Abstract
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.
Keywords
PsychologyMultilevel modelDevelopmental psychologyComputer scienceMachine learning
Affiliated Institutions
Related Publications
Qualitative methodologies II: a brief guide to applying interpretative phenomenological analysis in musculoskeletal care
Abstract In this paper, we conclude our two‐part series discussing how readers of Musculoskeletal Care might employ qualitative methods. In the first paper of the series, we foc...
Model Uncertainty, Data Mining and Statistical Inference
This paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a mo...
Novel methods improve prediction of species’ distributions from occurrence data
Prediction of species’ distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occ...
Application of hierarchical linear models to assessing change.
Developments over the past 10 years in the statistical theory of hierarchical linear models (HLMs) now enable an integrated approach for (a) studying the structure of individua...
Practical Bayesian Density Estimation Using Mixtures of Normals
Abstract Mixtures of normals provide a flexible model for estimating densities in a Bayesian framework. There are some difficulties with this model, however. First, standard ref...
Publication Info
- Year
- 2001
- Type
- article
- Volume
- 42
- Issue
- 1
- Pages
- 141-162
- Citations
- 110
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
110
OpenAlex
Cite This
Michael H. Boyle,
J. Douglas Willms
(2001).
Multilevel Modelling of Hierarchical Data in Developmental Studies.
Journal of Child Psychology and Psychiatry
, 42
(1)
, 141-162.
https://doi.org/10.1111/1469-7610.00706
Identifiers
- DOI
- 10.1111/1469-7610.00706