Factor Analysis
In research and marketing, analyzing huge amounts of data can prove quite exhausting, time-consuming, and expensive. Therefore, it becomes necessary to scale down the data into smaller chunks representing the entire dataset for easier analysis. And that is actually where factor analysis comes in handy.
So, What Exactly Is Factor Analysis?
Factor analysis is a data reduction and summarization technique that researchers use to figure out the interrelationships between a large number of variables‒to reduce them into fewer representatives and simplify the analysis process.
During factorization, we take into account a specific maximum common variance which now helps us to filter them into a common score.
For instance, if we have a population of 100 customers that we need to interview to get some insights about a product, it’s almost impossible to interview each one of them individually, right? But we can scale them to about ten based on a common variance for quick analysis.
There are two steps involved in accomplishing the factor analysis process.
- Factor extraction: The researcher decides what type of model they want in the final result and the preferred number of factors. This is accomplished using two methods: principal components analysis and common factor analysis.
- Factor rotation: Once you have figured out the model and number of factors you need, it’s time to narrow down to obtain a simplified version for easier interpretation. The two methods used in this case are orthogonal and oblique rotation.
What are the main types of factor analysis?
Exploratory Factor Analysis (EFA)
Exploratory factor analysis is the most common factor analysis technique that researchers use. Besides reducing the dataset, the method focuses on finding out the theoretical structure and the interrelationships between the variable and the respondent.
Two methods are used in exploratory factor analysis, based on where the factors are calculated from.
- R-type method‒when we use the correlation matrix
- Q-type method‒when we use the individual respondent
Based on the driving factor, exploratory factor analysis is classified into two:
- Principle component factor analysis‒the goal is to explain the highest amount of variance with the least number of factors
- Common factor analysis‒used when the common error variance and nature of the factor are unknown
Confirmatory factor analysis (CFA)
Confirmatory factor analysis is used in hypothesis testing‒to test any correlation between measured variables and their corresponding latent constructs.
Even though EFA and CFA are pretty similar, they have some distinguishing differences. In EFA, researchers just explore and come up with the number of factors for representing a given dataset. Also, all measured and latent variables are interrelated.
However, in confirmatory factor analysis, we actually specify the number of factors representing the dataset and what measured variables relate to latent variables.
And as the name suggests, confirmatory factor analysis confirms or rejects a given measurement theory.
Structural Equation Modeling (SEM)
Structural Equation Modeling brings together factor analysis and multiple regression analysis to analyze the structural relationship between measured and latent constructs.
It comes in handy when the researcher wants to estimate multiple interrelationships in one analysis. To achieve that, the technique uses two types of models:
- Measurement model‒shows how measured variables represent the theory
- Structural model‒the interrelationships between different constructs
Structural equation modeling uses two types of variables:
- endogenous variables, which resemble dependent variables
- exogenous variables, which resemble independent variables
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