Study 1 sample (N = 500) was randomly split into two datasets of equal size, a “discovery sample” (n = 250) and a “confirmation sample” (n = 250). The first sample was used to investigate the factor structure of the ETMCQ using exploratory factor analysis (EFA). Item responses were treated as ordinal categorical variables, and polychoric correlations were estimated. Identification of the potential number of factors was informed by the eigenvalue >1.0 rule [33]. To confirm the model identified from the EFA, the confirmation sample was used to run a confirmatory factor analysis (CFA), with the correlation between identified factors allowed. The CFA compared a fully unconstrained model (Model 1); a 3-factor model with all 18 original items (Model 2), a 3-factor model with 15 items (i.e., excluding the item with the lowest loading on each factor) (Model 3), a 3-factor model with 15 items which also correlated residuals between items which were considered to be similar in content (Model 4). We report model fit indices for each Model (Table 3). In order to evaluate the goodness of fit of the factor structure, the following fit indices were used: the root mean square error of approximation (RMSEA), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and Standardized Root Mean Square Residual (SRMR). CFI and TFI scores above 0.9 were considered acceptable and over 0.95 considered good, whereas RMSEA and SRMR values below 0.08 and 0.05 were considered acceptable and good respectively [34, 35]. The best fitting CFA model was also constructed using the Study 2 sample. Stata16 [36] was used to conduct the EFA, and CFA was conducted using Mplus8 [37].

Correlational analyses of ETMCQ with developmental, psychological and psychopathology measures, whilst controlling for demographic features (e.g., age, gender, annual income and level of education) were conducted. Results were corrected for multiple comparisons using a 5% false discovery rate (FDR), based on the sequential Benjamini–Hochberg FDR correction algorithm [38].

The potential role of the three ETMCQ factors as mediators of the association between childhood adversity (measured by the CTQ total score) and current mental health symptoms (measured by the BSI total score) was explored using mediation analysis with the indirect effect of adversity through each of the factors estimated in a single model. Age, gender, income and level of education were included as covariates. Bias-corrected bootstrapped confidence intervals were estimated (5000 bootstrap replications). Analyses were performed separately in Studies 1 and 2, and results compared. Likewise, the moderating role of the three epistemic factors in the relationship between adversity, insecure attachment, or low self-efficacy and psychopathology was explored in both samples.

Further analyses, which were not preregistered, were then conducted to explore differences in scores on the ETMCQ between participants based on scores of their attachment style. In order to explore differences in epistemic stance based on attachment styles, participants were grouped into ‘high’ and ‘low’ scorers based on a median split of the data on both the ‘anxious’ and ‘avoidant’ subscales of the ECR-R [39]. Only Study 1 was used for this analysis as the ECR-R was not available in Study 2. Four groups were created from these splits: “Secure” (low anxious and low avoidant), “preoccupied” (high anxious, low avoidant), “dismissing” (low anxious, high avoidant) and “fearful” (high anxious, high avoidant) [40]. One-way ANOVAs and pairwise comparisons were performed between these groups for each of the three factors in Stata16. Results were corrected for multiple comparisons using a 5% false discovery rate (FDR) [38].

To aid interpretation and comparison, and as results in both studies were very similar, we report the results for both studies together.

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