Author(s) This pre-registration is currently anonymous to enable blind peer-review. It has 2 authors.
Pre-registered on 2020/07/28 - 05:07 PM (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? The main question is if children distinguish awe experiences from neutral experiences.
3) Describe the key dependent variable(s) specifying how they will be measured. We will measure four key dependent variables:
The Small-Self Phenomenon: the intensity of participant’s diminished sense of self in comparison to the world
Sense of Connectedness: participant’s feeling of being part of everything in the world
Interest in Exploration: participant’s desire to explore unknown things in the world
Need for understanding: participant’s feeling of not knowing many things in the world
Each of the four dependent variables will be measured with graphical, 7-point Likert scales after the participant watches each video.
All four variables will also be measured with a binary selection comparison question at the end of the study after the participants watch each of the two videos (test and control videos, presented in randomized order).
4) How many and which conditions will participants be assigned to? This study comprises two within-subjects conditions: a test condition in which participants watch an awe-inspiring video involving scenes of grand natural landscapes and a control condition in which participants watch a neutral video involving scenes of a garden. All participants will watch both of the videos and will answer questions to measure the dependent variables after each video.
5) Specify exactly which analyses you will conduct to examine the main question/hypothesis. We may conduct an overall linear model to see if there is an interaction between condition and measure in predicting children’s responses to the two videos:
Lmer (response~condition*measure + (1|ID), data=data)
We’ll use the “drop1” function to see if the interaction term significantly improves model fit. We will drop the terms that are not significant from the model. If there is an interaction effect, then for each of the four dependent variables, we will conduct separate linear mixed models to see if condition significantly affects participants’ responses.
lmer(response~condition + (1|ID), data=data)
For the comparative measures, we will conduct binomial tests to see if children’s choices significantly differ from chance level (.50).
6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations. No participants will be excluded from outliers. Exclusion criteria consist of removing consent or assent, not being in the designated age range, not being a typically developing child, or does not complete study.
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 test a sample size n of 60 children between the ages of 4 and 9.
8) Anything else you would like to pre-register? (e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?) We will also explore the effects of age. If there is a significant age effect, we will examine younger and older children’s responses separately using a median split of age. We’ll also examine the correlations between children’s responses to the four DVs, as well as the effects of gender, race, and testing location.
