2.50
Hdl Handle:
http://hdl.handle.net/10755/158517
Type:
Presentation
Title:
Analyzing Longitudinal Data Within Parent Couples
Abstract:
Analyzing Longitudinal Data Within Parent Couples
Conference Sponsor:Midwest Nursing Research Society
Conference Year:2005
Author:Wu, Yow-Wu, PhD, MS
P.I. Institution Name:University at Buffalo/SUNY
Title:Associate Professor
Contact Address:School of Nursing, 920 Kimball Tower, Buffalo, NY, 14215, USA
Contact Telephone:716 829-3207
Co-Authors:Janet Pinelli, DNS, Professor; Powhatan Wooldridge, PhD
Longitudinal data analysis for husband and wife pairs has not been
discussed in depth in the nursing literature. Traditionally, husband data
and wife data were analyzed separately. Such analyses ignored the fact
that husbands' and wives' scores tend to be related to each other. The
purpose of this poster is to show readers to use hierarchical linear
models to analyze husbands' and wives' longitudinal data in relation to
one another, using couple as the unit of analysis. This was not found in
nursing literature. Raudenbush, Brennan and Barnett (1995) used a
multivariate hierarchical linear model to study psychological change
within married couples. In their study, each person's psychological
characteristics were viewed as changing over time as a function of both
the individual's personal characteristics and the influence of his/her
partner. The methodology they used is flexible in allowing randomly
missing data, varying spacing of time points, unbalanced designs, and
time-varying and time-invariant covariates. Pinelli et al (2003) studied
the family adjustment of parents who had their newborn in the neonatal
intensive care unit (NICU). One hundred and fifty-two families were
studied. Each father's and mother's scores were gathered while in the
NICU, and at 3, 6 and 12 months later. We measured adjustment, coping,
resources, and stress for each father or mother at each of these 4 time
points. We will describe sample statistics and then model first level
(within individuals) data using time as the predictor. Linear and
quadratic growth trajectories will be modeled for each subject at the
first level, then we will test whether these growth trajectories vary
significantly from person to person. If the answer is positive, we will
then model the effects of other predictor variables on growth
trajectories, using couple as a second level of analysis.
Repository Posting Date:
26-Oct-2011
Date of Publication:
17-Oct-2011
Sponsors:
Midwest Nursing Research Society

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleAnalyzing Longitudinal Data Within Parent Couplesen_GB
dc.identifier.urihttp://hdl.handle.net/10755/158517-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Analyzing Longitudinal Data Within Parent Couples</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Midwest Nursing Research Society</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2005</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Wu, Yow-Wu, PhD, MS</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">University at Buffalo/SUNY</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Associate Professor</td></tr><tr class="item-address"><td class="label">Contact Address:</td><td class="value">School of Nursing, 920 Kimball Tower, Buffalo, NY, 14215, USA</td></tr><tr class="item-phone"><td class="label">Contact Telephone:</td><td class="value">716 829-3207</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">nurwu@buffalo.edu</td></tr><tr class="item-co-authors"><td class="label">Co-Authors:</td><td class="value">Janet Pinelli, DNS, Professor; Powhatan Wooldridge, PhD</td></tr><tr><td colspan="2" class="item-abstract">Longitudinal data analysis for husband and wife pairs has not been <br/> discussed in depth in the nursing literature. Traditionally, husband data <br/> and wife data were analyzed separately. Such analyses ignored the fact <br/> that husbands' and wives' scores tend to be related to each other. The <br/> purpose of this poster is to show readers to use hierarchical linear <br/> models to analyze husbands' and wives' longitudinal data in relation to <br/> one another, using couple as the unit of analysis. This was not found in <br/> nursing literature. Raudenbush, Brennan and Barnett (1995) used a <br/> multivariate hierarchical linear model to study psychological change <br/> within married couples. In their study, each person's psychological <br/> characteristics were viewed as changing over time as a function of both <br/> the individual's personal characteristics and the influence of his/her <br/> partner. The methodology they used is flexible in allowing randomly <br/> missing data, varying spacing of time points, unbalanced designs, and <br/> time-varying and time-invariant covariates. Pinelli et al (2003) studied <br/> the family adjustment of parents who had their newborn in the neonatal <br/> intensive care unit (NICU). One hundred and fifty-two families were <br/> studied. Each father's and mother's scores were gathered while in the <br/> NICU, and at 3, 6 and 12 months later. We measured adjustment, coping, <br/> resources, and stress for each father or mother at each of these 4 time <br/> points. We will describe sample statistics and then model first level <br/> (within individuals) data using time as the predictor. Linear and <br/> quadratic growth trajectories will be modeled for each subject at the <br/> first level, then we will test whether these growth trajectories vary <br/> significantly from person to person. If the answer is positive, we will <br/> then model the effects of other predictor variables on growth <br/> trajectories, using couple as a second level of analysis.</td></tr></table>en_GB
dc.date.available2011-10-26T21:08:03Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T21:08:03Z-
dc.description.sponsorshipMidwest Nursing Research Societyen_GB
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