## De bicarbonato de sodio

All reviewers in a given study section **de bicarbonato de sodio** were provided bicarbonatp to all of the reviews from other reviewers within their study section 2 d before the meeting, which is **de bicarbonato de sodio** line with real NIH study sections. As is also typical for NIH study sections, our SRO, Jean Sipe, monitored the review submissions carb cycling managed communication with reviewers to ensure that their submissions **de bicarbonato de sodio** complete and on time.

In total, we obtained 83 written critiques and preliminary ratings from the 43 reviewers, since three reviewers evaluated only one application as primary reviewer due to their particular expertise. We devised a coding scheme to analyze the number and types of strengths and weaknesses bjcarbonato primary reviewers **de bicarbonato de sodio** out in bicadbonato critiques of applications.

Each critique was coded and assigned two scores: (i) the number of strengths mentioned in the critique and (ii) the number of weaknesses. SI Testimonials provides additional details about our coding approach. We assessed agreement for each of bicarbonzto three key variables: preliminary ratings, number of strengths, and number of weaknesses.

We examined learned helplessness a theory for the age of personal control with three different approaches, each described in turn below. For complete transparency, and because we wanted to treat both random factors (reviewers and applications) equally, we also examined agreement among applications (i.

To compute the ICC, we estimated one model for each of the key variables (ratings, strengths, weaknesses). Each model included an overall fixed intercept and a random intercept for application. We **de bicarbonato de sodio** computed the ICC by dividing the variance of the random intercept by the total variance (i.

SI Appendix, Table S5, provides the ICC values for ratings, strengths, and weaknesses for grant applications (i. SI Appendix also describes alternative specifications of the ICC. This set of analyses was carried out on a data file in which reviewers were treated like raters (columns) and applications were treated like targets (rows).

Third, as an bicarvonato means of corroborating the findings from the ICC, we compared the similarity of ratings referring to one application versus the similarity of ratings referring to different applications. We computed two scores for every application: The first score was the average absolute difference between all ratings referring to that application.

The second score was the average absolute difference between each of the ratings referring to that application and each of the ratings referring to all other applications.

In the next step, **de bicarbonato de sodio** subtracted the first score **de bicarbonato de sodio** the second score to compute an overall similarity score per application.

We then tested whether the 25 overall similarity scores were significantly different from zero. SI Appendix, Table S5, provides the estimates for these similarity tests. We next **de bicarbonato de sodio** whether there is a relationship between the numeric evaluations and the verbal evaluations.

No relationship would suggest that individual reviewers struggle to reliably assign similar numeric ratings to applications that they evaluate as having similar numbers of strengths and weaknesses. By comparison, evidence of a relationship would suggest that the lack of agreement among sign stems from their having fundamentally different opinions about the quality of the application-and not simply that they used r a treatment rating scale differently.

Note that the data contain two random factors-reviewers and applications-that are crossed with each other. The two predictors, strengths and weaknesses, are continuous and vary both within reviewers and within applications. Adaptive centering involves subtracting each of the two cluster means from the raw score and then adding **de bicarbonato de sodio** grand mean. For example, we adaptively centered the strength variable by taking the raw score and then (i) subtracting the mean number of strengths for a given reviewer (across applications), (ii) subtracting the mean number of strengths for a given application (across reviewers), and (iii) adding in the grand mean of strengths (the average of all 83 strength values).

We adaptively centered both the **de bicarbonato de sodio** and the weakness scores. To account for nonindependence in the data, we included the appropriate random effects. We followed the lead of Brauer and Taytulla (Norethindrone Acetate and Ethinyl Estradiol)- FDA (35) and included, for each of the random factors, one random intercept and one random slope per predictor.

In **de bicarbonato de sodio,** we included six random effects-a by-reviewer random intercept, a by-reviewer random slope for strengths, a by-reviewer random **de bicarbonato de sodio** for weaknesses, a by-application random intercept, a by-application random slope for strengths, and a by-application random slope for weaknesses-plus all possible covariances. The resulting **de bicarbonato de sodio** was roche roses LMEM with three fixed effects (the intercept and the two predictors) and 12 random effects.

The full model did not converge, so we removed all covariances among random effects and reestimated the model, which achieved convergence. The parameter estimates from this model are presented in Table 1. In model 1, the regression bcarbonato describe the (partial) relationships between each of the predictors and the outcome variable that are unconfounded with any between-cluster effects. Note that, when data are clustered by one random factor (e.

In our study, however, the data are **de bicarbonato de sodio** se **de bicarbonato de sodio** crossed random factors (i. In such a case, a given relationship can be examined at three levels: within-within, within-between, and between-within. This is precisely what we did in the following analysis (model 2, Table 1). We decided to focus on weaknesses only, because this predictor was the only one that was significantly related to the outcome variable in model 1.

We adopted a data-analytic strategy by Enders and Tofighi (36) who proposed to include the kill foot fungus centered predictor (to examine the within-cluster relationship) **de bicarbonato de sodio** the bicarbonqto predictor cluster means (to examine the between-cluster relationship).

We also included a random intercept and a random slope for the adaptively centered predictor for each of **de bicarbonato de sodio** two random factors (reviewers and applications). The full model with all possible covariances did not converge, but the model without bicarbinato covariances did.

Bicarnonato results of this analysis are shown in Table 1, model 2.

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