Factor analysis: Definition, sample questions Data Silos

Factor analysis: Definition, sample questions

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What is factor analysis?

Factor analysis, also known as dimension reductions, is a statistical method of reducing data of larger volume to a smaller data set. As the name suggests, factor analysis basically reduces the dimensions of your data and break it down into fewer variables. This small data set is now more manageable and easy to understand. 

Factor analysis finds a repeating pattern in a dataset and observes the common characteristics in the patterns. Hence the “factor” refers to observed variables sharing similar responsive patterns. There are major two types of factor analysis: 

  • Explanatory factor analysis – it is used when you don’t have any idea of the data structure of your dataset or the patterns it inculcates. 
  • Confirmatory factor analysis – it is used when you have an idea of the structure and dimension of your data and you want to verify it.

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Validity in factor analysis

Factor analysis works by measuring both unique and shared variances across the variables. But to be focused on one, we only consider the variables that share their variances with the other variables and not the unique variables. This is then used to identify various patterns in the variables and group them accordingly. 

Factor analysis basically creates a new scale by eliminating the unique variables or the ones that do not share their variances with some other variables. Along with its many applications, factor analysis is commonly used by survey researchers where they want to know whether they can simplify their question responses into shorter ones. 

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How does factor analysis work?

Factor analysis helps you in condensing variables and uncovering clusters of responses. Let us look at how it actually works with an example scenario:

You decide to ask some questions like below that cover somewhat similar grounds of customer satisfaction –  

  1. How did you like our product?
  2. Will you consider referring us to your friends and family? 

As for evaluating the entire performance of the organization, it would be convenient for you to have only one variable that represents the customer experience score. It can be done in two ways:

  • Get an average of both the questions
  • Run a PCA (Primary Component Analysis) and have a factor dependent variable. 

PCA technique is generally more effective than the average method as it calculated the weightage of the variables along with the calculations. 

There might be a case where you want to ask several questions to the customer but don’t exactly know which responses can be grouped together and which will be kept totally different. Example: purchase barriers of the target customers. The reasons for the same could be:

  1. Price is prohibitive
  2. Overall implementation costs
  3. We can’t reach a consensus in our organisation
  4. Product is not consistent with our business strategy
  5. I need to develop an ROI, but cannot or have not
  6. We are locked into a contract with another product
  7. The product benefits don’t outweigh the cost
  8. We have no reason to switch
  9. Our IT department cannot support your product
  10. We do not have sufficient technical resources
  11. Your product does not have a feature we require
  12. Other (please specify)

Factor analysis helps you to group these responses like:

Factor analysis: Definition, sample questions Data Silos

We have three labels for the groups and according to them, factor analysis uses heat maps to groups the factors clearly to tell us what affects the responses and what they imply.

Clustering the above information with its three components will tell us the customer trends that are high in Cost and Org but not both. 

Factor analysis: Definition, sample questions Data Silos

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Sample questions for factor analysis

After all the miracles that factor analysis does, it takes a good observational skill to figure out which questions need factor analysis and which don’t. to make it easier for you, in this section we will be looking at some sample questions where the factor analysis can be a good fit:

  • Psychographics 

These are the “Agree – Disagree” questions that tell the opinions of the customers. 

Example: 

I feel the importance to be loyal to one brand

I value quality more than money

  • Behavioural 

These “Agree – Disagree” questions bring out the behaviour of the customers.

Example:

I prefer the costliest option

I am a bargaining customer

  • Attitudinal 

These questions measure the attitudes of the customers in an “Agree – Disagree” manner.

Example:

I did not like the customer service

I am pleased with the product price

  • Activity-based

These “Agree – Disagree” questions tell you what the customer usually does.

Example:

I always prefer shopping online

I love to visit the zoos

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Great Research
Fast Insights
Best-in-class ROI

Voxco’s platform helps you gather omnichannel feedback, measure sentiment, uncover insights and act on them.

Join 500 + global clients across 40+ countries