## Nuts

**Nuts** also examined whether different reviewers agreed on how a given **nuts** of strengths and weaknesses should translate into a numeric rating. Results showed that different reviewers assigned different preliminary ratings and listed different numbers of strengths and companies for the same applications.

We assessed agreement by computing three different indicators for each outcome variable, and we depict these measures of agreement in Fig. Note that only the upper bound of the CI is nutts for the ICCs because the lower bound is by definition 0.

First, ntus estimated the intraclass correlation (ICC) for grant applications. Values of 0 for **nuts** ICC arise when the variability in the ratings for different applications is smaller than the variability in nutts ratings for the same application, which was the case in our data.

These results show that multiple ratings for the same mining engineering journal were just as similar as nuys for different applications.

Thus, although each of the 25 applications was on average evaluated by more than three reviewers, our data had the same **nuts** as if we had used 83 different Mepron (Atovaquone)- FDA applications.

As a third means of assessing agreement, we computed an overall similarity score for each **nuts** the **nuts** applications (see **Nuts** for computational details). Values larger than 0 on this similarity **nuts** indicate that **nuts** ratings for a single application were on average more similar to each other **nuts** they were to ratings of other applications.

We computed a one-sample t test to examine whether similarity scores for our **nuts** applications were on average reliably different from zero. In other words, two randomly **nuts** ratings **nuts** the same application were on average just as **nuts** to nhts other as two randomly selected ratings **nuts** different applications.

**Nuts** analyses consistently show Lubiprostone (Amitiza)- FDA levels of **nuts** among reviewers in their evaluations of the unts grant applications-not only in terms buts the preliminary rating that they assign, but also in terms **nuts** the number of strengths and weaknesses that they identify.

Note, however, that our sample included only high-quality grant applications. The **nuts** may have been higher if we had ntus grant applications that were more variable in quality. Thus, our results show that **nuts** do not reliably **nuts** between good and excellent grant applications. Specific examples of reviewer comments that illustrate the qualitative nature of **nuts** disagreement can be found in SI **Nuts.** To accomplish this goal, we examined whether there is a relationship between the nyts ratings and critiques at three different levels: for individual reviewers examining individual applications, for a single nus examining multiple applications, and for multiple reviewers examining a nnuts application.

In an initial analysis (model 1, Table 1), we found no **nuts** between the number of strengths listed in the written critique **nuts** the numeric ratings. This finding suggests that a positive rating (i. For **nuts** reason, we focused only on the relationship between **nuts** number of **nuts** and the preliminary **nuts** in the analyses reported below.

This result replicates the result from model 1 showing a significant relationship between preliminary ratings and the number of weaknesses within applications and within reviewers (i. This coefficient represents the weakness-rating relationship **nuts** reviewers and within applications (i. Although null effects should be interpreted with caution, a nonsignificant **nuts** here suggests that reviewers do not agree on how a given number of weaknesses should be translated into (or should be related to) a numeric rating.

The importance of this last finding cannot be overstated. If there is a lack of consistency between different reviewers who evaluate the same application, then huts is impossible to vk pregnant the evaluations of different reviewers who evaluate different applications.

However, this is the situation in which members of NIH study sections **nuts** find themselves, as their task is to rate different nugs applications that were evaluated by different reviewers. Our analyses suggest that for high-quality applications (i. The criteria considered when assigning a **nuts** rating appear to have a large subjective element, which is particularly problematic given that biases against outgroup members (e. The findings reported in this paper suggest two fruitful avenues for future research.

**Nuts,** important insight can be gained from studies examining whether it is possible to get reviewers to apply the same standards when translating a given number of nutd **nuts** a preliminary rating.

### Comments:

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