Assessing Potential Heteroscedasticity in Psychological Data: A GAMLSS approach

Juan C Correa, Thomas Kneib, Ospina Raydonal, Julian Tejada, Fernando Marmolejo-Ramos

Research output: Contribution to journalArticlepeer-review


This paper provides a tutorial for analyzing psychological research data with GAMLSS, an R package that uses the family of generalized additive models for location, scale, and shape. These models extend the capacities of traditional parametric and non-parametric tools that primarily rely on the first moment of the statistical distribution. When psychological data fails the assumption of homoscedasticity, the GAMLSS approach might yield less biased estimates while offering more insights about the data when considering sources of heteroscedasticity. The supplemental material and data help newcomers understand the implementation of this approach in a straightforward step-by-step procedure.
Original languageEnglish
Pages (from-to)331-344
Number of pages14
JournalThe Quantitative Methods for Psychology
Issue number4
Publication statusPublished - 3 Dec 2023


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