Research is a huge part of digital marketing as it’s a great way to understand your audience while building your business.
Not only that but asking for reviews and carrying out surveys with your business’s customers is another form of research that can have a huge impact on your marketing.
However, with research comes a bunch of terms you may not be used to – so what is scale of analysis?
Here, we are going to be covering common terms used in market research but are never really explained. This way, you can understand more as you carry out your own research to boost your business’s online presence.
What Is Scale Of Analysis?
Scale analysis is a pretty common term now in marketing research but is sometimes switched out for more other terms like research scales or data measurement scales.
This term refers to the scales used in marketing research to help quantify the results market research gathers and transform them into statistics.
These measurement scales help turn latent variable answers into data that can be used easily when it comes to analysis.
For example, imagine you are running a survey on your website to ask visitors what they think of it. You’re hoping to use this feedback to help improve your business’s website.
You have the option of letting each visitor write whatever they want into the feedback box – but this will give you widely variable results that you can hardly transform into graphs to help display your results.
So, this is where scaling comes in. Rather than let your survey participants run wild with variable answers, you use scales to help their qualitative data transform into quantitative data – or, in order words, you turn their words into numbers and thus statistics.
The scales you can use in your survey vary but a quick example would be ranking the visitor’s experience with your website from one to ten. By using the numbers given, you can apply statistical analysis to your data.
So, rather than allowing your data to be a bunch of words which are difficult to compare with one another, data measurement scales help transform these paragraphs into basic answers.
These make comparing your research results a lot easier and help you understand the results a lot better when it comes to the analysis step.
Dichotomous VS Polytomous In Scale Analysis
So, scale analysis refers to the methods used to turn variable, qualitative data from surveys into quantitative data that is easier for researchers and marketers to analyze.
There are two main ways to do this and they are by providing your survey respondents with two types of questions, or items: dichotomous or polytomous.
Dichotomous questions only allow respondents to choose between two answers. These are usually yes or no questions, agree or disagree questions, or even correct or incorrect questions.
For example, a dichotomous question in a survey about the quality of your business’s website could be:
“I found this website to be easy to navigate and find what I was looking for.” – Do you agree or disagree with this statement?
The respondent will then be able to agree or disagree. This is an example of a dichotomous question in a survey, and basically helps turn all those latent variables into basic, numerical data.
Analysts can then assign a number to each answer and compare the data with ease.
On the other hand, there are polytomous questions.
These are questions that offer more than two answers and are generally used in order to harvest more accurate answers that better reflect your respondents’ feelings and opinions without allowing answers to get too varied.
Let’s go back to the example dichotomous question above.
That question does not have to be dichotomous; instead of offering your respondents a basic agree or disagree answer, you can also offer them the ability to strongly agree or strongly disagree, or even a neutral answer for those who don’t feel strongly in either direction.
This will give your respondents the ability to choose between five different answers. In turn, you get more accurate answers while still being able to scale the respondent’s answers into quantitative data.
So, when planning your surveys, you will need to decide if you want to use dichotomous or polytomous (or both) questions.
Common Scales Used In Scale Analysis
So, we have covered the difference between dichotomous and polytomous questions and how important they are when writing surveys – now let’s take a look at some of the different analysis scales you can use too!
One of the most popular models of measurement scales used in surveys is the Likert scale. This is the scale used in the example polytomous question above.
It uses five or seven points to offer options that range from one extreme (such as very satisfied, strongly agree) to another complete opposite (very dissatisfied, strongly disagree).
Thus this scale assigns each answer a numerical value ranging from one to five or seven, depending on how many points are used in the scale.
The Likert scale is closely related to the semantic differential scale which measures subjective perception by offering a range of reactions to something.
In your survey, you may ask a respondent what they think about the landing page of your business and offer them options including reactive feelings like ‘busy’ or ‘confusing’ or ‘impressive’ or ‘informative’.
Another common scale used in scale analysis is the interval scale. This type of ordinal scale helps classify results by offering numbers in the answers themselves.
For example, you may enquire about the ages of your survey respondents and use an interval scale to group certain age groups together.
Each point will cover a certain span of numbers, be it in tens or general stages of age in a persons’ life or time of day.
So, scale of analysis is a super important method to use when including surveys in your market research.
It helps you turn qualitative data into quantitative data so you can easily analyze your results and find the answers you need to boost your business!
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