Forecasting the Results of Experiments: Piloting an Elicitation Strategy

Last registered on September 08, 2020

Pre-Trial

Trial Information

General Information

Title
Forecasting the Results of Experiments: Piloting an Elicitation Strategy
RCT ID
AEARCTR-0005211
Initial registration date
December 26, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
January 03, 2020, 5:16 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
September 08, 2020, 1:15 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Primary Investigator

Affiliation
University of California, Berkeley

Other Primary Investigator(s)

PI Affiliation
University of California, Berkeley
PI Affiliation
Australian National University

Additional Trial Information

Status
Completed
Start date
2019-12-18
End date
2020-01-09
Secondary IDs
Abstract
This study involves collecting forecasts of results of three field experiments. We randomly vary four features when eliciting forecasts: (1) small versus large reference values in an example preceding the predictions; (2) whether forecasts are in standard deviations or raw units; (3) slider versus text-entry responses; and (4) small versus large slider bounds.
External Link(s)

Registration Citation

Citation
DellaVigna, Stefano, Nicholas Otis and Eva Vivalt. 2020. "Forecasting the Results of Experiments: Piloting an Elicitation Strategy." AEA RCT Registry. September 08. https://doi.org/10.1257/rct.5211
Experimental Details

Interventions

Intervention(s)
(1) small (0.1 SD) versus large (0.3 SD) reference values in an example preceding the predictions; (2) whether forecasts are in standard deviations or raw units; (3) slider versus text-entry responses; and (4) small (+-0.5 SD) versus large (+-1.0 SD) slider bounds.
Intervention Start Date
2019-12-18
Intervention End Date
2020-01-09

Primary Outcomes

Primary Outcomes (end points)
Forecasts of experimental results for three studies preliminarily accepted as Registered Reports to the Journal of Development Economics.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We collect forecasts of the results of three experiments preliminarily accepted (before October 2019) that publicly posted their proposals, but that had not yet released any experimental results. We draw on a pool of academics, researchers, and practitioners. Consenting participants provide predictions for results of up to three studies. At the start of the survey, participants are randomized across the four conditions described in the “Interventions” section above.
Experimental Design Details
Randomization Method
Randomization takes place automatically when the survey is initiated.
Randomization Unit
Randomization is at the individual level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We plan to collect forecasts from at least 80 individuals.
Sample size: planned number of observations
We plan to collect forecasts from at least 80 individuals.
Sample size (or number of clusters) by treatment arms
Our main treatments will have N/2 participants, where N is our total sample size.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Committee for the Protection of Human Subjects, University of California, Berkeley
IRB Approval Date
2019-11-20
IRB Approval Number
2019-10-12690
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
Yes

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Abstract
Forecasts of experimental results can clarify the interpretation of research results, mitigate publication bias, and improve experimental designs. We collect forecasts of the results of three Registered Reports preliminarily accepted to the Journal of Development Economics, randomly varying four features: (1) small versus large reference values, (2) whether predictions are in raw units or standard deviations, (3) text-entry versus slider responses, and (4) small versus large slider bounds. Forecasts are generally robust to elicitation features, though wider slider bounds are associated with higher forecasts throughout the forecast distribution. We make preliminary recommendations on how many forecasts should be gathered.
Citation
DellaVigna, S., Otis, N. and Vivalt, E., 2020. Forecasting the Results of Experiments: Piloting an Elicitation Strategy. AEA Papers and Proceedings (Vol. 110, pp. 75-79).

Reports & Other Materials