Understanding the quality of data: a concept map for ‘the thinking behind the doing’ in scientific practice

Abstract

Recent school science curriculum developments in many countries emphasise that scientists derive evidence for their claims through different approaches; that such practices are bound up with disciplinary knowledge; and that the quality of data should be appreciated. This position paper presents an understanding of the validity of data as a set of conceptual relationships, illustrating the application of the network of ideas and their inter-relationships necessary for the ‘thinking behind the doing’ with examples from practice. We explore ways in which this understanding of data is inherently related to underpinning disciplinary ideas. We suggest how the recognition of a conceptual basis for understanding the quality of data represents an ontological shift with respect to widespread characterisations of scientific practices which addresses some long-standing issues in science education research, policy, curricula and practice.

Keywords:

• concept map
• concepts of evidence
• investigations
• practical work
• scientific practice
• validity of data

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Other terms are in common usage; for example, variables can be referred to as factors; the IV and DV being input and output factors. Some descriptions such as ‘the thing you measure’ for the DV can be misleading since all variables’ values are measured.

2 This involves scuffling at a regular intensity for a known period of time in a defined area to dislodge organisms from the substrate for collection in a net immediately downstream. Some people may consider such a technique to be so disruptive that they would not use it – an interesting ethical dimension that should be considered.

3 Proxy measures are very important in ‘historical’ sciences, such as geology/earth science and in the study of climate change e.g. tree rings and ice cores as proxy measures of climate conditions (see Hall, 2010).

4 Identification error is a potential threat to the quality of ‘citizen science’ surveys and checks on the data are often built into the procedure (Silvertown, 2009).

5 Non-parametric tests of difference are important for data that are not normally distributed.

6 Not only by ‘experiment’ – this refers to science's empirical basis.

Additional information

Notes on contributors

Ros Roberts

Ros Roberts is a senior lecturer in science education in the School of Education, Durham University, having previously taught in comprehensive schools and FE. Her research interests include the curriculum, teaching and assessment of scientific evidence; the role of practical work and fieldwork; and scientific literacy.

Philip Johnson

Philip Johnson taught science for 13 years in non-selective secondary schools in England before joining Durham University School of Education in 1992. He retired from the post of senior lecturer in science education in 2011. He began research into the development of students' understanding in science whilst teaching in schools and continues to do so.