In the world of data analysis, there’s not always one clearly appropriate analysis for every research question. There are so many factors to take into account, including the research question to be answered, the measurement of the variables, the study design, data limitations and issues, the audience, practical constraints like software availability, and the purpose of the data analysis.
So what do you do when a reviewer rejects your choice of data analysis? This reviewer can be your boss, your dissertation committee, a co-author, or journal reviewer or editor. What do you do?
There are ultimately only two choices: You can redo the analysis their way. Or you can fight for your analysis. How do you choose?
The one absolute in this choice is that you have to honor the integrity of your data analysis and yourself. Do not be persuaded to do an analysis that will produce inaccurate or misleading results, especially when readers will actually make decisions based on these results. (If no one will ever read your report, this is less crucial).
But even within that absolute, there are often choices. Keep in mind the two goals in data analysis:
- The analysis needs to accurately reflect the limits of the design and the data, while still answering the research question.
- The analysis needs to communicate the results to the audience.
So first and foremost, if your reviewer is asking you to do an analysis that does not appropriately take into account the design or the variables, you need to fight.
For example, a few years ago I worked with a researcher who had a study with repeated measurements on the same individuals. It had a small sample size and an unequal number of observations on each individual. It was clear that to take into account the design and the unbalanced data, the appropriate analysis was a linear mixed model.
The researcher’s co-author questioned the use of the linear mixed model, mainly because he wasn’t familiar with it, and thought the researcher was attempting something fishy. His suggestion was to use an ad hoc technique of averaging over the multiple observations for each subject.
This was a situation where fighting was worth it. Unnecessarily simplifying the analysis to please people who were unfamiliar with an appropriate method was not an option because the simpler model would have violated assumptions. This was particularly important because the research was being submitted to a high-level journal.
So it was the researcher’s job to educate not only his coauthor, but the readers, in the form of explaining the analysis and its advantages, with citations, right in the paper.
In contrast, often the reviewer is not really asking for a completely different analysis, but a different way of running the same analysis, or reporting different specific statistics.
For example a confirmatory factor analysis can be run either in standard statistical software like SAS, SPSS, or Stata, or it can be run it in structural equation modeling software like Amos or MPlus. The analysis is essentially the same, but the two types of software will report different statistics.
If your committee members are familiar with structural equation modeling, they probably want to see the type of statistics that structural equation modeling software will report, including overall model fit statistics like RMSEA or model chi-squares.
This is a situation where it may be easier, and produces no ill-effects, to jump through the hoop. Running the factor analysis in the software they prefer, assuming you have access to the software, won’t violate any assumptions or produce inaccurate results. Especially if the reviewer is someone who has the ability to stop your research in its tracks, it may be worth it to rerun the analysis to get the statistics they want to see reported.
Now you do have to decide whether the cost of jumping through the hoop, in terms of time, money, and emotional energy, is worth it. If the request is relatively minor, it usually is. If it’s a matter of rerunning every analysis you’ve done to indulge a committee member’s pickiness, it may be worth standing up for yourself and your analysis.
When you’re dealing with anonymous reviewers, the situation can get sticky. You cannot ask them to clarify their concerns and you have limited ability to explain the reasons for choosing your analysis. It may be harder to discern if they are being overly picky, don’t understand the statistics themselves, or have a valid point.
If you choose to stand up for yourself, be well armed. Research the issue until you are absolutely confident in your approach (or until you’re convinced that you were missing something). A few hours in the library or talking with a trusted expert is never a wasted investment. Compare that to running an unpublishable analysis to please a committee member or coauthor.
Often, the problem is actually not in the analysis you did, but in the way you explained it. It’s your job to explain why the analysis is appropriate and, if it’s unfamiliar to readers, what it does. Rewrite that section, making it very clear. Ask colleagues to review it. Cite other research that uses or explains that statistical method.
Whatever you choose, be confident that you made the right decision, then move on.