#15109 | AsPredicted

As Predicted:Study 1a: Forgotten Inference (#15109)


Created:       10/15/2018 08:06 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?
We predict that participants will make inferences about an unknown attributes based on correlation data between products (ie, probabilistic consistency). In contrast, when the same attribute is forgotten, we expect correlation data will be less used as an input to inference making. Instead, we expect inferences about a forgotten attribute based will be based on memory reconstruction which will result in an inference consistent with the valence of remember product information (ie, evaluative consistency).

Thus, we predict that the probabilistic consistency will drive inference when a target attribute is unknown whereas evaluative consistency will drive inferences when a target attribute is forgotten.

3) Describe the key dependent variable(s) specifying how they will be measured.
Attribute inference measured on a scale of 1-100

4) How many and which conditions will participants be assigned to?
Between subject conditions:

Inference condition forgotten: A target attribute value is shown in part 1 and then participants make an inference in part 2 where the attribute is now hidden
Inference condition unknown: A target attribute value is hidden in part 1 and then participants make an inference in part 2 where the attribute is still hidden



Within subject conditions:

Evaluative level condition high: The target bike performs high on most attributes.
Evaluative level condition low: The target bike performs low on most attributes.

Accessible Info condition positive: Accessible information shows a positive correlation between the missing attributes and known attributes
Accessible Info condition negative: Accessible information shows a negative correlation between the missing attributes and known attributes




5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
I will conduct an interaction analysis where the dependent variable is the target inference and the independent variables are Inference condition (IC), Evaluative level condition (EC), Accessible Info condition (AC), and ICxEC and ICxAC while control for the true value of the target attribute (when it was shown forgotten condition) and clustering standard errors on participant.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
I will exclude people that do not forgot the value of the target attribute between phase 1 and 2.

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.

I will recruit 400 participants in phase 1.

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

Nothing else to pre-register.