Considering the cut-off, removing items with low loadings may be the best strategy when conducting the Exploratory Factor Analysis. But, is the "Scale purification” ( the process of eliminating items from multi-item scales) sensitive to the sample size and the sample characteristics?
Hello Fatemeh,
1) Eliminating items solely based on their EFA loadings is generally dangerous as it depends on the factor model being the appropriate data generating process. If the process is different (e.g., a network structure or a simple aggregate) then the loadings simply don't mean anything. Second, it is not uncommon that you have highly valid indicator that however measure their underlying latent variable in isolation (i.e., there are no other indicators that measure this latent). This latent factor cannot be identified by the EFA and these items will be mislocated to a factor with which the true factor is most strongly correlated. Their loading on this factor will be much lower as it is the product of the correlation between both factors and the true loading. If this is not clear, I can provide an example and a simulation.
2) In low sample sizes, you get a stronger variation of any estimated parameter. Hence, a good item may have a bad loading just due to sampling error
3) The sample matters in sofar, as you have to have a sample of that population for which measurment model holds. That is as in any other moderation situation, only that in this time a group/population characteristics moderates the effect of the latent variable on the item response (assumed that the latent variable exists in both groups/population which does not have to be case).
HTH
--Holger
Dear Fatemeh Khoshnavay fomani ,
I agree with Holger Steinmetz . The answer is certainly "yes". A stabilised scale "purification" should only be accepted after checking it for sample size adequacy using the accepted standard validation methods. I would not recommend an EFA with a sample size less than 100 but requirements for at least 300 are excessive as it depends upon the strength of the factors identified. Please see my study guide for more information.
The sample size always matters and I agree with Holger Steinmetz and Peter Samuels. But, We have to think wisely about sample characteristics.
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