#### frank100

##### New Member
When carrying out factor analysis (Exploratory factor analysis) using SPSS, I found that people supress the value under certain values in factor loading and only take values above certain point are considered as significant loading.
Many suggest this value to be 0.30 (and load only value above 0.30 as significant factor)
I found the if value is high, then only high loading factors are in each factor which looks good but it tends to ignore the high loading cross loading. For example, if any item is cross loading in 2 factors may be with 0.60 in one and 0.48 in another, that 0.48 will be supressed or ignored or cross loading won’t be visible.
If we take 0.30 as boundary to decide significant loading, then even low value (i.e 0.31 or 0.35) may be loaded in each factor and there may be many cross loading.
So, I am bit confused. High value is good to take significant loading or low value?
0.50 is better or 0.30
In my case, the factors are loading same for both values which left me wondering which value should I take?

#### spunky

##### Doesn't actually exist
Rules of thumb like these ones you are describing (e.g. "only interpret factor loadings greater than X or Y value") are basic heuristics and have very little justification in statistical theory. That's why you can easily find yourself in situations like this one where the heuristic either contradicts itself or makes the interpretation difficult.

You'd need to switch your model of analysis to something like Confirmatory Factor Analysis (CFA) or a form of Exploratory Factor Analysis (EFA) that provides significance testing and p-values for the factor loadings.