I have calculated Cronbach's alpha in R using the
psych package and I am trying to interpret them now. I want to properly understand this, as it is part of my thesis. I am using the R help files to gain insight. I do not understand some parts of it though:
underestimates the reliability of a test and overestimates the first factor saturation
first-factor saturation is the extent to which a certain factor is present in all items. But how can this be interpreted, maybe an example?
it is a reasonable estimate of the first-factor saturation, although if the test has any microstructure (i.e., if it is “lumpy")
What is microstructure? Does it have to do with unidimensionality?
For tests with equal item loadings, alpha > G6, but if the loadings are unequal or if there is a general factor, G6 > alpha.
I already know that my items have different difficulties, thus they have different factor loadings (?) and G6 > alpha is true indeed (according to the output).
The reason behind all this is that my Cronbach's alpha conducted on a dichotomous dataset is "questionable" (.65) and I am trying to improve it.