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. 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 much 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 these are now being 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.
Table 1: Methods of assessing hygiene behaviour for remote data collection
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 current COVID-19 pandemic, telephone surveys have been 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.
Where possible it is useful to combine quantitative measures with qualitative measures.
Want to know more about remote quantitative and qualitative approaches for understanding COVID-19 related behaviours and perceptions?
- What aspects of COVID-19 programming is useful to explore qualitatively?
- How can qualitative data collection be done remotely?
- How can quantitative data collection be done remotely?
- What are the practical considerations of doing remote quantitative and qualitative data collection?