Description - Some examples from empirical TS

Gyde Hansen, June 2, 2005 - Copenhagen Business School

 

 

What is it that makes descriptions reflective, precise, careful, consequent, honest and complemen­tary, when we try to aspire to a certain degree of objectivity? Some examples:

          Being reflective means keeping under control the complex relationship between know­ledge production, the context of the research process and the involvement or influence of the personality of the observer. It means awareness as to the most common kinds of bias because of influence from the experimental situation like for example observer's influence, perspective, role and interests during the experiments and afterwards when interpreting data and results. Also potential institu­tional interests can have an influence. If such aspects are mentioned - or other aspects like for example   special incidents (e.g. misunderstandings) during the experiments - the reader of the description can take a stand on the study and its results in relation to that information.

          Being precise, careful, consequent and honest has to do with the scientific norm of being systematic and "ideally leaving no stone unturned". Having a hypothesis, often - even unconsciously - researchers tend to ignore observations, which don't exactly fit. Automatically we go for getting our ideas corroborated and confirmed, but observa­tions, which don't fit at first glance, should not end as trash. Later in an empirical study, in connection with other results and new patterns, they suddenly can emerge as being extremely relevant. Working systematically when describing data also involves keeping documentation, reflections and conclusions apart. The reader then gets a chance to draw his/her own conclusions from the same data.

            Describing complementary means categorizing and describing a phenomenon in focus both isolated and alternately according to its relations to other relevant pheno­mena in its surroundings. In research in translation processes for example, pauses are of great interest, because they can be measured. They also can be characterized and cate­go­rized, but in order to get insight into what is going on during the pauses, it is also necessary to look at non-pauses, i.e. what happens just before and immediately after the pauses and also at the completed translation product.