Doing a survey? Sure. This is how the UX Research team at Halodoc went about doing one for Covid-19.
Quarantine has become THE thing. Hanging out with friends after work? Not a chance. Dinner outside with family on weekends? Sounds like a far and scary dream.
Still, is that what this pandemic is all about? Being stuck within the confines of our homes, uncertainty looming over possibilities of pre-pandemic good times?
We’d like to say that it isn’t, but jumping into conclusions wouldn’t make it any better. As one of the players in the healthcare industry, understanding whether Covid-19 impacted Indonesians’ health-related activities and behaviours is one of our ninja ways. Maybe some good came out of this after all, right?
So, here’s how we utilised the Likert scale in our survey to do just that.
Hold up, what’s a Likert scale?
Have you ever been asked to rate how much you agree with a statement? That is one example of a Likert scale in action. To be exact, it’s a bipolar scale that measures responses within a specified range. You can use odd or even-numbered scales, depending on the need to accommodate a neutral option. The ranges in between allows us to measure variables while keeping subtleties intact, especially since we’re exploring behaviour changes.
So how does it fit into our survey?
You ask, what’s next. As with all research, we had a few hypotheses about which activities would be affected by the pandemic. After deliberation, we settled on exploring online doctor consultations, health article reading, mask use, hand sanitizer use, food consumption, and supplements consumption.
Then, we narrowed down our line of questioning. Even though a bunch of questions popped up in our heads, considering respondents’ time and cognitive load is important to maintain the validity of responses. With that in mind, we honed in on examining health-related activities from three different lenses: behaviours, attitudes, and future intentions.
Let’s break them down one by one.
This forms the pillar of our survey. Do people perceive a difference in their behaviours before and during the pandemic? To figure this out, we presented respondents with two statements. Check out the examples below:
Respondents rated these statements on a 5-point Likert scale based on frequency. An odd-numbered scale is appropriate here to afford the “sometimes” option, represented by the midpoint. After all, respondents may not just wear masks in extreme frequencies.
It’s unlikely that respondents remember exactly how many times they wore their masks, so asking for a specific number won’t cut it. Moreover, the frequency of using a mask depends on how often they leave their house. Likert scales work in our favour here since we can adjust our scales for those contexts.
Attitudes are slightly trickier to probe. While behaviours are observable, attitudes by definition are implicit. This presents a wringer: the social desirability bias. Long story short, the social desirability bias means that people tend to answer questions in a way that puts them in a favourable light. When people randomly click on a survey, they’re not signing up to have their world view shattered.
In times like these, Likert scales are pretty much a godsend. Instead of asking for respondents’ takes on, say, supplements consumption, we can ask them to rate how much they agree with statements about it. Here’s one example where we used a 5-point scale of agreement:
To the naked eye, these statements are pretty much identical. However, presenting both positive and negative statements allow us to understand how strongly people hold these attitudes. Let’s say hypothetically that a respondent strongly agrees that supplements can protect them from illness but also strongly agree that their health will be protected without it. This would mean that while supplements are deemed helpful, they wouldn’t necessarily be game changers.
On to future intentions.
No crystal balls were used in this survey, promise.
As with behaviours, questions about future intentions are not meant to accurately depict how people will behave. It’s all a matter of perception. Do people perceive themselves to behave similarly once the pandemic has blown over?
At this point, you know that Likert scales have been good to us. It is versatile - a few tweaks to the scale labels and you’ve got respondents answering a whole lot differently. While attitudinal questions used an agreement scale, this time we go back to playing with frequencies. Let’s take the classic Halodoc service, online doctor consultations, as an example:
An odd-numbered scale is key here so the midpoint can accommodate for no changes occurring at all. These scale labels also consider that people start off at different frequencies to begin with. How respondents rate these statements aren’t indicative of future behaviours, but it provides insight into how malleable these behaviours are with varying context and time.
All in all,
Likert scales can take your survey up a notch with its ability to fit different contexts and provide nuance. However, it’s not always fun and games.
It’s not only the statements that make Likert scales work, but the labels too. A respondent might understand the statement but become unsure once they read the labels.
Is an even-numbered scale a better fit for your survey? Does it make sense to measure agreement? There should be a clear justification as to why specific ranges and labels are chosen. In other words, setting up a Likert scale requires an understanding of the context we want to evaluate.
We also shouldn’t forget the main culprit of unaddressed objectives: bias. It’s so easy to develop blindspots once we are fully immersed in the context. We could unknowingly dismiss key behaviours from being evaluated or even develop no-brainer statements. Hence, recognising where we are in our endeavour to address the objective is essential.
So, what’s your take on Likert scales?
We are always looking out to hire for all roles for our team. If challenging problems that drive big impact enthral you, reach out to us at email@example.com.
Halodoc is the number 1 all around Healthcare application in Indonesia. Our mission is to simplify and bring quality healthcare across Indonesia, from Sabang to Merauke.
We connect 20,000+ doctors with patients in need through our Teleconsultation service. We partner with 1500+ pharmacies in 50 cities to bring medicine to your doorstep. We've also partnered with Indonesia's largest lab provider to provide lab home services and to top it off, we have recently launched a premium appointment service that partners with 500+ hospitals that allow patients to book a doctor appointment inside our application.
We are extremely fortunate to be trusted by our investors, such as the Bill & Melinda Gates Foundation, Singtel, UOB Ventures, Allianz, Gojek and many more. We recently closed our Series B round and, in total, have raised USD$100million for our mission.
Our team works tirelessly to make sure that we create the best healthcare solution personalised for all of our patient's needs and are continuously on a path to simplify healthcare for Indonesia.