Identifying Unit-Dependency and Time-Specificity in Longitudinal Analysis: A Graphical Methodology
- Laura Dugan
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Longitudinal analysis in criminology and other social sciences has become an important research tool because it allows us to draw conclusions from observing how multiple units change over time. Unfortunately, its results are more vulnerable to potential influences of unusual observational units or periods of time. Current leverage diagnostics are designed for cross-sectional analysis and are fallible when applied to longitudinal models. This article introduces a graphical diagnostic methodology to systematically examine the sensitivity of longitudinal results to extreme observational units and periods of time—unit-dependency and time-specificity. Further the article illustrates its use with an example testing policy effects on black and white female victimization of intimate partner homicide. Results are displayed in an easily understood graph that provides a snapshot of the results' time-specific patterns and robustness to unit-dependency. Currently, comparable tests for panel analysis are tedious and cumbersome. With this new illuminating methodology, researchers and policy-makers can easily decide whether a time-specific or unit-dependent pattern is consequential.
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- Identifying Unit-Dependency and Time-Specificity in Longitudinal Analysis: A Graphical Methodology
Journal of Quantitative Criminology
Volume 18, Issue 3 , pp 213-237
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers-Plenum Publishers
- Additional Links
- longitudinal analysis
- observation dependency
- spousal homicide
- time specific effects
- graphical diagnostics
- Laura Dugan (1) (2)
- Author Affiliations
- 1. Department of Criminology and Criminal Justice, University of Maryland, College Park, Maryland
- 2. National Consortium on Violence Research, Carnegie Mellon University, Pittsburgh, Pennsylvania