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Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression

Author

Listed:
  • Nikos Tzavidis
  • Nicola Salvati
  • Timo Schmid
  • Eirini Flouri
  • Emily Midouhas

Abstract

type="main" xml:id="rssa12126-abs-0001"> Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M-quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

Suggested Citation

  • Nikos Tzavidis & Nicola Salvati & Timo Schmid & Eirini Flouri & Emily Midouhas, 2016. "Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 427-452, February.
    • Handle: RePEc:bla:jorssa:v:179:y:2016:i:2:p:427-452
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    File URL: http://hdl.handle.net/10.1111/rssa.2016.179.issue-2
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    Citations

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    Cited by:

    1. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    2. Marco Alfò & Maria Francesca Marino & Maria Giovanna Ranalli & Nicola Salvati & Nikos Tzavidis, 2021. "M‐quantile regression for multivariate longitudinal data with an application to the Millennium Cohort Study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 122-146, January.
    3. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    4. Beatrice Foroni & Luca Merlo & Lea Petrella, 2023. "Expectile hidden Markov regression models for analyzing cryptocurrency returns," Papers 2301.09722, arXiv.org, revised Jan 2024.
    5. Luciano Ciravegna & Federica Nieri, 2022. "Business and Human Rights: A Configurational View of the Antecedents of Human Rights Infringements by Emerging Market Firms," Journal of Business Ethics, Springer, vol. 179(2), pages 431-450, August.
    6. Samantha Ofili & Lucy Thompson & Philip Wilson & Louise Marryat & Graham Connelly & Marion Henderson & Sarah J. E. Barry, 2022. "Mapping Geographic Trends in Early Childhood Social, Emotional, and Behavioural Difficulties in Glasgow: 2010–2017," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    7. Luca Merlo & Lea Petrella & Nikos Tzavidis, 2022. "Quantile mixed hidden Markov models for multivariate longitudinal data: An application to children's Strengths and Difficulties Questionnaire scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 417-448, March.
    8. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.
    9. Bibou-Nakou, I. & Markos, A. & Padeliadu, S. & Chatzilampou, P. & Ververidou, S., 2019. "Multi-informant evaluation of students' psychosocial status through SDQ in a national Greek sample," Children and Youth Services Review, Elsevier, vol. 96(C), pages 47-54.
    10. Antonella D’Agostino & Francesco Schirripa Spagnolo & Nicola Salvati, 2022. "Studying the relationship between anxiety and school achievement: evidence from PISA data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 1-20, March.
    11. Valéry Dongmo Jiongo & Pierre Nguimkeu, 2018. "Bootstrapping Mean Squared Errors of Robust Small-Area Estimators: Application to the Method-of-Payments Data," Staff Working Papers 18-28, Bank of Canada.

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