Linked Life Data

11205622

Source:http://linkedlifedata.com/resource/pubmed/id/11205622

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2001-2-1
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0021-9630
pubmed:author
pubmed:issnType
Print
pubmed:volume
42
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
141-62
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2001
pubmed:articleTitle
Multilevel modelling of hierarchical data in developmental studies.
pubmed:affiliation
Centre for Studies for Children at Risk, McMaster University and Hamilton Health Sciences Corporation, Ontario, Canada. boylem@fhs.csu.mcmaster.ca
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't