The anatomy of Covid-19 related conspiracy beliefs: exploring their nomological network on a nationally representative sample Goran Knežević1, Ljiljana B. Lazarević1, Ljiljana Mihić2, Milica Pejović Milovančević3,4 , Zorica Terzić3,5, Oliver Tošković1, Olivera Vuković3,4, Jovana Todorović3,5, Nađa P. Marić3,4 1 University of Belgrade, Faculty of Philosophy, Serbia University of Novi Sad, Faculty of Philosophy, Serbia 3 University of Belgrade, Faculty of Medicine, Serbia 4 Institute of Mental Health, Belgrade, Serbia 5 Institute for Social Medicine, Belgrade, Serbia 2 Abstract Background: The outbreak of the Covid-19 pandemic was followed by the widespread proliferation of conspiracy beliefs (CBs) regarding the origin and harmfulness of the virus and a high level of hesitancy and resistance to vaccination. We aimed to test a series of hypotheses on the correlates of CBs and vaccination. Methods: The sample (N=1203), was based on a multistage probabilistic household sampling designed to represent the general population of Serbia. We investigated correlates of CBs and vaccination, including socio-demographic factors, personality (HEXACO + Disintegration trait), somatic health, stressful experiences during pandemics (i.e. Covid-19 related and other threatening events), and psychological distress. The subjects were randomly split into two approximately equal subgroups, enabling cross-validation of the findings. Based on the significant regression predictors found in the exploratory, the SEM model was tested in the confirmatory subsample. Results: The SEM model based on the finding in the first subgroup had excellent Goodness-ofFit indices. The most important correlates of CBs were Disintegration (proneness to psychoticlike experiences), low Openness, lower education, Extraversion, living in a smaller settlement, and being employed. The correlates of vaccination were older age, CBs, and larger places of living. Evidence on the role of both stressful experiences and psychological distress in CBs and vaccination was not found. Conclusions: The findings of moderately strong and robust (cross-validated) paths, leading from Disintegration to CBs and from CBs to vaccination were the most important ones. Our findings seem to emphasize the role of cognitive/perceptual processes in CBs and vaccination. Keywords: Covid-19 conspiracy beliefs; vaccination; personality; HEXACO, Disintegration; psychotic-like experiences & behavior; stressful experiences during pandemics; Covid stress syndrome PLEASE NOTE THAT THIS IS A PRE-PRINT, WHICH HAS BEEN SUBMITTED TO A JOURNAL. WHILE THIS VERSION OF THE DOCUMENT IS THE MOST COMPLETE AND MOST RECENT, IT SHOULD BE REGARDED AS A WORK IN PROGRESS. WE URGE ANY PARTIES INTERESTED IN THIS WORK TO CONTACT THE LEAD AUTHOR (LISTED ABOVE) FOR FURTHER DETAILS OR UPDATES. Corresponding author: prof. Goran Knežević, Faculty of Philosophy, University of Belgrade, Serbia Address: Čika Ljubina 18-20, 11000 Belgrade email: gknezevi@f.bg.ac.rs Word count: 4523 words + 64 words in a footnote Authors' contributions All authors - investigation, methodology, resources, review, and approval GK – Conceptualization, Writing original draft GK, LL, NM, LM – Methodology OT, GK - Software GK - writing the first and original draft and formal analysis GK, LL – Validation, Data curation GK – formal analyses All authors – Investigation, Resources, Writing- Review & Editing NM, MPM, LM, OV, ZT, JT - Supervision NM, LL, GK - funding acquisition NM, LL - project administration Conflict of interest statements: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Financial support: This work was supported by the Science Fund of the Republic of Serbia, grant number #7528289. The special research program on Covid-19 is financed by a World Bank loan through Serbia Accelerating Innovation and Entrepreneurship Project – SAIGE. Ethical standard: Ethical Committees of the Faculty of Medicine (1322-VII/31) and Faculty of Philosophy in Belgrade (02-33/273) and Faculty of Philosophy in Novi Sad (05-27, br.893/1) approved the protocol. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional guides on the care and use of laboratory animals. Transparency statements Preregistration statement: The study presented in this paper is based on the data collected as part of the project (grant number #7528289) which was preregistered at Clinical Trials. Trial Registration: NCB 04896983. https://clinicaltrials.gov/ct2/show/NCB04896983 Sampling statement: We describe how the sample size was determined for each study. We also disclose any data exclusions and explain the rationale for these exclusions. Open material statement: All materials that were used in the study are publicly available at https://osf.io/uzct6, except for M.I.N.I.7.0.2. which is under copyright. Open data statement: The data needed to reproduce the results are open and available at https://osf.io/e85dc/. The same data set was used in Maric et al. (2022). Reproducible script statement: We provide openly accessible data analysis scripts that allow to reproduce all reported results and include any information necessary to access these scripts. Analytical scripts are available at https://osf.io/pwtc9/. Effects statement: We report basic descriptive statistics, effect sizes, exact p-values, and 95% confidence (credible) intervals. Introduction The pandemic outbreaks are examples of the conditions over which we do not have effective control. Uncontrollable conditions may present a persistent threat to physical and psychological well-being which can, in turn, generate a sense of vulnerability and distress. Such stressful conditions may facilitate the tendency to accept spurious causal narratives that account for one’s distress. The idea that the events over which we do not have control may increase illusory pattern perception and irrational beliefs - including conspiracy beliefs (CBs) - seems to be not only convincing and plausible but even experimentally supported (e.g., Whitson & Galinsky, 2008). The Covid-19 pandemic outbreak was followed by the widespread proliferation of CBs regarding the existence, origin, and harmfulness of the SARS-CoV-2 virus (e.g., Freeman et al., 2022). Another conspicuous feature of the pandemic was the level of hesitancy and resistance to vaccination (e.g., Hyland et al., 2021; Murphy et al., 2021). Different factors and their interactions could play a role in the etiology of CBs: sociodemographics, somatic factors, personality, stress exposure, and psychopathology. Associations with general characteristics such as age, gender, or socioeconomic status are inconsistent, but abundant evidence suggests that people with lower income, education, and poorer physical health tend to hold stronger CBs (e.g., Freeman & Bental, 2017; van Mulukom et al., 2022). The findings on the role of personality are inconclusive - generally, they tend to be small and inconsistent, some of them even in opposite directions: reported correlations between Openness (O) and CBs were found to be positive (e.g., Swami et al., 2010) but also negative (Orosz et al., 2016). Thus, the recently reported meta-analytical findings on the zero-correlations between Big Five and CBs (Goreis & Voracek, 2019) were of little surprise. However, Bowes et al. (2021) - despite reporting only modest or weak effect sizes between personality and conspiratorial ideation - recently concluded that those prone to CBs show “a complex mixture of traits including distress, immodesty, impulsivity, and negative affect” (p. 11). The most consistent correlates of CBs were various constructs reflecting psychotic-like phenomena, such as schizotypy, paranoid ideation, magical thinking, unusual beliefs and experiences, and schizophrenia (Barron et al., 2018; Darwin et al., 2011; Dyrendal, et al., 2021; Escola-Gaskon, 2022; Oliver & Wood, 2014). Our position is that these various psychotic-like phenomena in the general population emanate from a stable dispositional tendency, conceptualized by our model of Disintegration as a trait-like disposition, separate from other personality traits (Knezevic et al., 2016; 2017; 2019; 2022; Lazarević et al., 2016). Disintegration predicts various constructs and behaviors, such as Post-Traumatic Stress Disorder and Depression (Knezevic, Savic, et al., 2022) or Narcissism (Lazarević, Knežević, et al., 2021), but also non-clinical ones, such as closed-mindedness (Knežević, Lazarević, et al., 2022), sociopolitical attitudes (Knezevic & Keller, 2022), militant extremism (Medjedovic & Knežević, 2019), health-related behaviors (Lazarević et al., 2021; Stanković et al., 2022), and quality of life during Covid-19 pandemics (Marić et al., 2021). We hypothesized that those high on Disintegration tend to see, cognize and feel connections among the events where there are none, i.e., they are prone to illusory pattern perceptions, making false-positive errors, to see patterns in randomness (apophenia). Our recent empirical evidence (Knezevic, Keller, et al., 2022) supports the connection between Disintegration and illusory pattern perception. Moreover, the relationships between illusory pattern perceptions and irrational beliefs, such as CBs, have already been demonstrated (e.g., van Elk, 2013; Van Prooijen et al., 2018). The negative correlations between the CBs and vaccination1 have been documented, but the majority of studies investigated intentions to get vaccinated, not being vaccinated as a behavioral measure (e.g., Jolley & Douglas, 2014; Ghaddar et al., 2022). It was also found that respondents fearing COVID-19 were more willing to get vaccines than those who had no fear (Sekizawa et al., 2022). We aimed to test a series of hypotheses on the correlates of both CBs and vaccination, including sociodemographics, somatic and mental health status, personality, and stressful experiences during pandemics. There are several important strengths of the study: representative sample enabling generalization to the general population; structured clinical interview, assessed in person, enabling control for the mental disorders that might be associated with CBs (see Method for details); vaccination status as a behavioral measure enabling investigation of the direct influence of the dispositional and contextual factors on health-related behavior; stressors related to the pandemic separated from other threatening events since the pandemic outbreak enabling to investigate net effects of both; specific symptoms of the Covid stress syndrome assessed separately from the symptoms of general depression and anxiety enabling to investigate the role of situation-specific distress on CB and vaccination. In line with the previous findings, we hypothesize that: H1 - Anxiety and depression are associated with CBs above sociodemographic data, somatic health, and personality traits; H2 Disintegration predicts CBs above sociodemographics and other personality traits; its role in CB is not due to psychosis or other types of psychopathology. H3 - Stressful experiences (either Covid-19 related or those not directly related to Covid-19) are associated with CBs above sociodemographics, somatic health, and personality trait; H4 - Marginalized people with lower socioeconomic status and lower education hold stronger CBs; H5 - The presence of somatic illnesses is associated with CBs above sociodemographics and personality; H6 - Proneness to CBs predicts whether a person will get vaccinated, above all other variables; H7 - As elderly people were most endangered by the SARS-CoV-2 virus, we expect that age predicts vaccination 1 According to the WHO, Serbia was one of the first countries with a widely rolled out vaccination programme (World Health Organization). At the time of our data collection (mid 2021), the strict restrictions had been imposed and lifted again, and substantial parts of the Serbian population had been vaccinated (about half, Šidjanin et al., 2021). The immunization facilities were able to handle walk-in vaccinations. status above all other variables, and H8 - Vaccination is predicted by the intensity of the Covid stress syndrome above all other variables. Method Sample and procedure The sample consisted of 1203 participants representing the Serbian general adult population selected by the multistage probabilistic household sampling method (for details see Marić et al., 2021; 2022), collected as part of a larger study, whose primary aim was to investigate the influence of Covid-19 related experiences on the mental health of the Serbian population. Data were collected by trained research assistants, employing face-to-face interviewing and computer-assisted or paper and pencil methods to administer inventories. Data are available at https://osf.io/e85dc/. Instruments and variables COVID-19 related conspiracy beliefs were assessed by a 6-item self-report measure, designed for this study with the Likert type answering (“Completely disagree”-1, to “Completely agree” 5). The items were created based on van Mulukom et al. (2022) and Biber et al. (2020). Vaccination was assessed using one item measure: Did you take the COVID-19 vaccine? (yes-1 and no - 0). Personality traits. Six basic personality traits were assessed using a 60-item HEXACO-PI inventory, 10 items per personality trait (Ashton & Lee, 2009): Honesty/Humility (H), Emotionality (E), Extraversion (X), Agreeableness (A), Conscientiousness (C), and Openness (O) - Serbian version (Medjedović et al., 2019). The Disintegration trait (D) was measured by a 20-item DELTA questionnaire (Knežević et al., 2017). Both instruments have a 5-point Likerttype response scale (1 - completely disagree, 5 - completely agree). A global measure of subjective social status - Social ladder (Adler et al., 1994). Participants used a 10-step ladder to indicate their standing in the community with which they identify relative to other people in their community (1- the lowest standing, 10 - the highest standing). Religiosity is assessed using the item “I consider myself a religious person” with the Likert-type answering format ranging from 1 - completely disagree, to 5 - completely agree. Other variables included (Marić et al., 2021 for detailed description): Sociodemographic variables - sex (binary coded as male and female), age (in years), education (in years of schooling), marital status (coded as married, single, divorced, widowed), employment status (responses: employed, unemployed, student, retired, and other), and the size of the settlement: small -up to 20000; medium - 20000 - 99999, and large - more than 100000 inhabitants. Current and past mental disorders. Observer-rated on the Mini International Neuropsychiatric Interview (MINI Standard 7.0.2.) (Sheehan et al., 1998) in 12 DSM-5 diagnostic categories classified into mood, anxiety, substance use, psychotic and any disorder categories (see Maric at al, 2022). Symptom severity. Patient Health Questionnaire-9 (PHQ-9, Kroenke et al., 2001) and General Anxiety Disorder-7 questionnaire (GAD-7, Spitzer et al., 2006) to assess depression and anxiety in the last two weeks. Both instruments have a 5-point Likert-type response scale (0 - completely disagree, 4 - completely agree). The COVID stress scale (CSS, Taylor et al., 2020) - Serbian translation (Mihić et al., 2022), was used to assess various dimensions of distress related to the COVID-19 pandemic (Covid stress syndrome) in the last two weeks. The answering format was a 5-point Likert type scale, from 0 completely disagree, to 4 - completely agree. Covid-19 related stressors - events that were a direct result of infection of the participant or a close relative, or events associated with an increased risk of Sars-CoV-2 infection (being positively tested on Covid-19; having a close relative positively tested on Covid-19; being in quarantine/self-isolation for a period of time, and having a lack of Covid-19 protective equipment at their workplace when needed). Other threatening events since the pandemic outbreak (not directly related to the infection) (LTE-12; Brugha et al., 1985). If participants reported that a parent, child, spouse, close friend, or other relative died due to Covid-19 or that their serious illness was related to Covid-19, it was considered only as a Covid-19 related stressor (i.e., having a close relative with Covid-19 infection) and not counted in this list. Somatic illnesses. Participants were asked if they have current physical disorders and if at least one somatic illness was listed the score was set to 1, otherwise, it was 0. All materials are available at: https://osf.io/uzct6/. Analytical strategy We report descriptive statistics for all variables used in the study, and Cronbach’s alphas and McDonald’s omegas (McDonald, 1999) as reliability estimates. Variables with skewness above 1 (Bulmer, 1979) are normalized using a rank-based normalization algorithm (Blom, 1958). Because our sample is large enough to detect even small effects, to test for stability and generalizability of results we opt for the cross-validation of our findings. We randomly split our sample into approximately two halves: the exploratory subsample serves to establish the predictive regression models for CBs and vaccination as the dependent variables, while the second serves to cross-validate results. Even by splitting our sample in two, we still have enough power to detect correlations as small as .12 with the power of .80, at a .05 alpha level. In the case of CBs as a dependent variable, multiple linear regression is used, while in the case of vaccination, logistic regression is used. Correlations among all variables are presented, for each subsample separately. Cross-validation on the second, confirmatory subsample is based on the variables found to significantly contribute to these two regression functions or to correlate with CBs and vaccination >= .20 in the exploratory subsample. However, on this occasion, the analytical tool is not the same: instead of regression models, Structural Equation Modeling (SEM) is used. In the SEM model, CBs and vaccination are endogenous, dependent variables between which the path from conspiracy to vaccination is postulated. The CBs variable is constructed as a latent, with its six items as observed variables. As CBs on 5G radiation and Bill Gates’ chipped vaccines share specificities related to the malevolent technological influence on humans, error covariance between them is assumed. As vaccination is a categorical variable, the Weighted Least Square Mean and Variance Adjusted (WLSMV) is used as the parameter estimation algorithm. Several goodness-of-fit (GoF) indices evaluating misspecification in the model (Root Mean Square Error of Approximation - RMSEA, Comparative Fit Index - CFI, and TuckerLewis Index - TLI) are examined. Hu and Bentler (1999) suggested CFI and TLI should be greater than 0.95 (values from 0.90 to 0.95 might be acceptable if other GoFs are satisfactory; Marsh et al., 2010), whilst RMSEA should be less than 0.06. Mplus version 7 software is used for SEM analysis (Muthen & Muthen, 2010). All other analyses will be performed in SPSS Version 21.0.0.1 or JASP 0.16.1. software. Results Endorsement percentages for the six conspiracy statements are presented in Table 1 (n=1203). Among 54% of respondents who endorsed CBs at least to some extent, 20% completely or predominantly agreed with those statements (alternatives 4 or 5). The strong convergence of the six conspiracy statements is documented by the high amount of variance explained by the first Maximum Likelihood (ML) unrotated factor (54.66%). Regarding psychiatric diagnoses (current and past), CBs were related to psychotic disorders only (Table 2). To test the part of the second hypothesis assuming that subclinical variation in proneness to psychotic-like experiences and behaviors are implicated in CBs - not psychotic symptoms appearing within the clinical picture of psychotic disorders - we excluded participants that were diagnosed with psychoses (n= 35) from the further analyses. Additionally, the correlation between the diagnosis of psychosis and CBs after controlling for D dropped to .03, p=.33, while the partial correlation between D and CBs remained significant after controlling for the psychotic disorder (.21 p<.001), pointing to the primacy of psychotic-like experiences over diagnosis. To cross-validate our findings on the relationship between CBs, vaccination, and their correlates, we randomly divided our remaining 1168 participants into two subsamples of approximately equal size (n1= 578, and n2= 590). Descriptives are presented in Table 3. The reliability of the scales to measure personality and psychopathological symptoms were acceptable. Correlation coefficients are presented in Tables S1 and S2 in the Supplementary material (https://osf.io/2uybq/). Correlations between age, CBs, and vaccination, and between D, education, and CBs were the largest (in the medium range) and found in both subsamples. We constructed two hierarchical regression models to predict CBs and vaccination status in the exploratory subsample (Table 4). The first model - based on multiple linear regression analysis - consisted of 41 predictors classified into 5 blocks: socio-demographics (gender, age, years of education, marital status, employment, size of settlement, SES, religiousness), personality traits (HEXACO+D), symptom severity (depressive symptoms, anxiety symptoms, and Covid stress syndrome), stressful events (Covid-19-related and other threatening events), health (somatic illness), and 20 interaction effects between stressful events and personality + symptom severity (two types of stressful events x seven personality traits + depression, anxiety, and Covid stress syndrome). This regression function - to predict CBs - reveals the following profile (order of the variables reflects the size of beta coefficients): prone to psychotic-like experiences and behaviors, close-minded, emotionally stable, living in the smaller settlement, extraverted, being employed, being female, less agreeable. None of the interaction effects significantly contributed to the regression function. The overall percentage of explained variance in CBs amounted to 18.7%. The second regression model - based on the multiple logistic regression analysis contained 37 predictors classified in 6 blocks: the first five were the same as in the previous model, while the last block contained CBs. This regression function - predicting vaccination was defined (again, the order of the variables reflects the size of beta coefficients) by older age, stronger CBs, higher Covid stress syndrome, and a large settlement. The overall percentage of explained variance in vaccination status was 28.4%. As the next step in our analyses, all variables having correlations with CB and vaccination >= .20 or significantly constituting these two regression functions were included in the SEM model tested on the confirmatory subsample. This model had excellent fit: (2(77) = 91.46; p= .124; 2/df = 1.19; RMSEA(90% C.I.)= .018(.000-.031); CFI= 0.981; TLI= .975). As variables that only weakly constituted previous regression functions (E, A, and gender in case of CBs, and Covid stress syndrome in case of vaccination) had insignificant paths to CBs or vaccination, they were fixed to zero. This model also had excellent GoFs: (2(81)= 95.10; p= .135; 2/df =1.17; RMSEA(90% C.I.)= .017(.000-.030); CFI= 0.981; TLI= .977; 2(4)= 3.54; p= 0.530) which means that fixing these four parameters to zero did not cause deterioration of the model. Therefore, this model (Figure 1) is considered to be the most adequate and parsimonious representation of the covariance structure among the variables. The variables that did not pass cross-sample validation were not considered to be a part of the model of the relationship between our predictive variable set, CBs, and vaccination. Discussion Key results The endorsement of Covid-19-related CBs by the Serbian general population seems to be widespread. On average, half of the respondents endorsed CBs at least to some extent (20% of the sample endorsed alternatives 4 or 5) regarding the existence, origin, and harmfulness of the SARS-CoV-2 virus, or the danger and downsides of vaccination. The fact that the first ML factor extracted from the six items explains over half of their total variance, reflects the strong generalizability of the Covid-19 related CBs, even though these statements cannot be simultaneously valid explanations of the Covid-19 situation. Thus, the belief that the virus is a hoax launched by powerful groups to make a profit contradicts other conspiracies assuming the existence of the virus: however this statement loads on the first ML factor as strongly as other statements. The strongest personality correlate of CBs was D, even when the participants with current or past diagnoses of psychosis had been excluded from the analysis. The advantage of this study is the opportunity to control for the mental disorders that might be associated with CBs. Our findings strongly support hypothesis H2. The crucial relevance of D in predicting CBs was based on the assumption of the existence of the common mechanism driving individual differences in both. Apart from D, the relationships between low O, X, and CBs proved to be robust. Anxiety and depressive symptoms neither correlated with conspiracy nor moderated the relationships between stressful events and CBs. Even the context-dependent distress (i.e., Covid stress syndrome) did not robustly predict CBs. On the contrary, we found a weak tendency that individuals who are less distressed by Covid-19 are more prone to CBs. Therefore, we did find evidence supporting H1. The H3 was also not supported: neither Covid-19-related stressors nor other threatening events since the beginning of the pandemic predicted CBs. Similar to distress, none of the personality traits moderated the relationships between stressors and either CBs or vaccination. The absence of the role of interactions between stressful experiences and either personality or psychological distress means that the role of stressful events in CBs is not even conditional on the potential vulnerabilities of a person. Of sociodemographic variables, lower education, living in small settlements, and being employed were related to CBs. Thus, our H4 is only partially supported: education plays a role in CBs, but not self-reported SES. It should be noted that some other correlations appeared, which did not remain significant either after multivariable analyses, i.e., after controlling for the effects of other variables (such as low A, C, and SES in the case of CBs, or gender and A in case of vaccination) or after cross-validation (variables without paths, Figure 1). Vaccination Young age and CBs were the most important predictors of low vaccination status, thus supporting H6 and H7. Additionally, more participants got vaccinated in the larger settlements. Importantly, of all dispositional predictors of vaccination proneness to CBs was not only the strongest one but the only relevant one. Of note, Covid stress syndrome was not a robust predictor of vaccination (it was an incremental predictor of vaccination in the exploratory, but this effect was not found in the confirmatory subsample). Therefore, H8 is not supported. Limitations The fact that this study was a part of a larger one, with the broader aim to investigate the influence of Covid-19 related experiences on mental health, inevitably influenced the design of the study presented here. It reflects, mostly, in limiting the number of measures of potential importance for CBs and vaccination, such as cognitive measures (cognitive reflection, thinking styles, abilities), measures tapping illusory pattern perception, or measures oriented toward a conspiracy mentality. Nevertheless, the number and types of variables within the study design enabled further insights into the nature of both CBs and vaccination. The second limitation is a cross-sectional design, making causal inferences less grounded. However, these associations are informative enough to strengthen some and weaken other explanations of CBs and vaccination. Additionally, vaccination status in our study was self-reported, not corroborated by the certificate. Nevertheless, there were no reasons to believe that the participants were insincere when they reported their vaccination status. Besides, the number of participants vaccinated in our study match the data on the population vaccination rate in the same period (September 2021), reported by Šiđanjin et al. (2021). Finally, the reasons for the absence of correlations between distress and vaccination could be in the fact that the scales tapping anxiety, depression, and the Covid stress syndrome require reporting symptoms in the last 7-14 days, creating thus a possibility that many of our participants who had been vaccinated prior to these two weeks have their heightened anxiety & depression already decreased in the time of testing. The same reasoning could be applied to CBs: even if negative affectivity had been increased during the outbreak of the pandemic, in the time of testing they might have decreased - due to the reestablishment of the sense of control through adherence to CBs that the negative affectivity might have initially caused. However, such an interpretation is diminished by the fact that those prone to CBs are found to have lower scores on E in our exploratory subsample, a stable predisposition to experience higher levels of distress. Interpretation The widespread endorsement of Covid-19 related CBs in Serbia is similar to what has been reported in the UK (Freeman et al., 2022), the USA (Shaeffer, 2020), Croatia (Tonković et al., 2021), and six Western Balkan countries (Bieber et al., 2020), based on the samples claimed to be representative for the general population. It seems that there is a general pattern: only onethird to a half of the population tend to completely dissociate themselves from various circulating Covid-19 CBs - meaning that the remaining large part of the population endorses conspiracy statements at least to a degree. Compared with Biber et al.’s (2020) findings (October 2020), these CBs in Serbia seem to be remarkably stable. The role of D in CBs is in line with the consistent reporting on the importance of the constructs capturing psychosis-like-experiences and behaviors in both general and specific CBs (e.g., Barron et al., 2018; Bowes et al., 2021; Dyrendal et al., 2021; Hart & Graether, 2018). Barkun (2003, p. 4) noted that everything is connected in the mind of a conspiracist: “Because the conspiracists’ world has no room for the accident, the pattern is believed to be everywhere, albeit hidden from plain view. Hence the conspiracy theorist must engage in a constant process of linkage and correlation in order to map the hidden connections''. It seems that the primacy of this underlying reality of deeply felt connections among the unrelated events, i.e., reality in which everything becomes salient and related (apophenia, Conrad, 1958) might influence the experience of rational and scientific interpretations of reality as being less convincing and appealing, perhaps distant, strange or dangerous, and, as such, being forced upon us by alienated powerful groups serving their murky interests (CBs). Therefore, we assume illusory pattern perception to represent the common mechanism driving individual differences in both – D and CBs. When it comes to personality, the combination of D and low O, together with C (and occasionally X, Ekehammar, 2004) was found to be consistently associated with socio-political attitudes, such as right-wing authoritarianism and ethnic prejudice, but also world views (the world as a dangerous place, especially) (Knežević & Keller, 2022). Understanding the role of personality in CBs can help to shed light on the reasons behind correlations of CBs and various constructs capturing socio-political attitudes. The relationships between CBs and right-wing authoritarianism (e.g., Đorđevic et al., 2021), or extremist political views no matter whether right or left (Imhoff et al., 2022) appear to be a robust finding. Our findings emphasize the precedence of cognitive/perceptual processes assumed to be shared by D and CBs over general psychological distress. Although intelligence might explain part of the low education - CBs correlation, we believe that important factors behind this relationship lie in the competencies that the educational process facilitates sui generis: analytical and critical thinking and evidence-based knowledge. Documenting the importance of both education and intelligence in CBs, Furnham and Grover (2021) explained the role of education by emphasizing skepticism, less religiousness, and less attraction to populist theories of those more educated. Of sociodemographic variables, living in the small settlements, and being employed were related to beliefs in conspiracy theories. Many variables found to be predictors of CBs by Freeman and Bentall (2017) such as religiosity, health status (presence of somatic illnesses), marital and socioeconomic status were not found to be related either to CBs or vaccination in our study. As the virus turned out to be more dangerous for the elderly (Yanez et al., 2020), it was understandable that the vaccination rate was higher in this segment of the population. Additionally, more participants got vaccinated in the larger settlements, which might be related either to the understanding that the higher mobility of people and the frequency of contact could facilitate the spreading of the viruses or to the availability and accessibility of vaccination points. Generalizability To the extent that Covid-19 CBs capture conspiratorial propensity, the generalizations of our findings (regarding the set of variables predicting Covid-19 CBs) on other CBs and other populations should be expected. Insights such as those that content and contradictions of Covid19 related CBs are of less importance as long as the wrongdoings of the powerful destructive groups behind the curtains are implicated (e.g., Lukić et al., 2019) - serve as a basis for this expectation. Conclusions As in other countries, Covid-19 CBs in Serbia were widespread in the second year of pandemics. The findings of moderately strong and robust (cross-validated) paths, leading from D to CBs and from CBs to vaccination were the most important ones. Apart from the D-CBs path, the robust paths from low O, low education, X, smaller place of living, and being employed to CBs were found. Apart from weaker CBs, vaccination was robustly predicted by age and larger place of living. The current negative affective states (anxieties, depression) were not associated with stronger CBs or vaccination. Moreover, distress captured by Covid stress syndrome was found to be negatively related to CBs, and inconsistently related to vaccination. Neither the main effect of stressful experiences nor their interactions with personality and distress were robustly associated with either of the two. The weak tendency of individuals with the diagnosis of psychosis to endorse more CBs appears to be a consequence of the stable individual differences in psychotic-like tendencies playing a role in both, psychosis and CBs. Roughly, our findings are in line with the following narrative: what looks like an outbreak and proliferation of CBs in the Covid-19 pandemic is likely a contextually induced content modulation of uniformly widespread generic conspiratorial thinking tendencies; these tendencies - directly influencing health-related behavior such as vaccination - are, in a considerable extent, manifestations of the mechanisms that are part of our stable and broad thinking/motivational/behavioral tendencies, primarily proneness to psychotic-like experiences & behaviors. References Adler, N. E., Boyce, T., Chesney, M. A., Cohen, S., Folkman, S., Kahn, R. L., & Syme, S. L. (1994). Socioeconomic status and health: the challenge of the gradient. American Psychologist, 49(1), 15. https://doi.org/10.1037/0003-066X.49.1.15 Ashton, M. C., & Lee, K. (2009). The HEXACO–60: A short measure of the major dimensions of personality. Journal of Personality Assessment, 91(4), 340-345. https://doi.org/10.1080/00223890902935878 Barron, D., Furnham, A., Weis, L., Morgan, K. D., Towell, T., & Swami, V. (2018). The relationship between schizotypal facets and conspiracist beliefs via cognitive processes. Psychiatry Research, 259, 15-20. https://doi.org/10.1016/j.psychres.2017.10.001 Barkun, M. (2003). A Culture of Conspiracy: Apocalyptic Visions in Contemporary America. Berkeley, CA: University of California Press. Bieber, F., Prelec, T., Jović, D. & Nechev, Z. (2020). The Suspicious Virus: Conspiracies and COVID19 in the Balkans. Policy Analysis. Balkans in Europe Policy Advisory Group. Blom, G. (1958). Statistical estimates and transformed beta-variables (Doctoral dissertation, Almqvist & Wiksell). Bowes, S. M., Costello, T. H., Ma, W., & Lilienfeld, S. O. (2021). Looking under the tinfoil hat: Clarifying the personological and psychopathological correlates of conspiracy beliefs. Journal of Personality, 89(3), 422-436. https://doi.org/10.1111/jopy.12588 Brugha, T., Bebbington, P., Tennant, C., & Hurry, J. (1985). The List of Threatening Experiences: a subset of 12 life event categories with considerable long-term contextual threat. Psychological Medicine, 15(1), 189-194. doi:10.1017/S003329170002105X Bulmer, M. G. (1979). Principles of statistics. Dover Books, New York. Conrad K (1958). Die beginnende Schizophrenie (The beginning of schizophrenia). Stuttgart, Germany: Thieme Verlag. Darwin, H., Neave, N., & Holmes, J. (2011). Belief in conspiracy theories. The role of paranormal belief, paranoid ideation and schizotypy. Personality and Individual Differences, 50(8), 1289-1293. https://doi.org/10.1016/j.paid.2011.02.027 Dyrendal, A., Kennair, L., & Bendixen, M. (2021). Predictors of belief in conspiracy theory: the role of individual differences in schizotypal traits, paranormal beliefs, social dominance orientation, right wing authoritarianism and conspiracy mentality. Personality and Individual Differences, 173, 110645. https://doi.org/10.1016/j.paid.2021.110645 Đorđević, J. M., Žeželj, I., & Đurić, Ž. (2021). Beyond general political attitudes: conspiracy mentality as a global belief system predicts endorsement of international and local conspiracy theories. Journal of Social and Political Psychology, 9(1), 144-158. https://doi.org/10.5964/jspp.5609 Ekehammar, B. & Akrami, N. Gylje, M., & Zakrisson, I. (2004). What matters most to prejudice: Big Five personality, social dominance orientation, or right-wing authoritarianism? European Journal of Personality, 18, 463-482. DOI: 10.1002/per.526 Escola-Gaskon, A. (2022). Impact of conspiracist ideation and psychotic-like experiences in patients with schizophrenia during the COVID-19 crisis. Journal of Psychiatric Research, 146, 135–148. https://doi.org/10.1016/j.jpsychires.2021.12.022 Freeman, D., & Bentall, R. P. (2017). The concomitants of conspiracy concerns. Social Psychiatry and Psychiatric Epidemiology, 52(5), 595-604. https://doi.org/10.1007/s00127-017-1354-4 Freeman, D., Waite, F., Rosebrock, L., Petit, A., Causier, C., East, A., . . . Lambe, S. (2022). Coronavirus conspiracy beliefs, mistrust, and compliance with government guidelines in England. Psychological Medicine, 52(2), 251-263. https://doi.org/10.1017/S0033291720001890 Furnham, A., & Grover, S. (2021). Do you have to be mad to believe in conspiracy theories? Personality disorders and conspiracy theories. International Journal of Social Psychiatry, https://doi.org/10.1177/00207640211031614 Ghaddar, A., Khandaqji, S., Awad, Z., & Kansoun, R. (2022). Conspiracy beliefs and vaccination intent for COVID-19 in an infodemic. PLoS ONE 17(1), e0261559. https://doi.org/10.1371/journal.pone.0261559 Goreis, A., & Voracek, M. (2019). A systematic review and meta-analysis of psychological research on conspiracy beliefs: Field characteristics, measurement instruments, and associations with personality traits. Frontiers in Psychology, 10, 205. https://doi.org/10.3389/fpsyg.2019.00205 Hart, J., & Graether, M. (2018). Something’s going on here. Journal of Individual Differences, 39, 229-237. https://doi.org/10.1027/1614-0001/a000268. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118 Hyland, P., Vallières, F., Shevlin, M., Bentall, R. P., McKay, R., Hartman, T. K., ... & Murphy, J. (2021). Resistance to COVID-19 vaccination has increased in Ireland and the United Kingdom during the pandemic. Public Health, 195, 54-56. https://doi.org/10.1016/j.puhe.2021.04.009 Imhoff, R., Zimmer, F., Klein, O., António, J. H., Babinska, M., Bangerter, A., ... & Van Prooijen, J. W. (2022). Conspiracy mentality and political orientation across 26 countries. Nature Human Behaviour, 1-12. https://doi.org/10.1038/s41562-021-01258-7 Jolley, D., & Douglas, K. M. (2014). The effects of anti-vaccine conspiracy theories on vaccination intentions. PLoS ONE, 9(2), e89177. https://doi.org/10.1371/journal.pone.0089177 Kneževic, G., & Keller, J. (2021). Proneness to Psychotic-Like Experiences: A Neglected Personality Correlate of Right-Wing Authoritarianism and Prejudice. Manuscript submitted to publication. Knežević, G., Keller, J., Lazarević, L. & Lukić, P. (2022). Apophenia as a candidate endophenotype of …what trait, actually? Manuscript in preparation. Knežević, G., Lazarević, L. B., Bosnjak, M., Purić, D., Petrović, B., Teovanović, P., ... & Bodroža, B. (2016). Towards a personality model encompassing a Disintegration factor separate from the Big Five traits: A meta-analysis of the empirical evidence. Personality and Individual Differences, 95, 214-222. https://doi.org/10.1016/j.paid.2016.02.044 Knežević, G., Savić, D., Kutlesic, V., & Opacic, G. (2017). Disintegration: A reconceptualization of psychosis proneness as a personality trait separate from the Big Five. Journal of Research in Personality, 70, 187-201. https://doi.org/10.1016/j.jrp.2017.06.001 Knežević, G., Lazarević, L. B., Purić, D., Bosnjak, M., Teovanović, P., Petrović, B., & Opačić, G. (2019). Does Eysenck's personality model capture psychosis-proneness? A systematic review and meta-analysis. Personality and Individual Differences, 143, 155-164. https://doi.org/10.1016/j.paid.2019.02.009 Knežević, G., Lazarević, L. B., Bosnjak, M., & Keller, J. (2022). Proneness to psychotic-like experiences as a basic personality trait complementing the HEXACO model-a preregistered cross-national study. Personality and Mental Health, 1-19. https://doi.org/10.1002/pmh.1537 Knežević, G., Savić, D., Vermetten, E., & Vidaković, I. (2022). From war-related trauma exposure to PTSD and depression: A personality perspective. Journal of Research in Personality, 96, 104169. https://doi.org/10.1016/j.jrp.2021.104169 Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ‐9: validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606-613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x Lazarević, L. B., Bošnjak, M., Knežević, G., Petrović, B., Purić, D., Teovanović, P., Opačić, G., & Bodroža, B. (2016). Disintegration as an additional trait in the psychobiological model of personality: Assessing discriminant validity via meta-analysis. Zeitschrift für Psychologie, 224(3), 204–215. https://doi.org/10.1027/2151-2604/a000254 Lazarević, L. B., Knežević, G., & Bosnjak, M. (2021). Does the disposition towards psychoticlike experiences incrementally predict grandiose narcissism? Representative evidence from Germany. Current Psychology, 1-12. https://doi.org/10.1007/s12144-021-02112-9 Lazarevic, L. B, Purić, D., Teovanovic, P., Knezevic, G., Lukic, P., & Zupan, Z. (2020). What drives us to be (ir) responsible for our health during the COVID-19 pandemic? The role of personality, thinking styles and conspiracy mentality, Personality and Individual Differences, 176. https://doi.org/10.1016/j.paid.2021.110771 Lukić, P., Žeželj, I., & Stanković, B. (2019). How (ir)rational is it to believe in contradictory conspiracy theories? Europe’s Journal of Psychology, 15, 94–107. https://doi.org/10.5964/ejop.v15i1.1690 Marić, N. P., Lazarević, L. B., Mihić, L., Milovancevic, M. P., Terzić, Z., Tošković, O., ... & Knežević, G. (2021). Mental health in the second year of the COVID-19 pandemic: protocol for a nationally representative multilevel survey in Serbia. BMJ Open, 11(9), e053835. doi: 10.1136/bmjopen-2021-053835 Marić, N. P., Lazarević, L. B., Priebe, S., Mihić, L., Milovančević, M. P., Šupić, Z. T., Tošković, O., Vuković, O., Todorović, J., & Knežević, G. (2022). Covid-19-related stressors and mental disorders and distress: A cross-sectional, nationally-representative, face-to-face survey in Serbia. Epidemiology and Psychiatric Sciences. Marsh, H. W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A. J., Trautwein, U., & Nagengast, B. (2010). A new look at the big five factor structure through exploratory structural equation modeling. Psychological Assessment, 22(3), 471-491. https://doi.org/10.1037/a0019227 McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum Associates. doi:10.1111/j.2044-8317.1981.tb00621.x Medjedović, J., Čolović, P., Dinić, B. M., & Smederevac, S. (2019). The HEXACO personality inventory: Validation and psychometric properties in the Serbian language. Journal of Personality Assessment, 101(1), 25-31. https://doi.org/10.1080/00223891.2017.1370426 Međedović, J., & Knežević, G. (2019). Dark and peculiar. Journal of Individual Differences, 40(2). https://doi.org/10.1027/1614-0001/a000280. Mihić, L., Terzić-Šupić, Z., Todorović, J., & Marić, N. P. (2022). The Serbian COVID-19 Stress Scale (Serbian-CSS) and vaccine acceptance: Is there a place for COVID-19-related distress in explaining attitudes towards vaccination?. Public Health, 205, 37-42. https://doi.org/10.1016/j.puhe.2022.01.015 Murphy, J., Vallieres, F., Bentall, R. P., Shevlin, M., McBride, O., Hartman, T. K., & Hyland, P. (2021). Psychological characteristics associated with COVID-19 vaccine hesitancy and resistance in Ireland and the United Kingdom. Nature Communications, 12(1), 29. https://doi.org/10.1038/s41467-020-20226-9 Muthén, L.K. & Muthén, B.O. (1998-2012). Mplus User’s Guide. Seventh Edition. Los Angeles, CA: Muthén & Muthén. Oliver, J. E., & Wood, T. (2014). Medical conspiracy theories and health behaviors in the United States. JAMA Internal Medicine, 174(5), 817-818. doi:10.1001/jamainternmed.2014.190 Orosz, G., Krekó, P., Paskuj, B., Tóth-Király, I., Bőthe, B., & Roland-Lévy, C. (2016). Changing conspiracy beliefs through rationality and ridiculing. Frontiers in Psychology, 7, 1525. https://doi.org/10.3389/fpsyg.2016.01525 Schaeffer, K. (2020). A look at the Americans who believe there is some truth to the conspiracy theory that COVID-19 was planned. Pew Research Center. https://www.pewresearch.org/fact-tank/2020/07/24/a-look-at-the-americans-whobelievethere-is-some-truth-to-the-conspiracy-theory-that-covid-19-was-planned/ Sekizawa, Y., Hashimoto, S., Denda, K., Ochi, S., & So, M. (2022). Association between COVID-19 vaccine hesitancy and generalized trust, depression, generalized anxiety, and fear of COVID-19. BMC Public Health, 22(1), 1-17. https://doi.org/10.1186/s12889-02112479-w Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., ... & Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59(20), 22-33. Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of Internal Medicine, 166(10), 10921097. doi:10.1001/archinte.166.10.1092 Stanković, S., Lazarević, L., & Knežević, G. (2022). The Role of Personality, Conspiracy Mentality, REBT Irrational Beliefs, and Adult Attachment in COVID-19 Related Health Behaviors. Studia Psychologica, 64, 26-44. https://doi.org/10.31577/sp.2022.01.837 Swami, V., Chamorro‐Premuzic, T., & Furnham, A. (2010). Unanswered questions: A preliminary investigation of personality and individual difference predictors of 9/11 conspiracist beliefs. Applied Cognitive Psychology, 24(6), 749-761. https://doi.org/10.1002/acp.1583 Šiđanin, I., Njegovan, B. R., & Sokolović, B. (2021). Students’ Views on Vaccination against COVID-19 Virus and Trust in Media Information about the Vaccine: The Case of Serbia. Vaccines, 9(12), 1430. https://doi.org/10.3390/vaccines9121430 Taylor, S., Landry, C. A., Paluszek, M. M., Fergus, T. A., McKay, D., & Asmundson, G. J. (2020). Development and initial validation of the COVID Stress Scales. Journal of Anxiety Disorders, 72, 102232. https://doi.org/10.1016/j.janxdis.2020.102232 Tonković, M., Dumančić, F., Jelić, M., & Biruški, D. Č. (2021). Who believes in COVID-19 conspiracy theories in Croatia? Prevalence and predictors of conspiracy beliefs. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.643568 van Elk, M. (2013). Paranormal believers are more prone to illusory agency detection than skeptics. Consciousness & Cognition, 22, 1041–1046. https://doi.org/10.1016/j.concog.2013.07.004 van Mulukom, V., Pummerer, L., Alper, S., Cavojova, V., Farias, J. E. M., Kay, C. S., ... & Zezelj, I. (2022). Antecedents and consequences of Covid-19 conspiracy beliefs: A systematic review. Social Science & Medicine. https://doi.org/10.1016/j.socscimed.2022.114912 Van Prooijen, J. W., Douglas, K.M., & de Inocencio, C. (2018). Connecting the dots: Illusory pattern perception predicts belief in conspiracies and the supernatural. European Journal of Social Psychology, 48, 320–335. https://doi.org/10.1002/ejsp.2331 Yanez, N. D., Weiss, N. S., Romand, J. A., & Treggiari, M. M. (2020). COVID-19 mortality risk for older men and women. BMC Public Health, 20(1), 1-7. https://doi.org/10.1186/s12889-020-09826-8 Whitson, J. A., & Galinsky, A. D. (2008). Lacking control increases illusory pattern perception. Science, 322(5898), 115-117. https://doi.org/10.1126/science.1159845 7.6 29.6 13.3 16 61.1 6.1 23.9 5.2 3.7 42.2 5.9 33.8 10.0 8.1 59.6 6.1 25.0 5.6 43.6 6.2 26.0 12.8 11.4 37.0 5.6 28.0 14.6 14.8 46.2 6.2 27.7 3.7 9.9 5 - Completely agree 2 - Partially disagree 33.6 3 - Neutral 1 - Completely disagree Covid-19 related conspiracy beliefs Pharmaceutical companies created and spread the COVID-19 virus so they could sell their drugs and vaccines It seems that COVID-19 symptoms are related to the 5G network radiation. US army developed the COVID-19 as a bioweapon. Bill Gates created the chip (tracking device) and it is injected along with the COVID-19 vaccine. COVID-19 is a hoax developed by powerful groups to gain money. COVID-19 was created deliberately to reduce the world population. Average endorsement percentage accross the statements Percentage of participants with at least one statement endorsed in each of the answering category Percentage of participants with at least one statement endorsed in the answering categories 4-5 Percentage of participants with at least one statement endorsed in the answering categories 3-5 Percent of participants with at least one statement endorsed in the answering categories 2-5 4 - Partially agree Table 1. Conspiracy beliefs items - endorsement percents 9.9 72.5 21.6 59.2 30.4 26.3 45.1 73.0 78.1 Loadings (1th ML unrotated factor) .73 .66 .73 .71 .80 .80 Table 2. Conspiracy beliefs, vaccination status and four groups of psychiatric diagnoses, current or past Conspiracy beliefs Groups of psychiatric diagnoses ANY DISORDERa MOOD DISORDERS (major depressive episode, suicidality) ANXIETY DISORDERS (panic disorder current or past; generalized anxiety disorder, agoraphobia, social anxiety disorder, obsessive-compulsive disorder, and PTSD, all current SUBSTANCE USE DISORDERS (alcohol or drug substance use disorder, current) % with diagnosis Yes - 277 (22.9%) No - 926 (77.1%) Yes - 165 (13.7 %) No - 1038 (86.3 %) Yes - 60 (5.0 %) Mean (SD) 2.38 (1.09) 2.29 (1.06) 2.30 (1.05) 2.31 (1.07) 2.27 (1.09) Vaccinated Yes (49%)/ No (51%) 144 / 133 443 / 483 76 / 89 489 / 540 30 / 30 No - 1143 (95.0 %) 2.31 (1.06) 557 / 586 Yes - 96 (8.0 %) 2.44 (1.12) 46 / 50 No - 1107 (92.0 %) 2.30 (1.06) 541 / 566 Yes - 35 (2.9%) 18 / 17 2.83 (1.20)** PSYCHOTIC DISORDERS, No - 1168 (97.1%) 569 / 599 current and past 2.30 (1.06) ** Note: a - diagnostic assessment using M.I.N.I.7.0.2. standard, Sheean et al. (1998); *p<0.05; **p<0.01 Table 3. Socio-demographic, somatic and psychological characteristics of the samples Age Years in education Marital status (married/single/divorced/w idowed) Settlement (up to 20000/2000099999/100000 and more) Employment (% yes) Gender (% female) Current somatic illness (% yes) Vaccination status (% yes) Social ladder Religiosity Other threatening events since the pandemic outbreak, total number+ Covid-19 related stressors, total number+ COVID-19 related conspiracy beliefs Disintegration+ Honesty-Humility Emotionality Extraversion Agreeableness Conscientiousness Openeness Exploratory sample (n=578) SD Skew Kurt α 13.49 / / / 2.82 / / / Theoretical range 18-65 0-24 M 43.91 12.49 0-100 59.2/27.8 /6.9/6.1 / / / / 0-100 0-100 0-100 30.6/48.1 /21.3 58.5 50.0 / / / / / / / / / 0-100 0-100 1-10 1-5 33.2 49.7 5.46 3.79 / / 1.75 1.34 / / 0.00 -0.98 0-12 0.94 1.21 0-6 1.50 1-5 1-5 1-5 1-5 1-5 1-5 1-5 1-5 2.32 1.51 3.83 2.95 3.51 3.39 3.63 3.07 ω / / Confirmatory sample 2 (n=590) M SD Skew Kurt α 43.53 13.57 / / / 12.90 2.94 / / / ω / / / 61/26.1/ 7.5/5.4 / / / / / / / / / / / 27.8/46. 3/25.9 58.3 52.4 / / / / / / / / / / / / / / / / / 0.22 -0.21 / / / / / / / / 34.4 49.7 5.45 3.88 / / 1.78 1.25 / / .09 -1.07 / / .01 .17 / / / / / / / / 1.97 6.86 / / 1.00 1.29 2.50 12.57 / / 1.44 1.02 0.73 / / 1.59 1.42 .82 .00 / / 1.07 0.66 0.68 0.68 0.61 0.67 0.67 0.86 0.36 2.00 -0.25 -0.04 -0.25 -0.14 -0.17 0.01 -0.75 4.37 -0.61 0.10 -0.04 -0.32 -0.24 -0.55 .88 .93 .66 .65 .65 .66 .69 .74 .88 .93 .63 .65 .63 .66 .69 .77 2.32 1.46 3.86 2.95 3.53 3.35 3.65 3.06 1.07 0.55 0.70 0.69 0.61 0.67 0.65 0.85 .36 1.79 -.64 -.04 -.22 -.19 -.21 -.09 -.75 3.12 .49 .09 .08 .08 -.33 -.65 .87 .90 .69 .66 .64 .67 .67 .76 .88 .90 .69 .66 .65 .67 .67 .77 PHQ-9+ GAD-7+ Covid stress syndrome+ 0-27 0-21 0-144 3.25 2.07 16.63 4.03 3.29 17.89 2.42 2.70 1.69 7.91 9.35 3.76 .86 .89 .95 .86 .89 .95 2.96 1.97 15.55 3.35 2.81 16.84 1.69 2.16 1.71 3.41 6.16 3.73 .80 .85 .95 Note: + variables normalized; M - Mean; SD – Standard deviation; Skew - Skewness; Kurt - Kurtosis; α - Cronbach alpha; ω - Mc Donald's omega .82 .86 .95 Table 4. Multivariable relationships (Hierarchical linear regression) between five blocks of predictors and 1) Covid-19 conspiracy beliefs, 2) Vaccination status Covid-19 conspiracy beliefs Model Step 1 (Sociodemographic characteristics) Gender (males vs females) Age Years in education Marital status (married vs alone) Marital status (married vs devorced) Marital status (married vs widowed) Employment SES (social ledder) Settlement (1 - small, 2 - medium, 3 - large) Religiousness Step 2 (Personality: HEXACO + Disintegration) H E X A C O D Step 3 (Distress) Depressive symptoms (PHQ-9) ΔR2 0.069*** Vaccination status Beta t 0.11* 0.03 -0.09 0.05 -0.04 0.00 0.11** 0.08 -0.12** 0.01 2.38 0.52 -1.80 1.02 -1.00 0.00 2.63 1.84 -2.87 0.16 0.115*** Nagelkerke ΔR2 0.206** 95% CI for Exp (B) Exp B Lower Upper Wald 1.35 1.07*** 1.06 1.07 1.72 0.85 0.99 0.89 1.07 1.04 0.86 1.05 0.97 0.65 0.76 0.36 0.66 0.79 0.81 0.90 2.10 1.09 1.15 1.75 3.89 2.01 1.50 1.00 1.40 1.21 1.74 50.55 1.81 0.07 1.68 0.14 0.00 3.64 0.21 0.35 0.75 1.25 0.85 1.14 1.06 0.89 0.93 0.55 0.91 0.60 0.84 0.75 0.69 0.73 1.04 1.73 1.19 1.54 1.48 1.16 1.20 3.05 1.86 0.88 0.71 0.11 0.75 0.30 1.15 0.85 1.55 0.80 0.028 0.07 -0.15** 0.12** -0.10* -0.02 -0.18*** 0.29*** 1.49 -3.28 2.76 -2.32 -0.53 -3.91 6.21 0.001 0.009 0.02 0.39 Anxiety symptoms (GAD-7) Covid stress syndrome (CSS) Step 4 (Stress) C-19 related stressors Other threatening events since the pandemic outbreak Step 5 Somatic illness Step 6 (20 interaction terms) Step 7 Conspiracy beliefs Total -0.05 0.02 -0.84 0.44 0.001 0.65 1.04 1.28 1.59 0.28 5.30 0.007 -0.03 -0.73 0.80* 0.64 1.00 3.94 0.01 0.15 1.03 0.81 1.32 0.07 1.02 0.66 1.60 0.01 0.65*** 0.54 0.80 17.88 0.000 .000 0.01 0.000 0.91 1.29* 0.32 0.000 0.034** 0.187*** F(23,554)=5.52 0.284** Chi-square(24)=138.63 Note: ΔR2 - increase in R2; B - standardized regression coefficient; t - t-test; Nagelkerke ΔR2 - increase in Nagelkerke R2; Exp B odds ratio; Wald - Wald's statistic*** p < 0.001; ** p < 0.01; * p < 0.05; bolded. In Step 6, 20 interactions (personality & symptoms x C19-related & other stressors) were entered into equation (using stepwise method), but none of them remained in the equation. In all steps (except 6) enter method was used Figure 1. The final SEM model of the relations between sociodemographic variables (age, education, size of the place of living), personality (D, O, and X), and conspiracy beliefs (consp) and vaccination status. Boxes without paths represent variables whose paths are fixed to zero (they were significant predictors of conspiracy beliefs (CBs) or vaccination in the exploratory sample, but not in this, confirmatory sample): gender – gender; a – Agreeableness; e – Emotionality. CBs is modeled as a latent variable (consp) with six conspiracy statements as manifest variables: farma – Pharmaceutical companies created and spread the COVID-19 virus; usa_army – US army developed the virus as a bioweapon.; net_5g – COVID-19 symptoms are related to the 5G network radiation.; bgates – Bill Gates created the chip and it is injected along with the COVID-19 vaccine; hoax – The virus is a hoax developed by powerful groups to gain money; depopul – The virus was created to reduce the world population. Vaccination status is categorical variable (vaccinated/not vaccinated). The other manifest exogenous variables are d – Disintegration; o – Openness; x – eXtraversion; age – age in years; educ – years of education; employ – employment; settle – size of the place of living. Path (standardized partial regression) coefficients and loadings are indicated by the arrows between variables. All path coefficients were significant at least at the 0.05 level.