Monday, June 26, 2017

Who to Invite for Your Next Methods and Practices Symposium

Planning a symposium or panel on methods and practices in psychology? Here's a collection of top notch speakers to consider inviting.* Inspired by a recent post on PsychMAP as well as #womenalsoknowstuffnot to mention the frequency with which people ask me to recommend female speakers because they can't think of anythese are all women. So now there is no excuse for the 100% male panel on the subject. In fact, you could easily have a 100% female panel of stellar experts (and it's been done! exactly once, as far as I know). Keep in mind that many of these scholars could also be excellent contributors to special issues and handbooks on methods and practices topics.

Here are names and institutions for potential speakers across a range of career stages. These scholars can all speak to issues that relate to our field's unfolding conversations and debates about replicability and improving research methods and practices. When possible, I've linked the name to a relevant publication as well so that you can get a sense of some of their work.


(And of course, this list is incomplete. If you or someone you know should be on it, please leave a comment with the scholar's name, position, institution, relevant speaking topics, and a link to a relevant paper if applicable!)


Samantha Anderson, PhD student, University of Notre Dame

Statistical power, replication methodology, more nuanced ways to determine the "success" of a replication study

Jojanneke Bastiaansen, Postdoc, Groningen

Citation distortion, bias in reporting 

Christina Bergmann, Max Planck Institute Nijmegen, The Netherlands

Crowd-sourced meta-analyses, open science, improving research practices in infancy research 

Dorothy Bishop, Professor, Oxford
Reproducibility, open science

Erin Buchanan, Associate Professor, Missouri State University 
Effect sizes and confidence intervals, alternatives to NHST, Bayesian statistics, statistical reporting

Katherine Button, Lecturer, University of Bath

Power estimation, replicability

Krista Byers-Heinlein, Associate Professor, Concordia University

Organizing large multi-lab collaborative studies and RRRs (she leads the ManyBabies Bilingual project, an RRR at AMPPS currently in data collection), working with hard-to-recruit/hard-to-test/hard-to-define populations (bilingual infants), and making sure the media gets your science right.

Katie Corker, Assistant Professor, Grand Valley State University
Meta-analysis, replication, perspectives on open science from teaching institutions

Angelique Cramer, Associate Professor, Tilburg University

Slow science, open science, exploratory vs. confirmatory hypothesis testing, hidden multiple-testing issues in ANOVA, replication issues in the context of psychopathology research

Alejandrina Cristia, Researcher, Ecole Normale Supérieure
Crowd-sourced meta-analyses, research practices in infancy research

Pamela Davis-Kean, Professor, University of Michigan

Large developmental data sets, replication

Elizabeth Dunn, Professor, University of British Columbia
Pre-registration, how researchers think about Bayes Factors, the NHST debate

Arianne Eason, PhD student, University of Washington

Research practices in infancy research

Ellen Evers, Assistant Professor, University of California, Berkeley

Statistical power, reliability of published work

Fernanda Ferreira, Professor, UC Davis
Open science, open access, replication, how to design appropriate replication studies when original studies involve stimuli that may be specific to certain time periods or contexts (e.g., words used in an experiment in psycholinguistics)

Jessica Flake, Postdoc, York University

Construct validation, measurement, instrument design

Susann Fiedler, Research Group Leader, Max Planck Institute for Research on Collective Goods, Bonn, Germany

Economics and ethics of science, reproducibility, publication bias, incentive structures, digital scholarship and open science

Shira Gabriel, Associate Professor, SUNY Buffalo
Editor perspective on changes in the field and implementing new ideas in journals

Kiley Hamlin, Associate Professor, University of British Columbia

How to improve methods when you study hard-to-recruit populations; personal experiences with the dangers of failing to document everything and how to prevent this problem in your own lab.

Erin Hennes, Assistant Professor, Purdue University

Simulation methods for power analysis in complex designs

Deborah Kashy, Professor, Michigan State University

Reporting practices, transparency

Melissa Kline, Postdoc, MIT

Improving practices in infancy research

Alison Ledgerwood, Associate Professor, UC Davis

Practical best practices; how to design a study to maximize what you learn from it (strategies for maximizing power, distinguishing exploratory and confirmatory research); how to learn more from exploratory analyses; promoting careful thinking across the research cycle.

Dora Matzke, Assistant Professor, University of Amsterdam

Bayesian inference

Michelle Meyer, Assistant Professor and Associate Director, Center for Translational Bioethics and Health Care Policy at Geisinger Health System

Topics related to responsible conduct of research, research ethics, or IRBs, including ethical/policy/regulatory aspects of replication, data preservation/destruction, data sharing and secondary research uses of existing data, deidentification and reidentification, and related IRB and consent issues.

Kate Mills, Postdoc, University of Oregon 

Human neuroscience open data, multi-site collaboration

Lis Nielson, Chief, Individual Behavioral Processes Branch, Division of Behavioral and Social Research, NIH
Improving reproducibility, validity, and impact

Michèle Nuijten, PhD student, Tilberg University

Replication, publication bias, statistical errors, questionable research practices

Elizabeth Page-Gould, Associate Professor, University of Toronto

Reproducibility in meta-analysis

Jolynn Pek, Assistant Professor, York University
Quantifying uncertainties in statistical results of popular statistical models and bridging the gap between methodological developments and their application.

Cynthia Pickett, Associate Professor, UC Davis

Changing incentive structures, alternative approaches to assessing merit.

Julia Rohrer, Fellow, Deutsches Institut Für Wirtschaftsforschung, Berlin

Metascience, early career perspective on replicability issues

Caren Rotello, Professor, UMass Amherst

Measurement issues, response bias, why replicable effects may nevertheless be erroneous.

Victoria Savalei, Associate Professor, University of British Columbia

The NHST debate, how people reason about and use statistics and how this relates to the replicability crisis, how researchers use Bayes Factors.

Anne Scheel, PhD student, Ludwig-Maximilians-Universität, Munich

Open science, pre-registration, replication issues from a cognitive and developmental psychology perspective, early career perspective

Linda Skitka, Professor, University of Illinois at Chicago

Empirically assessing the status of the field with respect to research practices and evidentiary value; understanding perceived barriers to implementing best practices.

Courtney Soderberg, Statistical and Methodological Consultant, Center for Open Science

Pre-registration and pre-analysis plans, sequential analysis, meta-analysis, methodological and statistical tools for improving research practices.

Jessica Sommerville, Professor, University of Washington

Research practices in infancy research.

Jehan Sparks, PhD student, UC Davis

Practical strategies for improving research practices in one's own lab (e.g., carefully distinguishing between confirmatory and exploratory analyses in a pre-analysis plan).

Barbara Spellman, Professor, University of Virginia

Big-picture perspective on where the field has been and where it’s going; what editors can do to improve the field; how to think creatively about new ideas and make them happen (e.g., RRRs at Perspectives on Psychological Science)

Sara Steegen, PhD student, University of Leuven, Belgium

Research transparency, multiverse analysis

Victoria Stodden, Associate Professor, University of Illinois at Urbana-Champaign
Enabling reproducibility in computational science, developing standards of openness for data and code sharing, big data, privacy issues, resolving legal and policy barriers to disseminating reproducible research.

Jennifer Tackett, Associate Professor, Northwestern

Replicability issues in clinical psychology and allied fields

Sho Tsuji, Postdoc, UPenn and LSCP, Paris

Crowd-sourced meta-analysis

Anna van t'Veer, Postdoc, Leiden University

Pre-registration, replication

Simine Vazire, Associate Professor, UC Davis; Co-founder, Society for the Improvement of Psychological Science (SIPS)

Replication, open science, transparency

Anna de Vries, PhD student, Groningen

Citation distortion, bias in reporting, meta-analysis

Tessa West, Associate Professor, NYU
Customized power analysis, improving inclusion in scientific discourse

Edit (6/27/17): Note that this list doesn't even try to cover the many excellent female scholars who could speak on quantitative methods more broadly—I will leave that to someone else to compile (and if you take this on, let me know and I'll link to it here!). In this list, I'm focusing on scholars who have written and/or spoken about issues like statistical power, replication, publication bias, open science, data sharing, and other topics related to core elements of the field's current conversations and debates about replicability and improving research practices (i.e., the kinds of topics covered on this syllabus). 


Thursday, June 15, 2017

Guest Post: Adjusting for Publication Bias in Meta-Analysis - A Response to Data Colada [61]

A recent blogpost on Data Colada raises the thorny but important issue of adjusting for publication bias in meta-analysis. In this guest post, three statisticians weigh in with their perspective.

Datacolada Post [61] Why p-curve excludes ps>.05
Response of Blakeley B. McShane, Ulf Böckenholt, and Karsten T. Hansen

The quick version:
Below, we offer a six-point response to the recent blogpost by Simonsohn, Simmons, Nelson (SSN) on adjusting for publication bias in meta-analysis (or click here for a PDF with figures). We disagree with many of the points raised in the blogpost for reasons discussed in our recent paper on this topic [MBH2016]. Consequently, our response focuses on clarifying and expounding upon points discussed in our paper and provides a more nuanced perspective on selection methods such as the three-parameter selection model (3PSM) and the p-curve (a one-parameter selection model (1PSM)).

We emphasize that all statistical models make assumptions, that many of these are likely to be wrong in practice, and that some of these may strongly impact the results. This is especially the case for selection methods and other meta-analytic adjustment techniques. Given this, it is a good idea to examine how results vary depending on the assumptions made (i.e., sensitivity analysis) and we encourage researchers to do precisely this by exploring a variety of approaches. We also note that it is generally good practice to use models that perform relatively well when their assumptions are violated. The 3PSM performs reasonably well in some respects when its assumptions are violated while the p-curve does not perform so well. Nonetheless, we do not view the 3PSM or any other model as a panacea capable of providing a definitive adjustment for publication bias and so we reiterate our view that selection methods—and indeed any adjustment techniques—should at best be used only for sensitivity analysis.


The full version:
Note: In the below, “statistically significant” means “statistically significant and directionally consistent” as in the Simonsohn, Simmons, Nelson (SSN) blogpost. In addition, the “p-curve” refers to the methodology discussed in SNS2014 that yields a meta-analytic effect size estimate that attempts to adjust for publication bias.(1)

Point 1: It is impossible to definitively adjust for publication bias in meta-analysis 
As stated in MBH2016, we do not view the three-parameter selection model (3PSM) or any other model as a panacea capable of providing a definitive adjustment for publication bias. Indeed, all meta-analytic adjustment techniques—whether selection methods such as the 3PSM and the p-curve or other tools such as trim-and-fill and PET-PEESE—make optimistic and rather rigid assumptions; further, the adjusted estimates are highly contingent on these assumptions. Thus, these techniques should at best be used only for sensitivity analysis.
[For more details in MBH2016, see the last sentence of the abstract; last paragraph of the introduction; point 7 in Table 1; and most especially the entire Discussion.]

Point 2: Methods discussions must be grounded in the underlying statistical model
All statistical models make assumptions. Many of these are likely to be wrong in practice and some of these may strongly impact the results. This is especially the case for selection methods and other meta-analytic adjustment techniques. Therefore, grounding methods discussions in the underlying statistical model is incredibly important for clarity of both thought and communication.
SSN argue against the 3PSM assumption that, for example, a p=0.051 and p=0.190 study are equally likely to be published; we agree this is probably false in practice. The question, then, is what is the impact of this assumption and can it be relaxed? Answer: it is easily relaxed, especially with a large number of studies. We believe the p-curve assumptions that (i) effect sizes are homogenous, (ii) non-statistically significant studies are entirely uninformative (and are thus discarded), and (iii) a p=0.049 study and a p=0.001 study are equally likely to be published are also doubtful. Further, we know via Jensen’s Inequality that the homogeneity assumption can have substantial ramifications when it is false—as it is in practically all psychology research.
[For more details in MBH2016, see the Selection Methods and Modeling Considerations sections for grounding a discussion in a statistical model and the Simulation Evaluation section for the performance of the p-curve.]

Point 3: Model evaluation should focus on estimation (ideally across a variety of settings and metrics)
SSN’s simulation focuses solely on Type I error—a rather uninteresting quantity given that the null hypothesis of zero effect for all people in all times and in all places is generally implausible in psychology research (occasional exceptions like ESP notwithstanding). Indeed, we generally expect effects to be small and variable across people, times, and places. Thus, “p < 0.05 means true” dichotomous reasoning is overly simplistic and contributes to current difficulties in replication. Instead, we endorse a more holistic assessment of model performance—one that proceeds across a variety of settings and metrics and that focuses on estimation of effect sizes and the uncertainty in them. Such an evaluation reveals that the 3PSM actually performs quite well in some respects—even in SSN’s Cases 2-5 and variants thereof in which it is grossly misspecified (i.e., when its assumptions are violated; see Point 6 below).
[For more details in MBH2016, see the Simulation Design and Evaluation Metrics subsection.]

Point 4: The statistical model underlying the p-curve is identical to the model of Hedges, 1984 [H1984]
Both the p-curve and H1984 are one-parameter selection models (1PSM) that make identical statistical assumptions: effect sizes are homogenous across studies and only studies with results that are statistically significant are “published” (i.e., included in the meta-analysis). Stated another way, the statistical model underlying the two approaches is 100% identical and hence if you accept the assumptions of the p-curve you therefore accept the assumptions of H1984 and vice versa.
The only difference between the two methods is how the single effect size parameter is estimated from the data:
H1984 uses principled maximum likelihood estimation (MLE) while p-curve minimizes the Kolmogorov-Smirnov (KS) test statistic. As MLE possesses a number of mathematical optimality properties; easily generalizes to more complicated models such as the 3PSM (as well as others even more complicated); and yields likelihood values, standard errors, and confidence intervals, it falls on SSN to mathematically justify why they view the proposed KS approach to be superior to MLE for psychology data.(2)
[For more details in MBH2016, see the Early Selection Methods and p-methods subsections.]

Point 5: Simulations require empirical and mathematical grounding
For a simulation to be worthwhile (i.e., in the sense of leading to generalizable insight), the values of the simulation parameters chosen (e.g., effect sizes, sample sizes, number of studies, etc.) and the data-generating process must reflect reality reasonably well. Further still, there should ideally be mathematical justification of the results. Indeed, with sufficient mathematical justification a simulation is entirely unnecessary and can be used merely to illustrate results graphically.
The simulations in MBH2016 provide ample mathematical justification for the results based on: (i) the optimal efficiency properties of the maximum likelihood estimator (MLE; Simulation 1), (ii) the loss of efficiency resulting from discarding data (Simulation 2), and (iii) the bias which results from incorrectly assuming homogeneity as a consequence of Jensen’s Inequality (Simulation 3). We remain uncertain about the extent to which Cases 2-5 of the SSN simulations reflect reality and thus seek mathematical justification for the generalizability of the results. Nonetheless, they seem of value if viewed solely for the purpose of assessing the 3PSM model estimates when that model is misspecified.
[For more details in MBH2016, see the Simulation Evaluation section.]

Point 6: The 3PSM actually performs quite well in SSN’s simulation—even when misspecified.
Only in Case 1 of the SSN simulation is the 3PSM properly specified (and even this is not quite true as the 3PSM allows for heterogeneity but the simulation assumes homogeneity). SSN show that when the 3PSM is misspecified (Cases 2-5), its Type I error is far above the nominal α=0.05 level. We provide further results in the figures here.
• The blue bars in the left panel of Figure 1 reproduce the SSN result. We also add results for the 1PSM as estimated via KS (p-curve) and MLE (H1984). As can be seen, the Type I error of the 1PSM MLE remains calibrated at the nominal level. In the right panel, we plot estimation accuracy as measured by RMSE (i.e, the typical deviation of the estimated value from the true value). As can be seen, the 3PSM is vastly superior to the two 1PSM implementations in some cases and approximately equivalent to them in the remaining ones.
• In Figure 2, we change the effect size from zero to small (d=0.2); the 3PSM has much higher power and better estimation accuracy as compared to the two 1PSM implementations.
• In Figure 3, we return to zero effect size but add heterogeneity (τ=0.2). The 1PSM has uncalibrated Type I error for all cases while the 3PSM remains calibrated in Case 1; in terms of estimation accuracy, the 3PSM is vastly superior to the two 1PSM implementations in some cases and approximately equivalent to them in the remaining ones.(3)
• In Figure 4, we change the effect size from zero to small and add heterogeneity. The 3PSM generally has similar power and better estimation accuracy as compared to the two 1PSM implementations (indeed, only in Case 1 does the 1PSM have better power but this comes at the expense of highly inaccurate estimates). 

In sum, the 3PSM actually performs quite well compared to the two 1PSM implementations—particularly when the focus is on estimation accuracy as is proper; this is especially encouraging given that the 1PSM is correctly specified in all five cases of Figures 1-2 while the 3PSM is only correctly specified in Case 1 of the figures. Although these results favor the 3PSM relative to the two 1PSM implementations, we reiterate our view that selection methods—and indeed any adjustment techniques—should at best be used only for sensitivity analysis.


Footnotes
(1) The same authors have developed a distinct methodology also labelled p-curve that attempts to detect questionable research practices. This note does not comment on that methodology.
(2) Both MLE and KS are asymptotically consistent and thus asymptotically equivalent for the statistical model specified here. Consequently, any justification will likely hinge on small sample properties which can be mathematically intractable for this class of models. Justifications based on robustness to model specification are not germane here because if a different specification deemed more appropriate, the model would be re-specified according to this more appropriate specification and that model estimated.
(3) A careful reading of SNS2014 reveals that the p-curve is not meant to estimate the population average effect size. As shown here and in MBH2016, it cannot as no 1PSM can. This is important because we believe that the heterogeneous effect sizes (i.e., τ > 0) are the norm in psychology research.


References
[H1984] Hedges, L. V. (1984). Estimation of effect size under nonrandom sampling: The effects of censoring studies yielding statistically insignificant mean differences. Journal of Educational and Behavioral Statistics, 9, 61–85.

[MBH2016] McShane, B.B., Böckenholt, U., and Hansen, K.T. (2016), “Adjusting for Publication Bias in Metaanalysis: An Evaluation of Selection Methods and Some Cautionary Notes.” Perspectives on Psychological Science, 11(5), 730-749.

[SNS2014] Simonsohn,U., Nelson, L.D. and Simmons, J.P. (2014) “p-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Result”, Psychological Science, 2014, Vol.9(6), 666-681.