Missing-data-analysis-Open-Access-Articles | Journal Of Biometrics And Biostatistics

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Missing data arise in almost all serious statistical analyses. Missing data are ubiquitous in social science research. It is important to consider the issues raised by missing data at the research design stage. As unplanned missing data inevitably introduce ambiguity into the inferences that can be drawn from a study, the design should be carefully scrutinised to minimise the scope for missing data to arise. Considerable care over this aspect of design will pay a substantial dividend when the study is analysed. Inevitably, however, missing data will arise. Ambiguity in the analysis can be reduced if the chance of the data being missing depends only on observed data; the so-called ‘missing at random’ scenario. Open access to the scientific literature means the removal of barriers (including price barriers) from accessing scholarly work. There are two parallel roads towards open access: Open Access articles and self-archiving. Open Access articles are immediately, freely available on their Web site, a model mostly funded by charges paid by the author (usually through a research grant). The alternative for a researcher is self-archiving (i.e., to publish in a traditional journal, where only subscribers have immediate access, but to make the article available on their personal and/or institutional Web sites (including so-called repositories or archives)), which is a practice allowed by many scholarly journals. Open Access raises practical and policy questions for scholars, publishers, funders, and policymakers alike, including what the return on investment is when paying an article processing fee to publish in an Open Access articles, or whether investments into institutional repositories should be made and whether self-archiving should be made mandatory, as contemplated by some funders.
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Last date updated on June, 2021