

Some survey respondents do not complete the entire survey. This could be because they get impatient with it, because they do not want to respond to questions about specific topics, or simply because they are responding to the survey during a quick break or while waiting in line at Starbucks. (Remember, Facebook users see the invitation when they’re browsing the Facebook news feed, which could be any time someone might pull out their phone and check Facebook.)

As a result, questions that appear later in the survey, including demographics, can be blank in 10-20% of survey responses. Similar to overall non-response, this is an issue when such behavior does not occur at random relative to the questions you are analyzing; for example, if individuals who are particularly concerned about COVID-19 are more likely to take the time to finish the survey.

Also, the CTIS survey instrument is deliberately designed so that most items are optional—Qualtrics will not attempt to force respondents to answer questions that they leave blank. This allows respondents to leave an item blank if they prefer not to answer it, rather than entering a nonsense answer. This can lead to missingness in the middle of the survey, even among respondents who answer later questions. As noted above, this missingness is almost certainly not at random. Data users should examine and report the missingness in the questions they use. Imputation methods are an option; users should consider whether the assumptions of imputation models appear to be met for the data.