Abstract

Outliers, Consistency, and Context: The Importance of Reporting Variability in Editorial Office Performance Data

Highlights

  • Is the problem: many journal offices report key indicators as single values, such as total submission volume, number of reject decisions, total accepted manuscripts, number of accepted review invitations

  • Are you reporting what you think is a key value in your performance reports? Does that statistic tell the whole story? Does your audience take away a full, data-derived understanding of your journal stakeholder behaviors? Are you basing processing protocols off data points without full context?

  • What is a measure of variance and why is it useful to provide such a measure? A measure of variance is a summary statistic that represents the amount of spread or scattering in a data set, most commonly given as either a Standard Deviation or Interquartile Range

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Summary

Understanding Simple Statistics That Add Context

What is a measure of variance and why is it useful to provide such a measure? A measure of variance is a summary statistic that represents the amount of spread or scattering in a data set, most commonly given as either a Standard Deviation or Interquartile Range. Both journals presented in these visual examples have a mean turnaround time to post an initial decision of 30 days. If you employed this table, you would be reporting that on average it takes a manuscript 41 days to receive an initial decision, but it could happen in as few as 7 days or as much as 130 days, as was the case in the most extreme examples over the time period measured. Due to the potential presence of outliers, the range is not a good statistic to report (at least in isolation, without further statistics) because it can give a false impression of what is happening routinely

Standard Deviations and Interquartile Ranges
Standard Deviation
Findings
Why Should You Care?
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