Regression to Causality: Regression-style presentation influences causal attribution
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
Regression to Causality : Regression-style presentation influences causal attribution. / Bordacconi, Mats Joe; Larsen, Martin Vinæs.
I: Research & Politics, Bind 1, Nr. 2, 1, 09.2014, s. 1-6.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Regression to Causality
T2 - Regression-style presentation influences causal attribution
AU - Bordacconi, Mats Joe
AU - Larsen, Martin Vinæs
PY - 2014/9
Y1 - 2014/9
N2 - Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression models should note carefully both their models’ identifying assumptions and which causal attributions can safely be concluded from their analysis.
AB - Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests regression models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression models should note carefully both their models’ identifying assumptions and which causal attributions can safely be concluded from their analysis.
U2 - 10.1177/2053168014548092
DO - 10.1177/2053168014548092
M3 - Journal article
VL - 1
SP - 1
EP - 6
JO - Research and Politics
JF - Research and Politics
SN - 2053-1680
IS - 2
M1 - 1
ER -
ID: 123068809