Testing measurement invariance in a conditional likelihood framework by considering multiple covariates simultaneously.

This article addresses the problem of measurement invariance in psychometrics. In particular, its focus is on the invariance assumption of item parameters in a class of models known as Rasch models. It suggests a mixed-effects or random intercept model for binary data together with a conditional likelihood approach of both estimating and testing the effects of multiple covariates simultaneously.

Addressing Mental Health Disability in Unsheltered Homelessness: Outpatient Conservatorship in Los Angeles.

Objective: The authors sought to describe a pilot program for gravely disabled individuals experiencing unsheltered homelessness in Los Angeles County that illustrates a promising public health framework to address mental health-related disability in homeless populations.

Methods: Homeless outreach teams implementing the outpatient conservatorship (OPC) pilot program adopted a population health approach, multisystem care coordination, and prioritization of the least restrictive environments. The program allowed initiation of a Lanterman-Petris-Short (LPS) conservatorship outside of a hospital, with the goal of serving highly vulnerable individuals in the least restrictive settings.

Development of Melanocortin 4 Receptor Agonists by Exploiting Animal-Derived Macrocyclic, Disulfide-Rich Peptide Scaffolds.

G protein-coupled receptors are among the most widely studied classes of drug targets. A major challenge in this field is to develop ligands that will selectively modulate a single receptor subtype to overcome the disadvantages of undesired "off target" effects caused by lack of target and thus signaling specificity. In the current study, we explored ligand design for the melanocortin 4 receptor (MC4R) since it is an attractive target for developing antiobesity drugs.

Penalization approaches in the conditional maximum likelihood and Rasch modelling context.

Recent detection methods for Differential Item Functioning (DIF) include approaches like Rasch Trees, DIFlasso, GPCMlasso and Item Focussed Trees, all of which - in contrast to well established methods - can handle metric covariates inducing DIF. A new estimation method shall address their downsides by mainly aiming at combining three central virtues: the use of conditional likelihood for estimation, the incorporation of linear influence of metric covariates on item difficulty and the possibility to detect different DIF types: certain items showing DIF, certain covariates inducing DIF, or certain covariates inducing DIF in certain items. Each of the approaches mentioned lacks in two of these aspects.

Power Analysis for the Wald, LR, Score, and Gradient Tests in a Marginal Maximum Likelihood Framework: Applications in IRT.

The Wald, likelihood ratio, score, and the recently proposed gradient statistics can be used to assess a broad range of hypotheses in item response theory models, for instance, to check the overall model fit or to detect differential item functioning. We introduce new methods for power analysis and sample size planning that can be applied when marginal maximum likelihood estimation is used. This allows the application to a variety of IRT models, which are commonly used in practice, e.