#47354 | AsPredicted

As Predicted:Categories of conspiracy thinking (#47354)


Created:       09/09/2020 01:34 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 availble 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?
-There are four distinguishable categories in conspiracy thinking: intentional harm targeting the self (1), incidental harm targeting the self (2), intentional harm targeting others (3), incidental harm targeting others (4).
-Paranoia will be most strongly associated with endorsement of category 1 conspiracy theories (CT), followed by category 2, then 3 and then 4.
-Endorsement of conspiracy theories is higher overall in individuals with high paranoia.
-Individuals scoring high in paranoia will endorse self-referential conspiracy theories to a greater extent than those scoring lower in paranoia.
-Endorsement is higher for CTs that are perceived to be popular with an in-group; this pattern is reduced in individuals scoring high in paranoia.


3) Describe the key dependent variable(s) specifying how they will be measured.
a. Paranoia score – measured by the persecution subscale and the self-reference subscale of the R-GPTS
b. Conspiracy ideation –measured by asking participants to rate the extent to which they endorse conspiracy theory items in a pre-designed conspiracy ideation questionnaire from 1-5 (Strongly disagree, disagree somewhat, neither agree or disagree, agree somewhat, strongly agree). This conspiracy ideation questionnaire has a total of 24 items: 4 items in each of the 4 categories above, as well as 8 items with higher specificity that fall under category 3 and 4 above (4 in each subcategory)
c. Perception of severity of CT: participants will be asked to rate how severe the harm described in the conspiracy theory would be if it were true, from 1-5 (Not at all – Extremely).
d. Perception of in-group popularity of CT: participants are asked whether they think that people similar to them are likely to believe in the CT (yes/no/unsure)
e. General conspiracy mindset – this will be measured by the questionnaire designed by Bruder et al., 2013.
f. Positive control theory ideation - participants will be asked to rate the extent to which they endorse statements that describe the positive version of each conspiracy theory, from 1-5 as in the CT ideation questionnaire.
g. Social and economic conservatism – measured by scale from Everett et al., 2013


4) How many and which conditions will participants be assigned to?
All participants will complete all parts of the experiment. They will first be asked a demographic questionnaire asking their age, gender, ethnicity, nationality. They then will complete the full R-GPTS and conspiracy ideation questionnaire (24 questions) and positive control questionnaire, order randomised between participants. To finish they complete the questionnaires for general conspiracy mindset and social and economic conservatism .

Participants will be recruited from the US via the platform prolific.co. They will be paid in line with minimum wage, and can earn a bonus for correctly answering 5 attention check questions that will be asked at random during the questionnaires.


5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
To test hypothesis 1 we will estimate a network involving paranoia (using both subscales of the R-GPTS each as a separate node) and 16 individual items (each included as a node in the network) from the conspiracy ideation questionnaire. These are the items that fall into categories 1-4 listed above, all pertaining to “general” specificity condition. The data set contains ordinal (conspiracy items) and continuous (paranoia score) variables so we will estimate the network using a mixed graphical model. We will use least absolute shrinkage and selection operator (LASSO) regularization with EBIC model selection (Eskamp and Fried, 2017; Eskamp, 2016) in order to provide conservative estimates and a sparse network. We will perform spinglass algorithm analyses to statistically identify clusters or “communities” of nodes in the estimated network. This analysis will allow us visually determine whether the conspiracy items clustered into the four groups laid out above.

To test hypothesis 2 we will examine which cluster each node representing paranoia (ie both subscales of the R-GPTS) may be included in. We will perform tests for significant differences between edge weights to test our prediction.

To test hypotheses 3-5 we will conduct a single cumulative link mixed model (clmm, Christensen, 2015) using an information-theoretic approach with multi-model averaging (Burnham & Anderson, 2002; Grueber et al., 2011):
Outcome variable: conspiracy ideation (endorsement)
Input variables: paranoia (R-GPTS persecution subscale score), target (me/others), target*paranoia, in-group popularity, in-group popularity*paranoia, age, gender, ethnicity, nationality, (1|participant ID), (1|CT).
This analysis only includes CT items with “general” specificity (categories 1-4 listed above).


6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
Participants who took less than 10 minutes to complete the study will be excluded from analyses. We will repeat the main clmm analysis excluding people who failed >1 attention check and report any qualitative differences.

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 aim at initially recruiting 1000 participants to take part in the study. After recruiting 1000 we will determine the proportion of the sample who scored over 75/120 in endorsement of the conspiracy ideation questionnaire – an average of 3.15 in response to each conspiracy theory. If l the proportion of the sample meeting this condition is less than 7% we will recruit more participants, until this criterion is met – in accordance with distribution of paranoid thinking in our previous studies (Saalfeld et al., 2018), with an upper limit of 2000 participants in total. Any participants recruited after the initial 1000 will only be included in the sample if they score over 75/120 in the conspiracy ideation questionnaire.

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

We will perform exploratory analyses on the the predictability of each node in the network, node centrality estimates (strength, betweenness, closeness and expected influence), edge-weight accuracy, stability of centrality measures.
We will re-conduct the main analyses on positive control questions, and note any qualitative differences to results on the conspiracy theory questions. We will re-conduct the main analysis, excluding all data where participants endorsed both a CT and its corresponding positive control.
We will test whether CT theme (from 4 possible themes) predicted endorsement, and severity ratings, and whether CT theme interacted with target type on endorsement.
We will test how endorsement of CTs in the conspiracy ideation questionnaire varied with general conspiracy mindset. We will test the prediction that paranoia associated with general conspiracy mindset.
We will repeat the main clmm analysis with the social reference subscale of the R-GPTS.
We will test whether strength of endorsement of conspiracy theories varies according to a) intentionality (intentional/incidental) and b) specificity, and whether paranoia interacts with these effects.
We will investigate whether having heard of the conspiracy before impacts endorsement.
We will test whether paranoia is associated with how severe the harm in each CT is perceived to be. We will also test whether paranoia interacts with severity on endorsement of CTs.
We will test our prediction that a) endorsement and b) perceived severity of CT increases with agent presence, and if this effect is exaggerated in paranoia.
We will test whether economic and social conservatism impacts CT endorsement, or moderates any effects from the main analysis.