This post is a guest blog by Dr Helen Aveyard, Principal Lecturer for Student Experience in the Faculty of Health and Life Sciences at Oxford Brookes University. Helen has presented and published widely on nursing ethics and health care research.
Helen reviewed our upcoming SAGE Campus online courses that are inspired by SAGE’s popular Little Quick Fixes book series. In this blog post, Helen discusses the trickiness in teaching topics covered in our Statistical Significance course in particular, launching this February.
merging different disciplines…
The way in which we teach statistics to students in the health and social care disciplines has long been a point of discussion and often, unease. In my view, the unease comes from the merging of different disciplines - let's say the study of nursing and the study of statistics - both academic disciplines in their own right. Yet in order to understand nursing as a discipline, there is a need to understand the research that underpins it; in fact any subject that is research or evidence based will draw on research methods which in turn incorporate the use of statistics in the analysis of quantitative data.
In this way, the disciplines merge and students need an understanding of statistics in order to understand the research that informs their practice. This merging of different disciplines is not specific to the teaching of statistics; students are often asked to engage with subjects that are from a different discipline to the one in which they are primarily engaged. For example, many health and social care students study aspects of psychology, sociology, physiology and anatomy in addition to their primary health care discipline.
many students don’t like maths…
The study of statistics raises particular unease as many students perceive that the subject is difficult. They don't like maths; it is well established that many students fear working with numbers. Furthermore statistics is a complex academic discipline. The question arises, how do we teach this subject to students who do not necessarily have the background required for in-depth understanding of statistical theory? Hence the tension arises as we consider how to teach a complex subject to students whose own area of study might be far removed from the study of numbers.
This creates a tension; many disciplines place great emphasis on the critical appraisal of research to ensure that their students do not take the research they read at face value but instead attempt to see the strengths and weaknesses of the papers so that they can weigh up the contribution made by individual research papers to the state of knowledge in their area. In order to truly understand many quantitative research papers, staff and students need to engage with the statistical analysis that has been undertaken.
In order to understand the statistics used in a quantitative research paper, students and their lecturers need to engage with the study of statistics, which is not necessarily a discipline which is specifically related to their own academic endeavours.
deciding what exactly needs to be taught in statistics…
The discussion does not end here, however. Once it is established that the understanding of statistics for non statisticians is an important aspect of a curriculum, debate begins about what exactly needs to be taught. Do students need to engage with the mathematical processes involved with different statistical tests or do they need a broad brush understanding of the purposes of tests and what they mean? The questions become how much statistical theory is it possible to teach within a short module for students who do not necessarily have a mathematical background or indeed whether it is possible to teach statistics without such an underpinning knowledge.
This is a question that is grappled with on a frequent basis by those who teach research methods to students whose primary discipline is not mathematics or statistics.
One option is to take a comprehensive approach and to teach the statistical tests, their uses and interpretations to students. This can amount to more than one module's worth of teaching and the whole curriculum might need to be reconsidered in order to accommodate this. We need to question whether this is the best use of time in the taught curriculum.
Another option is to teach a more simplistic approach which glosses over statistical theory and instead informs students of the basic principles of what the tests mean. For example with simplified phrases such as ‘the lower the p value the less likely the results are to be due to chance' ; this is likely to equip students with a basic understanding of a paper but not likely to provide a deeper level of understanding of the concepts involved and which might not withstand a more extensive application of the use of statistics in a different academic paper. Hence students might understand the basic concepts in one paper but not be able to transfer them to another paper in which the statistics are undertaken and presented in a different way.
Finding resources to support teaching and learning…
One answer to the above comes from the availability of engaging and user friendly and well signposted resources for students and lecturers for the teaching and learning of statistics.
Resources that are geared towards the discipline in which the student is studying and which present the concepts in a tried and tested way that acknowledges the tensions described above; providing materials that are geared for courses that have committed to a significant level of teaching of the core concepts of statistics in addition to those which have decided to adopt a more broad brush approach. The SAGE Campus online courses are user-friendly resources for the understanding of statistics, mostly geared towards students who do not have a background in statistical theory.
Faculty can assign our upcoming online courses to their students and researchers to equip them with the statistics knowledge they need. Register your interest now to demo a sample module of our Statistical Significance online course.
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