I would advise against using statistical methods for determining the "best" variable. You have only 98 observations (how are "yes" and "no" answers distributed?) and more variables than cases. This is a recipe for disaster in the sense that any attempt to build a model with all variables is prone to overfit the data. You will find packages that try to do the trick and some careful cross-validation might help you to avoid some obvious pitfalls, but do not assume that you will learn much on a conceptual level. My suggestion would therefore be to eliminate variables that are weak on theoretical grounds before moving to the analysis step or to collect more cases if that is possible. And: test some simple models (equal weighting of variables) as competitors to get some benchmarks.
There is less literature on the soft and fuzzy process of conceptual variable selection than on algorithmic ways to "solve" this problem, but this is not necessarily due to the superiority of the latter. Some pointers in the literature could be:
Dawes, R. M. (1979). The robust beauty of improper linear models in decision making. American psychologist, 34(7), 571.
Freedman, D. A. (1991). Statistical models and shoe leather. Sociological methodology, 21, 291-313.
Freedman, D. (1999). From association to causation: some remarks on the history of statistics. Statistical Science, 14(3), 243-258.