On Quantitative studies.
It seems to me that in the field of natural sciences and technology, quantitative methods are the ones that are traditionally more popular. The focus is on easily testable and repeatable experiments from which knowledge may be deduced or induced. A simple example: a ball is dropped a thousand times and we conclude that balls fall to the ground if they are dropped. It doesn't matter if it is the first or the thousand-and-first ball that doesn't fall, both will falsify the hypothesis. However, it is only the one were we try a thousand times that is considered scientific and it is the larger quantity that makes it scientific.
As far as I know, based on the articles I have read, there are two kinds of quantitative research: surveys and measurements. Surveys are subjective opinions pushed into a template. In this way, subjective opinions are made quantifiable and as we reach a high number of participants it becomes less and less subjective. This assumes that the participants of the survey are a cross-section of the population that it strives to investigate. If it isn't we are obviously investigating something else so this is very important when recruiting people for your survey if you strive to make conclusions about that population.
Measurements are seen as less subjective but of course have the same challenges. Everything is based on what datapoints are included in your dataset. There is always a selection and often also exclusion of outliers etc. Datasets may easily be pushed into similar templates that better match your research with the risk of losing scientific relevance and significance.
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