'Changing misconceptions about the effects of rent control.' (AsPredicted #44055)
Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 4 authors.
Pre-registered on 07/04/2020 01:55 AM (PT)
1) Have any data been collected for this study already? No, no data have been collected for this study yet.
2) What's the main question being asked or hypothesis being tested in this study? Our first main hypothesis is that communicating scientific evidence about a specific economic policy through a refutational text is more effective to dispel popular misconceptions about this policy than a non-refutational text.
We will test this hypothesis for the misconception that imposing a price ceiling on rents would make more housing available.
As a second research question, we will test the role of a specific participants’ cognitive reflection trait on the misconception and on the change of belief. Our hypothesis is that higher cognitive reflection values will induce a higher positive change on the misconception.
3) Describe the key dependent variable(s) specifying how they will be measured. We will measure, on a five level scale, participants’ degree of agreement with the following statement: “Establishing rent controls, such that rents do not exceed a certain amount of money, would increase the number of people who have access to housing facilities.” We will do this before and after participants have read either the refutation text or the control text, thus obtaining a measure of opinion change.
The key dependent variables are i) the participant’s response change, in the control and in the treatment groups, and, ii) opinion changes across conditions, to assess the sign and size of the causality effect of the refutation text
4) How many and which conditions will participants be assigned to? There will be two conditions: the refutation text (treatment) and the control text.
One half of the participants will be randomly assigned to the refutation condition and the other half will be in the control condition.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. Statistical and Regression analysis.
To test for the first hypothesis we will formulate a baseline regression model where the dependent variable is the participant’s response change and the independent variable of interest is a dummy variable equal to one for those assigned to the treatment condition. This model will also control for participants’ characteristics such as education and age. The reason to include those controls is that assignment to conditions is balanced in terms of gender but not in terms of other characteristics.
To test for the second hypothesis we will build on the baseline model to add participants’ cognitive reflection measures and we will interact these measures with the treatment dummy.
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. We don't expect to exclude outliers; we plan to analyze them and test for sensitivity of results.
7) How many observations will be collected or what will determine sample size? No need to justify decision, but be precise about exactly how the number will be determined. We will collect 700 observations, 350 in each of the conditions. This sample size is determined by the goal to reach a statistical power close to 80% in case of a small effect size.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We will explore whether the type of text is correlated with indicators of comprehension.
