Persona:
Martínez Huertas, José Ángel

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0000-0002-6700-6832
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Martínez Huertas
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José Ángel
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  • Miniatura
    Publicación
    A Failed Cross-Validation Study on the Relationship between LIWC Linguistic Indicators and Personality: Exemplifying the Lack of Generalizability of Exploratory Studies
    (MDPI, 2022-10-13) Moreno, José David; Olmos, Ricardo; Martínez Mingo, Alejandro; Jorge Botana, Guillermo de; Martínez Huertas, José Ángel
    (1) Background: Previous meta-analytic research found small to moderate relationships between the Big Five personality traits and different linguistic computational indicators. However, previous studies included multiple linguistic indicators to predict personality from an exploratory framework. The aim of this study was to conduct a cross-validation study analyzing the relationships between language indicators and personality traits to test the generalizability of previous results; (2) Methods: 643 Spanish undergraduate students were tasked to write a self-description in 500 words (which was evaluated with the LIWC) and to answer a standardized Big Five questionnaire. Two different analytical approaches using multiple linear regression were followed: first, using the complete data and, second, by conducting different cross-validation studies; (3) Results: The results showed medium effect sizes in the first analytical approach. On the contrary, it was found that language and personality relationships were not generalizable in the cross-validation studies; (4) Conclusions: We concluded that moderate effect sizes could be obtained when the language and personality relationships were analyzed in single samples, but it was not possible to generalize the model estimates to other samples. Thus, previous exploratory results found on this line of research appear to be incompatible with a nomothetic approach.
  • Miniatura
    Publicación
    Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging
    (PsychOpen, 2022-12-22) Olmos, Ricardo; Martínez Huertas, José Ángel
    Random effects contain crucial information to understand the variability of the processes under study in mixed-effects models with crossed random effects (MEMs-CR). Given that model selection makes all-or-nothing decisions regarding to the inclusion of model parameters, we evaluated if model averaging could deal with model uncertainty to recover random effects of MEMs-CR. Specifically, we analyzed the bias and the root mean squared error (RMSE) of the estimations of the variances of random effects using model averaging with Akaike weights and Bayesian model averaging with BIC posterior probabilities, comparing them with two alternative analytical strategies as benchmarks: AIC and BIC model selection, and fitting a full random structure. A simulation study was conducted manipulating sample sizes for subjects and items, and the variance of random effects. Results showed that model averaging, especially Akaike weights, can adequately recover random variances, given a minimum sample size in the modeled clusters. Thus, we endorse using model averaging to deal with model uncertainty in MEMs-CR. An empirical illustration is provided to ease the usability of model averaging.
  • Miniatura
    Publicación
    The Revised Memory and Behavior Problems Checklist for Nursing Homes: Validation among Different Spanish Territories
    (MDPI, 2022-02-08) Velasco, Cristina; López, Javier; Pérez Rojo, Gema; Noriega, Cristina; Martínez Huertas, José Ángel
    Memory and behavioral difficulties among older people living in nursing homes can cause burden and other consequences in professional caregivers. There is a lack of instruments that evaluate these behaviors and their influence in formal caregivers. The aim of this study is to develop and psychometrically test—the Revised Memory and Behavior Problems Checklist for Nursing Homes (RMBPC-NH). A cross-sectional study was carried out. The sample was made up of 312 formal caregivers working in nursing homes from different territories in Spain, 87.5% were women and 12.5% were men. The average age of participants was 39 years (SD = 12.2). The sample was recruited from January 2019 to March 2020. Participants completed a self-administered questionnaire, which included sociodemographic information, and assessed quality of technical equipment, level of training, experience of working with older people, job satisfaction, professional quality of life, burnout, and conception of negative stereotypes held towards aging. The four-factor structure of the RMBPC-NH showed a good fit, namely in relation to memory, functional, and emotional factors, and other problems. It has shown adequate psychometric properties, internal consistency, and validity (correlations with professional quality of life, job satisfaction, burnout, and negative stereotypes). The RMBPC-NH is a useful instrument to evaluate the frequency of older people’s memory and behavior problems and professional caregivers’ burden. The practical application in nursing homes is discussed.