Despite several theoretical, structural, and statistical issues reported against Five Facets Mindfulness Questionnaire (FFMQ), most studies in the educational sector of Pakistan use it without analyzing its factor structure. Since culture might change the structure, this first systematic study filled the gap and explored the factor structure of…

Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure. These Multilevel Factor Models (MFMs)…

A large part of the empirical research in the field of visual attention has focused on various concrete paradigms. However, as yet, there has been no clear demonstration of whether or not these paradigms are indeed measuring the same underlying construct. We collected a very large data set (nearly 1.3 million trials) to address this question. We…

Exploratory factor analysis (EFA) is often conducted with ordinal data (e.g., items with 5-point responses) in the social and behavioral sciences. These ordinal variables are often treated as if they were continuous in practice. An alternative strategy is to assume that a normally distributed continuous variable underlies each ordinal variable.…

Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify…