In statistical analysis, factor analysis tools for analysing the factors and is significant in the data set. Factor analysis is hereby a statistical method utilised to describe the variability among observed and correlated variables in terms of a potentially lower number of unobserved variables called factors. Through this article, it is possible to analyse the factors involved with the data set, and for analysing the impacts of observed factors, this factorial analysis is applicable.
Factor analysis is considered a powerful data reduction technique, which mainly enables the researchers to investigate concepts that cannot easily be measured directly, hence, the unobserved factors can also be considered through this analysis. By boiling down a large number of variables into a handful of comprehensible underlying factors, the researchers can utilise this factor analysis that results in easy-to-understand, actionable data. For example, it is possible that variations in six observed variables mainly reflect the variations in two unobserved variables.
There are two types of factor analyses which are, exploratory and confirmatory. Common factor analysis models can be estimated by using various estimation methods such as principal axis factoring and maximum likelihood, and it is also possible to compare the practical differences between these two methods. In statistics, the factor analysis is of mixed data, which is the factorial method devoted to the data tables in which a group of individuals is described both by the quantitative variable and qualitative variable. In SPSS, it is possible to explore the factors engaged with the data and the researchers can analyse diverse factors in the data set including the observed and unobserved factors. Hence, the researchers and data analysts are willing to utilise factor analysis to conduct different researches in the fields of biology, marketing, operational research, market research, production, financial research and psychometric researches to explore observed and unobserved factors engaged with the data set.
The Exploratory factor analysis or EFA is utilised to identify the complex interrelationships among the items and group items that are part of unified concepts. On the other hand, Confirmatory factor analysis (CFA) is a more complex approach, which mainly tests the hypothesis that the items are associated with specific factors. It is hereby a technique that is used to reduce a large number of variables into fewer numbers of factors. Extracting maximum common variance is possible in this factor analysis to include the major factors associated with the data set. In SPSS, it is possible to conduct factor analysis for considering all the factors.
For testing the data set, it is important to identify the factors associated with the data and Factor analysis explicitly assumes the existence of the latent factors underlying the observed data. Both the observed and unobserved data are included in this analysis. It is hereby essential for the researchers to consider diverse factors, both observed and unobserved to perform statistical consulting and data analysis. There are direct, distributed, and augmentative factors that are statistically interpreted to identify the significance level of the data set. It is helpful for the researchers to include all the observed and unobserved variables in the data set for testing the significant value and variance of the factors that have effects on the dependent variable.