2.50
Hdl Handle:
http://hdl.handle.net/10755/158351
Type:
Presentation
Title:
Using Hierarchical Linear Models to Analyze A National Nursing Survey Study
Abstract:
Using Hierarchical Linear Models to Analyze A National Nursing Survey Study
Conference Sponsor:Midwest Nursing Research Society
Conference Year:2006
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:Carol Brewer, PhD; Christine Kovner, PhD; and Ying Cheng, PhD(c)
Researchers studying the nurse work force are interested in knowing what factors may have impact on nurses' working status. These factors can be gathered at both an individual level or at the Metropolitan Statistical Area (MSA) level. For example, age, educational background, and number of children under age of six are individual level data; and the unemployment rate, percent of families below poverty, and number of physicians per 1000 residents are MSA level data. The National Sample Survey of Registered Nurses (NSSRN) in the year 2000, randomly selected over thirty thousand nurses from 326 metropolitan statistical areas (MSA). One important variable collected in this study was nurses' working status (working or not work. We merged MSA level data into the NSSRN data set. Traditionally, such data were analyzed either at individual nurses level or at an aggregated MSA level. In this study, we argue that data must be analyzed at both levels simultaneously. The purpose of this presentation is to use the NSSRN data to demonstrate that data at different levels must be analyzed appropriately for the research purpose. The primary goal here focuses on methodological clarification rather than substantive explanations. A brief introduction of this data set is followed by demonstrating how these data can be analyzed by hierarchical liner models (HLM). The number of subjects in each MSA varies from 5 to746 nurses. The average number of nurses in each MSA is about 86 with a standard deviation of 111. In this study, data will be analyzed by multi-level logistic regression approach. The odds ratio of work and not work will be examined in detail. Why the HLM approach is more appropriate than either using individual or MSA level analysis alone will be discussed at the end. [Poster Presentation]
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.titleUsing Hierarchical Linear Models to Analyze A National Nursing Survey Studyen_GB
dc.identifier.urihttp://hdl.handle.net/10755/158351-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Using Hierarchical Linear Models to Analyze A National Nursing Survey Study</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">2006</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">Carol Brewer, PhD; Christine Kovner, PhD; and Ying Cheng, PhD(c)</td></tr><tr><td colspan="2" class="item-abstract">Researchers studying the nurse work force are interested in knowing what factors may have impact on nurses' working status. These factors can be gathered at both an individual level or at the Metropolitan Statistical Area (MSA) level. For example, age, educational background, and number of children under age of six are individual level data; and the unemployment rate, percent of families below poverty, and number of physicians per 1000 residents are MSA level data. The National Sample Survey of Registered Nurses (NSSRN) in the year 2000, randomly selected over thirty thousand nurses from 326 metropolitan statistical areas (MSA). One important variable collected in this study was nurses' working status (working or not work. We merged MSA level data into the NSSRN data set. Traditionally, such data were analyzed either at individual nurses level or at an aggregated MSA level. In this study, we argue that data must be analyzed at both levels simultaneously. The purpose of this presentation is to use the NSSRN data to demonstrate that data at different levels must be analyzed appropriately for the research purpose. The primary goal here focuses on methodological clarification rather than substantive explanations. A brief introduction of this data set is followed by demonstrating how these data can be analyzed by hierarchical liner models (HLM). The number of subjects in each MSA varies from 5 to746 nurses. The average number of nurses in each MSA is about 86 with a standard deviation of 111. In this study, data will be analyzed by multi-level logistic regression approach. The odds ratio of work and not work will be examined in detail. Why the HLM approach is more appropriate than either using individual or MSA level analysis alone will be discussed at the end. [Poster Presentation]</td></tr></table>en_GB
dc.date.available2011-10-26T20:57:49Z-
dc.date.issued2011-10-17en_GB
dc.date.accessioned2011-10-26T20:57:49Z-
dc.description.sponsorshipMidwest Nursing Research Societyen_GB
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