I am doing Exploratory Factor Analysis with 7 items. One of them (call it
v1) have high correlations with 3 others, and other 3 are mutually moderately correlated. I tried extracting 2 and 3 factors, but multiple items have loadings on 2 factors.
If I remove
v1, and extract 2 factors, then two items do not load highly on any of them. But 4 loads highly on factor1 and the last one loads highly on factor2. I am doing EFA to learn if there is any underlying concepts that give rise to the items (and not for just reducing the number of items), the two factors and the loadings of variables on them seem intuitive to me. But I am not sure if I can just remove an item. I saw this regarding PCA, and I understand the reason for not including collinear variables. But I need some reference to defend this in a paper I am writing.
My second concern is, the chi-square statistic is high and significant, meaning the model does not fit data well, no matter how many factors I extract (I cannot extract more than 4 with only 7 items). Is there any way to fit the model better?