Browsing by Author "Gonzalez Burgos, Jorge"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A Critical View on the NEAT Equating Design: Statistical Modeling and Identifiability Problems
    (SAGE PUBLICATIONS INC, 2022) San Martin Gutiérrez, Ernesto; Gonzalez Burgos, Jorge
    The nonequivalent groups with anchor test (NEAT) design is widely used in test equating. Under this design, two groups of examinees are administered different test forms with each test form containing a subset of common items. Because test takers from different groups are assigned only one test form, missing score data emerge by design rendering some of the score distributions unavailable. The partially observed score data formally lead to an identifiability problem, which has not been recognized as such in the equating literature and has been considered from different perspectives, all of them making different assumptions in order to estimate the unidentified score distributions. In this article, we formally specify the statistical model underlying the NEAT design and unveil the lack of identifiability of the parameters of interest that compose the equating transformation. We use the theory of partial identification to show alternatives to traditional practices that have been proposed to identify the score distributions when conducting equating under the NEAT design.
  • Thumbnail Image
    Item
    Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
    (John Wiley and Sons Inc, 2022) Marmolejo-Ramos, Fernando; Tejo, Mauricio; Brabec, Marek; Kuzilek, Jakub; Joksimovic, Srecko; Kovanovic, Vitomir; Gonzalez Burgos, Jorge; Kneib, Thomas; Bühlmann, Peter; Kook, Lucas; Briseño Sánchez, Guillermo; Ospina, Raydonal
    The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA. This article is categorized under: Application Areas > Education and Learning Algorithmic Development > Statistics Technologies > Machine Learning.