What is the difference between Qualitative and Quantitative Research?

[Sorry, this is a really long post, but I have been asked several times to explain the key differences between qual and quant. Here is my attempt to do that, but I would really appreciate comments from others.]


Chalk and cheese friends illustration. Opposites attracting, getting on, or complimenting each other. Could refer to successful integration or end of negotiations or bipartisanship. From the saying: like chalk and cheese, meaning opposites.

Qual and Quant, like Chalk and Cheese in this image, different – but they can get along with each other.

Quite often we see references to qual and quant research merging, with larger samples being used for qual and unstructured data (such as pictures or videos) being used for quant. However, the claim that qual and quant are coalescing into a single entity is a mistake based on a lack of understanding about what qual and quant really are. This post highlights the essence of what qual and quant are, why they are different, and why it matters.

Very simple definitions of qual and quant

For many people a very simple, probably over-simplified, definition of qual and quant research is all they are ever going to need, so it is worth setting that out first – but remember this is not the full picture.

Qual: a qual researcher typically uses unstructured information (such as the discussions in a focus group) to gain an understanding of why people do or believe something. Qual does not provide numbers that can be extrapolated to a wider world. Qual can say things like nearly everybody will be able to understand this advert, but it can’t say things like 22% of customers who see this advert will buy the product.

Quant: a quant researcher typically uses numbers, for example survey responses, to create measurements (usually from a sample) that predict how the population would answer the same questions, and/or to predict what the population would do.

Quant research

Quant research assigns numbers to phenomena and is based on the assumptions that things exist and that quant research can discover them. For example NPS is based on an assumption that there is a link between Likely to Recommend scores and behaviour and that the link can be discovered. The quant researcher’s view of the world is like a treasure hunter finding a hidden chest of money and assigning the value to the contents by counting the value of the money.

It is worth noting that all quant research starts as being a qualitative process. For example, if you decide to measure (count) how many children are playing in the street you have to make decisions, qualitative decisions, about things like: how old can somebody be and still be a child, what is the boundary of the street, and what do we mean by playing. Creating rules for these sorts of questions is called operationalizing and this process facilitates methods based on measurements.

Quantitative research tends to conduct its analysis across people rather than within people. For example, if we are trying to assess the impact of height on various behaviours we sample people with different heights and measure them on the various criteria. Results that vary by height are deemed to be discoveries – for example we might find that taller people are more likely to play some sports than others.

Quantitative research employs algorithms to produce the outputs; where the algorithms can range from very simple to very complex. For example, the algorithm to find out how many customers are male and how many female is the simple process of adding up the number who are male and those who are female; often converting these numbers to percentages. A complex algorithm might collect GPS location data from 1000s of customers to detect which travel patterns were most predictive of higher purchase levels.

Sometimes people define the difference between qual and quant as being that qual explains the why and quant explains the what. However, this is also a bit of an over-simplification, inasmuch as quant research can sometimes explain the why. However, it is fair to say that quant research often can’t explain the why.

Qual research

Qualitative research is all about meaning and seeks to provide understanding rather than enumeration. Qual research can be based on any form of input. Although the discussions in focus groups and depth interviews are common forms of data for qual research, so are pictures, videos, diaries, direct observation, and social media.

In qualitative research the researcher is an active part of the research process. Qualitative researchers seek to use their subjectivity to better understand why people believe the things they believe and do the things they do. The qual researcher is trying to actively use their experience of life to understand the phenomena they are researching. Consequently, it is possible for two qual researchers to arrive at quite different conclusions if they start with different prior life experiences and assumptions. By contrast, quant researchers typically try to minimise, rather than utilise, their subjectivity.

Qual research focuses its analysis on individuals and on the interactions between individuals. A qual researcher will try to understand a process, for example choice of a new mobile phone, by understanding each person being researched. By contrast quant research will typically take data for lots of people and seek to explain averages or groups of people.

The definitions of qual research are a bit more varied than those for quant and there are some practitioners who belief set is similar to quant researchers in that they see their role as trying discover naturally occurring phenomena and describe it. These researchers are in some ways like the Victorian botanists, seeking to describe as accurately as possible the plants they find.

However, more generally, qual research is based on a philosophy that when humans research humans there is no such thing as objective truth. The view of the world that is often associated with qualitative research is ‘constructionist’, because the research does not discover a story, it creates a story. In terms of market research the objective to create a story, after reviewing the evidence, that is useful to the client, i.e. it helps them make better business decisions. Note, just because no one story is the single most correct story does not mean that every story is equally useful.

The validity of quantitative research often rests upon the methods employed (e.g. sampling, sample size, and algorithm). In qualitative research two additional essential factors are the ability of the researcher and the coherence of the findings/story.

The growing similarities in how qual and quant information are collected

Changes in technology, such as the use of mobile devices, the collection of images and video, have resulted in some of the traditional differences in how qual and quant are conducted being eroded. However, these differences in the sort of data collected have not impacted the nature of the difference between qual and quant.

For example, consider the difference in how social media comments are processed. The data is unstructured and traditionally that sort of text material was considered qual, resulting in some people concluding that when they ran their text analytics they were conducting qual at scale – or quant. But when we look closely at what is happening it is simply another example of operationalizing a process to convert it from Qual (being based on the human insight gathered by reviewing information) to Quant. Typical processes for this sort of quant analysis are employing coders to turn the text into numerical codes/scores and using sentiment analysis to code the text into positive, negative and neutral.

The key differences between qual and quant

Quant research is based on measurements, an assumption that phenomena can be discovered, and predictions made by using the differences between people. 

Qual research is based on understanding what is happening within cases and using that understanding to make generalizable findings. The researcher is creating (not discovering) an explanation. The focus of the research is more about producing useful insight, rather than a ‘correct’ representation.

In theory, different quant researchers who use the same algorithms on the same data should produce the same results. In theory, qualitative researchers who have different backgrounds, different life experiences, working on the same data might produce different stories.

Do the differences matter?

Quite often when the topic of methodology crops up somebody will say that ‘clients don’t care about methodology’ – implying that the topic is not important for discussion. Whilst it is clearly the case that many clients are not particularly interested in methodology, it is important for somebody to be interested. This is because clients are interested in the end result, the impact on the business, and the results are impacted by the method. This is like the difference to the taxi customer about the type of fuel used. The customer is not, generally, interested in the type of fuel, but we want the driver to be aware of the difference so that they fill it with the right type of fuel and we want whoever is in charge of servicing the car to use the right schedule and the right tools.

In terms of the difference between qual and quant the key issue is that the researcher knows the power and limitation of the technique they are using and should be aware of the benefits and drawbacks of the main alternatives. In my experience it is not uncommon for quant researchers to offer quant research in ignorance of the epistemological problems of quant and being equally unaware of the benefits of qual (and vice versa).

AI and the end of Quant Research?

Artificial Intelligence (AI) could spell the end of the dichotomy of qual and quant. One interesting AI development is the creation of software that can interpret material in an intuitive way. This opens the door to conducting qualitative data at scale with AI providing large-scale, intuitive, consistent research.

At that point we might we see an end to the need to have quant research. Quant research, as discussed above, needs to trade-off a large amount of sensitivity and the ability to deal with within case information in order to produce measurements. With large-scale AI, the current simplifications of quant research may be redundant. We might see a world where AI intelligently selects information, engages in discourses, and constructs experiments with the need to structured data and without any explicitly process of operationalizing.

However, although the advocates of AI often claim we are on the verge of creating machines that will interpret qualitative information in an intuitive way, my feeling is that we are still a few years away from this.


[So, what are your thoughts? How would you improve this post?]

8 thoughts on “What is the difference between Qualitative and Quantitative Research?

  1. Great post! Rather than… “The quant researcher’s view of the world is like a treasure hunter finding a hidden chest of money and assigning the value to the contents by counting the value of the money”… I’d say:

    “Like a treasure hunter finding a hidden chest of valuables and estimating the value of the contents by extrapolating from the known value of some, presumably representative pieces.”

  2. AI still requires humans to teach it what is useful/not useful, what is important/not important, etc. So, it seems AI would make personal biases potentially more pronounced, as they would be programmed based on the programmer’s own interpretation of the world and their personal experiences. If the machine learning program is told that the relationship between two variables is more important than others, it could become possible to overlook potential correlated variables, wouldn’t it, because the programmer would be telling the machine to ignore some pairings?

  3. Ray, I love this post! Thanks so much for sharing your perspective. As a now-purely qualitative consultant, I sometimes struggle when potential clients tell me they want to “hybridize” (their word, not mine!) qual and quant, so that they can get the benefits of both in a single study. Yikes! As you can imagine, I tell these clients that I am probably not the consultant for them, but I’d rather help with their education about what each broad category of methods can and should do for them.

    I think what some of these well-intentioned folks are looking for is a way to remove the subjective nature of qualitative, and it’s simply not possible. Quallies can do more in analysis (text analytics as a start) to provide more structure, but fundamentally, qual and quant are designed to do different things, and when working in tandem often are the best way to answer the most perplexing business questions. This blog post will now become a favorite of mine.

  4. Nice post. A few thoughts on AI.

    First, I am not sure that AI will replace quant but rather become a part of quant at least initially. It is often based on quant tools (regression, clustering, neural networks) and built into tools quant researchers have (eg R or SPSS.

    I can also see AI taking a bite out of qual. Identifying ideas and themes in the same way it identifies cats within a photo.

    Finally the programmer doesn’t necessarily have to say a lot about what is important. Eg especially on clustering based techniques .

    Exciting times for qual and quant ahead I think.

  5. Great post. Qual and quant are often the perfect complement to each other (note the “e” in complement giving away that I’m a Brit!) But, as you clearly state they are very different from each other. Qual seeks to understand emotions, beliefs, attitudes, opinions, etc – why they are held, how they came about and what has or might influence them. Quant seeks to put numbers on answers to questions. We need both, but they answer different needs!

  6. Qual vs Quant, the old debate never ends. Good sign: a research community still exists. Dear Friends, in the Ray post and in the following discussion the whole matter is analysed in depth. But the iscussion allows for a more simple and empirical way to describe the difference.

    The best way to understand the difference is to put the results of your survey (qual or quant) in an Excel spreadsheet. One column for each respondent; one row for each “answer”.
    The current opinion is that if the spreadsheet has many columns, i.e. many respondents (more than 100 ?) the research would be rather quantitative; absolutely quantitative if in each cell the answer is represented by a number (as you coded it). On the contrary the prevalent feeling goes that if the columns are fewer(i.e. less than 50) and the answers are still in the original text, unstructured, the research is qualitative.

    Neither position hits the point in my opinion. The real differenze between Qual and Quant relies on what the research will do on the spreadsheet regardless to its format and size. The number of columns and the content of the cells in each row is absolutely unimportant. What matters is the work the researcher does.

    From the beginning the researcher can attack the matrix with two different approaches: look and try to understand the patterns on each colomn (all answers of the same respondent), or vice versa, the researcher can try to understand the patterns hidden on each row (all respondents on the same answer).

    I would say that the first procedure describes a Qual research, the secon one a Quant research. Nor the size of the sample (number of columns) nor the number of questions (number of rows) nor the content of cells (coded, structured or unstructured) have any importance in our decision.

    So the main dfference relies on what the researcher does with the data. The nature of the data and the way they are collected seems to me less important.

    I would love to see your comments.

    With all my best to Ray and everybody else in this discussion

    Carlo

  7. Great post most people rely on quantitative research mostly. Easier to do and definitely a cheaper alternative. Quantitative research methods people can make decisions off the data because they know they had a representative sample.

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