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EFA is an abbreviation for Exploratory Factor Analysis. As the name suggests, this analysis has to be exploratory in nature.
Haig (2010) puts exploratory factor analysis or EFA as “a multivariate statistical method designed to facilitate the postulation of latent variables that are thought to underlie – and give rise to – patterns of correlations in new domains of manifest variables.”
In this web blog, you will get an understanding of factor analysis and what to do to ensure the best results. Below are the six general steps that you need to follow for performing an EFA -
Every single data file that is available for analysis helps in achieving the result. They directly affect the outcome of the analysis and thus, we need them to be completely free of error.
Data cleaning is the process of removing any files from the source data which contains errors, are corrupted or can cause the analysis to have poor outcomes. Missing data and wrong entries are also one of the causes of wrong interpretation.
In a book written by Jason Osborne, the extraction techniques are used for the data analysis. These extraction techniques are a cluster of several methods that are used for the evaluation of the correlation/covariation between all the variables. These methods are used to extract the hidden variables from the measurable variables.
If you are using SPSS for the exploratory factor analysis, there are a number of factor analysis methods you can choose from.
If the extraction gave you 50 variables, you cannot all 50 for analysis. Thus, you need to choose the factors that you perform an analysis on. But it is an issue that is the result of outdated ideas and software that we are using.
If you are using statistical software, you will always get as many factors as there are variables to account for 100% variance. And that is why you need to decide the factors to retain because most of these extracted factors are meaningless.
Rotation often causes confusion to most of the students like you. The aim of rotation is to give a clear factor structure so that the results obtained after the EFA are understandable.
The professionals classify rotations into two categories - orthogonal and oblique. The axes in orthogonal are at 90° and factors are uncorrelated. At the same time, the factors are correlated in an oblique rotation where axes are not at 90°.
The aim of using an EFA is to check whether the data we have is suitable for a particular method or not. Producing results from EFA is easy but the real challenge is getting sensible results.
Please ensure to not use the EFA for confirming of a hypothesis and testing models. It is a misapplication of EFA. Also, do not use a confirmatory language when you are using EFA.
Science allows other researchers to use the results and then work on them further. If you will go down the road of EFA, you will see that the results are not often the best and clear. Even when the results are clear, it is not always possible to replicate them with a dataset that is similar.
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