Using Generalized Estimating Equations for Longitudinal Data Analysis  

Oleh:

Ballinger, Gary A.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Organizational Research Methods vol. 7 no. 2 (Apr. 2004), page 127-150.
Topik: EQUATIONS; longitudinal regression; nested data analysis; geenralized linear models; logistic regression; poisson regression
Fulltext: 127.pdf (184.94KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: OO3.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

The generalized estimating equation (GEE) approach of zeger and liang facilitates analysis of data collected inlongitudinal, nested or repeated measures designs. GEEs use the generalized linear model to estimate more efficient and unbiased regression parameters relative to ordinary least squares regression in part because they permit specification of a working correlation matrix that accounts for the for of within- subject correlation of responses on dependent variables of many different distributions, including normal, binomial and poisson. The author briefly explains the theory behind GEEs and their beneficial statistical properties and limitations and compares GEEs to suboptimal approaches for analyzing longitudinal data through use of two examples. The first demonstration applies GEEs to the analysis of data from a longitudinal lab study with a counted response variable, the second demonstration applies GEEs to analysis of data with a normally distributed response variable from subjects nested within branch offices of an organization.

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