scispace - formally typeset
Search or ask a question
Journal ArticleDOI

No relationship between researcher impact and replication effect: an analysis of five studies with 100 replications.

24 Mar 2020-PeerJ (PeerJ)-Vol. 8
TL;DR: Although there was substantial variability in replication success and in the h-factor of the investigators, the present results provide no evidence for the hypothesis that systematic replications fail because of low ‘expertise and diligence’ among replicators.
Abstract: What explanation is there when teams of researchers are unable to successfully replicate already established 'canonical' findings? One suggestion that has been put forward, but left largely untested, is that those researchers who fail to replicate prior studies are of low 'expertise and diligence' and lack the skill necessary to successfully replicate the conditions of the original experiment. Here we examine the replication success of 100 scientists of differing 'expertise and diligence' who attempted to replicate five different studies. Using a bibliometric tool (h-index) as our indicator of researcher 'expertise and diligence', we examine whether this was predictive of replication success. Although there was substantial variability in replication success and in the h-factor of the investigators, we find no relationship between these variables. The present results provide no evidence for the hypothesis that systematic replications fail because of low 'expertise and diligence' among replicators.
Citations
More filters
Journal ArticleDOI
TL;DR: The authors discuss the relevance and value of explicit replications in education and psychology fields, and discuss the importance of explicit replication in these fields. But explicit replication is rare in many fields, including education and science.
Abstract: Replication is a key activity in scientific endeavors. Yet explicit replications are rare in many fields, including education and psychology. In this article, we discuss the relevance and value of ...

39 citations

Journal ArticleDOI
TL;DR: In this paper, the authors conducted two preregistered lab studies applying the classic worldview defense paradigm and found that the expected interaction effects were not significant, while Bayesian analyses favored the null hypothesis.

9 citations

Journal ArticleDOI
TL;DR: In this paper , a meta-analysis was conducted on studies that manipulated mortality salience and social norm salience to increase confidence in the idea that MS and norm saliency interact to influence behavior.
Abstract: Terror management theory postulates that mortality salience (MS) increases the motivation to defend one’s cultural worldviews. How that motivation is expressed may depend on the social norm that is momentarily salient. Meta-analyses were conducted on studies that manipulated MS and social norm salience. Results based on 64 effect sizes for the hypothesized interaction between MS and norm salience revealed a small-to-medium effect of g = 0.34, 95% confidence interval [0.26, 0.41]. Bias-adjustment techniques suggested the presence of publication bias and/or the exploitation of researcher degrees of freedom and arrived at smaller effect size estimates for the hypothesized interaction, in several cases reducing the effect to nonsignificance (range gcorrected = −0.36 to 0.15). To increase confidence in the idea that MS and norm salience interact to influence behavior, preregistered, high-powered experiments using validated norm salience manipulations are necessary. Concomitantly, more specific theorizing is needed to identify reliable boundary conditions of the effect.

2 citations

References
More filters
Book
01 Dec 1969
TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Abstract: Contents: Prefaces. The Concepts of Power Analysis. The t-Test for Means. The Significance of a Product Moment rs (subscript s). Differences Between Correlation Coefficients. The Test That a Proportion is .50 and the Sign Test. Differences Between Proportions. Chi-Square Tests for Goodness of Fit and Contingency Tables. The Analysis of Variance and Covariance. Multiple Regression and Correlation Analysis. Set Correlation and Multivariate Methods. Some Issues in Power Analysis. Computational Procedures.

115,069 citations


"No relationship between researcher ..." refers background or result in this paper

  • ...There are also a number of reasonswhy replication effortsmay fail to replicate a real effect including lack of power in the replications (Cohen, 1969), lack of fidelity among researchers to the procedures of the original study (see Gilbert et al., 2016b), unacknowledged variance in auxiliary…...

    [...]

  • ...But what about findings that corresponds to real effects? There are also a number of reasonswhy replication effortsmay fail to replicate a real effect including lack of power in the replications (Cohen, 1969), lack of fidelity among researchers to the procedures of the original study (see Gilbert et al....

    [...]

Journal ArticleDOI
TL;DR: It is concluded that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity, and one or both should be presented in publishedMeta-an analyses in preference to the test for heterogeneity.
Abstract: The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity.

25,460 citations

Journal ArticleDOI
TL;DR: The index h, defined as the number of papers with citation number ≥h, is proposed as a useful index to characterize the scientific output of a researcher.
Abstract: I propose the index h, defined as the number of papers with citation number ≥h, as a useful index to characterize the scientific output of a researcher.

8,996 citations

Journal ArticleDOI
28 Aug 2015-Science
TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
Abstract: Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.

5,532 citations

Journal ArticleDOI
TL;DR: It is shown that despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings, flexibility in data collection, analysis, and reporting dramatically increases actual false- positive rates, and a simple, low-cost, and straightforwardly effective disclosure-based solution is suggested.
Abstract: In this article, we accomplish two things. First, we show that despite empirical psychologists' nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process.

4,727 citations