Multi-level simulation analysis: The dynamics of HIV/AIDS

Steven T. Seitz, Charles Hulin

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish (US)
Title of host publicationThe many faces of multi-level issues
EditorsFrancis J Yammarino, Fred Dansereau
Pages353-380
Number of pages28
DOIs
StatePublished - Dec 1 2002
Externally publishedYes

Publication series

NameResearch in Multi-Level Issues
Volume1
ISSN (Print)1475-9144

ASJC Scopus subject areas

  • Economics, Econometrics and Finance (miscellaneous)

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