Making the most of bad qualitative data

 

A cardinal rule of most research projects is things don’t always go to plan. Qualitative data collection is no difference, and the variability in approaches and respondents means that there is always the potential for things to go awry. However, the typical small sample sizes can make even one or two frustrating responses difficult to stomach, since they can represent such a high proportion of the whole data set.


Sometimes interviews just don’t go well: the respondent might only give very short answers, or go off on long tangents which aren’t useful to the project. Usually the interviewer can try and facilitate these situations to get better answers, but sometimes people can just be difficult. You can see this in the transcript of the interview with ‘Julie’ in the example referendum project. Despite initially seeming very keen on the topic, perhaps she was tired on the day, but cannot be coaxed into giving more than one or two word answers!


It’s disappointing when something like this happens, but it is not the end of the world. If one interview is not as verbose or complete as some of the others it can look strange, but there is probably still useful information there. And the opinions of this person are just as valid, and should be treated with the same weight. Even if there is no explanation, disagreeing with a question by just saying ‘No’ is still an insight.


You can also have people who come late to data collection sessions, or have to leave early resulting in incomplete data. Ideally you would try and do follow up questions with the respondent, but sometimes this is just not possible. It is up to you to decide whether it is worth including partial responses, and if there is enough data to make inclusion and comparison worthwhile.


Also, you may sometimes come across respondents who seem to be outright lying – their statements contradict, they give ridiculous or obviously false answers, or flat out refuse to answer questions. Usually I would recommend that these data sources are included, as long as there is a note of this in the source properties and a good justification for why the researcher believes the responses may not be trusted. There is usually a good reason that a respondent chooses to behave in such a way, and this can be important context for the study.


In focus group settings there can sometimes be one or two participants who derail the discussion, perhaps by being hostile to other members of the group or only wanting to talk about their pet topics and not the questions on the table. This is another situation where practice at mediating and facilitating data collection can help, but sometimes you just have to try and extract whatever is valuable. But organising focus groups can be very time consuming, and consume so many potentially good respondents in one go, so having poor data quality from one of the sessions can be upsetting. Don’t be afraid to go back to some of the respondents and see if they would do another smaller session, or one-on-ones to get more of their input.


However, the most frustrating situation is when you get disappointing data from a really key informant: someone that is an important figure in the field, is well connected or has just the right experience. These interviews don’t always go to plan, especially with senior people who may not be willing to share, or have their own agenda in how they shape the discussion. In these situations it is usually difficult to find another respondent who will have the same insight or viewpoint, so the data is tricky to replace. It’s best to leave these key interviews until you have done a few others; that way you can be confident in your research questions, and will have some experience in mediating the discussions.


Finally, there is also lost data. Dictaphones that don’t record or get lost. Files gone missing and lost passwords. Crashed computers that take all the data with them to an early and untimely grave! These things happen more often than they should, and careful planning, precautions and backups are the only way to protect against these.


But often the answer to all these problems is to collect more data! Most people using qualitative methodologies should have a certain amount of flexibility in their recruitment strategy, and should always be doing some review and analysis on each source as it is collected. This way you can quickly identify gaps or problems in the data, and make sure forthcoming data collection procedures cover everything.


So don’t leave your analysis too late, get your data into an intuitive tool like Quirkos, and see how it can bring your good and bad research data to light! We have a one month free trial, and lots of support and resources to help you make the most of the qualitative data you have. And don’t forget to share your stories of when things went wrong on Twitter using the hashtag #qualdisasters!