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The current culture of science thrives on peer review – that is, the willingness of your colleagues to read through your work, critique it, and thereby improve it. Science magazine recently collected a slew of tips on how to review papers, which give people getting started in the process of peer reviewing some lovely overarching strategies about how to prepare a review.
But how can you keep in your head all those pieces of good advice and apply them to the specifics of a paper in front of you? I’d argue that like many human endeavors, it’s impossible. There are too many complexities in each paper to collate loads of disparate recommendations and keep them straight in your head. To that end, I’ve created a template for reviewing papers our lab either puts out or critiques. Not incidentally, I highly recommend using your lab group as a first round of review before sending papers out for review, as even the greenest RA can parse the paper for problems in logic and comprehensibility (inculding teh dreded “tpyoese”).
To help my lab out in doing this, I’ve prepared the following template. It organizes questions I typically have about various pieces of manuscripts, and I’ve found that undergrads given nice reviews with its help. In particular, I find it helps them focus on things beyond the analytic details to which they may have not been exposed so that they don’t feel so overwhelmed. It may also be helpful for more experienced reviewers to judge what they could contribute as a reviewer in an unfamiliar topic or analytical approach. I encourage my lab members to copy and paste it verbatim when they draft their feedback, so please do the same if it’s useful to you!
Summarize in a sentence or two the strengths of the manuscript. Summarize in a sentence or two the chief weaknesses of the manuscript that must be addressed.
How coherent, crisp, and focused is the literature summary? Are all the studies discussed relevant to the topic at hand?
Are there important pieces of literature that are omitted? If so, note what they are, and provide full citations at the end of the review.
Does the literature summary flow directly into the questions posed by this study? Are their hypotheses clearly laid out?
Are the participants’ ages, sexes, and ethnic/racial distribution reasonably characterized? Is it clear from what population the sample is drawn? Are any criteria used to exclude participants from overall analyses clearly specified?
Are the measures described in brief but with enough data so that the naive reader knows what to expect? Are there internal consistency or other reliability statistics presented for inventories and other measures that can have these presented?
For any experimental task, is it described in sufficient detail to allow a naive reader to replicate the task and understand how it works? Are all critical experimental measures and dependent variables clearly explained?
Was the procedure sufficiently detailed to allow you to know what the experience was like from the perspective of the participant? Could you rerun the study with this description and that provided above of the measures and tasks?
Is each step that the authors took to get from raw data to the data that were analyzed laid out plainly? Are particular equipment settings, scoring algorithms, or the like described in sufficient detail that you could take the authors’ data and get out exactly what they analyzed?
Do the authors specify the analyses they used to test all of their hypotheses? Are those analytic tools proper to use given their research design and data at hand? Are any post hoc analyses properly described as such? Is the criterion used for statistical significance given? What measure of effect size do the authors report? Does there appear to be adequate power to test the effects of interest? Do the authors report what software they used to analyze their data?
How easily can you read the Results section? How does it flow from analysis to analysis, and from section to section? Do the authors use appropriate references to tables and/or figures to clarify the patterns they discuss?
How correct are the statistics? Are they correctly annotated in tables and/or figures? Do the degrees of freedom match up to what they should based on what’s reported in the Method section?
Do the authors provide reasonable numbers to substantiate the verbal descriptions they use in the text?
If differences among groups or correlations are given, are there actual statistical tests performed that assess these differences, or do the authors simply rely on results falling on either side of a line of statistical significance?
If models are being compared, are the fit indexes both varied in their domains they assess (e.g., error of approximation, percentage of variance explained relative to a null model, containing more information given the number of parameters) and interpreted appropriately?
Are all the findings reported on in the Results mentioned in the Discussion?
Does the discussion contextualize the findings of this study back into the broader literature in a way that flows, is sensible, and appropriately characterizes the findings and the state of the literature? If any relevant citations are missing, again give the full citation at the end of the review
How reasonable is the authors’ scope in the Discussion? Do they exceed the boundaries of their data substantially at any point?
What limitations of the study do the authors acknowledge? Are there major ones they omitted?
Are compelling future directions given for future research? Are you left with a sense of the broader impact of these findings beyond the narrow scope of this study?
REFERENCES FOR THIS REVIEW (only if you cited articles beyond what the authors already included in the manuscript)
This is a long post written for both professionals and curious lay people; the links below allow you to jump among the post’s sections. The links in all CAPS represent the portions of this post I view as its unique intellectual contributions.
Psychology is beset with ways to find things that are untrue. Many famous and influential findings in the field are not standing up to closer scrutiny with tightly controlled designs and methods for analyzing data. For instance, a registered replication report in which my lab was involved found that holding a pen between your lips in a smiling pose does not, in fact, make cartoons funnier. Indeed, less than half of 100 studies published in top-tier psychology journals replicated.
But it’s not only psychology that has this problem. Only 6 out of 53 “landmark” cancer therapy studies replicated. An attempt to induce other labs to reproduce findings in cancer research has scaled back substantially in the face of technical and logistical difficulties. Nearly two thirds of relatively recent economics papers failed to replicate, though this improved to about half when the researchers had help from the original teams. In fact, some argue that most published research findings are false due to the myriad ways researchers can find statistically significant results from their data.
One proposal for solving these problems is preregistration. Preregistration refers to making available – in an accessible repository – a detailed plan about how researchers will conduct a study and analyze its results. Any report that is subsequently written on the study would ideally refer to this plan and hew closely to it in its initial methods and results descriptions. Preregistration can help mitigate a host of questionable research practices that take advantage of researcher degrees of freedom, or the hidden steps behind the scenes that researchers can take to influence their results. This garden of forking paths can transmute data from almost any study into something statistically significant that could be written up somewhere; preregistration prunes this garden into a single, well-defined shrub for any set of studies.
Yet prominent figures doubt the benefits of preregistration. Some even deny there’s a replication crisis that would require these kinds of corrections. And to be sure, there are other steps to take to solve the reproducibility crisis. However, I argue that preregistration has three virtues, which I describe below. In addition to enhancing reproducibility of scientific findings, it provides a method for managing conflicts of interest in a transparent way above and beyond required institutional disclosures. Furthermore, I also believe preregistration permits a lab to demonstrate its increasing competence and a field’s cumulative knowledge.
Chief among the proposed benefits of preregistration is the ability of science to know what actually happened in a study. Preregistration is one part of a larger open science movement that aims to make science more transparent to everyone – fellow researchers and the public alike. Preregistration is probably more useful for people on the inside, though, as it helps people knowledgeable in the field assess how a study was done and what the boundaries were on the initial design and analysis. Nevertheless, letting the general public see how science is conducted would hopefully foster trust in the research enterprise, even if it may be challenging to understand the particulars without formal training.
Here are some of the problems preregistration promises to solve:
- Hypothesizing After the Results are Known (HARKing): You can’t say you thought all along something you found in your data if it’s not described in your preregistration.
- Altering sample sizes to stop data collection prematurely (if you find the effect you want) or prolong it (to increase the power, or the likelihood you have to detect effects): You said how many observations you were going to make, so you have a preregistered point to stop. Ideally, this stopping point would be determined from a power analysis using reasonable assumptions from the literature or basic study design about the expected effect sizes (e.g., differences between conditions or strengths of relationships between variables).
- Eliminating participants or data points that don’t yield the effect you want: There are many reasons to drop participants after you’ve seen the data, but preregistering reasons for eliminating any participants or data from your analyses stops you from doing so to “jazz up” your results.
- Dropping variables that were analyzed: If you collect lots of measures, you’ve got lots of ways to avoid putting your hypotheses to rigorous tests; preregistration forces you to specify which variables are focal tests of your hypothesis beforehand. It also ensures you think about making appropriate corrections for making lots of tests. If you run 20 different analyses, each with a 5% chance (or .05 probability) of yielding a result you want (a typical setup in psychology), then you’re likely to find 1 significant result by chance alone!
- Dropping conditions or groups that “didn’t work”: Though it may be convenient to collect some conditions “just to see what happens”, preregistering your conditions and groups makes you consider them when you write them up.
- Invoking hidden moderators to explain group differences: Preregistering all the things you believe might change your results ensures you won’t pull an analytic rabbit out of your hat.
Many of these solutions can be summed up in 21 words. Ultimately, rather than having lots of hidden “lab secrets” about how to get an effect to work or a multitude of unknown ingredients working their way into the fruit of the garden of forking paths, research will be cleanly defined and obvious, with bright and shiny fruit from its shrubbery.
Managing conflicts of interest
As I was renewing my CITI training (the stuff we researchers have to refresh every 4 years to ensure we keep up to date on performing research ethically and responsibly), I also realized that preregistration of analytic plans creates a conflict of interest management plan. Preregistered methods and data analytic plans ensure researchers to describe exactly what they’re going to do in a study. Those plans can be reviewed by experts to detect ways in which their own interests might be put ahead of the integrity of the data or analyses in the study, including officials at an individual’s university, at a funding agency, or in a journal’s editorial processes. Conscientious researchers can also scrutinize their own plans to see how their own best interests might have crept ahead of the most scientifically justifiable procedures to follow in a study.
These considerations led the clinical trials field to adopt a set of guidelines to prevent conflicts of interest from altering the scientific record. Far more than institutional disclosure forms, these guidelines force scientists to show their work and stick to the script of their initial study design. Since adopting these guidelines, the number of clinical trials showing null outcomes has increased dramatically. This pattern suggests that conflicts of interest may have guided some of the positive findings for various therapies rather than scientific evidence analyzed according to best practices. The preregistered shrub may not bear as much fruit as the garden of forking paths, but the fruit preregistered science bears is less likely to be poisonous to the consumer of the research literature.
Demonstrating scientific competence and cumulative knowledge
One underappreciated benefit of preregistration is the way it allows researchers to demonstrate their increasing competence in an area of study. When we start out exploring something totally new, we have ideas about basic things to consider in designing, implementing, and analyzing our studies. However, we often don’t think of all the probable ways that data might not comport with our assumptions, the procedural shifts that might be needed to make things work better, or the optimal analytic paths to follow.
When you run a first study, loads of these issues creep up. For example, I didn’t realize how hard it was going to be to recruit depressed patients from our clinic for my grant work on depression (especially after changing institutions right as the grant started), so I had to switch recruitment strategies. Right as we were starting to recruit participants, there was also a conference talk in 2013 that totally changed the way I wanted to analyze our data, as the mood reactivity item was better for what we wanted to look at than an entire set of diagnostic subtypes. In dealing with those challenges, you learn a lot for the second time you run a similar study. Now I know how to specify my recruitment population, and I can point to that talk as a reason for doing things a different way than my grant described. Over time, I’ll know more and more about this topic and the experimental methods in it, plugging additional things into my preregistrations to reflect my increased mastery of the domain.
Ideally, the transition from less detailed exploratory analyses to more detailed confirmatory work is a marker of a lab’s competence with a specific set of techniques. One could even judge a lab’s technical proficiency by the number of considerations advanced in their preregistrations. Surveying preregistered projects for various studies might let you know who the really skilled scientists in an area are. That information could be useful to graduate students wanting to know with whom they’d like to work – or potential collaborators seeking out expertise in a particular topic. Ideally, a set of techniques would be well-established enough within a lab to develop a standard operating procedure (SOP) for analyzing data, just as many labs have SOPs for collecting data.
In this way, the fruits of research become clearer and more readily picked. Rather than taking fruitless dead ends down the garden of forking paths with hidden practices and ad hoc revisions to study designs, the well-manicured shrubbery of preregistered research and SOPs gives everyone a way to evaluate the soundness of a lab’s methods without ever having to visit. Indeed, some journals take preregistration so seriously now that they are willing to provisionally pre-accept papers with sound, rigorous, and preregistered methodology. Tenure committees can likewise peek behind the hood of the studies you’ve conducted, which could alleviate a bit of the publish-or-perish culture in academia. A university’s standards could even reward an investigator’s rigor of research beyond a publication history (which may be more like a lottery than a meritocracy).
A model for confirmatory and exploratory reporting and review
In my ideal world, results sections would be divided into confirmatory and exploratory sections. Literally. Whether written as RESULTS: CONFIRMATORY and RESULTS: EXPLORATORY, PREREGISTERED RESULTS and EXPLORATORY RESULTS, or some other set of headings, it should be glaringly obvious to the reader which is which. The confirmatory section contains all the stuff in the preregistered plan; the exploratory section contains all the stuff that came after. Right now, I would prefer that details about the exploratory analyses be kept in that exploratory results section to make it clear it came after the fact and to create a narrative of the process of discovery. However, similar Data Analysis: Confirmatory and Data Analysis: Exploratory or Preregistered Data Analysis and Exploratory Data Analysis sections might make it easier to separate the data analytics from the meat of the results.
It’s also important to recognize that exploratory analyses shouldn’t be pooh-poohed. Curious scientists who didn’t find what they expected could systematically explore a number of questions in their data subsequent to its collection and preliminary analysis. However, it is critical that all deviations from the preregistration be reported in full detail and with sufficient justification to convince the skeptical reader that the extra analyses were reasonable to perform. Much of the problem with our existing literature is that we haven’t reported these details and justifications; in my view, we just need to make them explicit to bolster confidence in exploratory findings.
Reviewers should ask about those justifications if they’re not present, but exploratory analyses should be held to essentially the same standards as we hold current results sections. After all, without preregistration, we’re all basically doing exploratory analyses! As time passes, confirmatory analyses will likely hold more weight with reviewers. However, for the next 5-10 years, we should all recall that we came from an exploratory framework, and to an exploratory framework we may return when justified. When considering an article, reviewers should also look carefully at the confirmatory plan (which should be provided as an appendix to a reviewed article if a link that would not compromise reviewer anonymity cannot be provided). If the researchers deviated from their preregistered plan, call them on it and make them run their preregistered analyses! In any case, preregistration’s goals can fail if reviewers don’t exercise due diligence in following up the correspondence between the preregistration and the final report.
The broad strokes of a paper I’m working on right now demonstrates the value of preregistration in correcting mistakes and the ways exploratory results might be described. I was showing a graduate student a dataset I’d collected years before, and there were three primary dependent variables I planned on analyzing. To my chagrin, when the student looked through the data, that student pointed out one of those three variables had never been computed! Had I preregistered my data analytic plan, I would have remembered to compute that variable before conducting all of my analyses. When that variable turned out to be the only one with interesting effects, we also thought of ways to drill down and better understand the conditions under which the effect we found held true. We found these breakdowns were justifiable in the literature but were not part of our original analytic plan. Preregistration would have given us a cleaner way to separate these exploratory analyses from the original confirmatory analyses.
In any future work with the experimental paradigm, we’ll preregister both our original and follow-up analyses so there’s no confusion. Such preregistration also acts as a signal of our growing competence with this paradigm. We’ll be able to give sample sizes based on power analyses from the original work, prespecify criteria for excluding data and methods of dealing with missing values, and more precisely articulate how we will conduct our analyses.
Many people talk about the difficulties of preregistering studies, so I advance a template I’ve been working on. In it, I pose a bunch of questions in a format structured like a journal article to guide researchers through questions I’d like to have answered as I start a study. It’s a work in progress, and I hope to add to it as my own thoughts on what all could be preregistered grows. I also hope we can publish some data analytic SOPs along with our psychophysiological SOPs that we use in the lab (a shortened version of which we have available for participants to view). I hope it’s useful in considering your own work and the way you’d preregister. If this seems too daunting, a simplified version of preregistration that hosts the registration for you can get you started!