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Latent class profile analysis: An application to stage sequential processes in early onset drinking behaviours

Title
Latent class profile analysis: An application to stage sequential processes in early onset drinking behaviours
Authors
Chung H.Anthony J.C.Schafer J.L.
Ewha Authors
정환
Issue Date
2011
Journal Title
Journal of the Royal Statistical Society. Series A: Statistics in Society
ISSN
0964-1998JCR Link
Citation
vol. 174, no. 3, pp. 689 - 712
Indexed
SCI; SCIE; SSCI; SCOPUS WOS scopus
Abstract
In longitudinal research on early onset drinkers, much attention has been paid to the identification of subgroups of individuals who follow similar sequential patterns of drinking behaviours. However, research on the sequential development of drinking behaviour can be challenging in part because it may not be possible to observe the particular drinking behaviour stage at a given point in time directly. To address this difficulty, we can use a latent class analysis, which provides a set of principles for the systematic identification of homogeneous subgroups of individuals. In this work, we apply a latent class analysis in an investigation of stage sequential patterns of drinking behaviours among early onset drinkers, using data from the National Longitudinal Survey of Youth 1997. A latent class analysis approach is used to sort different patterns of drinking behaviours into a small number of classes at each measurement occasion; and the class sequencing of early onset drinkers over the entire set of time points is evaluated to identify two or more homogeneous early onset drinkers who exhibit a similar sequence of class memberships over time. This approach uncovers four common drinking behaviours in early onset drinkers over three measurements from early to late adolescence. The sequences of drinking behaviours can be grouped into three sequential patterns representing the most probable progression of early onset drinking behaviours. © 2010 Royal Statistical Society.
DOI
10.1111/j.1467-985X.2010.00674.x
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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