Towards simpler and more transparent quantitative research reports
The average quantitative research report in applied linguistics is needlessly complicated. Articles with over fifty hypothesis tests are no exception, but despite such an onslaught of numbers, the patterns in the data often remain opaque to readers wellversed in quantitative methods, not to mention to colleagues, students, and nonacademics without years of experience in navigating results sections. I offer five suggestions for increasing both the transparency and the simplicity of quantitative research reports: (1) round numbers, (2) draw more graphs, (3) run and report fewer significance tests, (4) report simple rather than complex analyses when they yield essentially the same results, and (5) use online appendices liberally to document secondary analyses and share code and data.
Article outline
 Round more
 Show the main results graphically
 Help readers get the gist of the results
 Show that the numerical results are relevant
 Forestall common misunderstandings
 Run and report much fewer significance tests
 Silly tests
 Tests in the output that are not relevant to the research question
 Omnibus tests followed by planned comparisons when testing a priori hypotheses
 Pseudoexploratory significance tests
 Sometimes, simple analyses suffice
 Mixed repeatedmeasures ANOVA versus ttests
 Multilevel models vs. clusterlevel analyses
 Nonparametrics vs. parametric tests
 Use appendices liberally
 Epilogue
 Acknowledgements
 Note

References
https://doi.org/10.1075/itl.20010.van