Hierarchical Linear Modeling in Organizational Research: Longitudinal Data Outside the Context of Growth Modeling  

Oleh:

Schonfeld, Irvin Sam ; Rindskopf, David

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Organizational Research Methods vol. 10 no. 3 (Jul. 2007), page 417-429.
Topik: hierarchical linear models; multilevel models; analysis of longitudinal data; methodology; occupational stress
Fulltext: 417.pdf (119.88KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: OO3.8 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

Organizational researchers, including those carrying out occupational stress research, often conduct longitudinal studies. Hierarchical linear modeling (HLM; also known as multilevel modeling and random regression) can efficiently organize analyses of longitudinal data by including within- and between-person levels of analysis. A great deal of longitudinal research has been conducted in the context of growth studies in which change in the dependent variable is examined in relation to the passage of time. HLM can treat longitudinal data, including data outside the context of the growth study, as nested data, reducing the problem of censoring. Within-person equation coefficients can represent the impact of Time t - 1 working conditions on Time t outcomes using all appropriate pairs of data points. Time itself need not be an independent variable of interest.

Opini Anda

Klik untuk menuliskan opini Anda tentang koleksi ini!

Kembali