#51416 | AsPredicted

As Predicted:Stock Flow Coronavirus JEP:A 2020-11-05 (#51416)


Created:       11/05/2020 10:24 AM (PT)

This is an anonymized version of the pre-registration.  It was created by the author(s) to use during peer-review.
A non-anonymized version (containing author names) should be made available by the authors when the work it supports is made public.

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?
Time series data can be presented as stocks (total number) or flows (change in number). We present data on COVID cases as either stocks (total number of cases) or flows (new cases) and see if this affects peoples judgments about current risk and their likelihood of engaging in risk-related behaviors (e.g., dining indoors at a restaurant).

3) Describe the key dependent variable(s) specifying how they will be measured.
There are two categories of DV (counterbalanced):
i) Risk judgment
"Imagining that you are currently living in this state: What do you think the current level of risk is with respect to coronavirus (COVID-19)?" (0 = no risk at all, 10 = very serious risk)"
ii) Behavior judgments:
13 questions "Imagining that you are currently living in this state: How likely would you be to do the following things?" (7 pt scale anchored by extremely unlikely and extremely likely):
1) Dine indoors at a restaurant?
2) Dine outdoors at a restaurant?
3) Take public transit, a cab, or an Uber/Lyft?
4) Invite a friend over to your house (and be indoors)?
5) Accept an invitation to a friend’s house (and be indoors)?
6) Go shopping (indoors) for non-essential items (like new clothes)?
7) Go to a gym or attend a workout class?
8) Send an elementary-school age child back to school (indoors and in person)?
9) Plan a get-together with friends/family over Thanksgiving?
10) Visit a doctor for a routine check-up?
11) Visit a dentist for a non-essential procedure?
12) Visit a salon/barber to get your hair cut?
13) Stock up on food/toiletries/cleaning products? <- reverse coded

4) How many and which conditions will participants be assigned to?
2 (SF: stock vs. flow) x 2 (FLOW: increasing flow vs. decreasing flow)

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
Everything will be analyzed using between subject OLS regression:
e.g., lm( Judgment ~ SF * FLOW )
Conditions will be effect coded (e.g., SF=stock -> -1, SF=flow -> +1)
We will analyze the behavior judgments both in aggregate (primary analysis, average across 13 measures) and also individually (secondary analysis to determine which behaviors are more or less affected)
We predict a significant interaction driven by a significant simple effect: When stocks and flows are both increasing (FLOW=increasing), we expect similar judgments of risk and likelihood of behaviors. When stocks are increasing but flows are decreasing (FLOW=decreasing), we expect to see lower judgments of risk and higher likelihood of engaging in risky behaviors in the SF=flow condition than the SF=stock condition.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
No exclusions

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.

Sample size = 600

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)

We will also collect exploratory demographic and psychographics measures:
1) What is your age?
2) What is your gender? (Male, Female, Other, Prefer not to respond)
3) Have you had coronavirus (COVID-19)?
4) Do you consider yourself “high risk” for coronavirus (COVID-19)?
5) What political party do you support? (Strong Democrat, Lean Democrat, Neither, Lean Republican, Strong Republican)
6) How would you describe the place you live? (Urban, Rural, Suburban, Other)