Verify original results through reanalysis before replicating

Promoting Data Sharing: The Moral Obligations of Public Funding Agencies

Sharing research data has great potential to benefit science and society. However, data sharing is still not common practice. Since public research funding agencies have a particular impact on research and researchers, the question arises: Are public funding agencies morally obligated to promote data sharing? We argue from a research ethics perspective that public funding agencies have several pro tanto obligations requiring them to promote data sharing. However, there are also pro tanto obligations that speak against promoting data sharing in general as well as with regard to particular instruments of such promotion. We examine and weigh these obligations and conclude that all things considered funders ought to promote the sharing of data. Even the instrument of mandatory data sharing policies can be justified under certain conditions.

Teachers' judgment accuracy: A replication check by psychometric meta-analysis

Teachers' judgment accuracy is a core competency in their daily business. Due to its importance, several meta-analyses have estimated how accurately teachers judge students' academic achievements by measuring teachers' judgment accuracy (i.e., the correlation between teachers' judgments of students' academic abilities and students' scores on achievement tests). In our study, we considered previous meta-analyses and updated these databases and the analytic combination of data using a psychometric meta-analysis to explain variations in results across studies. Our results demonstrate the importance of considering aggregation and publication bias as well as correcting for the most important artifacts (e.g., sampling and measurement error), but also that most studies fail to report the data needed for conducting a meta-analysis according to current best practices. We find that previous reviews have underestimated teachers' judgment accuracy and overestimated the variance in estimates of teachers' judgment accuracy across studies because at least 10% of this variance may be associated with common artifacts. We conclude that ignoring artifacts, as in classical meta-analysis, may lead one to erroneously conclude that moderator variables, instead of artifacts, explain any variation. We describe how online data repositories could improve the scientific process and the potential for using psychometric meta-analysis to synthesize results and assess replicability.

Replicability of simulation studies for the investigation of statistical methods: the RepliSims project

Results of simulation studies evaluating the performance of statistical methods can have a major impact on the way empirical research is implemented. However, so far there is limited evidence of the replicability of simulation studies. Eight highly cited statistical simulation studies were selected, and their replicability was assessed by teams of replicators with formal training in quantitative methodology. The teams used information in the original publications to write simulation code with the aim of replicating the results. The primary outcome was to determine the feasibility of replicability based on reported information in the original publications and supplementary materials. Replicasility varied greatly: some original studies provided detailed information leading to almost perfect replication of results, whereas other studies did not provide enough information to implement any of the reported simulations. Factors facilitating replication included availability of code, detailed reporting or visualization of data-generating procedures and methods, and replicator expertise. Replicability of statistical simulation studies was mainly impeded by lack of information and sustainability of information sources. We encourage researchers publishing simulation studies to transparently report all relevant implementation details either in the research paper itself or in easily accessible supplementary material and to make their simulation code publicly available using permanent links.

Replicability, Robustness, and Reproducibility in Psychological Science

Replication-an important, uncommon, and misunderstood practice-is gaining appreciation in psychology. Achieving replicability is important for making research progress. If findings are not replicable, then prediction and theory development are stifled. If findings are replicable, then interrogation of their meaning and validity can advance knowledge. Assessing replicability can be productive for generating and testing hypotheses by actively confronting current understandings to identify weaknesses and spur innovation. For psychology, the 2010s might be characterized as a decade of active confrontation. Systematic and multi-site replication projects assessed current understandings and observed surprising failures to replicate many published findings. Replication efforts highlighted sociocultural challenges such as disincentives to conduct replications and a tendency to frame replication as a personal attack rather than a healthy scientific practice, and they raised awareness that replication contributes to self-correction. Nevertheless, innovation in doing and understanding replication and its cousins, reproducibility and robustness, has positioned psychology to improve research practices and accelerate progress. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Knowledge and Attitudes Among Life Scientists Toward Reproducibility Within Journal Articles: A Research Survey

We constructed a survey to understand how authors and scientists view the issues around reproducibility, focusing on interactive elements such as interactive figures embedded within online publications, as a solution for enabling the reproducibility of experiments. We report the views of 251 researchers, comprising authors who have published in eLIFE Sciences, and those who work at the Norwich Biosciences Institutes (NBI). The survey also outlines to what extent researchers are occupied with reproducing experiments themselves. Currently, there is an increasing range of tools that attempt to address the production of reproducible research by making code, data, and analyses available to the community for reuse. We wanted to collect information about attitudes around the consumer end of the spectrum, where life scientists interact with research outputs to interpret scientific results. Static plots and figures within articles are a central part of this interpretation, and therefore we asked respondents to consider various features for an interactive figure within a research article that would allow them to better understand and reproduce a published analysis. The majority (91%) of respondents reported that when authors describe their research methodology (methods and analyses) in detail, published research can become more reproducible. The respondents believe that having interactive figures in published papers is a beneficial element to themselves, the papers they read as well as to their readers. Whilst interactive figures are one potential solution for consuming the results of research more effectively to enable reproducibility, we also review the equally pressing technical and cultural demands on researchers that need to be addressed to achieve greater success in reproducibility in the life sciences.

The REPRISE project: protocol for an evaluation of REProducibility and Replicability In Syntheses of Evidence

BACKGROUND

Investigations of transparency, reproducibility and replicability in science have been directed largely at individual studies. It is just as critical to explore these issues in syntheses of studies, such as systematic reviews, given their influence on decision-making and future research. We aim to explore various aspects relating to the transparency, reproducibility and replicability of several components of systematic reviews with meta-analysis of the effects of health, social, behavioural and educational interventions.

METHODS

The REPRISE (REProducibility and Replicability In Syntheses of Evidence) project consists of four studies. We will evaluate the completeness of reporting and sharing of review data, analytic code and other materials in a random sample of 300 systematic reviews of interventions published in 2020 (Study 1). We will survey authors of systematic reviews to explore their views on sharing review data, analytic code and other materials and their understanding of and opinions about replication of systematic reviews (Study 2). We will then evaluate the extent of variation in results when we (a) independently reproduce meta-analyses using the same computational steps and analytic code (if available) as used in the original review (Study 3), and (b) crowdsource teams of systematic reviewers to independently replicate a subset of methods (searches for studies, selection of studies for inclusion, collection of outcome data, and synthesis of results) in a sample of the original reviews; 30 reviews will be replicated by 1 team each and 2 reviews will be replicated by 15 teams (Study 4).

DISCUSSION

The REPRISE project takes a systematic approach to determine how reliable systematic reviews of interventions are. We anticipate that results of the REPRISE project will inform strategies to improve the conduct and reporting of future systematic reviews.

Replicating five pupillometry studies of Eckhard Hess

Several papers by Eckhard Hess from the 1960s and 1970s report that the pupils dilate or constrict according to the interest value, arousing content, or mental demands of visual stimuli. However, Hess mostly used small sample sizes and undocumented luminance control. In a first experiment (N = 182) and a second preregistered experiment (N = 147), we replicated five studies of Hess using modern equipment. Our experiments (1) did not support the hypothesis of gender differences in pupil diameter change with respect to baseline (PC) when viewing stimuli of different interest value, (2) showed that solving more difficult multiplications yields a larger PC in the seconds before providing an answer and a larger maximum PC, but a smaller PC at a fixed time after the onset of the multiplication, (3) did not support the hypothesis that participants' PC mimics the pupil diameter in a pair of schematic eyes but not in single-eyed or three-eyed stimuli, (4) did not support the hypothesis of gender differences in PC when watching a video of a male trying to escape a mob, and (5) supported the hypothesis that arousing words yield a higher PC than non-arousing words. Although we did not observe consistent gender differences in PC, additional analyses showed gender differences in eye movements towards erogenous zones. Furthermore, PC strongly correlated with the luminance of the locations where participants looked. Overall, our replications confirm Hess's findings that pupils dilate in response to mental demands and stimuli of an arousing nature. Hess's hypotheses regarding pupil mimicry and gender differences in pupil dilation did not replicate.

Efficient Scientific Self-Correction in Times of Crisis

Science has been invaluable in combating the COVID-19 pandemic and its consequences. However, science is not flawless: especially research that is performed and written up under high time pressure may be susceptible to errors. Luckily, one of the core principles of science is its ability to self-correct. Traditionally, scientific self-correction is achieved through replication, but this takes time and resources; both of which are scarce. In this chapter, I argue for an additional, more efficient self-correction mechanism: analytical reproducibility checks.

Analytic reproducibility in articles receiving open data badges at the journal Psychological Science: an observational study

For any scientific report, repeating the original analyses upon the original data should yield the original outcomes. We evaluated analytic reproducibility in 25 Psychological Science articles awarded open data badges between 2014 and 2015. Initially, 16 (64%, 95% confidence interval [43,81]) articles contained at least one 'major numerical discrepancy' (>10% difference) prompting us to request input from original authors. Ultimately, target values were reproducible without author involvement for 9 (36% [20,59]) articles; reproducible with author involvement for 6 (24% [8,47]) articles; not fully reproducible with no substantive author response for 3 (12% [0,35]) articles; and not fully reproducible despite author involvement for 7 (28% [12,51]) articles. Overall, 37 major numerical discrepancies remained out of 789 checked values (5% [3,6]), but original conclusions did not appear affected. Non-reproducibility was primarily caused by unclear reporting of analytic procedures. These results highlight that open data alone is not sufficient to ensure analytic reproducibility.

"statcheck": Automatically detect statistical reporting inconsistencies to increase reproducibility of meta-analyses

We present the R package and web app statcheck to automatically detect statistical reporting inconsistencies in primary studies and meta-analyses. Previous research has shown a high prevalence of reported p-values that are inconsistent - meaning a re-calculated p-value, based on the reported test statistic and degrees of freedom, does not match the author-reported p-value. Such inconsistencies affect the reproducibility and evidential value of published findings. The tool statcheck can help researchers to identify statistical inconsistencies so that they may correct them. In this paper, we provide an overview of the prevalence and consequences of statistical reporting inconsistencies. We also discuss the tool statcheck in more detail and give an example of how it can be used in a meta-analysis. We end with some recommendations concerning the use of statcheck in meta-analyses and make a case for better reporting standards of statistical results.

Effect Declines Are Systematic, Strong, and Ubiquitous: A Meta-Meta-Analysis of the Decline Effect in Intelligence Research

Empirical sciences in general and psychological science in particular are plagued by replicability problems and biased published effect sizes. Although dissemination bias-related phenomena such as publication bias, time-lag bias, or visibility bias are well-known and have been intensively studied, another variant of effect distorting mechanisms, so-called decline effects, have not. Conceptually, decline effects are rooted in low initial (exploratory) study power due to strategic researcher behavior and can be expected to yield overproportional effect declines. Although decline effects have been documented in individual meta-analytic investigations, systematic evidence for decline effects in the psychological literature remains to date unavailable. Therefore, we present in this meta-meta-analysis a systematic investigation of the decline effect in intelligence research. In all, data from 22 meta-analyses comprising 36 meta-analytical and 1,391 primary effect sizes (N = 697,000+) that have been published in the journal Intelligence were included in our analyses. Two different analytic approaches showed consistent evidence for a higher prevalence of cross-temporal effect declines compared to effect increases, yielding a ratio of about 2:1. Moreover, effect declines were considerably stronger when referenced to the initial primary study within a meta-analysis, yielding about twice the magnitude of effect increases. Effect misestimations were more substantial when initial studies had smaller sample sizes and reported larger effects, thus indicating suboptimal initial study power as the main driver of effect misestimations in initial studies. Post hoc study power comparisons of initial versus subsequent studies were consistent with this interpretation, showing substantially lower initial study power of declining, than of increasing effects. Our findings add another facet to the ever accumulating evidence about non-trivial effect misestimations in the scientific literature. We therefore stress the necessity for more rigorous protocols when it comes to designing and conducting primary research as well as reporting findings in exploratory and replication studies. Increasing transparency in scientific processes such as data sharing, (exploratory) study preregistration, but also self- (or independent) replication preceding the publication of exploratory findings may be suitable approaches to strengthen the credibility of empirical research in general and psychological science in particular.