Morning, Ana,

"Effective" means that people achieve their goals. So if you have goals (which I assume each organization/unit/person has), then you can measure effectiveness. 

How people achieve their goals is described by "efficiency". In this event, you should measure inputs as compared to outputs. For example: how much time/effort/money did people invest (input) for the goal achieved (output)?

As you can see, effectiveness is not the same as efficiency and they can exist separately. For example, one can be efficient but work on a wrong task. The wrong task produces no effectiveness. Vice versa, one can be effective and achieve his or her task, but in a pretty inefficient way.

Now if you look for different measures on the Internet (look for "HR effectiveness/efficiency metrics"), you can decide which of them are measuring the goal and which of them are focusing on the way to the (or any) goal. 

Sincerely,

Eugene

Dear  Yevgen Bogodistov:

Thank you very much for reflections. I agree with you on everything

Best Regards

Dear Ana

The best way is the mix of the deductive and inductive approach. You expertise will immensely matter.

Regards

It is best to use a more qualitative approach. Numerical indicators are too easy to manipulate. Think in terms of small focused case studies.

Dear @Rajiv Pandey 

Very good your point of view, Thank you very much for your contributions

Regards

Dear Frank Burchill:

Thank you very much for reflections. I agree with you on everything. But it is also good, but qualitative studies are often questioned by the way in which the hypotheses are tested

Best Regards

The same is even more true of quantitative studies. When Pearson of the Correlation Coefficient said that using it on studies of alcoholism it showed that children of alcoholics were less likely to be be alcoholic than children of non alcoholics - a counter intuitive result. John Maynard Keynes said it was so counter intuitive he should do some case studies. Put your money on Keynes! Keynes PhD was on mathematical statistics.

Dear Frank Burchill:

Thank you very much for your comments

regards