Author Archives: Joanna Diong

Common statistical mistakes when writing or reviewing manuscripts

Contributing to journal peer review is a good way to observe and mitigate the research conducted in a scientific field, and contribute to the growth of knowledge. I have peer reviewed for some years, and assessing manuscripts for publication now comes more easily. As a peer reviewer, I think it curious how simple statistical oversights are common at submission. As

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Reproducible research practices in qualitative research – Part 2

In the previous post we learned that qualitative studies can be reported in ways to ensure research design, measurement, data analysis and data itself are made transparent. Economics researchers Aguinis and Solarino conducted a literature search and developed 12 criteria for research transparency, covering research design, measurement, data analysis and data disclosure. They emphasize that transparency exists on a continuum.

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Reproducible research practices in qualitative research – Part 1

Recently, I reviewed a number of research proposals in which some applied qualitative or mixed (i.e. quantitative and qualitative) methods to answer health questions. I had my “reviewer hat” on as I assessed the proposals for research quality. After 3-4 proposals, it occurred to me that while assessing quantitative research proposals for research quality and reproducible practices was straightforward, doing

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Preparing computer code for peer-review: Nature journal guidelines

Computer code is used to analyse data in research studies across many fields including epidemiology, biomedical science, computational biology and physics. Many findings now depend on such analyses. What role do journals play in ensuring transparency and reproducibility of computer code used to generate research findings? How might this fit in with our efforts, as scientists, to reduce errors in

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Understanding interaction (subgroup) analysis in randomised studies

When we try to interpret findings from a study, we often like to understand whether an effect (of a treatment or test condition) might be different in subjects with different characteristics. If there was substantial variability among subjects, this may have masked a treatment effect in a select few. How can we understand effects in select groups of subjects that

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