A Crash Course in Good and Bad Controls
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| Title: | A Crash Course in Good and Bad Controls |
|---|---|
| Language: | English |
| Authors: | Carlos Cinelli (ORCID |
| Source: | Sociological Methods & Research. 2024 53(3):1071-1104. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 34 |
| Publication Date: | 2024 |
| Sponsoring Agency: | National Science Foundation (NSF), Division of Information and Intelligent Systems (IIS) Office of Naval Research (ONR) (DOD) |
| Contract Number: | 2106908 N0001417S12091 N000142112351 |
| Document Type: | Journal Articles Reports - Descriptive |
| Descriptors: | Regression (Statistics), Robustness (Statistics), Error of Measurement, Testing Problems, Causal Models |
| DOI: | 10.1177/00491241221099552 |
| ISSN: | 0049-1241 1552-8294 |
| Abstract: | Many students of statistics and econometrics express frustration with the way a problem known as "bad control" is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this "crash course" accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1434877 |
| Database: | ERIC |
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| Abstract: | Many students of statistics and econometrics express frustration with the way a problem known as "bad control" is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this "crash course" accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models. |
|---|---|
| ISSN: | 0049-1241 1552-8294 |
| DOI: | 10.1177/00491241221099552 |
