PubMed · 10243760
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B Blondell. 1976. Going metric.. https://pubmed.ncbi.nlm.nih.gov/10243760/
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Research scientists are trained to produce specialised bricks of knowledge, but not to look at the whole building. Increasing public concern about the social role of science is forcing science students to think about what they are actually learning to do. What sort of knowledge will they be producing, and how will it be used? Science education now requires serious consideration of these philosophical and ethical questions. But the many different forms of knowledge produced by modern science cannot be covered by any single philosophical principle. Sociology and cognitive psychology are also needed to understand what the sciences have in common and the significance of what they generate. Again traditional modes of ethical analysis cannot deal adequately with the values, norms and interests activated by present-day technoscience without reference to its sociological, political and economic dimensions. What science education now requires is 'metascience', a discipline that extends beyond conventional philosophy and ethics to include the social and humanistic aspects of the scientific enterprise. For example, students need to learn about the practices, institutions, career choices, and societal responsibilities of research scientists, and to rehearse in advance some of the moral dilemmas that they are likely to meet. They need also to realise that science is changing rapidly, not only in its research techniques and organisational structures but also in its relationships with society at large.
In this column, I have considered some simple tabular and graphical techniques that are helpful in explaining the substantive meaning of an interaction. When both of the explanatory variables are discrete, these techniques are easy to apply and provide both qualitative and quantitative interpretations of the interaction. As noted earlier, the more challenging case is one in which one or more of the explanatory variables are quantitative. One simple proposal is to construct two "reference levels" for each of the quantitative explanatory variables Then, given this set of reference levels, the explanation of the interaction can proceed along the same lines as for the case in which both explanatory variables are discrete. However, some care must be taken in the choice of reference levels.