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4 results

  • Chapter 4 - The Modelling of Time

  • Jos W. R. Twisk, Amsterdam University Medical Centers
  • Book:
    Applied Longitudinal Data Analysis for Medical Science
    Published online:
    20 April 2023
    Print publication:
    27 April 2023, pp 56-75

  • Summary

    In Chapter 4, the modelling of the time variable in longitudinal data analysis is discussed. In general, a distinction can be made between time as a continuous variable and time as a categorical variable represented by dummy variables. The latter is only possible in a longitudinal study with fixed time-points. Basically, three different situations are explained. (1) Growth curve analysis; i.e. when the time variable is the covariate of interest. Regarding growth curve analysis, the chapter also includes a discussion about latent class growth curve modelling. (2) The time variable as a potential confounder. When the time variable is a potential confounder it is argued that it is important to distinguish the time variable from age and (3) the time variable as effect modifier. When the time variable is an effect modifier, the interest is either in a different development over time for different groups or in a different relationship between an outcome variable and a covariate in different parts of the longitudinal study. Both situations are discussed in detail. All methods are accompanied by extensive real-life data examples.

  • Pediatric Online Disaster Preparedness Training for Medical and Non-Medical Personnel: A Multi-Level Modeling Analysis

  • Phung K. Pham, Solomon M. Behar, Bridget M. Berg, Jeffrey S. Upperman, Alan L. Nager
  • Journal:
    Prehospital and Disaster Medicine / Volume 33 / Issue 4 / August 2018
    Published online by Cambridge University Press:
    21 August 2018, pp. 349-354
    Print publication:
    August 2018
  • Introduction

    Terrorism and natural catastrophes have made disaster preparedness a critical issue. Despite the documented vulnerabilities of children during and following disasters, gaps remain in health care systems regarding pediatric disaster preparedness. This research study examined changes in knowledge acquisition of pediatric disaster preparedness among medical and non-medical personnel at a children’s hospital who completed an online training course of five modules: planning, triage, age-specific care, disaster management, and hospital emergency code response.

    Methods

    A multi-disciplinary team within the Pediatric Disaster Resource and Training Center at Children’s Hospital Los Angeles (Los Angeles, California USA) developed an online training course. Available archival course data from July 2009 to August 2012 were analyzed through linear growth curve multi-level modeling, with module total score as the outcome (0 to 100 points), attempt as the Level 1 variable (any module could be repeated), role in the hospital (medical or non-medical) as the Level 2 variable, and attempt by role as the cross-level effect.

    Results

    A total of 44,115 module attempts by 5,773 course participants (3,686 medical personnel and 2,087 non-medical personnel) were analyzed. The average module total score upon first attempt across all participants ranged from 60.28 to 80.11 points, and participants significantly varied in how they initially scored. On average in the planning, triage, and age-specific care modules: total scores significantly increased per attempt across all participants (average rate of change ranged from 0.59 to 1.84 points) and medical personnel had higher total scores initially and through additional attempts (average difference ranged from 13.25 to 16.24 points). Cross-level effects were significant in the disaster management and hospital emergency code response modules: on average, total scores were initially lower among non-medical personnel compared to medical personnel, but non-medical personnel increased their total scores per attempt by 3.77 points in the disaster management module and 6.40 points in the hospital emergency code response module, while medical personnel did not improve their total scores through additional attempts.

    Conclusion:

    Medical and non-medical hospital personnel alike can acquire knowledge of pediatric disaster preparedness. Key content can be reinforced or improved through successive training in an online course.

    PhamPK, BeharSM, BergBM, UppermanJS, NagerAL. Pediatric Online Disaster Preparedness Training for Medical and Non-Medical Personnel: A Multi-Level Modeling AnalysisPrehosp Disaster Med.2018;33(4):349–354.

  • Functions of Turkish evidentials in early child–caregiver interactions: a growth curve analysis of longitudinal data

  • Berna A. UZUNDAĞ, Süleyman S. TAŞÇI, Aylin C. KÜNTAY, Ayhan AKSU-KOÇ
  • Journal:
    Journal of Child Language / Volume 45 / Issue 4 / July 2018
    Published online by Cambridge University Press:
    21 February 2018, pp. 878-899

  • In languages with evidential marking, utterances consist of an informational content and a specification of the mode of access to that information. In this first longitudinal study investigating the acquisition of the Turkish evidential marker −mIş in naturalistic child–caregiver interactions, we examined six children between 8 and 36 months of age. We charted individual differences in child and caregiver speech over time by conducting growth curve analyses. Children followed a similar course of acquisition in terms of the proportion of the marker in overall speech. However, children exhibited differences with respect to the order of emergence of different evidential functions (e.g., inference, hearsay), where each child showed a unique pattern irrespective of the frequency in caregiver input. Nonfactual use of the marker was very frequent in child and caregiver speech, where high-SES caregivers mostly produced the marker during story-telling and pretend play, and low-SES caregivers for regulating the child's behavior.

  • Multilevel Modelling of Hierarchical Data in Developmental Studies

  • Michael H. Boyle, J. Douglas Willms
  • Journal:
    The Journal of Child Psychology and Psychiatry and Allied Disciplines / Volume 42 / Issue 1 / January 2001
    Published online by Cambridge University Press:
    14 March 2001, pp. 141-162
    Print publication:
    January 2001

  • This report attempts to give nontechnical readers some insight into how a multilevel modelling framework can be used in longitudinal studies to assess contextual influences on child development when study samples arise from naturally formed groupings. We hope to achieve this objective by: (1) discussing the types of variables and research designs used for collecting developmental data; (2) presenting the methods and data requirements associated with two statistical approaches to developmental data—growth curve modelling and discrete-time survival analysis; (3) describing the multilevel extensions of these approaches, which can be used when the study of development includes intact clusters or naturally formed groupings; (4) demonstrating the flexibility of these two approaches for addressing a variety of research questions; and (5) placing the multilevel framework developed in this report in the context of some important issues, alternative approaches, and recent developments. We hope that readers new to these methods are able to visualize the possibility of using them to advance their work.