We can say that these factorial analysis approach are generally used for two main purposes:
1) a more purely psychometric approach in which the objectives tend to verify the plausibility of a specific measurement model; and,
2) with a more ambitious aim in speculative terms, applied to represent the functioning of a psychological construct or domain, which is supposed to be reflected in the measurement model.
What do you think of these generals' use?
The opinion given for Wes Bonifay and colleagues can be useful for the present discussion:
Article Three Concerns With Applying a Bifactor Model as a Structure...
The AFE allows to generate structures of theoretical models and hypotheses that can be empirically tested (Gorsuch, 1983), without having previous specifications of the model or considering both the number of factors and the relationship between them (Gerbin & Hamilton, 1996).
The technique used by the AFE is to automatically extract the factors with a certain statistical criterion, obtaining the simplest factor structure in terms of its easiest and most significant interpretation (Thurstone, 1931 and 1947).
Confirmatory Factor Analysis (AFC)
The AFC allows to correct or corroborate, if any, the deficiencies of the AFE leading to a greater contrast of the specified hypotheses (Bollen, 1989), in the same way it analyzes the matrix of covariances instead of correlations, which helps establish whether the indicators are equivalent (Batista and Coenders, 1998).
The AFC is represented by flowcharts (path diagram), according to their particular specifications. The rectangles represent the items and ellipses, the common factors. Unidirectional arrows between common factors and items express saturation and bidirectional arrows indicate the correlation between common or unique factors (Jöreskog, 1969).
In conclusion, these models provide the appropriate statistical framework to assess the validity and reliability of each item instead of performing a global analysis (Batista, Coenders and Alonso, 2004), helping the researcher to optimize both the construction of a measuring instrument and The analysis of results.
In the AFC it is necessary to observe the factor loads that allow establishing the correlation between the variables and the factors (Garson, 2012). The closer they get to one, the greater the correlation will be. An empirical rule in the AFC states that the charges must be> = a 0.07. Although it is high and some factors and variables may be left out of the model, these should be taken at the discretion of the researcher, (Raubenheimer, 2004).
Taking the criteria of Garson (2012), Raubenheimer (2004) and Widaman (1993) in which they establish that, in order to eliminate collinearity between variables to the maximum, it is necessary to take the variables whose loads are> = 0.07, especially in the case of a Model proposal, in which case, its construction is based on the existing theory.
What you have described is essentially the difference between Confirmatory Factory Analysis and Exploratory Factor Analysis. In CFA, you have a predetermined theoretical model which you test through its fit to the data. In EFA, you develop a measurement model from the empirical pattern of correlation in the data.
factor analysis approach is very important for the validity of the constructs related to a model. In psychology we deal with qualitative variables that need to be measured, for example measuring autonomy or measuring motivation.
Dear Colleagues,
I don't think that CFA and EFA vary in this sense of inductive or deductive approach. It is right and true that there is the case in traditional book definitions, but in practice one can use EFA and CFA with a more o less deductive/inductive way. In fact, the FACTOR software is based on these assumptions, and talk more about the level of restriction (see: Article Program FACTOR at 10: Origins, development and future directions
) from https://www.researchgate.net/profile/Urbano_Lorenzo-Seva.So, this is the reason because I don't ask for the distinction between one or another approach, in fact there are many forms of doing factor analysis (ESEM, SCFA, bifactors models, CTCM models, between others). The question is if you think that the factorial analysis, in general, is useful for test measurement models, give evidence on an unobservable construct, or both options.
Best,
Hi! Factor analysis help to identify the underlying dimensions. Sometimes the measurement items varies based on various contexts. Besides if the measurement is not well established conducting factor analysis can produce clear dimensions that can be used for the particular research model. Thanks
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