TY - CHAP
T1 - Multi-level simulation analysis
T2 - The dynamics of HIV/AIDS
AU - Seitz, Steven T.
AU - Hulin, Charles
PY - 2002/12/1
Y1 - 2002/12/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=35448975618&partnerID=8YFLogxK
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U2 - 10.1016/S1475-9144(02)01042-1
DO - 10.1016/S1475-9144(02)01042-1
M3 - Chapter
AN - SCOPUS:35448975618
SN - 0762308052
SN - 9780762308057
T3 - Research in Multi-Level Issues
SP - 353
EP - 380
BT - The many faces of multi-level issues
A2 - Yammarino, Francis J
A2 - Dansereau, Fred
ER -