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Affect is involved in many psychological phenomena, but a descriptive structure, long sought, has been elusive. Valence and arousal are fundamental, and a key question-the focus of the present study-is the relationship between them. Valence is sometimes thought to be independent of arousal, but, in some studies (representing too few societies in th...
Many nonbelievers may engage in supernatural thinking despite their statements to the contrary. Using belief in the afterlife as a test case, we examine, across two studies, the possible discrepancy between what people say they believe and how they reason implicitly. In Study 1, participants completed a mindfulness task during which a light went of...
The psychological research on conspiracy beliefs to date has focused predominantly on conspiracy beliefs in Western democracies. The current study sought to fill this gap by examining beliefs in conspiracy theories in a democratic society and a society in transition. British (N = 298) and Macedonian (N = 312) participants completed an online questi...
Although anecdotal evidence suggests that control-threatening situations are associated with an increase in conspiracy beliefs, existing research does not support this "compensatory control" hypothesis. In the current study, we test a more refined hypothesis: that the link between control threat and conspiracy beliefs is domain specific, such that...
While lack of control is one plausible explanation for conspiracy beliefs, the experimental evidence is mixed. We present two naturalistic studies that offer some limited support for the control hypothesis. In the first, Macedonians living in (North) Macedonia (N = 116) completed a conspiracy ideation scale immediately after a national referendum o...
It is widely believed that conspiracy theory beliefs are the product of perceived lack of control. However, to date there is mixed evidence, at best, to support this claim. We consider the reasons why conspiracy theory beliefs do not appear to be based in any straightforward way on control beliefs, interrogating existing findings and presenting new...
Perceived lack of control is widely believed to motivate, at least partly, belief in conspiracy theories. We question the theoretical foundations of this belief and meta‐analyze existing published and unpublished studies to assess the overall effect of lack of control on conspiracy beliefs. The overall effect was small and not statistically signifi...
Beliefs in conspiracy theories, generally considered to be a unidimensional construct, are associated with negative outcomes. The existing measures of conspiracy theory beliefs have number of shortcomings. We present the development of a novel measure of the tendency to believe in conspiracy theories and report the discovery of a second factor that...
This study aimed to look at possible ways to reduce beliefs in conspiracy theories and increase the intention to have a fictitious child vaccinated. One hundred and sixty participants answered an online questionnaire. Three groups were used. The control group did not read any text prior to answering whereas the two experimental groups read either o...
Cardinality principle refers to the fact that the last number tag used in counting determines the cardinality of a set. Macedonian kindergarten children were tested with the give-a-number task for understanding of this principle. It was found that Macedonian children, unlike their western counterparts, pass through an additional stage, 5-knowers, b...
There are four variables in my dataset. It is in the long format and looks something like this:
ID = participant ID,
Object = various objects such as a cup, a plate, a notebook,
Like= likability rating for each object, and
Use= rating for how likely the participant is to use the object.
My hypothesis is that Like will predict Use but I want to take into consideration that the relationship between Like and Use may vary for each object and for each participant. Is something like that feasible at all? Is this the right way to model this in R:
intercept<-gls(Use~1, data=mydata, method="ML")
randomIntercept<-lme(Use~1, data=mydata, random=~1|ID, method="ML")
randomInterceptLike<-update(randomIntercept, .~. + Like)
I have three dependent variables, and 10 predictors and I am analyzing the data with multivariate regression. However, I need to compare the model and the contribution of each predictor with another groups. Any ideas how to proceed?