Understanding interaction models

Improving empirical analyses

Thomas Brambor, William Roberts Clark, Matthew Richard Golder

Research output: Contribution to journalArticle

2475 Citations (Scopus)

Abstract

Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist.

Original languageEnglish (US)
Pages (from-to)63-82
Number of pages20
JournalPolitical Analysis
Volume14
Issue number1
DOIs
StatePublished - Dec 1 2006

Fingerprint

political science
interaction
intuition
literature

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations

Cite this

Brambor, Thomas ; Clark, William Roberts ; Golder, Matthew Richard. / Understanding interaction models : Improving empirical analyses. In: Political Analysis. 2006 ; Vol. 14, No. 1. pp. 63-82.
@article{d6f07bcd7aa94c77a2c97d1bcdb2c0cb,
title = "Understanding interaction models: Improving empirical analyses",
abstract = "Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10{\%} of the articles in our survey followed the checklist.",
author = "Thomas Brambor and Clark, {William Roberts} and Golder, {Matthew Richard}",
year = "2006",
month = "12",
day = "1",
doi = "10.1093/pan/mpi014",
language = "English (US)",
volume = "14",
pages = "63--82",
journal = "Political Analysis",
issn = "1047-1987",
publisher = "Oxford University Press",
number = "1",

}

Understanding interaction models : Improving empirical analyses. / Brambor, Thomas; Clark, William Roberts; Golder, Matthew Richard.

In: Political Analysis, Vol. 14, No. 1, 01.12.2006, p. 63-82.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Understanding interaction models

T2 - Improving empirical analyses

AU - Brambor, Thomas

AU - Clark, William Roberts

AU - Golder, Matthew Richard

PY - 2006/12/1

Y1 - 2006/12/1

N2 - Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist.

AB - Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist.

UR - http://www.scopus.com/inward/record.url?scp=31044445688&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=31044445688&partnerID=8YFLogxK

U2 - 10.1093/pan/mpi014

DO - 10.1093/pan/mpi014

M3 - Article

VL - 14

SP - 63

EP - 82

JO - Political Analysis

JF - Political Analysis

SN - 1047-1987

IS - 1

ER -