Evidence on the uncertainty attached to school and individual scores is required to avoid overinterpretation of scores. Generalizability analysis provides a standard error (SE) that is a suitable indicator of uncertainty; a value can be obtained for a design already executed or for a design being considered for possible use. Assessments depart from traditional measurements in ways that require ...

When assessing causal effects, determining the target population to which results are intended generalize is a critical decision. Randomized and observational studies each have strengths limitations for estimating effects in population. Estimates from randomized data may internal validity but often not representative of Observational better reflect population, hence be more likely external vali...

The big data imposes the key problem of generalizability of the results. In the present contribution, we discuss statistical tools which can help to select variables adequate for target level of abstraction. We show that a model considered as over-fitted in one context can be accurate in another. We illustrate this notion with an example analysis experiment on the data from 13 university Massiv...

BACKGROUND There is a need for validated and easy-to-apply behavior-based tools for assessing interprofessional team competencies in clinical settings. The seven-item observer-based Modified McMaster-Ottawa scale was developed for the Team Objective Structured Clinical Encounter (TOSCE) to assess individual and team performance in interprofessional patient encounters. OBJECTIVE We aimed to im...

This article introduces the application of Generalizability Theory to assessing the reliability of measurements. Generalizability Theory can be used to assess reliability in the presence of multiple sources of error and also can be applied to assess reliability in the presence of different types of sources of error. In particular, the application of Generalizability Theory to measurements invol...

Machine learning classifiers have become increasingly popular tools to generate single-subject inferences from fMRI data. With this transition from the traditional group level difference investigations to single-subject inference, the application of machine learning methods can be seen as a considerable step forward. Existing studies, however, have given scarce or no information on the generali...