विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| Difference-in-Means Estimator× | Survey Experiment× | |
|---|---|---|
| क्षेत्र | Political Science | Political Science |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1923 | 2011 |
| प्रवर्तक≠ | Jerzy Neyman (design-based potential-outcomes framework) | Experimental political science; synthesized by Diana Mutz |
| प्रकार≠ | Design-based estimator of the average treatment effect | Randomized experiment embedded in a survey |
| मौलिक स्रोत≠ | Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. New York: W. W. Norton. ISBN: 9780393979954 | Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528 |
| उपनाम | Neyman estimator, Design-based ATE estimator, Difference of sample means, Mean-difference treatment effect estimator | Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment |
| संबंधित | 4 | 4 |
| सारांश≠ | The difference-in-means estimator is the design-based workhorse for analyzing randomized experiments: it estimates the average treatment effect simply as the difference between the average outcome among treated units and the average outcome among control units. Rooted in Jerzy Neyman's potential-outcomes framework and central to modern treatments by Imbens and Rubin and by Gerber and Green, it is unbiased under randomization, comes with a conservative Neyman variance estimator, and supports exact randomization inference, requiring no model of how outcomes are generated. | 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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