Everything, altogether, all at once: Addressing data challenges when measuring speech intelligibility through entropy scores.

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al.

Development, psychometric validation, and cross-cultural comparison of the "Instagram Motives Questionnaire" (IMQ) and the "Instagram Uses and Patterns Questionnaire" (IUPQ).

Background And Aims: Social network use is widespread, and the study of Instagram seems to have captured more attention in recent years. However, scale development and validation in the field has fallen short of providing sound scales of Instagram motives and usage patterns that consider the uniqueness of Instagram-related behavior. This paper describes the development, psychometric and cross-cultural validation of two new measurement instruments: the "Instagram Motives Questionnaire" (IMQ) and the "Instagram Uses and Patterns Questionnaire" (IUPQ).