At this time of year, applicants have often received two or more offers to a university program so the next step is to decide which one to accept. You could just randomly pick one, or survey your friends and family to see which one is the most recommended. But scientists and engineers tend to prefer more evidence-based and rational methods for choosing things, what is sometimes refered to as “decision analysis”. Most engineering programs introduce this, either formally or informally, at some point because engineers frequently have to decide from among several alternatives. Let’s illustrate it, as applied to the problem of selecting a university offer.
I’m going to use a variant of decision analysis popularized by the management consulting company Kepner-Tregoe a number of decades ago. In their method, given a number of possible choices there are two steps in making a decision about which one is “best”.
First we do an analysis on “needs”, i.e. which choices do or do not meet our fundamental requirements. Probably you did this to some extent already when you applied to university programs. For example, if you were only interested in Chemical Engineering, you would not have applied to the University of Windsor because they don’t have that program. Perhaps it is worthwhile repeating your needs analysis if you have discovered new things that are “must-haves” in your final decision. Any programs that do not meet those needs are eliminated from any further consideration.
Next we do the “wants” analysis, and this is the hard part. Having eliminated all the choices that don’t meet fundamental needs, we now compare programs on the basis of how well they meet our “wants”, or things that would be important or nice to have if possible. But these are not critical things, so you are prepared to do without them if necessary. So, we list our “wants”, and then assign a weight (from 1 to 10) that indicates how important it is to us (where 10 is very important). Then, for each offer choice we give it a score (from 0 to 10) indicating how well it meets this “want”. The weighted score is then the product of weight x score.
To illustrate, let’s assume we have 3 university choices (A, B, and C), and eight things that are important to us (shown in the table below). Having co-op opportunities is quite important so its weight is 9. Having a Formula SAE Team to participate in is also a “want”, but of lesser importance so its weight is 4. Your “wants” and their weights are going to be highly personal and specific to your circumstances (so don’t use mine!).
In the scores, U of A (not Alberta!) has no co-op program, so its score is zero, but U of B has a very extensive program so its score is 9. And so on, for each “want” you assign your evaluation of each university’s score in that category. Preferably, the “wants” should be things that can be measured or evaluated in some way so that assigning a score is not just a random guess. Below is the completed scoring table example.
For each university we calculate the weighted scores and sum them. In my example, U of B has the highest total score and is therefore the “best” choice, but U of A is a close second and maybe worth considering a bit more. U of C (not Calgary!) is farther back and probably not going to be a good choice. In the Kepner-Tregoe method, you might pick the top 2 or 3 scoring choices and do a further analysis to identify any negative consequences associated with each one. That can help to finalize the one choice with a higher score and fewest negatives.
The danger with these methods is that it is easy to manipulate the “wants”, weights and scores to get any answer you want. So if you are going to use it, you have to be careful and as objective as possible, otherwise you’re just fooling yourself and wasting your time.