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Factor Analysis Technique and Uses

Factor Analysis Technique and Uses.

Factor Analysis Technique and Uses.

Factor analysis is the practice of condensing many variables into just a few. This is so that your research data is easier to work with. The argument is that there are deeper causes driving the underlying concepts in your data that you can uncover and work with rather than the lower-level variables that cascade from them.

“Dimension reduction” is another term for factor analysis. You can combine one or more “super-variables,” also known as unseen variables or latent variables, to lower the “dimensions” of your data.

These deeper ideas aren’t readily apparent. They could indicate difficult-to-measure traits or inclinations like extraversion or IQ. There is a trade-off between the quality of the data and how easy it is to deal with. As with any procedure that simplifies complexity.


With factor analysis, the best solution is the one that yields a simplification that represents the true nature of your data, with minimum loss of precision.

Meaning of Factor Analysis

Factor analysis is a method for condensing a large number of variables into a smaller number of factors. This method takes the largest common variance from all variables and converts it to a single score.

Factor analysis is a collection of statistical tools that can be used to uncover the latent factors that drive observable data. Market research, as well as other areas such as technology, medicine, sociology, field biology, education, psychology, and others, frequently employ factor analysis.

Factor Analysis Technique and Uses

Key Concept of Factor Analysis

Variance: Variance, or how much your numerical results depart from the average, is one of the most significant concepts in factor analysis. When you do factor analysis, you’re trying to figure out how the various underlying factors affect the variance in your variables.

Every component will have an impact. But some will explain more variance than others, implying that the factor represents the variables it contains more precisely.

Eigenvalue: An eigenvalue is a measure of how much variance a factor explains. A factor solution with an eigenvalue of 1 or above explains more variance than a single observed variable. So it can help you reduce the number of variables you need.

Factor solutions with eigenvalues less than one account for less variability than a single variable. Therefore excluded from the study. In this sense, a solution would contain fewer factors than the original number of variables.

Factor Score: Factor score is another essential measure. This is a numerical measure of how strongly a variable from the original research data is associated to a certain component. Another term for this association or weighting towards a certain factor is factor loading.

Basic Assumption of Factor Analysis

The essential assumption of factor analysis is that there are a set of underlying variables called factors (smaller than the observed variables) that may explain the interrelationships among those variables given a set of observed variables.

Types of Factor Analysis

Here we look at two basic forms of factor analysis, exploratory and confirmatory.

Confirmatory Factor Analysis: The researcher begins by forming a hypothesis about their findings, which they hope to prove or refute. The location of the latent variables and the amount of variance they account for will be confirmed – or not – by factor analysis.

A prominent type of confirmatory factor analysis is principal component analysis (PCA). The researcher will use this strategy to do the analysis in order to generate several viable solutions that divide their data among a number of factors.

Items that are highly related to one another and may be grouped together by the researcher using their conceptual knowledge are those that load onto a single factor.

PCA will provide a variety of solutions with varying amounts of factors. These range from simple 1-factor answers to more sophisticated solutions. However, the fewer components included, the less variance in the answer will be accounted for.

Exploratory factor Analysis: Without a hypothesis in mind, exploratory factor analysis is carried out. It’s a method of investigation that aids researchers in determining whether there are any correlations between the original variables, and if so, where they lie and how they’re arranged.

How to Perform Factor Analysis

You may use a factor analysis tool in most major statistical software packages, such as SPSS and Stata, to examine your data. You’ll need the variables you’re interested in, as well as specifics about your initial hypothesis about their correlations and underlying variables, to get started.

Usefulness of Factor Analysis

Factor analysis can assist you understand grouping and clustering in your input variables by grouping them according to the latent variables. In addition to reducing the number of variables you have to explore.

Assume you ask a series of questions. All of them are intended to look at distinct, but connected, aspects of consumer satisfaction.

  1. How satisfied are you with our product?
  2. Would you recommend our product to a friend or family member?
  3. How likely are you to purchase our product in the future?

However, a customer satisfaction score should be represented by only one variable. One alternative is to average the answers to the three questions. Another way is to create a variable that is reliant on a factor. This can be accomplished by performing PCA with the first Principal Component retained (also known as a factor).

The benefit of PCA over an average is that it weights each of the variables in the calculation automatically.

Factor analysis stage is one of the most important stages in carrying out a research work because it is a technique and method that is used to reduce a large number of variables into fewer numbers of factors.

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