Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Causal Mediation Analysis in Politics× | Survey Experiment× | |
|---|---|---|
| Campo | Political Science | Political Science |
| Familia≠ | Regression model | Process / pipeline |
| Año de origen≠ | 2010 | 2011 |
| Autor original≠ | Imai, Keele, Tingley & Yamamoto (potential-outcomes causal mediation) | Experimental political science; synthesized by Diana Mutz |
| Tipo≠ | Causal-inference decomposition of a treatment effect into direct and indirect (mediated) components | Randomized experiment embedded in a survey |
| Fuente seminal≠ | Imai, K., Keele, L., & Tingley, D. (2010). A General Approach to Causal Mediation Analysis. Psychological Methods, 15(4), 309–334. DOI ↗ | Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528 |
| Alias | Causal mediation, Mechanism analysis, Direct and indirect effects, Potential-outcomes mediation | Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | Causal mediation analysis decomposes the effect of a treatment — often a randomized experimental manipulation, such as a campaign message or an information treatment — into the part transmitted through a specified intermediate variable, the mediator, and the part operating through all other pathways. Formalized in the potential-outcomes framework by Imai, Keele, Tingley, and Yamamoto, it defines the average causal mediation effect (ACME) and the average direct effect, makes explicit the sequential-ignorability assumption required to identify them, and supplies a sensitivity analysis for when that assumption fails. It lets political scientists move beyond 'does the treatment work?' to 'why does it work?' | A survey experiment embeds a randomized experiment inside a survey: respondents are randomly assigned to different versions of a question, frame, or stimulus, and their answers are compared to estimate a causal effect. By combining the internal validity of randomization with the representative samples and rich measurement of survey research, survey experiments — especially population-based ones — let political scientists draw causal inferences about how information, framing, or message attributes shape public attitudes and behavior. |
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