@Siwarit Here, what do you mean by good results? As we all know that higher communities lead to extraction of higher variance and higher loadings. Merely, getting clean pattern matrix with low loadings doesn't fulfill the criteria of doing EFA. Each factor that has emerged should be able to explain majority of variance (i.e., 50%; Avg loading > .708). If you're getting close to 50% variance, then it's good to go ahead.

Hello Sirwarit,

There are a lot of possible answers to your query as it was posted.

1. It could be that the set of variables you are attempting to factor simply do not share common latent variable underpinnings, and therefore no parsimonious factor structure (other than, perhaps, something approaching the "worst case" of k factors for k variables) exists.

2. It could be that the variables are poorly measured and/or suffer from high levels of specificity, such that the common variance is "low," according to your appraisal. Better versions or measurements of the variables might alleviate this problem.

3. You could have extracted too few factors to account for the variation in the variable set. Alternatively, a chunk of the variables in your set simply might not belong in the set, and this is causing your solution(s) to have low variance accounted for.

4. Your judgment that 50% of variance being explained by the factor structure you've selected from your EFA might be too pessimistic. I've seen a lot of published studies in which EFA solutions adopted by author/s account for 35-60 percent of variance. Often, this coincides with author/s' decision to jettison a number of the variables from the solution because of low variable-factor loadings. Are these ideal structures? Probably not, but one of the reasons for engaging in EFA is to refine a variable set into a group which does have common latent underpinnings...so, it is not unreasonable to expect that some variables might not end up being included.

I agree with Imran Anwar that it wasn't clear what you meant by having obtained good results.

Good luck with your work.

In social sciences, this value should be >0.50. What you need to do is constanly try to extract and add items, to reval the items that increase and decrease the most the TEV. If you're sending for publication, most probably it will be rejected with such results.