Changing the lens: The contingency of results from conjoint experiments on the outcome variable and the estimand
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conjoint experiments have become popular in political science for studying opinions, attitudes, and preferences on various issues. While the methodological literature discusses two dependent variables—forced-choice and rating outcomes—many studies continue to use (or report) only the former. Additionally, many studies primarily focus on analyzing causal quantities—Average Marginal Component Effects (AMCEs) and do not report the descriptive estimates—Marginal Means (MMs). This article highlights the contingency of results from conjoint experiments on the outcome variable and the estimand used. It calls for the inclusion of the rating outcome and for reporting the MMs alongside AMCEs. As the two outcome variables elicit distinct preferences, it explains how relying solely on one may obscure important findings and limit the insights gained from the experiment. This is particularly consequential for the analysis of MMs. These arguments are demonstrated by replicating and reanalyzing recently published conjoint studies. The article concludes with practical recommendations for applied researchers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it