TY  - CHAP
AB  - Multi-level causation generates serious methodological issues that are not always appreciated in contemporary social research. This chapter uses the dynamics of HIV/AIDS to illustrate three such issues. First, the failure to take both individual-level and group- or context-level forces into account leads to systematic bias in the statistical analysis of observed data. We decompose a fully specified cross-level regression model into separate individual- and group-level components to illustrate the resulting biases in data analysis. Second, we look at the application of fully specified cross-level regression models to processes that are not in equilibrium. Static cross-level regression models cannot properly estimate multi-level cause-and-effect when there are non-linear feedback effects among independent and dependent variables over time. Finally, we explore how computational modeling can be used to study these feedback dynamics in multi-level causal processes. We illustrate two computational methods that help researchers unravel such complex causal environments: counterfactuals and process decomposition.
VL  - 1
SN  - 978-0-76230-805-7, 978-1-84950-113-2/1475-9144
DO  - 10.1016/S1475-9144(02)01042-1
UR  - https://doi.org/10.1016/S1475-9144(02)01042-1
AU  - Seitz Steven T
AU  - Hulin Charles
ED  - Francis J. Yammarino
ED  - Fred Dansereau
PY  - 2002
Y1  - 2002/01/01
TI  - Multi-level simulation analysis: The dynamics of HIV/AIDS
T2  - The many faces of multi-level issues
T3  - Research in Multi-Level Issues
PB  - Emerald Group Publishing Limited
SP  - 353
EP  - 380
Y2  - 2024/09/20
ER  -