QUALITATIVE DATA

This is like quantitative data's brother. The two look at the world very differently!

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Quantitative data's favourite subject is Mathematics, but qualitative data looks for a bit more spice in life - that spice being soft data, like opinions, thoughts and ideas.

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Although the two can't agree on anything, they have to get along. Through the process of triangulation, both forms of data are put together to build a detailed picture.

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Nice analogy, wasn't it?

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What is qualitative data?

Quantitative data, by itself is not generally enough to reach any conclusions. This is where qualitative data comes in.

Qualitative data, often referred to as 'soft' data, deals with data that is not measurable statistically. It essentially characterizes responses through subjective approximation, which basically means it describes. 

Although this form of data is more difficult to collect, it is richer and more divulging.

It is also known as categorical data, because values can be put into separate, distinct categories that do not merge. These categories are based on characteristics and qualities of data collected; for example, 'red, blue, green, and yellow' are wholly separate from one another in terms of descriptive attributes.

Quantitative data is useful for measuring frequency, which is a way of quantifying observable phenomena, but can also help when collecting data for audience research, where people's opinions and feelings are what matter.

We'll be focusing on this aspect; on how quantitative data can be used in audience research.

 

 

 

 

 

 

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Case study

Ask we've been tasked with researching the possibility of producing a film aimed at teenagers, let's focus our audience researching energies mostly on this age group. 

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What questions to we want answered, and from who? How will we collect this data, and how useful will it be?

Let's refer to the table of questions we wrote in the beginning, and make a list of possible approaches we can take to collect answers.

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Click to enlarge.

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Here are some ideas.

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One approach is to do a one-on-one interview with a teenager.

• This method is viable and relatively straightforward. As an interviewer, we will have to keep the conversation going by asking open-ended (but relevant and not leading) questions, and we will have to record the whole interview (not just take notes!), so that we can always go back to review.

• We will get very in-depth answers, as it is easier to obtain detailed perceptions directly from an interviewee. We can ask highly detailed questions that receive high response rates.

• However, results depend highly on who is being interviewed. We will have to conduct interviews with a vast number of people of different genders, ages, ethnicities, socioeconomic groups, etcetera to get reliable results. This could become costly and time consuming.

• Also, responses will vary greatly with our questions, so we have to make sure they are as neutral and straightforward, but still prompting and detailed as possible.

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​We could analyse some films aimed at teenagers as part of our desk research.

• This will provide us with very in-depth information. We could literally analyse every single aspect of a film, which will allow us to use what we have learnt to our advantage when we actually make our film.

• These observations will provide factual information. If the camera is panning right, it's panning right! There should be no discrepancy between what we notice and what others notice. This is good, because although it is qualitative data, there is little room for disagreement and subjectivity.

• However, finding the correct film is a big challenge. On what grounds can we decide that one film is better to analyse than the other? This is why we have to be sure of what the purpose of our film is - do we want to make money, win an Oscar, or tell a story? Are we making a short film or a feature film? What is our budget? What are the ratings of the films we are watching? How much were their box office takings? Who was their target audience? Which criterion are relevant, and which are not?

• Moreover, we have to make sure our analysis skills are at top-notch level, because there's no point analyzing a film incorrectly, producing research that is of no use to anyone.

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Inductive and deductive approaches

 

Deductive: this is where a predetermined analysis structure is used. This means that we have a rule, and if something conforms to that rule, we can assign an attribute to it. 

 

For example:

1. All chairs are red

2. I have a chair

3. Therefore it is red.

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Or:

1. All cats meow

2. I heard a meow

3. Therefore it must have come from a cat.

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Or more in context:

1. 100% of boys said they like horror

2. I want to sell to boys

3. Therefore I should make horror.

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Inductive: a more time-consuming process, inductive reasoning forces us to design our own rules and framework based on multiple experiments and observations. This allows us to make sense of our results and identify and investigate trends.

  

For example:

1. This chair is black

2. That chair is black

3. Therefore all chairs are black.

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Or:

1. Black cats meow

2. White cats meow

3. Therefore all cats must meow

(Assuming only black and white cats exist in this world. If others exist, this is a form of falsification evidence that can lead to shifts in normal given paradigms - for example, we know that gravity exists all around the world. If someone finds a location on earth where gravity is not present, this can change our understanding about gravity on Earth.)

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Or more in context:

1. All teenage girls said they like horror

2. All teenage boys said they like horror

3. Therefore it must be that all teenagers like horror.

(This conclusion could spark controversy, as some may not identify as males or females. In this case, we might have to collect data about this group of people too.) 

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Evaluation

Now let's evaluate the strengths and weaknesses of quantitative data. We can always refer back to this when making our research choices, because it is a good way to check throughout the research stage on whether our experiments are actually of use.

Remember: researchers get paid money in real life for the work they do. If what they produce is not of actual use, then they haven't done their job.

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