You are confusing two issues that are easily confused if new to this field. But first off, why did you divide by 10? Why not 6?
The two issues are
The range of each variable is what you are looking at. 3 variables have range 0 to 1 and one doesn't. Dividing the 4th variable by 6 would fix that if identical range is required, but it's not required for PCA.
The SD, or equivalently the variance, of each variable, is what bites with PCA. Using a correlation matrix is equivalent to standardizing variables to mean 0 and SD or variance 1. But then the range is irrelevant. In practice it is likely that variables ranging between 0 and 1 have similar SDs (although there is no guarantee) and that a variable ranging between 0 and 6 has a larger SD (ditto).
There is no right or wrong answer here without knowing
It's common advice to use the correlation matrix when variables are on different scales, and that's usually better than mixing mice and giraffes together, but that still leaves PCA likely to overweight some variables and underweight others compared with their substantive importance.
People often seem to think that PCA is a kind of washing machine that takes dirty data and emits clean components, but unless you have a bundle of variables that belong together, and simple latent structure, the results often disappoint.
With 4 variables a scatter plot matrix will often be informative.