Ostensibly, the results section of an article should be the easiest to write. The analysis section of the manuscript tells the reader what the authors did with the data, and the results section simply presents the output of those analyses in a format that is, at least hypothetically, easy to understand and interpret. Unfortunately, things that are hypothetically one way don’t always play out that way in practice. As such, many results sections are poorly set up by the analysis section, dense, redundant with tables or figures, and/or confusing. This can be tragic regardless of the journal the work is published in, but a poorly constructed results section can be especially detrimental in an applied journal. When writing a results section, one must also take into account the readership of the journal if the work is going to enjoy a maximum impact. For example, the results section of a well-written article in an epidemiology journal might look quite different from one in a practice-oriented journal. The Journal of Public Health Management and Practice (JPHMP) has a large audience of practitioners and policy makers, many of whom do not have advanced training in statistics. As such, an appropriate analytical plan and results section should be written in a manner that encourages interpretation. To accomplish this for JPHMP, authors should consider a few points: writing the results section of a scientific article
- If possible, choose analyses that are as parsimonious as possible. While a complicated model that controls for multiple covariates may be technically correct, there is a difference between being correct and being useful. Furthermore, if a model must control for multiple covariates, consider strategies that aid in the interpretation of coefficients, such as centering variables on their mean in cases where zero has no meaning (eg, age).
- Relatedly, avoid presenting coefficients without interpreting them, especially in complex regression models where the interpretation is difficult or hampered by transformations. For example, if a value is log-transformed due to non-normal distribution, provide an example in the original scale to aid the reader in understanding the relationship between the variables. Related, avoid presenting standardized coefficients for variables where the unstandardized coefficients are more meaningful. Standardized coefficients are useful for understanding relative contributions but aren’t useful for absolute expected changes.
- When possible, present point estimates with confidence intervals. Never simply present P values. Also, at JPHMP, we use the American Medical Association Manual of Style, so statistical significance (ie, P values) should be expressed to 2 digits to the right of the decimal point unless the P value is less than .01, in which case the P value should be expressed to 3 digits to the right of the decimal point. writing the results section of a scientific article
- Never attempt to describe results that fail to achieve significance at the a priori threshold for statistical significance, such as suggesting that the results “approached significance” or displayed a “trend towards significance.” Similarly, statistics are never “highly significant.”
- Think carefully about the manner in which data are presented in tables. Tables should stand alone, as many readers prefer to glean your results from your tables rather than the text.
- Data visualization in figures should be a similarly thoughtful process. Many journals, including JPHMP, allow for readers to download tables, figures, and images separately from the article for inclusion in presentations. As such, more visually informative and attractive figures will be more likely disseminated.
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About the Author
- Justin B. Moore, PhD, MS, FACSM, is a Professor and Vice Chair in the Department of Implementation Science in the Division of Public Health Sciences at Wake Forest University School of Medicine. He serves as the Editor-in-Chief of the Journal of Public Health Management and Practice.
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