I have 257 variables, which influence behaviour altruism of humans. They can be assigned to the 3 following criteria: internal, external or selection. I check the influence of the individual variable on altruism, further I need to check the influence of all variables, which are part of internal. Can I just standardise these variables and then take the mean of them and consider this as an index?
As Gung said, we should know a lot more about your situation to give a really useful specific answer. Anyway, I'll try to give a general one.
The altruistic behaviour you are interested in may be influenced by some of your 257 variables. You need to know which variables do influence altruism, and therefore you should resort to tools like regression or ANOVA that could tell you which variables have a significant influence on altruism. Please notice that such tools check influence of every variable - at least, every variable you tryed to include in your analysis.
However, if you use an index (which in your proposal is just a single linear combination of your variables out of the infinite amount of linear combinations you could use), you will lose a lot of information, and the influence of single variables can be cancelled by the influence of other variables or just get lost in the sum.
Just to put an invented example with two variables: Let's imagine that people with more body weight are more altruistic, and that tall people is less altruistic that short people. If you ran a regression analysis you would find significant positive correlation between body weight and altruism and negative correlation between height and altruism - and that would be what you were searching for. Although, if instead of using body weight and height you use an index made of the (standardized) sum of both, the effects might be cancelled - that is, the most altruistic people (short people with overweight) will get about the same index value as the less altruistic ones (tall slim people), and therefore you wouldn't be likely to find any correlation between your index and altruism.
In summary, if you have a lot of variables, use tools to tell which of them have influence. You may design a useful index if you have good reasons to build it in a particular way, but if you just take an arbitrary index like the standardized sum of all variables, you are just losing information - a lot of information.