Understanding the key features and benefits of qualitative and quantitative data
Diana Spellman
In market research, a common early consideration is whether to include a qualitative and/or a quantitative element in a study. In basic terms, looking at the difference between qualitative and quantitative data, quantitative research will collect numeric and easily quantifiable answers, such as the number of visits to the cinema in the past month, age, height or weight of individuals or satisfaction with a service on a scale of 1-10. Qualitative research on the other hand, is about feelings, or exploring reasons and topics with no fixed questions or ridgid agenda.
Qualitative data can take many forms and is often used at the start of a study, to help define the topics and structure of the overall project. Forms of qualitative data include;
Qualitative research usually takes place in-person or via video, either in small groups or one-to-one. Because the conversation is not structured, or semi-structured, it is not restricted to topics already considered, enabling the exploration of new areas or thinking. Whilst there may be an ‘interview guide’ with questions to be covered, if new themes emerge they can be explored ‘off guide’.
Qualitative research output is based on interpretation and is expressed in language, images, or even sound bites and video.
Quantitative data, depending on the sample size, can be used to make statistically significant statements about the general population. It is collected in large quantities, typically thousands of respondents who are interviewed to represent different demographic groups in the general population, with analysis conducted to compare results among different groups (e.g. age groups, gender or region), discovering trends and patterns.
The actual data collected can be;
Often, quantitative data is collected over time to track changes in results, or could provide a benchmark before or after the launch of a new product or service.
Quantitative research data can be collected via a survey, online, in-person, mail or via telephone.
There will always be a balance between the comforting statistical significance of quantitative conclusions and the feeling that you may have missed a depth of insight or lost a nuance because of the quantitative structure required. Achieving the right balance depends on the specific business objectives – are you looking to fine tune a new product or to get an understanding of the success of a product launch? The former may benefit more from the depth of qualitative while the latter will need the definitive conclusions of quantitative data.
With quantitative research, specific and measurable findings are possible – in the example below we can say that a third of respondents are completely satisfied with the service provided by Acme.
Depending on the sample sizes, statistically significant differences in the satisfaction of certain groups of respondents could also be noted, such as between men and women, or older/younger service users. This would help guide the client company focus on groups where lower satisfaction levels are experienced.
Further, an attempt could be made to discover the reasons for satisfaction or dissatisfaction, via a question such as ‘For which of the following reasons did you give a score of 1 for satisfaction?’, listing areas such as slow, poor staff knowledge, etc. However, this could never be a complete list as you cannot predict all of the possible responses.You may also not get to the root of the issue, ‘poor staff knowledge’ would not tell you the specific things they could not help with.
Quantitative questions therefore need to be complimented by qualitative insights to get the full picture, where possible. Asking the open-ended question ‘Why did you give a score of 1 for satisfaction?’ will get the real, unrestricted reason for their rating.
This article has already contrasted the two approaches to research in depth, but let’s also consider the practical implications to help weight up the advantages and disadvantages of qualitative and quantitative research.
Weighing up the cost of completing a qualitative vs. quantitative project, the cost per interview will be significantly different. With a small budget where you may not be able ask a large enough sample size to make clear conclusions, you may still be able to complete some individual depth interviews (IDIs) to get greater depth of insights instead. On the other hand, asking 100 people ten questions, one of which is open-ended gives you an element of each.
Qualitative research can take longer due to respondent recruitment, length of interview (typically an hour or more for 1-2-1 interviews, or two hours for a group discussion (e.g. 6-8 people discussing a topic) and more time-intensive analysis.
Quantitative research analysis is statistical, looking at themes and trends in the data set, making comparisons between subsets of the data to come up with robust conclusions that can predict the real world or target market.
In a qualitative project, analysis involves reading transcripts of interviews, watching videos, reviewing behaviour diaries, or even images.
Qualitative reports will include individual quotes or video voxpops to illustrate the themes emerging. Quantitative results will consist of charts and tables and strong recommendations based on the statistical conclusions.
It is possible to get the best of both worlds with a hybrid survey, containing both closed quantitative and open-ended questions, which provide qualitative data within that quantitative survey structure.
Open-ended questions provide the best of both worlds because not only can you read each one individually to get a sense of the data in depth, or to follow up on specific customer issues, they can also be coded, using software such as Codeit, effectively converting qualitative data into quantitative data.
Coding is the systematic classification of open-ended verbatim comments into themes or groups. It enables analysis of themes and topics as well as sentiment within open-ended qualitative responses to give you a quantifiable or categorical set of actionable answers alongside other quantitative results. Find out more about the power of coding read the seven stages of analysing open-ended questions and learn the practical steps of how to analyse open ended questions.
Based on the benefits and disadvantages of qualitative and quantitative data, you will likely choose either one approach or a hybrid model, either with a qualitative phase followed by a quantitative phase, or a quantitative survey that enables you to capture qualitative insights in open-ended questions. Either way, you can use a dedicated coding platform such as Codeit to further enhance the depth and actionability of your research by classifying the open-ended qualitative data.
If you’d like to give Codeit a go, you can take up their 30 day free trial to help you start coding and analysing your open-ended question data today.
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