Regression to causality: Regression-style presentation influences causal attribution
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 model...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2014-08-01
|
| Series: | Research & Politics |
| Online Access: | https://doi.org/10.1177/2053168014548092 |
| _version_ | 1828340034265153536 |
|---|---|
| author | Mats Joe Bordacconi Martin Vinæs Larsen |
| author_facet | Mats Joe Bordacconi Martin Vinæs Larsen |
| author_sort | Mats Joe Bordacconi |
| collection | DOAJ |
| description | 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 t -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. |
| first_indexed | 2024-04-13T22:51:14Z |
| format | Article |
| id | doaj.art-82120fd44a3d42a7a721c8d983deca72 |
| institution | Directory Open Access Journal |
| issn | 2053-1680 |
| language | English |
| last_indexed | 2024-04-13T22:51:14Z |
| publishDate | 2014-08-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Research & Politics |
| spelling | doaj.art-82120fd44a3d42a7a721c8d983deca722022-12-22T02:26:11ZengSAGE PublishingResearch & Politics2053-16802014-08-01110.1177/205316801454809210.1177_2053168014548092Regression to causality: Regression-style presentation influences causal attributionMats Joe BordacconiMartin Vinæs LarsenHumans 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 t -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.https://doi.org/10.1177/2053168014548092 |
| spellingShingle | Mats Joe Bordacconi Martin Vinæs Larsen Regression to causality: Regression-style presentation influences causal attribution Research & Politics |
| title | Regression to causality: Regression-style presentation influences
causal attribution |
| title_full | Regression to causality: Regression-style presentation influences
causal attribution |
| title_fullStr | Regression to causality: Regression-style presentation influences
causal attribution |
| title_full_unstemmed | Regression to causality: Regression-style presentation influences
causal attribution |
| title_short | Regression to causality: Regression-style presentation influences
causal attribution |
| title_sort | regression to causality regression style presentation influences causal attribution |
| url | https://doi.org/10.1177/2053168014548092 |
| work_keys_str_mv | AT matsjoebordacconi regressiontocausalityregressionstylepresentationinfluencescausalattribution AT martinvinæslarsen regressiontocausalityregressionstylepresentationinfluencescausalattribution |