Qualitative methods generate rich non-numerical data and are well suited to understanding ‘how, what, and why questions’ or topics that we know very little about. Qualitative methods also allow researchers or programme implementers to understand issues from the perspective and in the language of the community members and key informants. Qualitative methods can be used alone, or combined with quantitative measures for a more robust understanding of your research question(s).

During an outbreak, it is particularly important to listen to communities and actively use their feedback to make programmatic adjustments, as well as bring their voices and opinions to policy- and decision-makers since they may not always have the opportunity to do this directly. Qualitative data can generate important narrative-style information on current needs, fears and perceptions of your focal disease.

Qualitative research can be useful during the following stages of programme implementation:

Formative research on behaviour - Formative research is done in advance of programme design and helps “form” programmes, ensuring they are context adapted and effective at addressing the particular needs and priorities of the local population. Formative research normally utilises a range of methods - qualitative and quantitative - to understand current practices, perceptions and the determinants of behaviour (i.e. the factors that enable or prevent a behaviour). It is often repeated during the intervention process to allow for adaptation and learning. Formative research to inform disease prevention activities involves selecting methods to answer the following types of questions:

Pre-testing intervention ideas - Prior to rolling out a programme at scale, it can be useful to test it among a small group of people. When doing so, qualitative methods can be used to learn about the feasibility and acceptability of the proposed behaviours and the intervention components. Qualitative methods can be developed to explore the following types of questions:

Monitoring acceptability and accountability - Throughout programme implementation, there should be ways of continuing to learn from populations. This should include selecting methods to answer the following types of questions:

Process evaluations - Qualitative methods can also be used to explore how change did or did not happen at an individual or community level. This should include selecting methods to answer the following types of questions:

Feasibility of implementation -

Qualitative methods can also be used with implementation teams to plan or assess the feasibility and logistical aspects of implementing the programme. This should include developing methods to answer the following types of questions:

Depending on your public health risk of concern, methods that avoid face-to-face interactions with participants may be key for minimising transmission. For this reason, many research institutions consider face-to-face data collection unethical during outbreaks of some diseases and suggest minimizing direct interactions when not urgent. Below we outline several method options for remote qualitative data collection:

Note that whilst many of the examples and resources from this section relate top COVID-19, principles and activities can be applied to programming for other infectious diseases.

This section was written with COVID-19 in mind; hence, it focuses on key COVID-19 preventative behaviours. However, principles and practices can be applied to diseases with similar preventative behaviours, and or adapted to different behaviours.

Quantitative measures generate results that can be summarised in numerical form, such as the number of people who hold a particular opinion or practice a certain behaviour. Quantitative measures are useful because they are relatively easy to collect (the same question is asked to all participants) and because they can help to track how opinions or behaviours change over time (e.g. prior to a programme being implemented, at the end of the programme’s implementation and sometimes at intervals in between).

When selecting any data collection method, it is important to be aware of potential biases. This is particularly important when measuring behaviour. A range of biases may be introduced, in particular if the participant is aware of the behaviour under observation. Bias in handwashing measures is often related to social desirability bias (the desire to give an answer that will meet social expectations), due to common knowledge of the perceived benefits of handwashing, for example. Handwashing measures are also often affected by recall bias (the inability of participants to accurately recall past behaviour), because it can be challenging to remember how frequently a routine or habitual daily behaviour is practiced.

While there is quite a lot of literature on measuring handwashing behaviour, there is less on mask use and physical distancing. However, many of the challenges with assessing handwashing behaviour are also likely to affect mask use and physical distancing, since, following the COVID-19 pandemic, these are now widely and publicly promoted. Thus, they are often seen as socially desirable.

Below we outline several quantitative indicators for measuring handwashing behaviour, along with their strengths and limitations. We also include comparable examples for mask use and physical distancing. For more information on measuring behaviour, see this article. This guide also provides a summary of some of the challenges with common hand washing measures.

Methods of assessing hygiene behaviour for remote data collection. Source: Ben Tidwell

For examples of some of the measures above being applied in research studies, see the following links for each type of indicator: behavioural intentions, self-reported behavior frequency, self-reported behavior by occasion, covert recall, existence of place for handwashing.

For mask use, similar questions may be asked about intentions to wear a mask, regularity of mask use (always/sometimes/rarely/never), covert recall, or having a mask at home. In both the SARS outbreak in 2003 and the COVID-19 pandemic, telephone surveys were employed to monitor changes in population behaviour and sentiments to public health measures in Hong Kong. For physical distancing, intentions and self-reported behaviour are also possible. For example, you could conduct a ‘social mixing’ survey, which captures the number of people that the participant came into contact with in the last week. This information can be further broken down based on whether these were people within or external to their households, and by age, gender, and physical proximity (e.g. beyond 1 metres/within 1 metres (or the physical distance recommend in national guidelines), or without physical touch/with physical touch such as a handshake or hug). While these methods for understanding mask use and physical distancing are newer, there is some evidence that self-reported physical distancing behaviour is quite closely aligned with actual behaviour. Since the start of the COVID-19 pandemic, there have been several key intervention studies which target mask use published (Study 1, Study 2, Study 3). In these studies, enacted mask usage was measured through direct observation within the community and self-reported behaviour frequency.

Where possible, it is useful to combine quantitative measures with qualitative measures.

There are a number of ways you can collect quantitative data remotely. This includes surveys through phone calls (also called CATI - Computer Assisted Telephone Interview), texts/ SMS, apps, interactive voice response (IVR) or online surveys. Detailed descriptions of these methods are provided in this report.

Whether you are switching from face-to-face data collection, or specifically designing a remote data collection tool, conducting remote data collection requires some specific considerations as outlined below.

For phone-based data collection, it is important to keep the duration of the interview/survey short for a number of reasons (including use of valuable phone time, battery usage, loss of interest from respondents). You may consider breaking up an interview into multiple phone calls if needed. More information about recommended durations of interviews/surveys via different modes of data collection are included in this report. Keeping interviews or surveys to short durations may require multiple rounds of piloting and revision of the questions. You will also need to consider which questions are most important and ensure these are at the beginning of the survey where they are more likely to be answered.

When collecting data over the phone, it is important to consider how to inform participants about the data collection activities and how to ask for their consent prior to collecting data. It’s also important to ensure you collect data ethically, especially during outbreaks, when people may be more vulnerable to discrimination and exclusion. For guidance on how to do this, refer to our resource on ethics, consent, protection, and risk here or see this ALNAP report.

If you would like to audio record a qualitative interview, you will need to obtain consent for making a recording. Recording devices should not be switched on before the interview/focus group begins and should be immediately turned off once it has concluded. There are apps that can be used to record phone conversations or alternatively, a recorder can be placed next to the phone while it is used on loud-speaker mode. Taking notes on a computer or with a pen and paper can be done as a back-up, or if the participant does not consent to having the interview recorded. However, note taking while simultaneously conducting in-depth interviews is challenging and is likely to compromise either the quality of the conversation, or the accuracy of documenting what is being said, so an additional person is recommended to serve as a note-taker. Some apps produce transcripts of verbal conversations.

For quantitative interviews, consider how data can be recorded to ensure high quality. It may be a good idea to include as many response options as you can think of in the survey forms to avoid the data collectors having to type lengthy notes. It is likely most efficient for data collectors to collect data on a computer or tablet that can then be sent directly to an online server, but this may not always be possible in resource-limited settings. If collecting data via paper-based methods, consider how data will be entered on a computer later and what will be done with the hard copies (they will need to be safely secured or destroyed after use).

Ensure that data collection staff can conduct the interview from a quiet room in their office or home, to avoid disturbance during the interview and maintain privacy. Consider how the data will be stored and how you will ensure data safety. Phones, tablets and/or computers used to collect data should be password protected. Consider how you will maintain confidentiality and anonymity for your participants if you need to share the findings of the research. If you are using technology-based platforms, make sure to check the security measures in place.

Consider if you can offer participants compensation for their time. This is particularly necessary if the interview is taking place via technologies that could cost them money. For example, compensation may be needed to cover the costs of re-charging phones, purchasing phone credit or data and other opportunity costs. There are a range of ways that compensation can be provided. For example, this could include sending phone credit or money through mobile banking platforms, such as M-pesa and Paytm.

In some cases, remote qualitative data collection has been found to allow participants to speak more freely, for instance about the work of international aid organisations. This may be because there is less of a personal relationship formed between the interviewer and participant. It is also possible that this distance between the interviewer and respondent may reduce other common types of biases. For example, participants may feel more ‘anonymous’ and therefore more able to accurately report on behaviours that are socially desirable. However, new biases may also be introduced during remote data collection. For example, it may be harder to hold the participant’s attention via the phone or other remote data collection formats. To minimise errors in reporting, it might be useful to diversify the way you ask questions. Examples of how to design questions to minimise reporting bias or neutral responses are described in this blog, along with information about other biases.

Many data collectors will have little or no experience of collecting data remotely. It will therefore be very important to provide thorough training on this new mode of data collection. These two blogs include useful guidance on hiring and training data collectors and on ensuring high data quality. During training, include scenarios where respondents ask for more information related to the focal disease, report carrying out unsafe practices, or report being a victim of abuse and teach data collectors how they should respond and what actions they should take. Some advice on this can be found in this report.

Consider what materials your data collectors will need, including whether they will require a different mobile number dedicated to this data collection activity. You will need to think through how you will keep track of data collected and what communication mechanisms you will put in place to regularly check in with your data collection teams. Detailed protocols should be written for how to conduct the data collection activity, from start to finish.

Authors: Fiona Majorin, Astrid Hasund Thorseth, Sian White and James B. Tidwell
Review: Julia Rosenbaum, Kondwani Chidziwisano, Sheillah Simiyu, Katrina Charles, Li Ann Ong, Rob Hope
Last update: 01.03.2023

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