'Beliefs and social media spread of disinformation'
(AsPredicted #78270)

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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?
It is predicted that individuals will be more likely to contribute to the spread of disinformation when it supports an issue-related belief. Therefore, Hypotheses 1 and 2 are that:
H1: Individuals who have lower trust in government handling of CV19 will report a greater likelihood of contributing to the spread of disinformation that undermines the government than those with higher trust.
H2: Individuals who have higher trust in government handling of CV19 will report a greater likelihood of contributing to the spread of disinformation that supports the government than those with lower trust.

It is also predicted that individuals will be more likely to judge spreading disinformation that supports an issue-related belief as more morally acceptable. Hypotheses 3 and 4 are therefore that:
H3: Individuals with lower trust in the government will report the sharing of disinformation that undermines government as more morally acceptable than those with higher trust in the government.
H4: Individuals with higher trust in the government will report the sharing of disinformation that supports government as more morally acceptable than those with lower trust in the government.

Finally, it is predicted that moral judgements surrounding the spread of a specific category of disinformation will mediate the relationship between related beliefs and spreading the same category of disinformation. Therefore, Hypotheses 5 and 6 are that:
H5: Moral judgement of sharing 'government undermining' disinformation will mediate the relationship between low trust and increased likelihood of spreading 'undermining' disinformation.
H6: Moral judgement of sharing 'government supporting' disinformation will mediate the relationship between low trust and increased likelihood of spreading 'supporting' disinformation.

3) Describe the key dependent variable(s) specifying how they will be measured.
For H1, H2, H5 and H6 the dependent variable will be the reported likelihood that participants would contribute to the social media spread of two specific categories of disinformation ('favourable' and 'unfavourable' towards the UK government). The present study will be trialling a scale that incorporates methods of contributing to and reducing the spread of disinformation on social media specifically.
The dependent variable for H3 and H4 will be participant's moral judgements of spreading these two categories of disinformation, measured from 'not at all acceptable' to 'completely morally acceptable'.

4) How many and which conditions will participants be assigned to?
This is a correlational research study. Participants will be asked to rate false or misleading content from two categories of false or misleading content from social media that has previously been pre-tested for allocation purposes. The first contains three items that undermine the UK government in some way, while the second contains three items that support the UK government.

To measure beliefs around trust, participants will also complete the Citizen Trust in Government Organisation scale from Grimmelikhuijen, S. & Knies, E. (2019). Validating a scale for citizen trust in government organisations. https://doi.org/10.1177/0020852315585950

They will also complete the COVID-19 Perceived Risk Scale from Yıldırım, M. & Güler, A. (2020) Factor analysis of the COVID-19 Perceived Risk Scale: A preliminary study, Death Studies, https://doi.org/10.1080/07481187.2020.1784311

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
H1 and H2 will be tested using multiple regressions, with citizen trust, perceived COVID risk, age and gender as predictors of the social media spread of each disinformation 'theme'.
H3 and H4 will also be tested using multiple regressions with citizen trust, perceived COVID risk, age and gender as predictors. However, moral acceptability will be the dependent variable.
H5 and H6 will be tested using mediation analysis, with moral acceptability mediating the relationship between citizen trust and social media spread. Additionally, where age and gender are found to be significant in H1-H4 analyses then they will be included as control variables.
Additional exploratory analysis is also anticipated.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
Data will be screened prior to analysis, with the following exclusion criteria applied for removal of responses:
1. Declining consent.
2. Not meeting the recruitment criteria – must be based in England, use social media on a regular basis (e.g. more than once a month) and be over 18 years of age.
3. An implausible completion time, defined by 2SD faster than (below) the mean completion time as would suggest inauthentic responding.
4. Any responses flagged as problematic by Qualtrics' proprietary screening software.

Furthermore, the following criteria will be applied for exclusions during the main analyses:
5. Zero variance between item responses in either the Citizen Trust and COVID-19 Risk Perception scales.
6. If suspicious patterns of responding are detected that may require further removal of participants, then analysis will be reported both with and without said participants.

Any participants who have missing data on the Citizen Trust scale or COVID-19 Risk Perception scale will not be included in analysis where that variable is used.
Where gender is not recorded as either M or F, participants will be excluded only from analyses that specifically involve gender.
For Social Media Spread scale and Moral acceptability responses, if participants have missing data for a specific 'type' of disinformation (e.g. 'Favourable' or 'Unfavourable' towards the government) they will be excluded from that group of analyses only.

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.
To ensure enough power for the mediation analyses (H5 and H6), sample size planning was conducted using MedPower (Kenny, 2017). A minimum effect size of β = .2 is thought to be the minimum effect size that would be practically significant in social science research (Ferguson, 2009). To detect β = .2 at 80% power, 250 participants would be required. This would also cover the number of participants required to test H1-H4. Again, using Ferguson's (2009) recommended minimum of r2 = .04, 191 participants would be needed to have 80% power. Allowing for data screening exclusions, the target sample size is 280 participants.

Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532–538. https://doi.org/10.1037/a0015808
Kenny, D. A. (2017, February). MedPower: An interactive tool for the estimation of power in tests of mediation. https://davidakenny.shinyapps.io/MedPower/.

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)
A focus of this study will be to trial a scale to understand how individuals contribute to the digital spread of a social media post. Reliability of this scale will be tested using Cronbach's Alpha.

Additional exploratory analysis will also occur. These may use other demographics that are collected in the study, for example participants' voting intentions.

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