Making sense of quantitative data

I discussed in a previous article how surveys are used to collect information from a large number of people.1 In surveys, closed - that is, forced choice-format questions are most often used, as they allow quantitative analysis to be carried out and the results to be generalised.

Imagine that you aim to investigate the possible effects of part time jobs on their course achievements among medical students in your medical school. You have just got 1000 questionnaires back, each with responses on details about their jobs, course assessment results, and personal details totalling more than 20 responses from each student. How are you going to make sense of them and present the information to other students? Armed with only a paper, a pencil, and a calculator, it will probably take many hours just to work out the average responses for each question asked. Just to calculate the correlation coefficient between any two variables would involve literally thousands of arithmetic operations and errors are almost inevitable. This is why the statistical software and a systematic method of analysing such quantitative data are essential.