Publication type
Conference Paper
Series
Understanding Society Scientific Conference 2015, 21-23 July 2015, University of Essex, Colchester, UK
Author
Publication date
July 22, 2015
Summary:
As a multifactorial trait, sleep is challenging to operationalise. We
assessed whether latent class analysis (LCA) might identify distinct
sleeping patterns that capture the complexity of sleep and are
meaningfully associated with socio-demographic and health
characteristics.
Data for Understanding Society’s seven sleep variables (duration,
latency, disturbance, medication, snoring/coughing, quality and daytime
sleepiness), collected from respondents with complete data in Waves 1
and 4, were subjected to LCA using Latent Gold (Statistical Innovations,
MA). LCA model fit was assessed using the Log-Likelihood
Bayesian/Akaike Information Criteria and classification error
parameters. Latent classes were interpreted according to their
association with the seven sleep variables; and with age, gender,
education, employment, household composition and subjective health.
The best fitting LCA models identified six latent sleep classes in
both waves, each class containing 6.5-31.6% of respondents. The
distribution of the seven sleep variables across these six sleep classes
suggested the latter might be described as: ‘long good sleepers’; ‘long
moderate sleepers’; ‘snoring good sleepers’; ‘snoring bad sleepers’;
‘short bad sleepers’; ‘struggle to sleepers’. These classes were
significantly associated with all of the socio-demographic/health
variables. For example: a disproportionate number of employed, healthy,
well-educated females living in a couple with children were ‘good long
sleepers’; while a disproportionate number of employed, well-educated
healthy males living in a couple with children were ‘good snoring
sleepers’.
Latent class analysis revealed six distinct sleeping patterns that
are associated with key socio-demographic and health characteristics and
appear stable across Waves 1 and 4 of Understanding Society.
Subject
Link
https://www.understandingsociety.ac.uk/scientific-conference-2015/papers/53
#523215