Why factor

Factor analysis is commonly used in market research , as well as other disciplines like technology, medicine, sociology, field biology, education, psychology and many more. One of the most important ideas in factor analysis is variance — how much your numerical values differ from the average.

The amount of variance a factor explains is expressed in an eigenvalue. If a factor solution has an eigenvalue of 1 or above, it explains more variance than a single observed variable — which means it can be useful to you in cutting down your number of variables. Factor solutions with eigenvalues less than 1 account for less variability than a single variable and are not retained in the analysis.

In this sense, a solution would contain fewer factors than the original number of variables. Another important metric is factor score. This is a numerical measure that describes how strongly a variable from the original research data is related to a given factor.

Another term for this association or weighting towards a certain factor is factor loading. There are two basic forms of factor analysis, exploratory and confirmatory.

In this type of analysis, the researcher starts out with a hypothesis about their data that they are looking to prove or disprove. Factor analysis will confirm — or not — where the latent variables are and how much variance they account for. Principal component analysis is a popular form of confirmatory factor analysis. Using this method, the researcher will run the analysis to obtain multiple possible solutions that split their data among a number of factors. Items that load onto a single factor are more strongly related to one another and can be grouped together by the researcher using their conceptual knowledge.

Using PCA will generate a range of solutions with different numbers of factors, from simplified 1-factor solutions to higher levels of complexity. However, the fewer number of factors employed, the less variance will be accounted for in the solution. As the name suggests, exploratory factor analysis is undertaken without a hypothesis in mind.

Most major statistical software packages, such as SPSS and Stata, include a factor analysis function that you can use to analyze your data. To get started, you will need the variables you are interested in and, if applicable, details of your initial hypothesis about their relationships and underlying variables. Say you ask several questions all designed to explore different, but closely related, aspects of customer satisfaction :.

But you only want one variable to represent a customer satisfaction score. One option would be to average the three question responses. Another option would be to create a factor dependent variable. The advantage of PCA over an average is that it automatically weights each of the variables in the calculation.

The following are possible barriers to purchase:. Factor analysis can uncover the trends of how these questions will move together. The following are loadings for 3 factors for each of the variables. Notice how each of the principal components have high weights for a subset of the variables. The first component heavily weights variables related to cost, the second weights variables related to IT, and the third weights variables related to organizational factors.

We can give our new super variables clever names. If we were to cluster the customers based on these three components, we can see some trends. Customers tend to be high in Cost barriers or Org barriers, but not both. Factor analysis, including PCA, is often used in tandem with segmentation studies. It might be an intermediary step to reduce variables before using KMeans to make the segments. Factor analysis provides simplicity after reducing variables. For long studies with large blocks of Matrix Likert scale questions, the number of variables can become unwieldy.

Choosing exactly which questions to perform factor analysis on is both an art and a science. Delivered live online with our video webinars We'll host your staff online and deliver your training virtually using tools like Zoom, Microsoft Teams, Skype, Citrix etc.

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