Dispositional and ideological factor correlate of conspiracy thinking and beliefs

Jan Ketil Arnulf; Charlotte Robinson; Adrian Furnham

doi:10.1371/journal.pone.0273763

. 2022 Oct 26;17(10):e0273763. doi: 10.1371/journal.pone.0273763

Dispositional and ideological factor correlate of conspiracy thinking and beliefs

Jan Ketil Arnulf ¹, Charlotte Robinson ², Adrian Furnham ^1,^*

Editor: Goran Knežević³

PMCID: PMC9604007 PMID: 36288289

Abstract

This study explored how the Big Five personality traits, as well as measures of personality disorders, are related to two different measures of conspiracy theories (CTs)The two measures correlated r = .58 and were applied to examine generalisability of findings. We also measured participants (N = 397) general knowledge levels and ideology in the form of religious and political beliefs. Results show that the Big Five and ideology are related to CTs but these relationships are generally wiped out by the stronger effects of the personality disorder scales. Two personality disorder clusters (A and B) were significant correlates of both CT measures, in both cases accounting for similar amounts of variance (20%). The personality disorders most predictive of conspiracy theories were related to the A cluster, characterized by schizotypal symptoms such as oddities of thinking and loose associations. These findings were corroborated by an additional analysis using Latent Semantic Analysis (LSA). LSA demonstrated that the items measuring schizotypal and related symptoms are cognitively related to both our measures of CTs. The implications for the studying of CTs is discussed, and limitations are acknowledged.

Introduction

Conspiracy theories (CTs) are essentially the belief that the causes of most major social, political and economic events are due to a plot by multiple, evil, people with a selfish, political goal in mind [1]. It has been argued, but disputed, that they usually form part of a monological belief system [2] in the sense that people have a conspiracist worldview, or thinking style. This means that they accept and integrate new CTs on a wide range of issues, which can be reliably measured [3–5]. It has also been suggested that people accept often strange and outlandish CTs because they serve a psychological function for people who feel powerless, excluded or disadvantaged [1,2,6,7].

In many ways CTs could be seen as superstitious, magical, and paranormal beliefs which has attracted people, and consequent research interest, over the years [8,9]. The question posed is why people believe in CTs when there is no credible scientific evidence for them; that is what functions do they fulfil? [10]. One way of examining this issue is to examine individual difference correlates of CTs. It should be noted that belief in CTs and conspiracy thinking are used essentially synonymously as the latter is seen to lead to the former.

This study attempts to move the literature on CTs forward in three ways. First, we use two different CT questionnaires to check the generalisability of results. There are a number of different measures of general CTs as well as measures of very specific beliefs about conspiracies in science, business, politics etc [5]. Further, the study is a part replication of Furnham and Grover [11] but using additional individual difference variables. They used the 15 item measure devised by the Swami research group working in the area [4]. Second, we examine personality disorder (PDs) correlates of CT beliefs which has hitherto been ignored. There have been a number of studies examining the DarkTriad and CTs [12–16] but these examined only two of the many PDs recognised by recent editions of American and European psychiatric manuals [17]. Indeed, there are good theoretical reasons to believe that the other disorders (i.e. schizotypy) are more strongly related to CTs [18]. For example, belief in unlikely conspiracies might have more in common with schizotypal thought disorders [19] and paranoia than with the calculative cold-heartedness of anti-social typologies [20]. Third, we examine the association between general knowledge and the belief in CTs.

There are long lists of topics that the CT researchers have investigated, including medicine [21] commerce [22], and very specific events [23]. These keep constantly being “updated” as in the case of COVID-19 [24]. However, because it is assumed that people tend toward “conspiracist outlooks and world views”, various researchers have developed questionnaires which ask participants to what extent they accept a range of relatively well-known theories. The fact that they nearly all demonstrate high internal reliability (as measured by Cronbach’s alpha) is taken as indication of the propensity of some individuals to accept and endorse a wide range of theories and integrate them into their belief system. In this study, we examine two measures of CTs, using them to see if we can generalise our findings [2,4].

Over the last decade there has been dramatic rise in studies in this area by people from many disciplines including political science, psychology, and sociology [25–29]. Many studies have examined individual difference correlates of CTs including ability, ideology, and personality [30–32]. The COVID-19 pandemic has been a great impetus to research in this area [33–36]. One difficulty in reviewing and comparing them is that they have used different measures trying to assess belief in CTs.

Recently, in a study of over 11,500 people from East and West Europe and America Walter and Drochon [2] tested and confirmed number of hypotheses, using a 10-item generic scale of CTs used in this study. They tested a number of hypotheses: First, the education hypothesis namely higher the education level, the lower people’s propensity to conspiracy thinking. Second, the right-wing hypothesis: that the more people perceive themselves as positioned towards the right of the political spectrum, the higher their propensity to conspiracy thinking. Third, the ideological extremity hypothesis: which stated that people who perceive themselves as positioned on the ideological extremes of the political spectrum have a higher propensity to conspiracy thinking than respondents who perceive themselves in the centre of the political spectrum. Fourth, the magical thinking hypothesis: people who have a high level of magical thinking have a greater tendency to conspiracy thinking which is associated with people who feel powerless and excluded. Fifth, the representation within the political system hypothesis: people who do not feel represented within the political system are more inclined to conspiracy thinking than people who do feel represented within the political system. Sixth, the institutional trust hypothesis: people who have low levels of institutional trust are more inclined to conspiracy thinking than people who have high levels of institutional trust. Seventh, the economic insecurity hypothesis: people who feel insecure about their economic situation have a higher propensity to conspiracy thinking. They confirmed most of their findings noting that CT believers were more inclined to position themselves on the right of the political spectrum, engage in magical thinking, feel distrust towards public officials and reject the political system.

The relationship between demographic (sex, age, education), ideological factors (religion, politics), and conspiracist beliefs has been explored [32]. Those who endorse CTs misattribute a great deal of agency and intentionality to others where it is clearly inappropriate to do so [27]. Many suggest that education is the best way to reduce belief in CTs [37]. It has also been demonstrated that religious and superstitious people are more likely to endorse CTs [38,39]. As regards personality trait correlates Goreis and Voracek [40] in a meta-analysis of studies that used personality theories concluded that that neither trait Agreeableness, nor Openness-to-Experience, or the other personality factors were significantly associated with conspiracy beliefs if effect sizes were aggregated. More recent studies however suggest that these relationship warrant revisiting [41].

In a study highly relevant to this Furnham and Grover [18] asked 475 adults to complete measures of Belief in CTs [4], two personality disorders tests as well as a short intelligence test and two self-evaluations. Belief in CTs was correlated with nearly all PDs as well as the three established higher order clusters (A- odd and eccentric; B- dramatic, emotional or erratic disorders, C- anxious or fearful disorders). A series of hierarchical regressions showed five of the variables were significant which indicated that less intelligent participants, scoring higher on two PD clusters (Cluster A and B) but lower on Cluster C believed more in the CTs. The present study was a part confirmation of Furnham and Grover [18] with four exceptions: we used two CT measures; we used a different, more robust, measure of IQ; we also measured non-clinical personality variables; and we examined ideological correlates (political and religious) beliefs. We aimed to examine both clinical and non-clinical traits in the same study.

In this study we examine four sets of factors that have been hypothesised and shown to be related to CTs. Our primary aim is the attempt to replicate the findings on the same population but using different CTs measures, as well as determine the amount of variance explained by each set of factors. We chose three classic demographic factors: sex, age and education and hypothesised the latter would be significantly associated with belief in CTs. That is higher education would be negatively correlated with belief in CTs (H1). We also used a measure of intelligence to test the hypothesis that higher IQ test scores would be negatively correlated with belief in CTs (H2). Secondly, we measured ideology with items asking about political and religious beliefs. We attempted to replicate previous findings to test the hypothesis that more religious people would endorse CTs (H3) and more politically conservative/right-wing people would endorse CTs (H4).

Our third set of variables was personality. Studies that have examined trait correlates of CTs have shown mixed results, but where correlations are significant they are modest. We examine two hypotheses based on the previous literature: that trait Agreeableness will be negatively (H5) but Openness positively (H6) related to CTs.

Our fourth set of variables was the personality disorders (PDs). People with Cluster A personality disorders tend to experience major disruptions in relationships because their behavior may be perceived as peculiar, suspicious, or detached. People who have a personality disorder from Cluster B tend to either experience very intense emotions or engage in extremely impulsive, theatrical, promiscuous, or law-breaking behaviors. People with personality disorders in Cluster C tend to experience pervasive anxiety and/or fearfulness. We predict a positive correlation between endorsing CTs and Cluster A (H7) and Cluster B (H8).

Finally, we wanted to explore how the cognition involved in conspiracy theories is related to the clinical dimensions of personality. The clusters B and C of personality disorders are primarily related to the individual’s ability to regulate emotions in the relationships to other people. Cluster A however involves cognitive components of egocentric or magical thinking with odd or loose associations related to the cognitive peculiarities of psychosis in general and schizophrenia in particular. This type of thinking has shown itself to be detectable through digital text analysis [19,42,43], more specifically Latent Semantic Analysis (LSA). This technique has recently gained more interest as a method in psychology [44–46] as it allows a statistical comparison of cognitive content in texts, adding to the use of rating scales [47,48]. The thought patterns peculiar to schizophrenia and Cluster A conditions have been found to show up in statistically significant ways using LSA, predicting these conditions better than human judges [49]. For this reason, we will subject the item contents to LSA to shed light on the response patterns [50], an approach akin to what has earlier been found to work the diagnostics of personality and psychopathology in the schizotypal spectrum [19,47,49]. If cognitive peculiarities are indeed central to a propensity for believing in CTs we expect that the semantics of the conspiracy theories are most closely related to the semantics of the items measuring schizotypal PDs (H9). The technique itself will be explained in more detail below.

Method

Participants

In all 397 people took part in this study: 195 male, 199 female and 3 non-binary, They ranged in age from 19 to 71 with a mean of 39.9 years (SD = 11.63 yrs). In all 54% were graduates; 93% were British nationals, and 60.3% owned their own homes. They were all working and indicated their occupation which were very varied to include accountants, health workers and people in IT. Asked their company rank, 5.0% indicated they were the CEO, 4.2% directors, 22.2% managers and 68.7% employed. They also rated their beliefs on various 10 point scales: Religious (Not at all = 0 to Very = 10) 2.29 (SD = 2.90); Politics (Conservative = 0 to Liberal = 10) 5.55 (SD = 2.46).

Measures

Conspiracy Thinking [2], which henceforth will be referred to as “CT1”. This was a 10-item scale devised as part of the Conspiracy and Democracy project at the University of Cambridge. It consisted of 10 statements that are generic in nature and not connected to any specific societal, economic or political systems. The scale was administered to over 11,000 people and was examined for its psychometric properties. In this study the Alpha was .60, which is lower than the usual .70 cut-off for acceptability.

Belief in Conspiracy Theories (BCTI), which will henceforth be referred to as “CT2” [4]. This is a 15-item measure that describes a range of internationally-popular conspiracy theories. Participants rated their belief that each conspiracy was true on a 9-point scale, ranging from 1 (Completely false) to 9 (Completely true). An overall score was computed as the mean of all items, with higher scores reflecting greater belief in conspiracy theories. Scores on this measure have been shown to be one-dimensional [4] and correlate strongly with scores from a generic measure of conspiracist ideation (r = .88) [26]. In the present study, Cronbach’s α for the BCTI was .91.

The Mini-IPIP [51] This is a 20-item short form of the 50-item International Personality Item Pool—Five-Factor Model measure. The Mini-IPIP scales, with four items per Big Five trait, had consistent and acceptable internal consistencies across five studies. The scales showed a comparable pattern of convergent, discriminant, and criterion-related validity (Studies 2–5) with other Big Five measures. In this study the reliabilities were Extraversion (.85) Neuroticism (.76) Openness (.74) Agreeableness (.78) and Conscientiousness (.67)

General Knowledge Test [52] is an open-answer item questionnaire [53]. The test has been used in numerous studies mainly done by Lynn and his colleagues. Scores were computed by adding together all correct answers (1 = correct; 0 = incorrect). We decided to use a short version which comprised 10 items like “Who wrote 1984; What disease stops blood clotting? Which Italian designer was shot in Miami in 1997? In what game can you bid a grand slam? Which is the principal street for finance in New York?”.

Coolidge Axis-II Inventory–Short Form (SCATI) [54]. This 70-item self-report measure assesses 14 personality disorders, 10 from DSM-V, 2 from Cluster B of the DSM-IV-TR (Depressive and Passive Aggressive) and 2 from DSM-III-R (Sadistic and Self-Defeating). The SCATI has good internal scale and test-retest reliability [55]. It has been used to predict PDs in subclinical [56] and clinical [57] populations. Using the DSM-5 classification the three clusters were calculated: A (odd and eccentric, alpha = .73), B (dramatic, emotional or erratic disorders) (alpha = .72), C (anxious or fearful disorders, alpha = .73).

Procedure

Latent Semantic Analysis (LSA) is a prevalently used vector-based representation of meaning in language that can be used for computational purposes [58,59]. The general principle behind the technology is to sample texts from naturally occurring sources such as newspapers, books and other streams of text, generating a word database with hundreds of thousands of words occurring in prevalent contexts. These text samples are turned into semantic spaces through singular value decomposition (SVD), a technique akin to principal component analysis (PCA). Through this technique, it is possible to obtain stable numerical estimates on the likelihood of sequences of words used in the diagnosis of thought disorders [49] or how closely groups of words such as item texts or generated free texts assemble each other [60–62], for example how close a given text comes to a diagnostically recognized statement [47,63]. The numerical output of LSA is usually a cosine, where numbers approaching 1 indicate identical meaning between the compared texts, and lower numbers indicate disparate or unrelated meanings in the compared texts. Open implementations of LSA can be found in the statistical packages R [64] or Python [65], and as a simple interface on the open website www.lsa.colorado.edu.

Ethics permission was sought and received (CEHP/514/2017). Participants were recruited through Prolific.ac, an online participant database. Prolific was chosen over alternative online recruitment websites, due to its greater diversity of participants. We specified that people had to be employed. The survey took an average of 14 minutes to complete and participants were paid £2.00 after completing the survey. The usual inspection of the data was done at the end to look for irregularities (patterned results, excessive missing data, very short or long completion time) and very few were found. We have a standard procedure used in all studies.

Results

We first analysed results from the 10 item Conspiracy Thinking measure. Table 1 shows the correlational results. Seven correlations with the CT measure were significant indicating that higher scores on CT were associated with non-degree and less intelligent, more politically conservative, Disagreeable and Conscientious as well as being higher on Cluster A (with odd and eccentric beliefs) and Cluster B (being dramatic, emotional and erratic). Whilst the correlation with Conscientious is significant but modest it does appear at odds with the previous literature.

Table 1. Means, SDs and correlations between CT1, demographics, ideology, five-factor personality and the three clinical clusters.

	Mean/ Sum	SD	CT1	Sex	Age	Ed.years	GK	Relig.	Pol.	Agree.	Consc.	Extrav.	Open.	Neurot.	Cl A	Cl B
CT1	1.25	1.24
Sex	1.51	0.50	-.06
Age	39.88	11.62	-.07	-.02
Ed.years	1.46	0.50	.03	-.03	-.05
GK	6.95	2.43	-.12^*	-.07	.16^***	.02
Relig.	2.29	2.90	.07	.08	.13^*	-.06	-.04
Pol.	5.61	2.42	-.21^***	.03	-.15^**	.17^**	.10	-.17^**
Agree.	15.58	2.61	-.12^*	.33^***	.10^*	.04	.06	.14^**	.18^**
Consc.	13.42	2.95	.10^*	.00	.00	-.01	.06	.09	-.12^*	.04
Extrav.	11.08	3.43	.04	.03	.05	-.03	.09	.15^**	-.02	.25^***	.10^*
Open.	14.45	2.94	-.06	-.08	-.05	.08	.12^*	-.06	.23^***	.20^***	.07	.17^***
Neurotic.	11.95	3.29	.06	.26^***	-.23^***	.15^**	-.15^**	-.01	.12^*	.08	-.31^***	-.19^***	-.03
Cl A	27.59	7.00	.31^***	.00	-.19^***	.04	-.11^*	.03	-.06	-.19^***	-.20^***	-.29^***	-.03	.47^***
Cl B	34.75	7.41	.20^***	-.05	-.27^***	.08	.02	.04	.04	-.08	-.26^***	.15^**	.12^*	.43^***	.54^***
Cl C	30.26	6.70	.06	.08	-.24^***	.11^*	-.04	-.06	.14^**	-.09	-.23^***	-.39^***	-.07	.54^***	.73^***	.52^***

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Scores for the traits and PDs are summed; Sex: Male = 1, female = 2

*p < .05

**p < .01

***p<001.

We then performed a multiple hierarchical regression with CT and the criterion variable and four sets of variables as predictors: demography (sex, age, education and intelligence), ideology (religious and political beliefs); the Big Five traits and then the three PD clusters. Table 2 shows that four variables were significant indicating that higher CT scores were associated with not having a university degree, being more Conscientious, while having higher Cluster A and lower Cluster C scores. These accounted for just under a quarter of the variance.

Table 2. Regression results for CT1.

	B	Std. Error	Std. Beta	t
Sex	-.102	.129	-.042	-.787
Age	-.002	.005	-.025	-.471
School years	-.040	.014	-.145	-2.877^**
regious	-.000	.021	-.000	-.004
Politics	-.040	.026	-.084	-1.538
GK	-.031	.028	-.056	-1.099
Agreeableness	-.022	.027	-.047	-.803
Conscientiousness	.046	.022	.112	2.120^*
Extraversion	.027	.022	.078	1.222
Openeness	-.023	.022	-.057	-1.066
Neuroticism	.020	.023	.056	.877
Cluster A	.078	.013	.454	5.615^***
Cluster B	.017	.012	.109	1.485
Cluster C	-.058	.016	-.322	-3.629^***

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*p < .05

**p < .01

***p<001.

Last, we did the same hierarchical regression but using all 14 PDs rather than clusters. We recognize the obvious problem of multicollinearity, as correlations between these different measures are relatively high, as to be expected [54] The result was significant (F(25,332) = 6.21, AdjR² = .23 with three PDs being significant: Borderline (Beta = .18, t = 2.12, p < .05) Dependent (Beta =.-18, t = 2.54, p < .01) and Schizotypal (Beta = .25, t = 3.84, p < .01).

We repeated the same analyses on the other measure of CT. Table 3 shows the correlational results. Eight of the correlations were significant, showing that higher CT were associated with: Not having a degree, being more religious and politically conservative, being Extraverted and Neurotic, as well as having higher scores on Clusters A, B and C.

Table 3. Means, SDs and correlations between CT2, demographics, ideology, five-factor personality and the three clinical clusters.

	Mean	SD	CT2	Sex	Age	Ed.years	GK	Relig.	Politics	Agreeable.	Conscient.	Extrav.	Open.	Neurot.	Cl A	Cl B
CT2	42.43	20.87
Sex	1.51	.50	.04
Age	39.88	11.62	-.09	-.02
Ed.years	1.46	.50	.01	-.03	-.05
GK	6.95	2.43	-.07	-.07	.16^**	.02
Relig.	2.29	2.91	.19^***	.08	.13^*	-.06	-.04
Politics	5.62	2.42	-.15^**	.03	-.15^**	.17^**	.10	-.17^**
Agreeable.	15.58	2.61	-.02	.33^***	.10^*	.04	.06	.14^**	.18^***
Conscient.	13.42	2.95	.09	.00	.00	-.01	.06	.09	-.12^*	.04
Extrav.	11.08	3.43	.10^*	.03	.05	-.03	.09	.15^**	-.02	.25^**	.10^*
Open.	14.45	2.94	-.04	-.08	-.05	.08	.12^*	-.07	.23^**	.20^**	.07	.17^***
Neurot.	11.95	3.29	.10^*	.26^***	-.23^**’	.15^**	-.15^**	-.01	.12^*	.08	-.31^***	-.19^***	-.03
Cl A	27.59	7.00	.35^***	.01	-.19^**	.05	-.11^*	.03	-.06	-.19^**	-.20^***	-.29^***	-.03	.47^***
Cl B	34.75	7.41	.28^***	-.05	-.27^**	.08	.02	.04	.04	-.08	-.26^***	.15^**	.12^*	.43^***	.54^***
Cl C	30.26	6.7	.16^**	.08	-.24^**	.11^*	-.04	-.06	.14^**	-.09	-.23^***	-.39^***	-.07	.54^***	.73^***	.52^***

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*p < .05

**p < .01

***p<001.

We did the same regression: See Table 4, acknowledging the same problems. This indicated that less well educated, religious Conscientious, Extraverts with high scores on Cluster A and B, low on Cluster C were more likely to endorse the CT theories. The regression accounted for almost exactly the same amount of variance as in the other regression.

Table 4. Regression results for CT2.

	B	Std. Error	Std. Beta	t
Sex	1.495	2.239	.036	.667
Age	-.066	.093	-.038	-.718
School years	-.333	.242	-.068	-1.371
religious	.581	.370	.080	1.569
Politics	-.367	.456	-.044	-.805
GK	-.167	.490	-.017	-.341
Agreeableness	.195	.476	.024	.410
Conscientiousness	.967	.381	.134	2.536^*
Extraversion	.803	.383	.133	2.097^*
Openeness	-.425	.382	-.060	-1.112
Neuroticism	-.250	.407	-.039	-.615
Cluster A	1.345	.240	.449	5.593^***
Cluster B	.488	.208	.171	2.347^*
Cluster C	-.604	.277	-.192	-2.178^*

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*p < .05

**p < .01

***p<001.

Again, we did the same hierarchical regression but using all 14 PDs rather than clusters. The result was significant (F(25,332) = 5.42, AdjR² = .24 with three PDs being significant: Avoidant (Beta = -.28, t = 2.65, p < .01), Passive-Aggressive (Beta = .15, t = 1.93, p < .01) and Schizotypal (Beta = .27, t = 4.19, p < .01).

The two measures of CT (thinking and beliefs) correlate strongly (.58), but our predictor variables do not seem to part in major ways in their effects on predicting CT1 or CT2.

The clinical clusters remain the most important predictors of CT, rendering the other variables insignificant or unimportant. We therefore proceeded to analyse the semantic relationships between the 70 items of the SCATI and the 10 items of CT1 that explicate the conspiracy theories, a total of 80 items. The analysis was made on the general LSA service available at the website lsa.colorado.edu, using the doc-to-doc procedure with 300 factors, similar to the procedure used by Nimon & al. in 2016 [66]. The ensuing output yields a pairwise comparison of overlap in meaning between the 80 items, a symmetric matrix of 3,160 unique pairs that previous studies has shown to be predictive of psychometric characteristics [62,67,68]. This matrix, termed a “semantic similarity matrix” [67] can be viewed as representing the mere overlap in semantic meaning between the items, i.e., purely cognitive features. It does not contain any knowledge about respondent ratings or other features of the scales, but it is structurally comparable to the correlation matrix of the item scores [67].

Our first analysis aimed to check if the semantic information was sufficient to determine the factor structure of the items used in SCATI and CT1. We subjected the semantic similarity matrix to a principal component analysis with oblimin rotation, asking for a total of 15 factors (14 clinical scales plus the CT1). The analysis returned 15 factors with root mean square of the residuals (RMSR) being .03. Uniquely high factor loadings were identifiable for the CT1 items and 13 of the 14 clinical scales, the only scale failing to appear as separate from the other clinical scales was Histrionic. A t-test indicated that the identified unique factors were significantly different from the cross loadings (p < .001). We therefore concluded that the semantic properties of the scales were sufficient to identify the single scales and their relationships with CT1, see Table 5:

Table 5. Semantically generated principal component analysis.

Factor loading for each unique scale (“focus factors”) compared to cross-loadings (“orbiting factors”) and p-values for the differences.

Scale	Focus factor loadings	Orbit (cross-loadings)	P-value for the difference
ANTISOCIAL	.25	.06	.043
AVOIDANT	.11	.08	.696
BORDERLINE	.25	.07	.015
DEPENDENT	.22	.06	.069
DEPRESSIVE	.13	.08	.512
HISTRIONIC	.02	.09	.264
NARCISSISTIC	.15	.08	.234
OBSESS.-COMPULS.	.26	.07	.006
PARANOID	.31	.05	.001
PASSIVE-AGGRESSIVE	.23	.08	.023
SADISTIC	.31	.06	.001
SCHIZOID	.15	.08	.360
SCHIZOTYPAL	.30	.06	.002
SELF-DEFEATING	.31	.06	.001
CT1	.29	.03	.000
Full study	.22	.07	.000

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As shown in Table 5, the SCATI and the CT1 items stand out as semantically separate in the principal component analysis based on semantics alone. We next went to calculate the average semantic relationships between the clinical scales and our dependent variable, CT1. This is done by averaging the semantically generated cosines in all item-to-item relationships in the matrix. Since each SCATI scales consists of 5 items, their mutual relationships are made of 5*5 = 25 unique item pairs. The relationships between each SCATI scales and the CT1 likewise consist of 5*10 = 50 unique item pairs. The averages of these relationships are calculated and presented in Table 6, along with the empirically observed correlations based on our sampled responses:

Table 6. Semantic relationships (in cosines) between SCATI semantics and CT1, compared with the empirically observed correlations from human responses (bottom line).

	ANTI-SOC.	AVOID.	BORD.	DEPEN.	DEPRES.	HISTRI.	NARC.	OBSES.	PARAN.	PASSIV.	SADIST.	SCHIZOID	SCHIZO-TYP.	SELF-DEF.
AVOIDANT	.45
BORDERLINE	.47	.49
DEPENDENT	.45	.42	.43
DEPRESSIVE	.44	.50	.50	.44
HISTRIONIC	.40	.41	.41	.40	.43
NARCISSISTIC	.43	.47	.48	.42	.50	.40
OBSESSIVE	.52	.49	.51	.46	.47	.44	.46	\|
PARANOID	.53	.53	.52	.49	.51	.46	.50	.56
PASSIVE-AGGR.	.48	.50	.50	.46	.47	.42	.48	.52	.58
SADISTIC	.37	.35	.38	.33	.38	.32	.36	.37	.42	.37
SCHIZOID	.29	.33	.34	.30	.30	.28	.32	.30	.32	.29	.24
SCHIZOTYPAL	.43	.43	.41	.44	.41	.38	.44	.45	.51	.48	.32	.30
SELFDEFEATING	.38	.45	.40	.41	.49	.35	.47	.37	.44	.46	.32	.27	.39
*CT1*	.07	.06	.05	.06	.03	.04	.06	.05	.08	.07	.06	.04	.09	.05	r = .70 ^*
Empirical Corr. ^*	.24	.07	.19	.11	.14	.15	.25	.20	.29	.22	.24	.18	.41	.21	r = .70 ^*

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*The semantic relationships with clinical scales are correlated .70 (rank-order, p < .01) with the observed correlational equivalents.

Table 6 shows that the semantic characteristics of the SCATI items that are most strongly related to beliefs in conspiracy items also predict the order in which the SCATI scales are correlated with CT1. H9 is supported in that the semantic (i.e., cognitive) properties of the SCATI items are indicative of the strength of their correlations with CT beliefs. The semantic relationships between conspiracy theories and the two SCATI scales schizotypal and paranoia are significantly stronger (p < .05) than those of the other scales.

Discussion

This study set out to explore how demographics, education, ideology, normal personality traits, and indications of personality disorders are contributing to beliefs in conspiration theories (CTs). Multiple regression found a weak, negative relationship between educational level and CTs, giving modest support to H1. General knowledge (that we used as a proxy for intelligence) was not directly related to CTs and H2 was thereby not supported. Religious beliefs were correlated with CT2 but not CT1, and the effects of religiousness waned in multiple regression, disconfirming H3. Political belongingness did correlate significantly with both measures of CT, but in both cases the effects disappeared in multiple regression, disconfirming H4. The Big Five personality traits were also correlated with CTs but only conscientiousness and extraversion retained significance in the multiple regression models. This finding disconfirmed H5 and H6. The Personality Disorders did show up as main predictors of belief in both CTs. However, we predicted clusters A and B, whereas clusters A and C emerged as significant for both CT1 and CT2. Finally, the latent semantic analysis (LSA) showed a significant relationship between the cognitive properties of the PD scales and belief in CT, in particular for schizotypal disorder. H9 was thereby supported.

The pattern of confirmed vs disconfirmed hypotheses suggests that beliefs in conspiracy theories are related to ideology or general levels of knowledge in terms of superficial correlations, but that these relationships tend to blur when matched against traits measuring personality disorders. These effects should not be taken to indicate that all people who believe in CTs are also showing a personality disorder (PD). Rather, it is likely that personality factors, knowledge levels and ideologically conducive environments contribute to the adoption of CTs, where full-fledged embracement of these may take on the character of personality disorders or be exacerbated by underlying tendences towards such.

This interpretation is supported by the fact that our sample does not on average display elevated scores on the SCATI compared to the clinically established norms. More importantly, not all PDs seem to predispose people to the same degree of CT endorsement. The PDs most strongly related to conspiracy beliefs are the schizotypal and paranoid subtypes. These groups could be characterized with affinity to loose associations, cognitive oddities and propensities towards delusion. On the opposite side of the spectrum are the avoidant, depressive and histrionic disorders who might simply be scared of thoughts with conspiracy content and react with different defense mechanism to being exposed to such ideas.

These findings are in line with previous research on the cognitive characteristics of schizophrenic and schizotypal disorders and the diagnostic value of semantic algorithms in detecting such cognitive propensities [19,42,49].

It is thus possible that the manifest agreement on conspiracy theories in groups of people is caused partly by social psychological mechanisms that drive people together in social networks where these ideas are being proposed, and partly by individuals with personality disorders who are willing and capable of propagating such theories with great conviction. Such a propagation of CTs would be a two-thronged approach where vulnerable individuals lend credibility to unlikely ideas that penetrate the capacity for critical thinking in less disturbed but otherwise socially committed network members.

There are three interesting issues that arise from this study both methodological and theoretical. The first refers to the generalisability of findings given the measures used. Any researcher is being increasingly faced with an alarming increase in CTs as a range of global crises unfold [24] and may ask the question about the specificity of particular CTs. That is, are the causes, consequences and correlates of beliefs in any/all CTs unique to that CT or generalisable? We are one of very few studies which used more than one, though inevitably strongly positively correlated, measures of beliefs in CTs to check the reliability of our findings. Our findings seem to suggest that there are generalisable aspects to the CTs in the sense that the scales themselves have some internal consistency, they are substantially correlated between themselves, and they seem to be related along similar lines to variables such as ideology, education, and personality traits. Maybe in an even more substantially way, the conspiracy theories all seem related to the same type of cognitive structures as identified through the semantic algorithms. This analysis shows quite clearly the relationship of ideation and cognition between conspiracy theories, paranoia, and schizotypal thinking. This finding relates in an interesting way to previous findings on schizophrenia [19].

The second issue concerns the variance explained in the many studies of this kind [4]. Many researchers have noted that although they assessed a wide variety of individual difference measures (biography, demography, ideology, personality) they could not account for very much of the variance (often less than 10%) which was often inflated by method invariance. The question asked was what other individual difference factors, not assessed, may account for these beliefs? In this study we measured a number of individual variables which accounted for around 20% of the variance in both analyses. We believe that what is unique about this paper is the number of predictor variables that we used at the same time to explore correlates of beliefs in CTs.

The third issue, is how people integrate specific CTs into their general CT world view such that when asking them about a number of them, as done in this study, there is high alpha indicating internal reliability. In this study there was a difference between the two alphas: .60 vs .91. The relatively low alpha of the first Conspiracy thinking measure may have resulted from some relatively “extreme” ideas being tested such as alien interventions. Interestingly Walter and Drochon [2] found only one factor when they factor analysed their data set but did note that their results left room for at least two complementary approaches namely determinants of, and consequences of, generic conspiracy thinking vs specific individual conspiracy beliefs. It is possible that they are some theories such as the “ancient alien astronaut theory” which only very few are able to endorse and integrate into their conspiracist world view [69].

Limitations and suggestions for further research

Most of the psychological research in this area has confirmed the hypotheses about what sort of people are more likely to hold conspiracist beliefs. Further they offer plausible explanations about why they do so. Yet we know little about the process. For instance, how do people hear about CTs: do they seek out media outlets that endorse these theories such as very specific television channels and website? Do they join, or shun, groups of believers? How do they answer their critics, and how much have they had to do so? Which are their most central CT beliefs, and how well integrated are these with their socio-political world view?

It would also be interesting to note how CT beliefs are related to other superstitious, magical or paranormal beliefs as well as attitudes to science and rationality. For instance, are those interested in, and believers concerning astrology, more likely to accept CT? Are people more skeptical about conventional medicine and attracted to alternative medicine more interested in CT?

Like all others this study had limitations. The population was skewed towards better educated middle class people from one country. They may be less inclined to accept certain CTs, but possibly more receptive to others. It is clearly always best to try to have a large, diverse and representative population in such studies. Our test of General Knowledge (GK) was very short. Though we believe it to be both reliable and valid, we see that a more traditional and encompassing IQ test measuring g (general intelligence) could yield better and more credible information about the effect of cognitive ability on CTs. Further, we had very brief measures of religious and political beliefs which were related to CTs and it would be interesting to explore these associations with more comprehensive measures of those beliefs, as well as knowledge about the respondents’ level of commitment to, and involvement in their ideological communities. For instance membership of, and interaction with, particular religious and political groups, may say a lot about beliefs in CTs over time.

The internal reliability of one CT measure was lower than the usual acceptable cut-off of .7. Next, as always many of our correlations, even though significant were low. Those shown in Tables 1 and 3 are not Bonferroni corrected which means many are not significant. This shows yet again the search for individual difference variables which explain belief in CT remain unknown, and which may throw light on the psychological function of holding CTs.

Supporting information

S1 File

(SAV)

Click here for additional data file.^{(305.1KB, sav)}

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0273763.r001

Decision Letter 0

Goran Knežević

17 Feb 2022

PONE-D-21-40074Bright and Dark-Side Personality and Conspiracy ThinkingPLOS ONE

Dear Dr. Arnulf,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I have obtained reviews from two competent reviewers. Please carefully read their comments.

I will start with my own.

The hierarchical regression analysis of which results are presented in Table 9 is highly problematic. First of all, the way you had constructed dependent variable (factor with loadings on conspiracy variables, based on the factor analysis of the overall set of variables) was not appropriate. The reason is that it contains - to an extent – the variance of the variables that you were using as predictors later in this regression analysis (this is the reason why your multiple correlation is so inflated in this analysis). Moreover, the rationale for this analysis is totally unclear: you showed the results with conspiratorial thinking and conspiratorial beliefs separately (I see the logic in this – I guess to further check similarity of these two by projecting them in the space defined by the same predictor set), and then, you are showing practically the same thing with a variable that is some unfortunate composite of these two (and the rest of the variables included in this FA). I would highly recommend to remove this analysis, the text attached to it, as well as Figure 1.

A little can be achieved by stretching the notion of dark-of side personality on cluster A. Cluster A is implicated in conspiracy beliefs because it measures psychotic-like phenomena, not because of its darkness (if dark side of personality has anything in common with what is measured by dark triad or dark tetrad scales, i.e. malevolent personality). In other words, If you partial out schizotypy from Cluster A it would cease to predict anything conspiratorial (beliefs, thinking, mind-set). Cluster B (more related to the notion of dark personality) is obviously of secondary importance, even part of its relatedness to conspiracy is due to the correlation with Cluster A. Therefore, my recommendation would be to take into consideration the first suggestion of r#2 very seriously. I would recommend to rely on the usual use of the notion of darkness in this field. Your own results revealed much more peculiarity in conspiracy than darkness.

You did not find support neither for H2 nor H5. Please, be more careful with the interpretation of your own results.

Your explanation of the mediator role of political ideology between conscientiousness and conspiracy does not seem to be convincing: openness and agreeableness are much more implicated in political ideology than conscientiousness.

Variable "degree" is not explained (Years of schooling? Educational levels (if levels, how many)?). In regressions with CT1 and CT2 as Dvs, it looks like higher level of education predicts conspiracy, not the other way around. It is strange that your educational levels start with higher levels and end with lower levels (years) of education. Please, be careful with this - I suggest higher levels of this variable to reflect higher education level.

Minor – rename Table 1 as Table 2.

Please, avoid using term interaction in describing your H9, you did not test the interaction between personality and ideology.

Please, use the same labels throughout the article (e.g. BCTI but not CT2, or CT2 not BCTI, or use both).

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: No

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper presents findings from an online study of 397 community residents ages 19-71 most of whom were British in nationality. They completed an extensive battery of personality, political, and other demographic differences along with two measures of acceptance of conspiracies, a more general measure and one assessing belief in specific conspiracies. The two measures of conspiratorial thinking were moderately correlated (r=.58), which is about what one would expect given the respective levels of reliability (.60 and .91). Regression analyses using the various correlates produced similar profiles of associations, and a factor model placed the two measures in roughly the same location in a two-dimensional space, defined by political ideology versus measures of personality disorders.

The findings show that both types of measures of conspiratorial thinking are largely related in the same ways to various characteristics. There are more of those for the specific conspiracies, but that is not explained, and so I’m not sure there is much new here other than that. The authors suggest that they are testing the hypothesis that “personality and ideological beliefs interact to explain a common pathway toward CT.” (page 6). But I see no effort to test that hypothesis as all of the analyses involve tests of single correlates. There is evidence that after controlling for the personality disorders, the relation with political ideology disappears. But I don’t see how that tests the hypothesis as it is stated. Apparently, persons with those disorders in this sample are more likely to be on the conservative end of the political spectrum. The authors claim that their findings support the prediction, but I don’t how they can say that other than that is what they are reading into the pattern of correlates.

The paper would benefit a lot from a more discerning discussion of what the findings say and what questions they answer.

There is some confusion at the bottom of page 13, when the authors say that various characteristics were related to endorsement of CT theories: Extraverts with high scores on Cluster A and B, low on Cluster C were more likely to endorse CT theories.” But that is not what the regressions tell us. Those are independent predictors. And does CT include theories?

Reviewer #2: The paper is very well written, concise, and clear. The research problem is very relevant and actual and the strength of this study is in the involvement of different variables in the prediction of CT. However, there are some concerns and suggestions for improvement of the manuscript:

- The title is not appropriate since the authors explored basic and psychopathological traits and not traits that are referred to the bright and dark side of personality in common sense (Dark Triad, Light Triad). Maybe title such is: Dispositional and ideological factors of conspiracy thinking and beliefs would be more appropriate.

- I am not sure what insight we get with separate analyses on CT1 and CT2. The final analysis presented in Table 9 seems that subsumed previous results and it would be economic to keep only these results since two conspiracy measures are already highly correlated. In the introduction, the authors did not explain differences between CT1 and CT2, nor their different correlates, thus separate analysis is not argumented. If they want to keep it (with more elaboration and arguments for that in Introduction), then in the 1st step of prediction of CT1, CT2 should be entered, and vice versa.

The other concern in this part is regarding stepwise regression analysis which is very criticized and should be avoided. Instead, please do a regular, enter regression. If there is a problem with multicollinearity you could try to merge some variables in composite scores if that is justified.

- The other main concerns in regarding General Knowledge Test. It is not clear whether the authors shortened the test for this study or short version of the test is already validated elsewhere? Please report alpha for the used tests in the Method section and not in the Discussion. Furthermore, authors identify GKT with crystallized int. but the correlations between GKT with Openness are the same as with fluency test, while fluency test does not correlate sig. with Openness. I think that authors will need to be more careful regarding the conclusion about relations between crystall. int. and conspiracy theories since the used measure (GKT) and general knowledge as a construct per se is a construct between personality and intelligence. The conclusion should be more in line with what GKT really measures. Also, IQ in all tables and results should be replaced with GKT.

- The joint factor analysis (p. 14) is not argumented in Introduction and it is not in line with aims. Please delete this part.

- In discussion, there is not much explanation of the results, for example, why low N and high C are associated with CT and not A and O as expected?

Minor points:

- p.3 „Indeed, there are good theoretical reasons to believe that the other disorders (i.e. schizotypy) are more related to CTs than the two assessed in the dark triad [18].“ – please correct this because Dark Triad is subclinical and not clinical construct. The traits in DT are not considered disorders.

- there is a lack of reference in some cases, e.g., in a statement such as „based on the previous literature“

- title in Table 1 is missing and please add a note in all tables, what is CT1 and CT2

- I am not sure what variable type is a degree (binary or ordinal), please specify in the sample description. If it is binary, add in a note how it is coded. It is somewhat odd to state „not having a degree“ at p.13.

- p.14 please check this: Passive-Aggressive (Beta=..15,

- delete Fig. 1, everything is already stated in Table 9.

- p.19 authors stated „two studies“ but this is one study, corr. and reg. analysis belong to the one study

- p.17 „allegiance to an ingroup (through conscientiousness)“, allegiance to an ingroup is more Agreeableness and not Conscientiousness

- p.17 „Thus, for instance, less intelligent and well-educated extraverts and neurotics may be less or more prone to holding theories than stable introverts“ I am not sure how much is this realistic since education and int. are correlated.

- p.18 the suggestions for future studies are something that is already very well documented, e.g. „It would also be interesting to note how CT beliefs are related to other superstitious, magical or paranormal beliefs as well as attitudes to science and rationality.“

- since the authors stated „Those shown in Tables 1 and 3 are not Bonferroni corrected which means many are not significant“ the question is why they did not use p-correction already? In any case, it is recommended to use p-correction, thus authors should use it here.

**********

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PLoS One. 2022 Oct 26;17(10):e0273763. doi: 10.1371/journal.pone.0273763.r002

Author response to Decision Letter 0

30 Mar 2022

Subject: PLOS ONE Decision: Revision required [PONE-D-21-40074] - [EMID:2f14363b9625c539]

PONE-D-21-40074

Bright and Dark-Side Personality and Conspiracy Thinking

PLOS ONE

Dear Dr. Knezevic

RESPONSE: Thank you for email concerning the above paper and attaching reviewers comments. I note below how we have responded to each.

Your Comments

RESPONSE: THIS PART IS NOW DELETED ENTIRELY

RESPONSE: THE DARKNESS PART IS NOW LARGELY DELETED FROM THE DISCUSSION AND THE FOCUS IS ON THE CLINICAL SCALES AND CLUSTERS.

You did not find support neither for H2 nor H5. Please, be more careful with the interpretation of your own results.

RESPONSE: ALL HYPOTHESES ARE NOW RESTATED AND THEIR EMPIRICAL STATUS COMMENTED IN A WRAP-UP AT THE BEGINNING OF THE DISCUSSION.

RESPONSE: WE HAVE DELETED THIS PART OF THE ANALYSIS AND INSTEAD FOCUSED ON THE CONTRIBUTIONS OF THE PERSONALITY DISORDER CATEGORIES IN MULTIPLE REGRESSION.

RESPONSE: WE PREVIOUSLY USED A REVERSE CODED, DICHOTOMOUS VARIABLE. NOW USING THE NUMBERS OF YEARS OF EDUCATION IN THE EXPECTED DIRECTION (HIGHER NUMBERS = MORE YEARS OF EDUCATION).

Minor – rename Table 1 as Table 2.

RESPONSE: DONE AS REQUESTED

Please, avoid using term interaction in describing your H9, you did not test the interaction between personality and ideology.

RESPONSE: NOW DELETED AS SUGGESTED

Please, use the same labels throughout the article (e.g. BCTI but not CT2, or CT2 not BCTI, or use both).

RESPONSE: DONE AS REQUESTED

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When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

RESPONSE: HOPEFULLY THIS HAS BEEN DONE

2. Please amend your current ethics statement to address the following concerns: a) Did participants provide their written or verbal informed consent to participate in this study? b) If consent was verbal, please explain i) why written consent was not obtained, ii) how you documented participant consent, and iii) whether the ethics committees/IRB approved this consent procedure.

RESPONSE: WE USED THE STANDARD PROCEDURE FOR PROLIFIC. THE ETHICS COMMITTEE CONSENTED TO PARTICIPANTS AGREEING TO HAVE THEIR ANONYMISED RESPONSES AGGREGATED, ANALYSED AND RESPONSE: SUBSEQUENTLY PUBLISHED. WE HAVE DONE THIS WITH NUMEROUS STUDIES.

2. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

RESPONSE: WE CONSISTENTLY NOTE THAT IF ANYONE WISHES TO SEE THE DATA WE WILL SEND THEM AN SPSS FILE WITH THE VARIABLES EXPLAINED. THE FILE IS COMPLICATED AND HAS NUMBER OF VARIABLES THAT WE DID NOT ANALYSE OR REPORT FOR THIS STUDY

3. Please include a copy of Table 5 which you refer to in your text on page 14. [Note: HTML markup is below. Please do not edit.]

RESPONSE: TABLES REVISED AND EDITED.

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Reviewer #1:

This paper presents findings from an online study of 397 community residents ages 19-71 most of whom were British in nationality. They completed an extensive battery of personality, political, and other demographic differences along with two measures of acceptance of conspiracies, a more general measure and one assessing belief in specific conspiracies. The two measures of conspiratorial thinking were moderately correlated (r=.58), which is about what one would expect given the respective levels of reliability (.60 and .91). Regression analyses using the various correlates produced similar profiles of associations, and a factor model placed the two measures in roughly the same location in a two-dimensional space, defined by political ideology versus measures of personality disorders.

RESPONSE: A FAIR SUMMARY

RESPONSE: WE BELIEVE THAT AS WE HAVE RESTATED THE HYPOTHESES AND REWRITTEN MUCH OF THE DISCUSSION WE HAVE NEW AND INTERESTING FINDINGS

Apparently, persons with those disorders in this sample are more likely to be on the conservative end of the political spectrum. The authors claim that their findings support the prediction, but I don’t how they can say that other than that is what they are reading into the pattern of correlates.

RESPONSE: WE HAVE ATTEMPTED AN EXPLANATION OF THIS POINT

The paper would benefit a lot from a more discerning discussion of what the findings say and what questions they answer.

RESPONSE: WE BELIEVE THAT WE HAVE NOW DONE THIS

RESPONSE: THANK YOU. WE HAVE RE-VISTED AND CORRECTED THIS POINT

Reviewer #2:

The paper is very well written, concise, and clear. The research problem is very relevant and actual and the strength of this study is in the involvement of different variables in the prediction of CT.

RESPONSE: EXCELLENT NEWS

However, there are some concerns and suggestions for improvement of the manuscript:

RESPONSE: EXCELLENT SUGGESTION THANK YOU, NOW CHANGED

RESPONSE: WE BELIEVE THAT OPERATING WITH TWO MEASURES OF CT ADDS STRENGTH TO OUR CLAIM THAT THERE EXISTS A GENERLISABLE TENDENCY TO ADOPT CTS IN MANY FORMS, AND THAT THIS GENERALISABLE TENDENCY IS LINKED TO THE PSYCHOLOGICAL MECHANISMS THAT WE OUTLINE.

THESE IDEAS ARE HOPFULLY NOW BETTER EXPLAINED IN THE TEXT.

RESPONSE: THE STEPWISE REGRESSION IS NOW DELETED.

RESPONSE: WE HAVE DELETED REFEFENCES TO IQ AND REPLACED THEM WITH THE ABBREVIATION GK INSTEAD.

- The joint factor analysis (p. 14) is not argumented in Introduction and it is not in line with aims. Please delete this part.

RESPONSE: DELETED AS SUGGESTED

- In discussion, there is not much explanation of the results, for example, why low N and high C are associated with CT and not A and O as expected?

RESPONSE: THE DISCUSSION IS NOW RE-DESIGNED TO EXAMINE THE REGRESSION RESULTS THAT FOCUS ON THE PERSONALITY DISORDERS.

Minor points:

RESPONSE: REFERENCES TO THE DARK TRIAD ARE NOW TAKEN OUT OF THE CORE PART OF THE STUDY AND ONLY SPORADICALLY REFERRED TO AS PART OF THE PREVIOUS RESEARCH IN THE FIELD.

RESPONSE: YOU AND THE REVIEWERS HAVE GIVEN US THE OPPORTUNITY TO SIGNIFICANTLY IMPROVE THE CLARITY, FOCUS AND ANALYSIS OF THE PAPER FOR WHICH WE ARE MOST GRATEFUL.

Attachment

Submitted filename: BDConspiracy comments to reviewers.docx

Click here for additional data file.^{(28.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0273763.r003

Decision Letter 1

Goran Knežević

23 May 2022

PONE-D-21-40074R1Dispositional and ideological factor correlate of conspiracy thinking and beliefsPLOS ONE

Dear Dr. Arnulf,

The article is now considerably improved. However, both reviewers had further recommendation that I would like you to address properly. While r#2 has more technical suggestions, the objections of r#1 are more general and substantive. The issue of importance and novelty of the findings in the context of existing knowledge should be more elaborated. As emphasized by both reviewers, the semantic analysis requires further explanation and elaborations. As none of us is familiar with the analysis (and probably most of our audience) the method and its rationale should be more elaborated. I am curious how you would respond to the following statement: to the extent that the relationship between any two constructs is based on the similarity of the words used to describe them, the more psychologically trivial is the relationship (i.e., constructs are too close to each other, predictor-criterion overlap is substantial). Apart from reviewers requested, I would suggest to have the values in the Table 6 better explained (Are correlations between PDS and CTs+GK presented in the last three columns?; How exactly sum factor scores from LSA are calculated and why this information is important beyond the three factors loadings?; Isn’t it the case that the correlations between the loadings on the three factors and correlations of PDs and CTs are of importance, not the correlations between the factor loadings and sum of loadings across the factors or their ranks).

Please submit your revised manuscript by Jul 07 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Goran Knežević

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: This revised paper focuses on a semantic analysis of various personality measures and tendencies toward conspiratorial thinking in a convenience sample of mostly British adults. The authors find that various clinical forms of thinking such as schizotypal tendencies are strong correlates of conspiracy thinking. This has been found before and so it seems that what is novel in this paper is the use of semantic analysis to identify why people who endorse those clinical symptoms are more likely to also believe in conspiracies. Publication in Plos One does not require novel findings, but if replication is the goal, then this is a poor sample for that purpose. It is limited to people who are employed, with no obvious justification for that restriction. And it is unlikely to be representative of any population since it is a convenience sample with no evidence of generalizability. The authors argue in the future directions section that the results raise interesting questions, but the ones suggested seem to have been studied. There is evidence that conspiracy believers use non-mainstream media and that they are attracted to alternative medical treatments. All of this prior work should be cited and if the present results add something of value to those findings, then it might be worth saying what that is.

With these concerns aside, there are questions about how the semantic analysis was conducted. How were the inter-relations between the semantic scores subjected to a principal component analysis? I can imagine how that was done, but providing the details would help.

The more important analysis of the relation between the semantic scores and conspiracy beliefs is totally opaque. It is not at all clear what is meant by the sum of factor loadings in Table 6 or how those were related to ratings of conspiracy beliefs. Nor is there any clear interpretation of the second factor that is claimed to correlate with conspiracy thinking.

The authors seem to gloss over the finding that conscientiousness is related to conspiracy thinking. Why is that? Given the unequal reliabilities of the various measures used to assess correlates of conspiracy thinking, regression analysis is a poor guide to detecting true relations. I would go with the simple correlations more so than the regressions.

The authors claim that the two measures of conspiracy mentality are only modestly correlated (.58) but given the reliabilities of the measures of .60 and .91 (as reported in the Discussion when it should be in the Methods), one would only expect a correlation of about .54. So, the correlation that was observed suggests that they two scales are virtually identical.

Reviewer #2: Dear authors,

Thank you for your answers and comments. The manuscript is improved, but there are still some minor issues that should be addressed before the acceptance for publication:

Introduction

- Please add a short explanation of similarities and differences between 2 measures of CTs since you built your case based on these 2 measures.

- Seems like the whole introduction is based on belief in CTs, and not conspiracy thinking. In the description of previous research please specify for each study which CTs measure is used

(specify the name of the measure, i.e., in Walter and Drochon study, etc.). This would contribute to a better understanding of the introduction of 2 measures of CT.

- Although the authors stated that they delete the term cryst. int., it is still present in the hypothesis:

"We also used a short measure of crystalised intelligence to test the hypothesis that higher IQ test scores

would be negatively correlated with belief in CTs (H2)." as well as in the whole text (e.g., in the discussion they used the term intelligence and not cryst. int.). Please specify that you expect negative relations between

the general knowledge as the indicator of cryst. int. and CTs, or just - negative relations between the general knowledge and CTs.

In addition, in this hypothesis, only beliefs in CTs are mentioned and not conspiracy thinking or CTs in general, as in other hypotheses.

Results

- There is no M and SD for CT2 in Table 1, please merge Tables 1 and 3. Please add alphas for all measures in Table 1 and report either sum or mean scores for used instruments

(mean scores are better). Please delete descriptives for sex, it is odd. Add a note with variable explanation in all tables.

- I am not sure why in LSA only CT1 was entered, and not CT2. Please explain it at the beginning of the analysis. Are there some technical issues? I am not familiar with LSA and I could assume that readers would not be also familiar, thus the comment "(CT2 was not part of the analysis at all and the number is

statistically significant even if N (the number of scales) is only 14)" should be explained more clearly.

- Seems like the rotation was not used in PCA, was it on purpose? Also, the statement "Here, the CT1 items have the strongest loadings,

followed by the items from schizotypal scale" is not true, since the Depressive scale had loading -.216.

If you have other rationale, please explain. Also sentence "If the CT1 factor loadings are removed from the table..." should be changed to "removed from the analysis..."

Discussion

- The discussion lack similarities and differences between CT1 and CT2. In addition, the statement "In this study we measured a number of individual variables which accounted for

22% of the variance in both analyses" should be replaced with - around 20%, since CT1 was explained by 20% and CT2 by 21%.

Other

- Check APA for use of capital letters (it should be personality disorder and conspiracy theories, but Dark Triad and not dark-triad or dark triad)

- Delete (Crystallised IQ) from the explanation of the General Knowledge Test.

- Delete M and SD from the description of the measures since they are already presented in Table 1.

- Results for model testing remove from Table 2 (Model Adj. R2 = .20. F = 6.868, p < .001) and Table 4 (Model Adj. R2 = .21. F = 7.250, p < .001) and report in text as you did for next analysis.

- Please specify on p.10 that you use hierarchical regression analysis.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Bojana M. Dinić

PLoS One. 2022 Oct 26;17(10):e0273763. doi: 10.1371/journal.pone.0273763.r004

Author response to Decision Letter 1

7 Jul 2022

Comments to reviewers - PONE-D-21-40074R1

Dispositional and ideological factor correlate of conspiracy thinking and beliefs

PLOS ONE

Dear prof. Knežević!

Please find below our comments and answers to the questions and concerns addressed by yourself and the reviewers:

The article is now considerably improved.

Thank you!

However, both reviewers had further recommendation that I would like you to address properly. While r#2 has more technical suggestions, the objections of r#1 are more general and substantive. The issue of importance and novelty of the findings in the context of existing knowledge should be more elaborated. As emphasized by both reviewers, the semantic analysis requires further explanation and elaborations. As none of us is familiar with the analysis (and probably most of our audience) the method and its rationale should be more elaborated.

The technique is now explicated in further detail at several points in the manuscript. The methodological approach has also been more aligned with traditional statistics tables. We have also added more references to the growing body of research using these methods. Hopefully these passages are more intuitively understandable now.

I am curious how you would respond to the following statement: to the extent that the relationship between any two constructs is based on the similarity of the words used to describe them, the more psychologically trivial is the relationship (i.e., constructs are too close to each other, predictor-criterion overlap is substantial).

This question is pertinent and interesting. However, it has been thoroughly dealt with in a number of the references in the text, these will be listed again here. The core of the problem is how almost all survey-based correlation matrices seem heavily influenced by semantic relationships. These semantic relationships seem to exist below the usual conventions for scale construction in psychometrics, such as various types of factor analysis, rotation of scales and thresholds for cross-loadings. Thus, several publications using semantic algorithms have shown that the predictor-criterion overlap problem even where the strictest psychometric techniques have been used. There is a growing need to determine the relationship between semantic properties of scales and the influence of attitude strength. In fact, the present manuscript offers an interesting window to the intricate relationship between attitudinal and semantic/cognitive mechanisms involved in survey responses, see table 6 which contains a direct comparison of semantic predicted with empirically observed relationships. The following publications address these questions directly:

The relationship between semantic patterns and attitude strength: Arnulf, J. K., Larsen, K. R., Martinsen, O. L., & Egeland, T. (2018). The failing measurement of attitudes: How semantic determinants of individual survey responses come to replace measures of attitude strength. Behavior Research and Methods, 50(6), 2345-2365.

The use of text algorithms to replace/complement Likert scales and other types of psychological measures: Kjell, O. N. E., Kjell, K., Garcia, D., & Sikstrom, S. (2019). Semantic Measures: Using Natural Language Processing to Measure, Differentiate, and Describe Psychological Constructs [Article]. Psychological Methods, 24(1), 92-115. https://doi.org/10.1037/met0000191

The pervasiveness of semantically produced relationships in established measurement scales: Arnulf, J. K., Larsen, K. R., Martinsen, O. L., & Bong, C. H. (2014). Predicting survey responses: how and why semantics shape survey statistics on organizational behaviour. PLoS ONE, 9(9), e106361. https://doi.org/10.1371/journal.pone.0106361

How semantic properties of scales may help us model scales and their relationships a priori: Rosenbusch, H., Wanders, F., & Pit, I. L. (2020). The Semantic Scale Network: An online tool to detect semantic overlap of psychological scales and prevent scale redundancies. Psychological Methods, 25, 380–392. https://doi.org/10.1037/met0000244

The philosophical problems of semantically determined scales: Arnulf, J. K., & Larsen, K. R. (2021). Semantic and ontological structures of psychological attributes. In D. Wood, S. J. Read, P. D. Harms, & A. Slaughter (Eds.), Measuring and modeling persons and situations (pp. 69-102). Academic Press. https://doi.org/10.1016/B978-0-12-819200-9.00013-2

Apart from reviewers requested, I would suggest to have the values in the Table 6 better explained (Are correlations between PDS and CTs+GK presented in the last three columns?; How exactly sum factor scores from LSA are calculated and why this information is important beyond the three factors loadings?; Isn’t it the case that the correlations between the loadings on the three factors and correlations of PDs and CTs are of importance, not the correlations between the factor loadings and sum of loadings across the factors or their ranks).

Tables 5 and 6 are now completely changed in ways that comply more with traditional ways of representing data, and with more extensive explanations that we hope will make everything more transparent.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

See below

• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

See attached

• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

See attached

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

________________________________________

4. Have the authors made all data underlying the findings in their manuscript fully available?

requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

All the necessary data will be uploaded with the manuscript

Reviewer #1: No

Reviewer #2: No

________________________________________

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

________________________________________

6. Review Comments to the Author

We believe that our sample of nearly 400 adults drawn from the Prolific platform is a good sample from which one could easily generalize. Indeed, we have published papers in PLOS ONE (2021) using a similar sample from the same provider. We specify employed people to reduce the number of students.

As to the latter point, we have noted the salient prior work relevant to this paper and tried to point out where we are adding something new

These details are now provided with more elaborate explanations.

Hopefully this is now easier to understand. The semantic analyses build on a widely published framework, now extensively cited and explained in the text. The techniques are explained and compared with the empirically obtained survey responses. In the most important table, number 6, we show that the semantic and empirical relationships are correlated .70 (p < .01) which hardly suggests a serendipitous result. The table should make it clear that the cognitive structures of the survey item texts alone show strongest relationships between cluster A scales and conspiration theories, and this pattern is detectable in the respondent data.

We have noted and discussed the conscientiousness finding. We have kept in the regressions recognizing that the problems highlighted by the reviewer are very common in this area.

We have changed the “modestly” correlated to “strongly” correlated though we cannot accept that they are virtually identical.

Reviewer #2: Dear authors,

Thank you for your answers and comments. The manuscript is improved, but there are still some minor issues that should be addressed before the acceptance for publication:

Introduction

- Please add a short explanation of similarities and differences between 2 measures of CTs since you built your case based on these 2 measures.

See above…we have done this.

- Seems like the whole introduction is based on belief in CTs, and not conspiracy thinking. In the description of previous research please specify for each study which CTs measure is used

(specify the name of the measure, i.e., in Walter and Drochon study, etc.). This would contribute to a better understanding of the introduction of 2 measures of CT.

We have tried to do that. We have also tried to explain similarities and differences between Belief if CTs and conspiracy thinking, and further we have changed the title into “…conspiracy thinking and beliefs” to reflect this.

- Although the authors stated that they delete the term cryst. int., it is still present in the hypothesis: "We also used a short measure of crystalised intelligence to test the hypothesis that higher IQ test scores would be negatively correlated with belief in CTs (H2)." as well as in the whole text (e.g., in the discussion they used the term intelligence and not cryst. int.).

Now done

Please specify that you expect negative relations between the general knowledge as the indicator of cryst. int. and CTs, or just - negative relations between the general knowledge and CTs.

The latter…now done

In addition, in this hypothesis, only beliefs in CTs are mentioned and not conspiracy thinking or CTs in general, as in other hypotheses.

OK…rectified to be consistent

Results

- There is no M and SD for CT2 in Table 1, please merge Tables 1 and 3. Please add alphas for all measures in Table 1 and report either sum or mean scores for used instruments (mean scores are better). Please delete descriptives for sex, it is odd. Add a note with variable explanation in all tables.

Done as requested

This passage is now changed. The reason CT2 is taken out is that long matrices of single items can get unwieldy in LSA, since the number of unique item pairs expands with every item added. We believe that the statistics and whole picture is easier to understand using only CT1, since the two are virtually interchangeable in effects, as per the comments of one of the reviewers.

- Seems like the rotation was not used in PCA, was it on purpose? Also, the statement "Here, the CT1 items have the strongest loadings, followed by the items from schizotypal scale" is not true, since the Depressive scale had loading -.216.

To change the presentation of data, we have now shown a factor structure of the SCATI and CT1 based on a PCA with oblimin rotation. The ensuing picture is clearer. Negative loadings are tricky from an interpretive point of view in semantics and we have now tried to make the display of data more aligned with usual statistical representations.

- The following analysis presented in Table 6 is not clear to me and it is not clear why GK is in the table. The answer on your H9 could be get based on the pattern structure of the factors in Table 5 (with the use of rotation). If you have other rationale, please explain. Also sentence "If the CT1 factor loadings are removed from the table..." should be changed to "removed from the analysis..."

This is now all changed.

Discussion

- The discussion lack similarities and differences between CT1 and CT2. In addition, the statement "In this study we measured a number of individual variables which accounted for

22% of the variance in both analyses" should be replaced with - around 20%, since CT1 was explained by 20% and CT2 by 21%.

Now changed thank you.

Other

- Check APA for use of capital letters (it should be personality disorder and conspiracy theories, but Dark Triad and not dark-triad or dark triad)

Done as requested

- Delete (Crystallised IQ) from the explanation of the General Knowledge Test.

Done as requested

- Delete M and SD from the description of the measures since they are already presented in Table 1.

Done as requested

- Results for model testing remove from Table 2 (Model Adj. R2 = .20. F = 6.868, p < .001) and Table 4 (Model Adj. R2 = .21. F = 7.250, p < .001) and report in text as you did for next analysis.

Done as requested

- Please specify on p.10 that you use hierarchical regression analysis.

Done as requested

We very much hope that you can accept this revision for the journal

________________________________________

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Submitted filename: PONE-D-21-40074R2 Comments to reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0273763.r005

Decision Letter 2

Goran Knežević

15 Aug 2022

Dispositional and ideological factor correlate of conspiracy thinking and beliefs

PONE-D-21-40074R2

Dear Dr. Arnulf,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Goran Knežević

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: Dear authors,

Thank you for your answers and corrections that you made. All my suggestions were incorporated in the manuscript, thus it could be accepted.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #2: Yes: Bojana M. Dinić

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PLoS One. doi: 10.1371/journal.pone.0273763.r006

Acceptance letter

Goran Knežević

28 Sep 2022

PONE-D-21-40074R2

Dispositional and ideological factor correlate of conspiracy thinking and beliefs

Dear Dr. Arnulf:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Prof Goran Knežević

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PLOS ONE

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Dispositional and ideological factor correlate of conspiracy thinking and beliefs

Jan Ketil Arnulf

Charlotte Robinson

Adrian Furnham

Roles

Abstract

Introduction

Method

Participants

Measures

Procedure

Results

Table 1. Means, SDs and correlations between CT1, demographics, ideology, five-factor personality and the three clinical clusters.

Table 2. Regression results for CT1.

Table 3. Means, SDs and correlations between CT2, demographics, ideology, five-factor personality and the three clinical clusters.

Table 4. Regression results for CT2.

Table 5. Semantically generated principal component analysis.

Table 6. Semantic relationships (in cosines) between SCATI semantics and CT1, compared with the empirically observed correlations from human responses (bottom line).

Discussion

Limitations and suggestions for further research

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Goran Knežević

Roles

Author response to Decision Letter 0

Decision Letter 1

Goran Knežević

Roles

Author response to Decision Letter 1

Decision Letter 2

Goran Knežević

Roles

Acceptance letter

Goran Knežević

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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