Abstract
Students of international politics often use data in which the covariates vary both within and across units of observation. This is particularly true for dyadic data, which has come to dominate quantitative studies of international conflict, but is also a concern in any work involving a time-series cross-sectional component. Standard regression methods treat both types of covariates as equivalent with respect to their influence on the dependent variable, ignoring possible differences between cross-dyad and within-dyad effects. Here, I discuss the potential pitfalls of this approach, and show how between- and within-dyad effects can be separated and estimated. I then illustrate the approach in the context of a logistic regression, using data on international disputes.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 433-445 |
| Number of pages | 13 |
| Journal | International Interactions |
| Volume | 27 |
| Issue number | 4 |
| State | Published - Dec 1 2001 |
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All Science Journal Classification (ASJC) codes
- Political Science and International Relations
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Estimating between- and within-cluster covariate effects, with an application to models of international disputes. / Zorn, Christopher.
In: International Interactions, Vol. 27, No. 4, 01.12.2001, p. 433-445.Research output: Contribution to journal › Article
TY - JOUR
T1 - Estimating between- and within-cluster covariate effects, with an application to models of international disputes
AU - Zorn, Christopher
PY - 2001/12/1
Y1 - 2001/12/1
N2 - Students of international politics often use data in which the covariates vary both within and across units of observation. This is particularly true for dyadic data, which has come to dominate quantitative studies of international conflict, but is also a concern in any work involving a time-series cross-sectional component. Standard regression methods treat both types of covariates as equivalent with respect to their influence on the dependent variable, ignoring possible differences between cross-dyad and within-dyad effects. Here, I discuss the potential pitfalls of this approach, and show how between- and within-dyad effects can be separated and estimated. I then illustrate the approach in the context of a logistic regression, using data on international disputes.
AB - Students of international politics often use data in which the covariates vary both within and across units of observation. This is particularly true for dyadic data, which has come to dominate quantitative studies of international conflict, but is also a concern in any work involving a time-series cross-sectional component. Standard regression methods treat both types of covariates as equivalent with respect to their influence on the dependent variable, ignoring possible differences between cross-dyad and within-dyad effects. Here, I discuss the potential pitfalls of this approach, and show how between- and within-dyad effects can be separated and estimated. I then illustrate the approach in the context of a logistic regression, using data on international disputes.
UR - http://www.scopus.com/inward/record.url?scp=0442303938&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0442303938&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0442303938
VL - 27
SP - 433
EP - 445
JO - International Interactions
JF - International Interactions
SN - 0305-0629
IS - 4
ER -