How 'situational' is judgment in situational judgment tests?

Whereas situational judgment tests (SJTs) have traditionally been conceptualized as low-fidelity simulations with an emphasis on contextualized situation descriptions and context-dependent knowledge, a recent perspective views SJTs as measures of more general domain (context-independent) knowledge. In the current research, we contrasted these 2 perspectives in 3 studies by removing the situation descriptions (i.e., item stems) from SJTs. Across studies, the traditional contextualized SJT perspective was not supported for between 43% and 71% of the items because it did not make a significant difference whether the situation description was included or not for these items. These results were replicated across construct domains, samples, and response instructions. However, there was initial evidence that judgment in SJTs was more situational when (a) items measured job knowledge and skills and (b) response options denoted context-specific rules of action. Verbal protocol analyses confirmed that high scorers on SJTs without situation descriptions relied upon general rules about the effectiveness of the responses. Implications for SJT theory, research, and design are discussed

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%