Title:      Inductive Event Data Scaling using Item Response
Theory

Authors:    Philip A. Schrodt

Entrydate:  2007-07-17 12:27:17

Keywords:   event data, IRT, latent trait, scaling, Rasch model,
Goldstein scale, WEIS, CAMEO

Abstract:   Political event data are frequently converted to an
interval-level measurement by assigning a numerical scaled value
to each event.  All of the existing scaling systems rely on
non-replicable expert assessments to determine these numerical
scores. This paper uses item response theory (IRT) to derive
scales inductively, using event data on Israeli interactions
with Lebanon and the Palestinians for 1991-2007. Monthly scores
on a latent trait are calculated using three IRT models: the
single-parameter Rasch model, and two-parameter models that add
discrimination and guessing parameters. The three formulations
produce generally comparable scores (correlations of 0.90 or
higher). The Rasch scales are less successful than the
expert-derived Goldstein scale in reconciling the somewhat
divergent sets of events derived from the Agence France Presse
and Reuters news services. This is in all likelihood due largely
to a low weighting given uses of force by the IRT because such
events are common in these two dyads. A factor analysis of the
event counts shows that a single cooperation-conflict dimension
generally accounts for about two-thirds of the variance in these
dyads, but a second case-specific dimension explains another 20%.
Finally, moving averages of the derived scores generally
correlate well with the Goldstein values, suggesting that IRT
may provide a route towards deriving purely inductive, and hence
replicable, scales.

http://polmeth.wustl.edu/retrieve.php?id=707