Replication Rate, Framing, and Format Affect Attitudes and Decisions about Science Claims

Barnes, Ralph M.; Tobin, Stephanie J.; Johnston, Heather; MacKenzie, Noah; Taglang, Chelsea M.

doi:10.3389/fpsyg.2016.01826

Cited by 3 publications

(3 citation statements)

References 45 publications

(55 reference statements)

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“…tend to also affect attitudes as well (see also [ 67 – 69 ]). Additionally, Barnes and colleagues [ 61 ] demonstrated that the attitude difference dependent measure that we employed in the present study predicted choice outcomes of participants who were given the option to pick one prescription drug or another.…”

Section: Discussionmentioning

confidence: 80%

“…For our purposes, we used attitude towards science claims to measure the impact of the various kinds of attacks directed against the claims. The basic procedure and attitude-based dependent measure used in the present studies are nearly identical to those used by Barnes, Tobin, Johnston, MacKenzie, and Taglang [ 61 ]. We chose the procedure and dependent measure used by Barnes and colleagues because they found that their attitude measures were in agreement with a choice measure.…”

Section: Introductionmentioning

confidence: 99%

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The effect of ad hominem attacks on the evaluation of claims promoted by scientists

et al. 2018

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Two experiments were conducted to determine the relative impact of direct and indirect (ad hominem) attacks on science claims. Four hundred and thirty-nine college students (Experiment 1) and 199 adults (Experiment 2) read a series of science claims and indicated their attitudes towards those claims. Each claim was paired with one of the following: A) a direct attack upon the empirical basis of the science claim B) an ad hominem attack on the scientist who made the claim or C) both. Results indicate that ad hominem attacks may have the same degree of impact as attacks on the empirical basis of the science claims, and that allegations of conflict of interest may be just as influential as allegations of outright fraud.

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Section: Discussionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

The effect of ad hominem attacks on the evaluation of claims promoted by scientists

et al. 2018

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“…Recent years have seen an increased focus on the "reproducibility crisis" in science, both in science at large (Baker et al, 2016;Munafò et al, 2017; "Replication studies offer much more than technical details", 2017), and perhaps even more acutely in the psychological sciences (Open Science Collaboration, 2015). Some of the reasons behind this crisis-including flawed statistical procedures, career incentive structures that emphasize rapid production of "splashy" (i.e., unlikely) results while punishing "failed" studies, and biases inherent in the publication system-have been articulated carefully in previous work, again both generally (Szucs, 2016;Barnes, Tobin, Johnston, MacKenzie, & Taglang, 2016;Wicherts et al, 2016), and for fMRI in particular (Carp, 2012;Button et al, 2013;Poldrack et al, 2017;Szucs & Ioannidis, 2017). Among these problems, the most frequently identified, and possibly the most easily remedied, is lack of statistical power due to too-small samples.…”

Section: Introductionmentioning

confidence: 99%

How sample size influences the replicability of task-based fMRI

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et al. 2017

Preprint

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Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven distinct tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N=100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

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Small sample sizes reduce the replicability of task-based fMRI studies

et al. 2018

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Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

show abstract

Replication Rate, Framing, and Format Affect Attitudes and Decisions about Science Claims

Cited by 3 publications

References 45 publications

The effect of ad hominem attacks on the evaluation of claims promoted by scientists

The effect of ad hominem attacks on the evaluation of claims promoted by scientists

How sample size influences the replicability of task-based fMRI

Small sample sizes reduce the replicability of task-based fMRI studies

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