I am doing factor analysis to check the factorial validity of a 14-items scale with four subscales. Two items have low (less than 0.3) correlations with other items in the subscale to which they belong. However, they load considerably (greater than 0.32) on the factor (latent variable) to which they correspond. Under such condition, should the two variables be dropped from the scale on grounds of low correlations or be retained within the scale on grounds of the acceptable loadings?
Update: After going further through the analysis--removing one item on substantive ground and another item due to low loading (in the range of .2)--I found the correlation matrix depicted below.
The highlighted correlations indicate the correlations of items with other items in the same subscale. However, some items also correlate with items in other subscales considerably (>0.3). Yet, the four factors themselves are inter-correlated which I think is the reason why items also show non-negligible correlations with other items of a different factor. Below is the inter-correlation of factors.
However, as you see hereunder, the loading of each item on the factor to which it belongs is substantial and no cross-loading.
Given these results, how should I go about interpreting my subscales? May I be justified to consider the sub-scales as being suited to measure the underlying constructs/factors of interest?