#19841 | AsPredicted

'Anxiety, Interpersonal Sensitivity, and Paranoia.'
(AsPredicted #19841)


Created:       02/21/2019 03:39 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?
1. Anxiety
State anxiety will be associated with increased harmful intent attributions (but not self-interest attributions) to partners when averaged across all trials within each partner.

2. State Anxiety x Paranoia
There will be an interaction between state anxiety and trait paranoia leading to increased attribution of harmful intent (but not self-interest attributions) to partners across all trials.

3. Interpersonal Sensitivity
High interpersonal sensitivity (as defined by Boyce & Parker, 1989) scores will be associated with increased harmful intent attributions (but not self-interest attributions) to partners when averaged across all trials within each partner.

High scores on subscales of ‘Fragile Inner Self’ and ‘Interpersonal Awareness’ (as defined by Boyce & Parker, 1989) will be associated with increased harmful intent attributions (but not self-interest attributions) to partners when averaged across all trials within each partner.

4. Trials-to-peak-decision
There will be an interaction between state anxiety and trait paranoia leading to a decreased number of trials before a high (>60) attribution score of harmful intent (but not self-interest attributions) to partners is triggered separately across unfair and fair dictators.


3) Describe the key dependent variable(s) specifying how they will be measured.
(1) Paranoia score (sum of scores for social reference and persecutory delusions subscales) recorded using the Green et al., Paranoid Thoughts Scale (Green et al., 2002) – paranoia score will be z-score transformed for all ordinal model where paranoia is an independent variable (see Analysis).

(2) Average Self-interest: continuous scores of subject inferences that dictator was motivated by desire to earn more averaged over each block of 6 trials. Average Self-Interest will be transformed in a 5-level ordinal variable for clmm models (see Analysis).

(3) Average Harmful-intent: continuous scores of subject inferences that dictator was motivated by desire to reduce the players bonus averaged over each block of 6 trials. Average Harmful-intent will be transformed in a 5-level ordinal variable for clmm models (see Analysis).

(4) Trials-to-peak-decision (Harmful-intent): trial number that a high (>60) harmful intent score is triggered by a participant.

(5) Trials-to-peak-decision (self-interest): trial number that a high (>60) self-interest score is triggered by a participant.

For each variable, the number of levels will depend on the distribution of the data. All levels will have > 20 observations


4) How many and which conditions will participants be assigned to?
Participants will first be sent the GTPS, Interpersonal Sensitivity Measure (IS; Boyce & Parker, 1989 – comprised of 5 subscales: Timidity (T), Interpersonal Awareness (IA), Separation Anxiety (SA), Fragile Inner Self (Fis), and Need for Approval (NA)), Penn State Worry Questionnaire (PSWQ; Meyer et al., 1990), and Trait Anxiety measure (TA; Spielberber et al., 1983). 7-days later they will then be sent the State Anxiety measure (SA; Spielberger et al., 1983) and the Helsinki Summit.

The Helsinki Summit version is the identical version listed in a previous preregistration (http://aspredicted.org/blind.php?x=8cj8zk)


5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
For all analyses, a model selection approach with model averaging will be used (MuMln packaged), with a delta set at <2 for top model inclusion. Input variables will be standardised / centred as appropriate. Zero-averaging will be used.

We will use cumulative link models (clm) and mixed models (clmm; Christensen et al., 2015). Mixed models allow random terms to be included to account for repeated measures.

Psychological factors associated with paranoia:
Model (i) will be a clm specified as follows: paranoia ~ SA + TA + IS + Worry + Gender + Age + Education

Prediction 1 & 2:
Model (i) will be a clmm specified as follows:
harmful intent (average) ~ SA + paranoia + dictator (fair/unfair/partially fair) + SA * paranoia + age + gender + (1|ParticipantID)

Model (ii) will be a clmm with identical explanatory terms except that 'self-interest (average) will replace 'harmful intent (average)'

Prediction 3:
Model (i) will be a clmm specified as follows:
harmful intent (average) ~ IS + paranoia + dictator (fair/unfair/partially fair) + IS * paranoia + age + gender + (1|ParticipantID)

Model (ii) will be a clmm specified as follows:
harmful intent (average) ~ IA + NA + SA + T + Fis + dictator (fair/unfair/partially fair) + age + gender + (1|ParticipantID)

Model (iii) will be a clmm with identical explanatory terms except that 'self-interest (average) will replace 'harmful intent (average)'

Prediction 5:
Model (i) Both harm intent and self-interest scores of participants will be set a value of 6 if they score 60 or over in their first trial, 5 if they score 60 or over by their second trial, 4 if they score 60 or more by their third trial, and so on. All trials following the threshold being reached will be coded as 0. Participants not reaching 60 for any trial will be coded 0 across all trials. Each of the following models will be conducted for fair dictators and unfair dictators separately. It will be a clm specified as follows:
Trialtrigger(harm intent) ~ SA + paranoia + dictator (fair/unfair/partially fair) + SA * paranoia + age + gender

Model (ii) will be a clmm with identical explanatory terms except that 'self-interest (average) will replace 'harmful intent (average)'


6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
We will include participants: - Aged between 18-65 - UK residents - Fluent in english – have not taken part in any online studies from our lab – have not received a diagnosis of a mental illness.

Participants that fail both comprehension questions on the task will be removed from our analysis.


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.

1000 participants will be recruited to take the initial GPTS, TA, Worry, and IS survey. These participants will be called back after a minimum interval of 7 days to take part in the Helsinki Summit task and to complete the SA. We aim at recalling as many of the original 1000 subjects as possible, but as a minimum we will aim for 500 subjects. To recall participants, we will first send an email to all participants who are eligible. Once the responses have slowed down to be < 10 per day, we will send another request email to the remaining subjects who have not responded. Once responses are < 2 per day, we will stop data collection.

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

Predictions 1-4: If the model fails to fit, we will instead run three separate models (clm) exploring responses to (i) fair; (ii) partially fair and (iii) unfair dictators - with the same explanatory terms as outlined above but without the 'participant id' random effect.