Objectives of your study - Clearly define your research questions. This will determine the type of data you need to collect and how to best capture this information.

Type of data to be collected - Some remote data collection methods, such as telephone calls, are more suited to qualitative, open-ended questions, whereas others are designed primarily for quantitative, multiple choice or closed-answer questions, such as Interactive Voice Record (IVR), and some can handle both.

The population you will be targeting, their needs and preferences for data collection - Define who your target participants are and where they are located. Consider the languages spoken by these groups, their literacy levels, the mobile coverage in the area, and access and use of mobile phones by the community, as this will help you to identify data collection methods that are suitable. For example, if smartphones are not very common in your population, using app-based interviews would not be appropriate.

The local context - It is important to have a good understanding of the local context, including cultural customs and norms. When choosing a data collection method, you should always check that it is appropriate for the local context and acceptable to the community, especially when using new methods, such as IVR, which they may be unfamiliar with.

Number and depth of questions to be asked - Some methods will allow you to ask more questions than others. For example, if you want to ask lots of in-depth questions, a telephone call or online survey would be a more suitable platform to collect data compared to using SMS.

Data quality and response rates - Some methods are more likely to have low response rates. This should be considered when calculating sample size and cost. Some methods may also be susceptible to response errors, particularly if the population is not familiar with this method of data collection, for example IVR.

Access to technology - Some methods, such as online surveys, require respondents to use a smartphone or computer. All the methods mentioned above require some level of phone access. This may exclude certain members of the population. For example, women typically have reduced access to phones, compared to men and people experiencing heightened vulnerability, such as people with disabilities or crisis-affected populations, who may also have reduced access to phones.

Cost - SMS and IVR are less expensive methods for researchers, however, they are relatively expensive for respondents, unless phone credit is provided. Costs incurred by the data collection team will depend on personnel costs, phone costs, whether incentives are given, length of the survey, number of call attempts and the mode of data collection.

What data security measures do they have in place?

How will you be able to access, analyse and share the data?

Does your organisation have the technical equipment and capacity needed to access and analyse the data?

What will the costs be to your organisation and to participants?

Does the company have processes in place for seeking informed consent from participants?

Are there any regulatory restrictions around conducting remote data collection in your context? These might include requirements for ethical approval. In some countries there may also be restrictions around random digit dialing.

Are any other organisations planning to do remote data collection in your study population? Collaborating on data collection may reduce ‘survey fatigue’ - where populations become frustrated if approached by multiple organisations doing data collection on COVID-19. We recommend sharing results of any data collection with national or regional governments and with other organisations working in your area. Where possible, data should also be shared with populations.

Existing information about people in the sample - Since it's hard to maintain people’s attention for long periods of time in remote surveys, it is a major advantage if you already have existing demographic information about your sample population.

Consent processes may be shorter - If participants have completed a more extensive informed consent process previously, you may only need to provide information on how the general consent process is being updated in the current circumstances and this could therefore save time. If an initial face-to-face consent process was used then this may lead to higher confidence in the research process among participants.

Reduction in (some kinds of) bias - Previous relationships may lead to less bias in self-reported behaviors, attitudes, or beliefs, particularly if these opinions or practices run contrary to existing norms or government guidelines.

Higher response rates - Having existing relationships can make a major difference to response rates. For example, one random digit dialing study in India had response rates of only about 25%, while similar studies among pre-existing cohorts in Kenya found that, of the 74% of potential participants who answered the call, only 1% declined to participate. Low rates of participation may introduce biases that are challenging to correct.

Missing sub-groups - The original sample may not be completely representative of the target population of the new sample, for example, if the original sample was based on a subgroup of the population (i.e. mothers of adolescents).

Social desirability bias - Knowledge of the objectives of a prior study (e.g., a study on healthy behaviors) may bias responses if participants know the views of those conducting the study.

Respondent fatigue - Respondents may be less interested in continuing to answer questions or may dedicate less attention to their responses.

Non-representative samples may exclude people who might be vulnerable to exclusion and discrimination, and people who live in inaccessible locations. These people may be less likely to be present in many convenience samples (e.g. sampling visitors to a clinic).

Non-representativeness introduced by remote data collection itself may exclude these groups because they cannot be reached remotely. For example, most remote data collection requires access to mobile phones and about 30% of people globally do not have phones.

Even a random digit dialing approach can be biased, because some families have more than one phone number, meaning that the probability of their household being selected is higher. Furthermore, certain age groups or genders may be more or less likely to answer the phone or to agree to respond to a survey on the phone. For instance, this World Bank study found that women are 15% more likely to answer the phone.

When the response rate is not high, those responding might differ systematically from those not responding to a survey. The data may be adjusted for missingness using a number of techniques (including multiple imputation).

When those in the sample frame are not representative of the target population, post-survey weighting may be employed using a variety of techniques, including raking and matching.

The population being studied

The number of ways you have to contact a particular respondent

How recently the phone numbers were collected

The number and times of day/days of the week you try to contact the respondent.

Ghana - This was an IVR-based, random-digit dialling study as part of a national health promotion campaign tackling a range of issues, including HIV/AIDS, malaria and water, sanitation, and hygiene. The data collection team managed to get 81% of those who were reached via mobile phone to complete at least half of the survey. This was reportedly not much different to response rates during previous rounds of face-to-face data collection. However, the remote data collection did not reach a representative sample. These refusals, combined with the coverage bias described above, resulted in fewer women, rural, and older residents responding.

Kenya - This study was about COVID-19 and used an existing list of phone numbers collected by organisations working in the area with data collection by live interviewers. Only one phone number was available per household and phones were only called once - this resulted in an overall response rate of 66%.

India - This was a random-digit dialing survey using live interviewers to explore prejudice against women in India. 22% of the people who answered the call agreed to participate and completed the survey. However, this figure doesn’t include people who did not pick up or phone numbers that were inactive or disconnected.

El Salvador - This World Bank study aimed to compare the response rate of self-reported surveys via WhatsApp and telephone surveys, in 600 randomly selected adults during the national COVID-19 lockdown. The study found that the response rate increased by 42% in telephone surveys, representing a 140% increase relative to the self-reported surveys delivered via WhatsApp.

Budget in time to make multiple attempts to contact each respondent.

Try reaching out at different times of day, such as evenings or weekends, when people may be more likely to answer the phone or have time to respond to your survey.

Consider testing and perfecting your introduction, especially if reaching out via SMS which many have character limits.

Try to enhance credibility by mentioning which organisation is conducting the survey and make sure to explain how the respondent will be helping others if they agree to participate (e.g. helping the government or organisations to understand the needs of people like them).

Also mention how long the survey will take and that you respect and appreciate their time.

Tell the person that if they are busy, you are happy to try again at another convenient time.

Consider asking trusted community members, such as community leaders or health workers, to support the study and encourage people to respond to messages or calls. This may be important if community members do not trust the request for data.

Education

Past experience

Ownership of a smartphone

Literacy

Fluency in the local language and dialects

Fluency in the language of the recruiting organisation.

Without a researcher on the ground to oversee the work, data may be collected from the wrong respondents or may be fabricated. To reduce this risk, fixers can be asked to log the GPS coordinates of respondents. Also try to regularly engage with fixers or local data teams that you may establish. Having weekly calls to discuss and overcome common challenges may improve data quality and the satisfaction of your team.

If the people participating in the data collection receive an incentive to complete an interview or survey, then they may be concerned about fixers collecting a “commission” from them. To mitigate this, respondents should be clearly informed about if they will receive an incentive and what this incentive is prior to data collection (i.e. during the consent process).