How does belief in conspiracy theories affect loneliness and connectedness? A longitudinal investigation

Conspiracy theories are contentious, and conversations about conspiracy theories can generate conflict. It is therefore possible that developing beliefs in conspiracy theories may isolate a believer from friends and family, resulting in loneliness and reduced connectedness to others. However, evidence of such effects remains equivocal. We therefore report a preregistered longitudinal analysis testing for effects of belief in specific conspiracy theories and conspiracy mentality on loneliness and connectedness (and vice versa). We conducted a longitudinal panel study (N = 839) using 12 waves collected over 11 months. We analyzed data using random intercept cross-lagged panel models (RI-CLPMs) along with several alternative statistical models for cross-lagged data. We found no evidence of an effect of belief in specific conspiracy theories on loneliness, nor of conspiracy mentality on either loneliness or connectedness. We did find evidence of a negative effect of belief in specific conspiracy theories on connectedness in our preregistered analyses, but this finding was not robust to alternative specifications. We found no evidence of converse effects (i.e., of connectedness and loneliness on belief in conspiracy theories or conspiracy mentality). Our findings suggest that average within-person effects on loneliness and connectedness (over a 1-month lag period) were small and not robust across plausible model specifications. Nevertheless, our study was subject to several limitations, including modest power, vulnerability to time-varying confounds, and a sample with low mean levels of belief in conspiracy theories. Effects that are smaller than we had adequate power to detect, unfold over different time lags, or that are concentrated within subgroups (such as highly committed “believers”) remain possible, but would require further research to establish.

Loneliness can drive us toward unlikely sources of comfort; sometimes, to communities of strangers united by the conviction that the world is systematically deceptive (Bierwiaczonek et al., 2020). Some of these communities center around conspiracy theories—beliefs that hidden, powerful groups secretly orchestrate significant public events, despite a lack of public evidence (Douglas et al., 2019). Online forums like Reddit's r/conspiracy, which stands at roughly 2.4 million members, exemplify how these communities can develop participatory cultures where users share alternative narratives about everything from vaccine safety to election integrity (de Wildt and Aupers, 2024; Klein et al., 2019). They promise understanding and belonging through shared skepticism and doubt in mainstream narratives. Yet some evidence suggests this apparent solution may come at a cost: Rather than restoring social bonds, conspiracy beliefs may undermine or frustrate the very connections they promise to provide, creating cycles of social rejection and further isolation (Albath et al., 2026). When our fundamental need for connection leads us to seek validation through shared distrust, we confront an interesting question—do we find the connections we seek, or do we inadvertently deepen our isolation?

Loneliness and social connectedness capture related but distinct dimensions of social life. Loneliness is a subjectively distressing “discrepancy between one's desired and achieved level of social relations” (Perlman and Peplau, 1981, p. 32). Loneliness can be experienced due to a lack of deep, intimate attachment (e.g., from a partner or best friend), or due to a lack of connections to a broader network of friends and community. Weiss (1973) described the former as “emotional loneliness” and the latter as “social loneliness”.

In contrast, social connectedness is the capacity “to feel comfortable and confident within a larger social context than family or friends” (Lee and Robbins, 1995, p. 233). Lee and Robbins conceptualized social connectedness as a facet of belongingness, which is typically conceptualized as the fulfillment of the need “to form and maintain at least a minimum quantity of lasting, positive, and significant interpersonal relationships” (Baumeister and Leary, 1995, p. 497)1. Although loneliness and social connectedness are closely related both conceptually and empirically (e.g., r = −0.80 in Lee et al., 2001, Study 2), they differ in the sense that loneliness specifically comprises the subjective distress that can arise from lacking social relations rather than feelings of connectedness more broadly. Loneliness can also be caused by the absence of either very close relationships or broader social connections, whereas social connectedness relates primarily to the latter. Together, they offer a richer picture of social wellbeing than either concept alone, given that one may be absent without the other being present.

Whether conspiracy beliefs shape these experiences, however, is theoretically ambiguous, with opposing narratives. One possibility is that conspiracy theories may damage the very relationships they promise to restore. For instance, conspiracy theories often position believers as holders of special knowledge while casting non-believers as gullible “sheeple” (see Harambam and Aupers, 2017). This may create tension in social relationships where conspiracy believers struggle to maintain connections with those they view as misguided about reality (Biddlestone et al., 2021). Beyond interpersonal friction, the content of many conspiracy theories, steeped in distrust of institutions, skepticism of expertise, and suspicion of official narratives, may cultivate worldviews fundamentally at odds with the social cohesion and trust that underpin meaningful connection (Van Prooijen et al., 2022).

Crucially, the erosion of social bonds may not rest solely with conspiracy believers themselves. Conspiracy theories are widely stigmatized in mainstream society, and people anticipate social exclusion and negative evaluation when expressing conspiratorial views (Lantian et al., 2018). Individuals described as endorsing or spreading conspiracy theories are judged as less competent, moral, and warm than those who do not (Gundersen et al., 2025), a social penalty that others impose rather than believers invite. In a cultural moment where evidence-based reasoning functions as social currency, where politicians are scrutinized for their sources and meme culture renders conspiracy belief not just wrong but uncool (Johansen et al., 2022), the sanctions conspiracy believers face are structural rather than interpersonal. They find themselves not only at odds with specific facts, but with the shared epistemic framework through which society determines what counts as knowledge. When people disagree on the basis of truth, the mutual understanding that social connectedness requires becomes difficult to sustain (Nera et al., 2022).

On the other hand, conspiracy theories may sometimes ameliorate loneliness rather than creating it. Conspiracy communities can supply ready-made social networks of like-minded believers, offering validation, shared identity, and a sense of belonging that mainstream social networks may have failed to provide (Klein et al., 2018, 2019). Baumeister and Leary's (1995) belongingness hypothesis suggests that the need for connection is powerful enough that people will seek alternative pathways when conventional ones are blocked. Conspiracy communities may represent precisely such an alternative, offering frequent interaction, shared worldview, and mutual validation: the ingredients the hypothesis identifies as essential for genuine belonging.

Taken together, these theoretical accounts suggest that the within-person consequences of conspiracy beliefs for loneliness and connectedness are genuinely uncertain. The same beliefs that strain relationships with non-believing family and friends may simultaneously strengthen bonds with fellow believers. Whether the net effect is an increase or a decrease in loneliness and connectedness is ultimately an empirical question, and one that, as we review below, existing evidence has yet to resolve.

The empirical evidence: does conspiracy belief lead to loneliness and connectedness?

Cross-sectional research suggests small negative correlations between loneliness and each of belief in specific conspiracy theories and conspiracy mentality (Hettich et al., 2022). In this context, conspiracy mentality refers to a generalized tendency to believe that events are the result of a conspiracy (Imhoff et al., 2022). Although we were unable to find prior studies focusing specifically on the relationship between conspiracy beliefs and social connectedness specifically, Panerati et al. (2026) reported small positive correlations between perceived social isolation and both conspiracy mentality and anti-LGBTQ+ conspiracy belief endorsement.

There is some evidence of different operationalisations of subjective isolation being differentially associated with conspiracy belief. Galgali et al. (2024) reported four cross-sectional studies using multiple regression to examine the extent to which emotional loneliness, existential isolation (i.e., a feeling that nobody else shares one's experience; Pinel et al., 2017), and alienation/anomie (i.e., a feeling of the breakdown in the social fabric and leadership of society; Teymoori et al., 2016) predicted conspiracist ideation. The authors found that alienation/anomie was a significant predictor of conspiracist ideation in all four of the studies, existential isolation in three of the four studies, and loneliness in none.

Moving on from cross-sectional evidence, some studies have also used latent growth curve modeling to describe trajectories in loneliness and conspiracism over time. Using a sample of high-school students first sampled in the early 1990s, Bierwiaczonek et al. (2024) reported that both initial levels of loneliness and increasing loneliness over life was associated with conspiracy mentality several decades later in midlife. Over a shorter timeframe of 3 years (with 4 waves), Zhou et al. (2026) found no evidence that either growth in social loneliness or growth in social loneliness was a significant predictor of conspiracy beliefs. However, they found that emotional loneliness at time 1 was a significant predictor of conspiracy beliefs at time 4.

Nevertheless, neither cross-sectional correlations nor relationships between trajectories over time are evidence of causal effects, and the available evidence base provided mixed support for the proposition that either belief in specific conspiracy theories or conspiracy mentality causes social disconnection. The most direct evidence comes from a series of experiments by Toribio-Flórez et al. (2024). Across multiple studies, participants anticipated lower relationship satisfaction when a hypothetical social contact or potential dating partner endorsed a conspiracy theory, and this pattern held whether the social contact was a friend, colleague, or romantic prospect. While these findings speak to anticipated rather than actual relationship outcomes, and to relationship satisfaction rather than loneliness and connectedness specifically, they offer evidence that conspiracy beliefs create meaningful barriers to relationship formation and maintenance in the minds of non-believers.

Converging experimental evidence reinforces this picture. Individuals described as endorsing or spreading conspiracy theories are consistently judged as less competent, moral, and warm than those who do not, suggesting that conspiracy beliefs carry a reputational cost that non-believers readily impose (Gundersen et al., 2025; Toribio-Flórez et al., 2023). These experimental studies provide preliminary evidence of a mechanism via which spreading or endorsing conspiracy theories could lead to loneliness and reduced connectedness (i.e., via reputational costs). However, they do not directly speak to the effects of conspiracy beliefs on loneliness or connectedness. They also focus on acute effects immediately following an experimental manipulation. Cross-lagged longitudinal designs offer a capacity to estimate longer-term effects, albeit without the same degree of protection from confounding.

In a study using 2 waves, Godžáková and Šrol (2026) found no evidence of cross-lagged relationships between loneliness and COVID-19 conspiracy beliefs (in either direction). However, the cross-lagged panel model they applied does not control all time-invariant confounding variables (Hamaker et al., 2015). Other longitudinal studies have been conducted using the random intercept cross-lagged panel model (RI-CLPM). The RI-CLPM separates stable between-person differences from within-person change, allowing temporal precedence to be established while controlling for time-invariant confounds. This permits credible, albeit tentative, inference about causal effects. Bertlich et al. (2025) report two panel studies using RI-CLPMs (N = 1,604 and N = 1,502 respectively), finding that although loneliness and conspiracy belief were correlated at the between-person level, neither predicted changes in the other over time. Although not focusing specifically on loneliness or connectedness, Albath et al.'s (2026) three-year panel study (also using RI-CLPMs) found no evidence of conspiracy beliefs predicting subsequent changes in belonging. Overall, as Van Prooijen et al. (2022) noted, while conspiracy beliefs are widely assumed to damage social relationships, direct longitudinal support for this claim remains limited.

The present investigation

Overall, these mixed results point to a gap in knowledge about the causal relationships between conspiracy beliefs and social connection. While the Toribio-Flórez et al. (2024) experiments capture anticipated responses to hypothetical social contacts, whether these translate into real-world shifts in loneliness and connectedness in everyday life remains unclear. Longitudinal designs are better placed to answer this question, yet existing work remains limited. Most studies rely on cross-sectional designs that cannot determine whether loneliness drives conspiracy belief, conspiracy belief erodes social bonds, or both processes operate simultaneously (e.g., Hettich et al., 2022; Zwar et al., 2022). Bertlich et al.'s (2025) longitudinal studies offer more credible causal estimates, yet used a relatively small number of waves (3and 5 respectively), while Albath et al. (2026) used four. The power of RI-CLPM analyses is highly sensitive to the number of waves (see Mulder, 2023).

This research aims to fill this gap in knowledge through a large-scale longitudinal investigation of bidirectional relationships between conspiracy beliefs and social connection. Using data from a 11-month panel study of adults in the United Kingdom, Australia, and New Zealand, we employ RI-CLPMs (Hamaker et al., 2015; Mulder and Hamaker, 2021) to test hypotheses about how conspiracy belief and social connection influence each other over time.

We examine both conspiracy mentality and specific conspiracy beliefs, recognizing these may show distinct relationships with social outcomes. Our social connection measures include both loneliness and connectedness. This dual approach allows us to examine whether conspiracy belief relates similarly to the absence of negative social experiences and the presence of positive social experiences.

We proposed four preregistered hypotheses based on the reviewed theory and evidence. We hypothesized that there would be positive cross-lagged effect of belief in conspiracy theories on loneliness (H1), such that individuals higher on conspiracy theory belief would report higher loneliness at subsequent time points. Furthermore, we hypothesized that there would be a negative cross-lagged effect of belief in conspiracy theories on connectedness (H2), with higher conspiracy beliefs predicting lower subsequent feelings of connection to others and society. We made similar hypotheses about the effects of conspiracy mentality: that there would be a positive cross-lagged effect of conspiracy mentality on loneliness (H3), and a negative cross-lagged effect of conspiracy mentality on connectedness (H4).

While our preregistered hypotheses pertain specifically to effects of belief in conspiracy theories and conspiracy mentality on loneliness and connectedness, our analyses also permit us to estimate effects in the opposite direction (i.e., of loneliness and connectedness on belief in conspiracy theories and conspiracy mentality). There does exist some prior literature showing that manipulations of the related constructs of social exclusion (Graeupner and Coman, 2017) and ostracism (Poon et al., 2020) lead to increased conspiracy beliefs.

We preregistered our hypotheses, data collection plan and analysis plan prior to collecting the data analyzed in this report. The preregistration is accessible at https://osf.io/juk54/.

Participants and procedure

This study was conducted as part of an overarching longitudinal project conducted between October 2022 and October 2024. This manuscript reports data collected in 12 monthly waves between November 2023 and October 2024, representing waves 14–25 of the overarching project. Data was collected via Prolific from a convenience sample of participants residing in the United Kingdom, Australia, and New Zealand. A detailed description of the initial screening and recruitment can be found in the preregistration for one of the other project components available at https://osf.io/z7k34/.

On the third of each month, a survey was opened on Prolific, and open only to a “custom allowlist” of participants who completed the first wave (in October 2022) without meeting any of the exclusion criteria specified for that wave (as specified in the preregistration linked above), and excluding one person who withdrew from the study during the first year of data collection. This custom allowlist had 994 participants in it, which was also the target sample size we entered in Prolific. Each survey wave was open for 1 week, closing on the 10th of the given month. Participants were paid GBP2 at wave 14 (which had additional one-off measures), GBP0.90 each for waves 15-18, and GBP1 for each of waves 19-25.

The interval between waves in this study (1 month) was selected based on practical constraints along with our judgment that this would plausibly be enough time for effects to emerge. Ideally, it would have been possible to select a duration based on a theory that specifies how long effects of conspiracy beliefs on loneliness and connectedness would take to emerge, but to our knowledge no such theory exists.

Sample size determination

The sample size for the analyses reported in this manuscript was determined by the goals and constraints of the overarching longitudinal project rather than a power analysis tailored to the specific hypotheses tested here. However, the target sample size for the original longitudinal study was partially guided by a power analysis for a single-indicator RI-CLPM model, suggesting that 500 participants and 13 waves produced power of 90% with α = 0.05 to detect a cross-lagged effect size (standardized b ' = 0.15). This analysis suggested that even with considerable attrition we might expect reasonable power to detect effects of this size or larger. We return to potential issues with power in the discussion section below.

Exclusion criteria

At the study level, any participant who finished at least two survey waves within the 12 waves used in this study was included in the sample analyzed here. This produced an observed sample size of N = 839.

Any response to a given wave that met any of the criteria below was excluded from that wave and consequently treated as missing. However, participants whose responses met these criteria continued to be invited back to subsequent waves. The exclusion criteria were:

Not providing a correct response to every attention check included in the wave. Each wave included one instructional manipulation check and one nonsensical item check, with the content of the checks changing in each wave.

Having a study duration (recorded by Qualtrics) of less than the sum of the number of items in the survey multiplied by two (see Huang et al., 2012).

“Returning” their submission in the Prolific system, indicating that they revoke their consent.

Duplicate submissions from the same participant to the same wave, as detected via Prolific ID.

Qualtrics status other than 0 (normal response)–i.e., removing duplicate, preview and test responses.

For more information about these exclusion criteria, please see the preregistration at https://osf.io/juk54.

Attrition and missing data

Table 1 displays the number of participants included at each wave after exclusions. Participants were able to decline answering individual questions, but missing data within submitted surveys was rare: just 14 missing item responses across the four main measures used in this study. Missing data (whether due to attrition or missing items) was dealt with by estimating models via full information maximum likelihood (see Enders and Bandalos, 2001; Lang and Little, 2018). Where scores created based on multiple items were used in analyses, any score for which an item response was missing was itself treated as missing.

Participant demographics

Participants provided demographic information at the beginning of the overarching longitudinal project (in October 2022). We use that information to describe the participants below, albeit some participants may have experienced changes in demographics over the course of the study.

Of the final sample of N = 839, 408 (48.6%) resided in the United Kingdom, 314 (37.4%) in Australia, and 117 (13.9%) in New Zealand. 54.5% identified as women, 44.6% as men, and 0.7% as gender diverse. On a measure of political orientation where 0 was the extreme left and 10 the extreme right, the median response was just to the left of center (4). Participants' ages ranged from 19 to 86 years of age, M = 42.1 (SD = 13.0). We incremented all ages by 1 in this calculation to better represent their ages at the time when the data analyzed in this paper was collected.

Measures

Loneliness

Loneliness was measured via a modified version of the 3-item UCLA loneliness scale (Hughes et al., 2004). Given the monthly data collection (with one-week open periods), items were adapted to refer to experiences over the preceding 3 weeks. An example item is “Over the last 3 weeks, how often did you feel that you lacked companionship?” Responses to each item were collected on a 5-point rating scale (Never, Almost never, Sometimes, Fairly often, Very often). A loneliness score was calculated for each participant at each wave by computing the mean of their responses to the three items at that wave.

We selected this scale due to its short length (an important asset in a longitudinal study), widespread use in the literature (see Alsubheen et al., 2023 for a review), and its suitability for adaptation to a 3-week recall period. In a study with midwife students (N = 1,742), Gosling et al. (2024) found that the 3-item and 20-item versions of the UCLA loneliness scale showed similar psychometric properties. In our study, Cronbach's α varied across waves from 0.91 to 0.94.

Connectedness

Connectedness was measured via the 6-item connectedness to others scale (Pavey et al., 2011). The items were prefaced with the instruction to “Please indicate the extent to which you agree with the following statements at the present moment.” An example item is “I feel a bond with other people” (Pavey et al., 2011, p. 909). Responses were collected on a 5-point Likert/agreement scale. A connectedness score was calculated for each participant at each wave by computing the mean of their responses to the six items at that wave. While we were unable to locate any strong evidence of the reliability or validity of this scale from existing studies, we selected it due to its brevity, clear questions, and focus on state rather than trait levels of loneliness. Cronbach's α ranged from 0.84 to 0.87 across waves.

Conspiracy mentality

Conspiracy mentality was measured via Bruder et al.'s (2013) 5-item conspiracy mentality questionnaire (CMQ). Responses to each item were recorded on a 11-point scale with endpoints of “certainly not (0%)” and “certain (100%)”. An example item is “I think that there are secret organizations that greatly influence political decisions” (Bruder et al., 2013, p. 5). A conspiracy mentality score was calculated for each participant at each wave by computing the mean of their responses to the five items at that wave. Kay and Slovic (2025) found positive evidence for the reliability and construct validity of this scale in a preregistered study, while Cirović and Pedović (2025) found evidence of factorial validity for a one-factor model (albeit with a correlated residual) and nomological validity in versions of the CMQ in four languages including English. Cronbach's α ranged from 0.88 to 0.90 across waves.

Belief in specific conspiracy theories

Belief in specific conspiracy theories was measured by 16 items2, each describing contemporary and notable conspiracy theories. These items comprised a mixture of items drawn from previous studies, items adapted from previous studies, and items created for this study. Each item was designed to refer to a claim that referred to a conspiracy theory (i.e., a theory alleging multiple actors, secrecy, and malevolence), that was of contemporary relevance, and that was sufficiently notable to have been discussed in mainstream media coverage. An example item is “The trails left behind airplanes are toxic chemicals released as part of a secret government programme.”

Eleven of these items were collected continuously since the beginning of the overarching longitudinal study (in October 2022), while five were introduced at the first of the waves analyzed here (in November 2023). The full items can be found in the questionnaires shared via our OSF project. Responses to each item were collected on a 5-point Likert/agreement scale. A belief in conspiracy theories score was calculated for each participant at each wave by computing the mean of their responses to the 16 items at that wave. Cronbach's α ranged from 0.94 to 0.95 across waves.

The theories we included are each ones that contradict accounts of relevant epistemic authorities, which is often considered a requirement for a claim to constitute a conspiracy theory (see Räikkä, 2018). This does not necessarily imply that all of the theories are false: For example, our items included theories alleging that COVID-19 pandemic was caused by a “lab leak” and that Jeffrey Epstein was murdered. It is within the realms of possibility that these theories may ultimately transpire to be warranted, even though they are not currently the mainstream received explanations.

Other measures

Given that this study forms part of an overarching longitudinal project, each survey contained other measures related to objectives and hypotheses not forming part of this preregistration. For full copies of the questionnaires from each wave, please see our OSF project.

Ethics approval

This study was conducted in accordance with the Massey University ethics code (Massey University, 2017). Ethics approval was granted by the Massey University Human Ethics Committee Southern A, application SOA 22/42. All participants provided informed consent at every wave.

Data analysis

We analyzed data using R (R Core Team, 2023) and RStudio (Posit team, 2023). We relied particularly on the packages lavaan (Rosseel, 2012), dplyr (Wickham et al., 2023), psych (Revelle, 2025), ICC (Wolak et al., 2012), rmcorr (Bakdash and Marusich, 2024) and semTools (Jorgensen et al., 2022).

Statistical models

In our preregistered analyses, we make extensive use of variants of the RI-CLPM (Hamaker et al., 2015; Mulder and Hamaker, 2021). The RI-CLPM is a form of structural equation model in which a participant's score on a construct (e.g., loneliness) is modeled as arising from a combination of a “random intercept” (e.g., that person's mean level of loneliness over time) and a within-person latent variable for that construct and wave. This permits a focus on within-person variance. The RI-CLPM includes cross-lagged paths between the within-person latent variables (e.g., the path from within-person conspiracy mentality at time 1 to within-person loneliness at time 2, controlling for within-person loneliness at time 1).

The RI-CLPM facilitates analysis of within-person variability (implicitly controlling for all time-invariant confounding variables) and of cross-lagged coefficients (thus establishing temporal precedence of cause before effect). It is thus helpful for causal inference, albeit by no means invulnerable to biases (especially time-variant confounding; see our Discussion section for more information). In its simplest form, the RI-CLPM just uses a single indicator for each construct (e.g., a loneliness score; see Figure 1). However, it can be extended to a multiple-indicator specification in which a given within-person factor loads onto multiple items, which allows measurement error to be modeled (Mulder and Hamaker, 2021). We used multiple-indicator RI-CLPMs to test hypotheses 3 and 4, which pertain to pairs of latent constructs (conspiracy mentality and loneliness, and conspiracy mentality and connectedness). This appeared conceptually consistent with the measures applied; for example, the CMQ is intended as a measure of an underlying “general susceptibility” to conspiracy theories (Bruder et al., 2013, p. 1). In contrast, while it would have been possible to hypothesize a latent variable underlying the belief in specific conspiracy theory items, it would then become unclear how such a variable is conceptually distinct from conspiracy mentality. We therefore did not apply the multiple-indicator model for H1 and H2 (and preregistered this decision).

In our preregistered analyses, we applied “stationary” versions of the RI-CLPM: That is, we assumed that the regression paths and covariances between within-person variables were constant over time. For our tests of H3 and H4 via multiple-indicator models, we additionally assumed equal factor loadings and intercepts. We report tests of measurement invariance over time in the Supplementary material.

We present unstandardized regression coefficients when reporting the results from RI-CLPMs. While it is possible to compute standardized coefficients, the fact that these coefficients are standardized on the within-person variances over time means that the standardized coefficients vary over waves even when the unstandardized coefficients do not (as is the case in our preregistered analyses). The unstandardized coefficients also apply the same units of measurement as the original measures, whereas standardized coefficients use the less familiar metric of within-person standard deviations.

While we employ RI-CLPMs in our preregistered analyses, we also apply a range of alternative models for cross-lagged data as robustness checks: the stable trait autoregressive trait and state (STARTS) model, the latent curve model with structured residuals (LCM-SR), the autoregressive latent trajectory (ALT) model, and the latent change score (LCS) model. Each of these models can be considered as generalizations of the RI-CLPM that also account for trait-like variance, but define the cross-lagged causal estimands differently, and loosen other auxiliary assumptions. A full introduction is beyond the scope of this article, but can be found in Usami et al. (2019). We estimated stationary versions of each of these models to enhance the probability of convergence given their greater level of complexity relative to the RI-CLPM. Our purpose in applying these additional models is to address the presence of uncertainty about the best model specification for producing unbiased estimates of causal effects (see Murayama and Gfrörer, 2025 for further discussion).

When reporting findings, we treat the preregistered models as primary, and interpret them according to preregistered inferential criteria. Doing so ensures that our hypotheses are subjected to severe tests (see Lakens, 2019). However, where the robustness checks for a hypothesis indicate that there exists a better-fitting model (per the Akaike Information Criterion, AIC), and that model produces substantively different conclusions (i.e., with respect to the sign and/or significance of a cross-lagged coefficient), we interpret the findings as equivocal. This rule for interpreting robustness checks was not preregistered, and was determined after analyzing data.

For H1, H3 and H4, the substantive conclusions were identical across the preregistered analyses and all reported robustness checks. Only for H2 was there substantial variation in findings across model specifications, which we discuss in more detail below.

Descriptive statistics

Means and standard deviations for the main study variables are displayed in Table 2. The majority of variance for each variable was between rather than within persons, especially for belief in conspiracy theories. There was nevertheless some non-trivial variance within persons for each of the variables.

Correlations between the four main variables (both between and within persons) are displayed in Table 3. All between-person correlations were statistically significant: Loneliness was positively and moderately correlated with both belief in conspiracy theories and conspiracy mentality, while connectedness had small negative correlations with both. The within-person correlations were smaller: Loneliness had statistically significant but very small within-person correlations with both belief in conspiracy theories and conspiracy mentality. On the other hand, connectedness was not significantly related to either.

Hypothesis 1: belief in conspiracy theories and loneliness

H1 was tested via an RI-CLPM. The preregistered model was stationary: That is, we assumed that the cross-lagged and autoregressive parameters, as well as the covariances between the within-person variables, were constant across waves. A simplified depiction of this model (without only a subset of waves shown) is displayed in Figure 1. In this depiction, the rectangles represent the observed variables, while the ovals in the center of the figure represent within-person variance in loneliness and conspiracy beliefs, and the ovals on the left (the “random intercepts”) represent stable between-person differences in loneliness and belief in conspiracy theories.

The fit of the preregistered model was imperfect but relatively good: scaled χ 2 (267) = 422.932, robust root mean squared error of approximation (RMSEA) = 0.037, comparative fit index (CFI) = 0.992, standardized root mean square error residual (SRMR) = 0.028, Akaike Information Criterion (AIC) = 12,394. For the RMSEA and CFI we apply the population-corrected method for robust scaling, given that the sample-corrected versions were intractably slow to compute with our dataset. See Brosseau-Liard et al. (2012) for a discussion of the differences between these statistics.

In this preregistered analysis, the unstandardized cross-lagged path from belief in conspiracy theories to loneliness was non-significant, b = −0.041, p = 0.352, 95% CI [−0.128, 0.046]. As such, H1 was not supported. The unstandardized cross-lagged path from loneliness to belief in conspiracy theories was likewise non-significant, b = 0.004, p = 0.383, 95% CI [−0.006, 0.015]. The autoregressive paths were nonetheless both positive and significant.

As one robustness check, we estimated a non-stationary RI-CLPM where the regression parameters and covariances were permitted to vary across waves. In this alternative model, we formed inferences by creating mean cross-lagged paths (for belief in conspiracy theories to loneliness, and vice versa, across all waves) as defined parameters in lavaan. This robustness check produced substantially unaltered results (i.e., both mean cross-lagged paths remained non-significant). The alternative model showed better fit relative to complexity than the preregistered model, with a lower AIC (Δ AIC = 42).

In an additional set of robustness checks, we estimated the STARTS, LCM-SR, ALT and LCS models. The STARTS and LCS models produced inadmissible solutions (negative variances) and were excluded from further consideration. The cross-lagged coefficients were non-significant in the remaining two models. For regression parameter estimates for robustness checks, please see the Supplementary material.

Hypothesis 2: belief in conspiracy theories and connectedness

H2 was also tested via a RI-CLPM; see Figure 2. In the preregistered (stationary) model, the cross-lagged path from connectedness to belief in conspiracy theories was non-significant, b = −0.017, p = 0.056, 95% CI [−0.035, 0.000]. However, the path from belief in conspiracy theories to connectedness was negative and significant, b = −0.074, p = 0.016, 95% CI [−0.134, −0.014], supporting H2. Model fit was again imperfect but relatively good: scaled χ 2 (267) = 385.662, robust RMSEA = 0.031, CFI = 0.995, SRMR = 0.027, AIC = 5657.

Despite these promising results for H2, a robustness check where parameters could vary across waves produced different conclusions. The mean cross-lagged path from belief in conspiracy theories to connectedness became non-significant, b = −0.103, p = 0.271, 95% CI [−0.287, 0.080], while the mean reverse path became significant, b = −0.057, p = 0.004, 95% CI [−0.096, −0.019]. This alternative model also showed improved fit relative to the preregistered model, AIC = 5,597.

We also completed additional robustness checks with the STARTS, LCM-SR, ALT and LCS models. The STARTS and LCS models produced inadmissible solutions (negative variances). Of the two remaining models, the LCM-SR produced a borderline-significant cross-lagged path from conspiracy theories to connectedness, b = −0.067, p = 0.049, 95% CI [−0.135, 0.000]. However, the ALT model was the best-fitting of the estimated models, scaled χ 2 (290) = 337.683, p = 0.028, robust RMSEA = 0.018, robust CFI = 0.998, SRMR = 0.018, AIC = 5,578, and neither cross-lagged coefficient was significant in this model. Given that H2 was supported in the preregistered analysis but not supported in alternative specifications with superior model fit (ALT, unconstrained RI-CLPM), we interpret the findings with respect to H2 as equivocal.

Hypothesis 3: conspiracy mentality and loneliness

Hypothesis 3 was tested via a multiple-indicator RI-CLPM (Mulder and Hamaker, 2021). A simplified depiction of this model is displayed in Figure 3. This multiple-indicator model permitted measurement error at the item level to be explicitly modeled. Within this model, conspiracy mentality and loneliness were treated as within-person latent variables that produce within-person variance in responses to their respective items.

As preregistered, we assumed that the within-person factor loadings and intercepts, as well as the cross-lagged and autoregressive parameters, were constant across waves. We likewise assumed that covariances between within-person terms were constant across waves. The within-person components of the latent variables were also treated as unidimensional with no correlated error terms. All random intercept terms were permitted to covary.

In this preregistered analysis, the unstandardized regression path from conspiracy mentality to loneliness was b = −0.035, p = 0.131, 95% CI [−0.080, 0.010]. As such, H3 was not supported. The path from loneliness to conspiracy mentality was likewise not significant, b = −0.010, p = 0.674, 95% CI [-0.055, 0.035]. Model fit was imperfect but relatively good: scaled χ 2 (4,554) = 6,087, p < 0.001, robust RMSEA = 0.026, CFI = 0.981, SRMR = 0.030, AIC = 162,754. A test of measurement invariance suggested the strong constraints over time in the preregistered model were not supported, albeit multiple-indicator RI-CLPMs without these constraints produced substantively identical conclusions; see the Supplementary material.

In one robustness check, we estimated a single-indicator RI-CLPM using scores for conspiracy mentality and loneliness. This model does not model measurement error at the item level, but avoids assuming that all variance unique to a wave, person and item constitutes measurement error (which might misclassify some true within-person changes over time as measurement error). In this robustness check, we permitted the regression paths and covariance terms to vary over time for the sake of reducing the number of applicable assumptions. In this model, the mean cross-lagged path from conspiracy mentality to loneliness was non-significant, b = 0.002, p = 0.951, 95% CI [−0.073, 0.077], as was the mean path from loneliness to conspiracy mentality, b = −0.027, p = 0.642, 95% CI [−0.138, 0.085].

We also completed additional robustness checks with stationary versions of the STARTS, LCM-SR, ALT and LCS models. Of these, the STARTS model did not converge. The paths from conspiracy mentality to loneliness and vice versa were both non-significant in the three remaining models.

Hypothesis 4: conspiracy mentality and connectedness

H4 was also tested with a stationary multiple-indicator RI-CLPM (see Figure 4). The unstandardized regression path from conspiracy mentality to connectedness was non-significant, b = −0.021, p = 0.233, 95% CI [−0.055, 0.013]. As such, H4 was not supported. The path from connectedness to conspiracy mentality was likewise not significant, b = −0.026, p = 0.465, 95% CI [−0.095, 0.044]. Model fit was imperfect but relatively good: scaled χ 2 (8,640) = 11,221, p < 0.001, robust RMSEA = 0.024, CFI = 0.973, SRMR = 0.030, AIC = 194,782. A test of measurement invariance suggested the strong constraints over time in the preregistered model were not supported, albeit removing these constraints did not alter the substantive conclusions; see the Supplementary material.

In one robustness check, we estimated a single-indicator RI-CLPM using scores for conspiracy mentality and connectedness, without constraints over time. In this model, the mean cross-lagged path from conspiracy mentality to connectedness was non-significant, b = −0.002, p = 0.911, 95% CI [-0.039, 0.035], as was the mean path from connectedness to conspiracy mentality, b = −0.050, p = 0.566, 95% CI [−0.222, 0.122].

In further robustness checks, we also estimated stationary STARTS, LCM-SR, ALT and LCS models. Of these, we excluded the STARTS results due to negative estimated variances. The cross-lagged paths from conspiracy mentality to connectedness and vice versa were not significant in any of the remaining models.

Summary of results

In Table 4, we summarize the results across our four pre-registered hypothesis tests, as well as non-preregistered robustness checks employing alternative longitudinal models, including single indicator non-stationary RI-CLPMs (allowing parameters to vary across waves) and the suite of alternatives described in Usami et al. (2019).

Overall, we found little evidence in favor of our hypotheses. We found no evidence of an effect of belief in conspiracy theories on loneliness (H1), nor of effects of conspiracy mentality on either loneliness (H3) or connectedness (H4). We did find evidence of a negative effect of belief in conspiracy theories on connectedness in our preregistered analysis testing H2, but this finding was not upheld in better-fitting alternative model specifications, making our conclusions about H2 equivocal. What might explain these results?

One possibility is that they stem from limitations with respect to statistical power or external validity. Despite the relatively large sample size and number of waves, the low quantity of within-person variance for most of our constructs resulted in only modest statistical power to detect small effects (see Mulder, 2023). The confidence intervals for the estimated effects produced in our analyses convey which effect sizes are incompatible with the data (Kruschke and Liddell, 2018). For example, via the preregistered test of H1, we can see that an effect of belief in conspiracy theories on loneliness outside the interval [−0.128, 0.046] can be rejected at the 0.05 alpha level. In other words, the data are incompatible with the true effect of belief in conspiracy theories on loneliness being very large, but fairly substantial negative (or even moderately positive) effects cannot be rejected. Interested readers can interpret the confidence intervals for our other hypotheses estimates in a similar way; most of the confidence intervals from our preregistered analyses are of comparable width to this one.

In addition to modest power, our estimates may also be subject to a risk of bias. While the RI-CLPM (and the other models we estimated) provide protection against time-invariant confounds that have time-invariant effects, they could be vulnerable to time-varying confounds (see Rohrer and Murayama, 2023), or confounds that are themselves stable but have effects that vary over time (Murayama and Gfrörer, 2025). In principle, such confounds could bias estimated effects either upwards or downwards. Similarly, it is entirely possible that causal effects were present, but operating more quickly or slowly than implicitly assumed by our 1-month lags.

Limitations with respect to construct validity (i.e., measurement) are also applicable. Our measures of loneliness and connectedness are by no means perfectly valid, and do not comprise objective measures of the social interactions of our participants (De Jong Gierveld et al., 2006). Endorsements of conspiracy theories may not necessarily reflect beliefs in those theories, but could rather reflect plausibility judgments (Nera and Sutton, 2026), or even expressions of humor or insincerity (Williams et al., 2026). Our conspiracy theory items also contained theories that vary in terms of politicization, social acceptability, and other characteristics; it is possible that changes in belief in some specific theories produce effects on loneliness or connectedness that are masked when these items are combined into a score. Interested readers could use our open data to conduct additional analyses with individual items or subsets of items.

Limitations with respect to external validity apply also, and could explain our findings: We applied convenience sampling, and it is possible that important causal effects between conspiracy beliefs or mentality and loneliness or connectedness do occur in the population as a whole, or a subset thereof that was underrepresented in our sample. For example, most of our sample could reasonably be classed as “non-believers” and it is possible that quite different effects apply within the subpopulation of people who strongly believe multiple conspiracy theories.

Nevertheless, our findings are consistent with those of Bertlich et al. (2025) who likewise found no evidence of either belief in specific conspiracy theories or conspiracy mentality affecting loneliness (or vice versa). It is therefore reasonable to entertain the possibility that these findings are valid: That belief in conspiracy theories and conspiracy mentality don't have substantial effects on loneliness or connectedness (at least not on average). After all, while developing an enthusiasm for conspiracy theories might well strain relationships with some family members, friends and co-workers, it could just as well enhance relationships with others. There are also a range of communities dedicated to discussing conspiracy theories (de Wildt and Aupers, 2024; Engel et al., 2022; Klein et al., 2019; Rogalla, 2022), which plausibly provide social benefits and a sense of connection to some of their members. Positive and negative effects may thus counteract each other, resulting in a near-zero net effect. That said, this scenario would not rule out the possibility of very substantial positive or negative effects of belief in conspiracy theories on loneliness or connectedness for some individuals.

Future directions

Our findings are inconsistent with either loneliness or social connectedness being important causes or consequences of belief in conspiracy theories. However, we cannot rule out the possibility that more nuanced, fine-grained analyses may find that particular forms of social isolation follow from, and/or partly cause, conspiracy beliefs, in at least some people. Future studies could be modeled on existing research that examines the diversity of causal pathways that underpin the relationships between social isolation and wellbeing (for reviews see Cacioppo et al., 2015; Heinrich and Gullone, 2006). This research has shown that different forms of isolation are associated with different consequences. For instance, objective social isolation (e.g., size of social network) is more strongly associated with physical wellbeing while loneliness (perceived social isolation) has a greater psychological impact (Hong et al., 2023; see also Wolters et al., 2023).

Relatedly, it is possible that conspiracy belief affects some forms of loneliness or isolation but not others. For instance, social loneliness may not result for conspiracy believers who join like-minded communities where people collaborate on projects and provide support and validation of believers' concerns (Franks et al., 2017; Hill et al., 2026). However, these groups likely cannot substitute for lost intimate relationships, and thus will not address emotional loneliness. By using instruments that measure subtypes of isolation and loneliness (e.g., Berkman and Syme, 1979; de Jong-Gierveld and Kamphuls, 1985) future studies may be able to establish if only specific types of social disconnection are linked to belief in conspiracies—either as a cause and a consequence.

In addition to considering different kinds of isolation and loneliness, it might be the case that the cause of the isolation is a key determinant of conspiracy belief. Experimental work shows that ostracism and social exclusion have been shown to impair cognition (Buelow and Wirth, 2017) and increase the endorsement of conspiracy theories (Poon et al., 2020). A hypothesis worth testing is that loneliness that results from active exclusion is especially likely to lead to belief in conspiracies, whereas loneliness that results from experiences such a bereavement or non-conspiracy-related difficulties with maintaining close relationships is not.

Finally, future research would do well to consider how different forms of social isolation could be incorporated into existing models of conspiracy belief formation, to better understand the conditions under which isolation and conspiracy belief might be causally connected. For instance, van Prooijen's (2020) existential threat model describes a process that requires a variety of cognitive and social conditions to coincide before conspiracy beliefs are likely to develop: perception of an existential threat; sense-making about the causes of the threat; and the presence of a salient antagonistic outgroup. Social isolation could impact this conspiracy development process in a number of ways. For instance, loneliness might elevate a perceived threat to ‘existential' levels. Indeed, chronic loneliness may constitute the primary existential threat that triggers extensive ‘ego protective' sense making (Bierwiaczonek et al., 2024). Similarly, hypervigilance driven by loneliness may make outgroups more salient as potential malicious antagonists (Spithoven et al., 2017; c.f., Faig et al., 2025). Objective social isolation could also impact people's sense-making by creating an epistemic bubble (Nguyen, 2020) where alternative perspectives that might help reduce the attractiveness of a conspiratorial explanation are absent or suppressed. Importantly though, even if loneliness or social isolation exacerbate these psychological and environmental drivers, conspiracy belief will not necessarily eventuate, if, for instance, no outgroup is identified or, if one is, it is not considered to be antagonistic. Thus, the pathway from social isolation to conspiracy belief may be contingent on a variety of conditions occurring.

We were unable to detect predictive, longitudinal relationships between either loneliness or social connectedness and conspiracy belief using multiple measures and model specifications in a sample that was mostly skeptical of conspiracy theories. If perceived deficits in social connection cause increased belief in conspiracies—or belief in conspiracies causes deficits in perceived social connection–the effects appear to be small, uncommon, or both. However, our findings do not rule out the possibility that social disconnection and conspiracy belief are causally connected in more subtle ways that are contingent on the presence of other factors, that occur over durations shorter or longer than we studied, or via connections that are concentrated in particular subgroups (e.g., people with very high levels of belief in conspiracy theories).