网络分析整合 Quantum-8-Personality 与 Big Five 人格特质：一项针对 1,052 名中国成人的研究

Abstract

Background:

The Big Five Personality (BFP) model is an established dimensional framework, whereas the Quantum-8-Personality (Q8P) model is an emerging culturally grounded framework derived from Chinese philosophical traditions. This study explored dimension-level relationships between the two frameworks.

Methods:

Network analysis was conducted using data from 1,052 Chinese adults who completed the Quantum-8-Personality Scale (Q8PS) and Big Five Inventory-2 (BFI-2).

Results:

The largest cross-framework connections involved Q8P Contradiction and BFP neuroticism, Q8P Extraversion and BFP extraversion, and Q8P Yanggang and BFP conscientiousness. These associations were small to moderate in magnitude.

Conclusion:

The findings identify preliminary areas of correspondence between the Q8P and BFP dimensions but do not establish equivalence, convergent validity, or incremental validity of the Q8PS. The results should be interpreted as exploratory because the factorial validity of the Q8PS remains provisional.

1 Introduction

Personality is the characteristic pattern of cognition, emotion, and behavior that differentiates individuals. It reflects a person’s relatively stable tendencies across situations and is a foundational element of psychological functioning (Corr and Matthews, 2020). Although personality traits exhibit considerable consistency over time, they are not entirely stable. They evolve in response to psychological maturation, life experiences, and social context (Hudson, 2021).

Personality is associated with social functioning and behavior across multiple areas of life. Meta-analyses have documented relationships between personality traits and physical and mental health, academic performance, and occupational outcomes (Terracciano et al., 2025; Sharifibastan et al., 2025; Du et al., 2025; Han et al., 2024; Chen et al., 2025; Lee et al., 2024). For example, conscientiousness is often associated with healthier behavior and stronger occupational performance, whereas neuroticism is associated with greater vulnerability to anxiety and stress-related problems. These findings illustrate the practical relevance of studying personality while not implying that any single framework provides a complete account.

The exploration of own and others’ personality has long been a central theme in psychological research. Multiple personality assessment methods exist, ranging from projective techniques and behavioral observations to structured interviews. Nevertheless, owing to its efficiency and scalability, self-report questionnaire is the most widely used approach. Several prominent models have been used in contemporary personality studies. These include the Big Five Personality Traits (also known as the Five-Factor Model), Myers–Briggs Type Indicator (MBTI), Eysenck’s Psychoticism, Extraversion, and Neuroticism (PEN) Model, HEXACO Personality Model, 16 Personality Factors (16PF), and Dark Triad (encompassing narcissism, Machiavellianism, and psychopathy). Each offers a distinct theoretical lens to analyze individual personality differences (Table 1).

Table 1

The main points of various personality assessment tools.

Of these, the Big Five Personality (BFP) has garnered extensive empirical support and academic attention. As of August 19, 2026, it has been cited over 2,650,000 times according to Google Scholar, reflecting its dominant role in the field. The model conceptualizes personality along five broad dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The HEXACO model builds upon this foundation by introducing a sixth dimension—honesty-humility—which captures variations in sincerity, fairness, and greed avoidance. In contrast, other models such as the PEN, 16PF, and Dark Triad have received comparatively less focus in recent research, although they are influential in specific applied contexts.

Culture is a crucial consideration in personality assessments. Cultural values, beliefs, and social norms shape the expression and development of personality traits, implying that instruments developed in one cultural context may not be fully appropriate in another (Bar and Otterbring, 2021; Allik and McCrae, 2004).

China, with its 5,000-year history of continuous civilization and rich philosophical heritage, presents a distinct cultural landscape. Many personality assessments used in China have been adapted from Western scales such as the Big Five Inventory-2 (BFI-2) and the MBTI. Although these adaptations provide valuable insights, they may have overlooked culturally specific constructs.

Notable exceptions exist in this regard. For example, the Five Pattern Scale, derived from the theory of traditional Chinese medicine, interprets personality through the lens of Wu Xing (the five elements)—metal, wood, water, fire, and earth—each associated with specific emotional and behavioral tendencies (Zhang et al., 2019). Similarly, the Quantum-8-Personality (Q8P) model is rooted in Chinese cultural philosophy, specifically the I Ching (Li, 2018) (Table 2).

Table 2

Quantum-8-personality and BaGua.

The I Ching is an ancient Chinese philosophical and divination system that has influenced East Asian thought for millennia (TenHouten and Wang, 2001). Its core components, the eight trigrams (BaGua), symbolize the dynamic and interdependent interplay between yin (represented by a broken line and associated with receptivity and negativity) and yang (unbroken line and activity and positivity). This system posits that all natural and human phenomena are results of a continuous transformation and balance between these two fundamental forces (Pham et al., 2006). Each trigram consists of three Yao (lines), and each Yao embodies both yin and yang qualities, reflecting the philosophy of duality and perpetual change (Table 2).

The Q8P model translates the ancient symbolism into a modern personality framework. The Q8P Scale (Q8PS) operationalizes this model with items largely derived from the theoretical work of Li (2018) and Ma (2024).

It proposes eight primary personality types, each corresponding to one of the trigrams and encompassing a unique combination of cognitive, emotional, and behavioral attributes rooted in the I Ching wisdom. Though eight-factor structure was not confirmed in an independent sample and that the scale should be regarded as provisional, this approach offers a culturally grounded alternative for understanding personality in the Chinese philosophical context.

The Q8PS exhibits the following key characteristics:

• Incorporates dimensions such as Yinrou, Introversion, Sensitivity, Deliberation, and Harmoniousness, which are distinct from the Big Five BFP facets.

• Employs culturally resonant representative figures to improve participant engagement and comprehension, offering an innovative approach for future AI-based personality assessments (Evin et al., 2022).

• It is highly practical, with an average completion time of 3 min (compared to 6 min for BFI-2; Zhang et al., 2022), and a response rate of 99–100% for 40 items.

• Uses a straightforward and interpretable scoring system. First, one personality corresponds to five items, and dimension scores are computed by summing the five corresponding item score. Second, the dimension with the highest score indicates the dominant personality type of the test-taker. Third, the evaluation incorporates overall score profiles to provide a comprehensive personality trait assessment. For instance, high scores on both Sensitivity and Contradiction dimensions may suggest a strong ability to perceive information, coupled with a limited “memory” capacity that impedes complex information processing; potentially leading to indecisiveness, internal conflict, and emotional behavior.

As a newly developed instrument, the Q8PS requires further validation. This study aimed to investigate the relationships between the Q8P and BFP dimensions through network analysis, facilitating cross-cultural integration and providing additional theoretical and methodological tools for personality research.

The two instruments differ in origin, validation status, and intended use. The BFI-2 is an established dimensional trait inventory supported by large-sample psychometric evaluation, including validation in Chinese populations (Zhang et al., 2022), and is used to compare broad personality domains. In contrast, the Q8PS is an emerging theory-derived descriptive instrument intended to characterize eight culturally grounded personality tendencies. It has not been validated for clinical diagnosis, personnel selection, nationally normed individual decisions, or use as a substitute for the BFI-2. Accordingly, the present study does not assume that the two instruments are equivalent or interchangeable; rather, it uses network analysis to examine exploratory dimension-level correspondence in Chinese adults while recognizing the provisional validation status of the Q8PS.

Within the Q8PS, the eight constructs are conceptualized as broad, relatively enduring, cross-situational tendencies spanning cognition, affect, interpersonal behavior, and behavioral style rather than as isolated actions, values, or abilities. Yanggang emphasizes persistence, responsibility, and assertive leadership; Yinrou emphasizes receptivity and nurturance; Introvertion emphasizes restraint and low outward expressiveness; Extraversion emphasizes social expressiveness; Sensitivity emphasizes rapid perceptual and affective responsiveness; Deliberation emphasizes a slow, cautious response style; Contradiction emphasizes ambivalence and internal conflict; and Harmoniousness emphasizes interpersonal attunement and accommodation. The eight scores are treated as correlated, nonorthogonal profile dimensions; the current model does not specify a higher-order hierarchy, and the highest score is descriptive rather than a mutually exclusive personality category. Conceptual overlap with the Big Five, HEXACO, interpersonal models, or trait emotional intelligence remains an empirical question requiring discriminant and incremental validation. Representative figures are used only as illustrations and are not part of scoring or validation.

2 Materials and methods

2.1 Participants

Participants were recruited using nonprobability convenience sampling. Trained university student investigators recruited adults through their social networks in their home provinces. Participants completed the Q8PS and BFI-2 as self-report measures in the same survey session.

The participants were 1,052 adults from various provinces (men: n = 412; women: n = 640; mean age = 26.18 years; SD = 11.2; range: 18–55 years). Confidentiality was assured, and participation was voluntary (Table 3).

Table 3

Basic characteristics of the population.

The analytic sample was predominantly young and highly educated: 676 participants (64.3%) were students, 786 (74.7%) reported a bachelor’s degree, and 135 (12.8%) reported postgraduate education. The largest province-level groups were Guangdong (n = 389, 37.0%), Beijing (n = 150, 14.3%), and Yunnan (n = 100, 9.5%); the complete distribution is reported in Table 3. All participants were recruited through the same general investigator-mediated social-network approach, but investigator-specific recruitment proportions were not retained. Because recruitment was nonprobability-based and many province-level cells were small, formal regional comparisons were not used to make population-representative claims.

The inclusion criteria were as follows: (1) age: 18–55 years; (2) education level of junior high school or above, with the ability to comprehend the scale’s content; and (3) no history of depression or mental illness self-reported.

2.2 Personality assessments

2.2.1 Quantum-8-personality scale (Q8PS)

The Q8PS was developed to operationalize eight personality constructs derived from traditional Chinese medicine mind–nature theory (Li, 2018; Ma, 2024): Yanggang, Yinrou, Introvertion, Extraversion, Sensitivity, Deliberation, Contradiction, and Harmoniousness. The item content was derived primarily from the theoretical descriptions of these eight constructs.

According to the development information available to the present authors, the developers first constructed an item pool and administered a preliminary version of the Q8PS to college students. Exploratory factor analysis was then used to screen the items, and items with primary factor loadings below 0.40 were removed. Participants were subsequently invited to comment on item comprehension and acceptability. Wording considered unclear or potentially uncomfortable was revised without intentionally changing the construct represented by the item. This process resulted in the current 40-item version of the Q8PS.

The available historical records did not contain the exact number of items in the original item pool, the sample size and demographic characteristics of the preliminary administration, the factor-extraction and rotation procedures used during the original item-screening analysis, the complete factor-loading and cross-loading matrix, or any prespecified item-deletion criteria beyond the loading threshold of 0.40. We therefore do not retrospectively infer or report procedures that could not be verified from the original development records. These limitations in the documentation of the initial scale-development process should be considered when evaluating the instrument.

To provide additional and reproducible psychometric evidence for the fixed 40-item version, we conducted a new exploratory factor analysis in the present sample of 1,052 adults from 25 Chinese provinces. The sample comprised 412 men and 640 women, with a mean age of 26.18 years (SD = 11.20; range = 18–55 years). All 40 items were complete and were treated as six-category ordered variables. The EFA was based on a polychoric correlation matrix. Sampling adequacy was acceptable (KMO = 0.881; minimum item MSA = 0.729), and Bartlett’s test of sphericity was significant, χ²(780) = 17,430.67, p < 0.001.

Factors were extracted using minimum residual estimation and direct oblimin rotation. Factor retention was evaluated primarily through parallel analysis using 500 simulated datasets, supplemented by the original and revised Velicer MAP procedures, the Kaiser criterion, comparisons of six- through nine-factor solutions, theoretical interpretability, and model parsimony. Parallel analysis supported eight factors, whereas the MAP procedures suggested six factors and the Kaiser criterion suggested nine. The eight-factor solution was retained for interpretation because it was supported by the primary empirical criterion and corresponded to the prespecified theoretical framework. This analysis evaluated the already finalized 40-item scale; it was not used to remove additional items.

The eight-factor solution provided only partial support for the proposed structure. Absolute primary loadings ranged from 0.296 to 0.769, maximum absolute cross-loadings ranged from 0.057 to 0.365, and communalities ranged from 0.328 to 0.640. Twenty-four of the 40 items met all four prespecified interpretive criteria: a primary loading ≥ 0.40, maximum cross-loading < 0.30, primary-to-cross-loading difference ≥ 0.20, and communality ≥ 0.30. The complete rotated pattern matrix is reported in Figure 1 and the accompanying item-level results.

Figure 1

The proposed correlated eight-factor model was subsequently examined in an independent sample of 1,022 participants who were not included in the EFA sample. Confirmatory factor analysis was performed using weighted least squares mean- and variance-adjusted estimation, with the items treated as ordered categorical variables. The model converged but showed inadequate fit: scaled χ²(712) = 11011.37, p < 0.001, CFI = 0.691, TLI = 0.661, RMSEA = 0.119 (90% CI [0.117, 0.121]), and SRMR = 0.123. Thus, the independent CFA did not confirm the proposed eight-factor structure. The eight-domain specification was retained as the theory-based scoring framework used in the present network analysis, but its factorial validity should be regarded as provisional.

The final Q8PS is a 40-item self-report measure assessing Yanggang, Yinrou, Introvertion, Extraversion, Sensitivity, Deliberation, Contradiction, and Harmoniousness. Items are rated on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree). Each subscale contains five items that are summed, with higher scores indicating a stronger expression of the corresponding trait.

In the present 1,052-participant sample, Cronbach’s α was 0.839 for the total Q8PS and ranged from 0.58 to 0.81 across the eight subscales (Table 4). Together with the mixed factor-analytic findings, these reliability estimates indicate that the psychometric evidence for the current 40-item scale remains provisional.

Table 4

Distribution of dimension.

Study variables: means, standard deviations, skewness, and kurtosis, Cronbach’s alpha n = 1,052.

2.2.2 Chinese Big Five inventory–2 (BFI-2)

Personality traits were assessed using the Chinese BFI-2 (Zhang et al., 2022). The instrument contains 60 self-report items measuring neuroticism, extraversion, openness, agreeableness, and conscientiousness, with 12 items per domain. Items are rated from 1 (strongly disagree) to 5 (strongly agree). The 24 reverse-keyed items were recoded as 6 minus the observed response before the 12 items in each domain were summed. In this study, the BFI-2 was used as an established comparison framework, not as an error-free gold standard or as sufficient evidence by itself for validating the Q8PS.

The psychometric performance of the BFI-2 was evaluated in the present 1,052-participant sample using all 60 items. A correlated five-factor CFA was estimated with robust maximum likelihood (MLR) in lavaan version 0.6–19, with WLSMV for ordered items as a sensitivity analysis. The conventional MLR indices were χ²/df = 4.967, RMSEA = 0.061 (90% CI [0.060, 0.063]), CFI = 0.578, TLI = 0.561, and SRMR = 0.086. The WLSMV analysis led to the same substantive conclusion (CFI = 0.663, TLI = 0.649, RMSEA = 0.073, and SRMR = 0.094). Raw-score Cronbach’s α values were 0.829 for neuroticism, 0.720 for extraversion, 0.362 for openness, 0.443 for agreeableness, and 0.707 for conscientiousness. Thus, the five-factor structure received limited rather than confirmatory support, and the low reliability of openness and agreeableness was considered when interpreting the network associations.

2.3 Statistical analysis

2.3.1 Descriptive statistic

The sample characteristics were examined using descriptive statistics via R version 4.2.1.

2.3.2 Network analysis

Following an initial assessment of the descriptive statistics (mean, standard deviation, kurtosis, and skewness) and internal consistency (Cronbach’s alpha) of the Q8PS and BFI-2 dimensions, we constructed a cross-sectional network comprising eight Q8PS dimensions and five BFI-2 domains. Each Q8PS dimension score was calculated by summing its five items, whereas each BFI-2 domain score was calculated by summing its 12 keyed items after the reverse-keyed items had been recoded. The analysis followed established reporting standards for cross-sectional psychological network studies (Burger et al., 2023).

Network analysis estimates and visualizes the conditional relationships among psychological variables (Epskamp et al., 2012, 2018). Compared with conventional correlation analyses, it allows the association between two nodes to be examined while controlling for all other nodes in the network (Bringmann and Eronen, 2018). This approach has previously been used to investigate relationships between dimensions derived from different personality questionnaires (Di Fabio et al., 2023; Zhang et al., 2025).

The network analysis was conducted as follows. First, zero-order associations among the 13 dimensions were calculated using Spearman rank correlations. The regularized partial correlation network was then estimated from a Pearson product–moment correlation matrix using the graphical least absolute shrinkage and selection operator with Extended Bayesian Information Criterion model selection (EBICglasso). The network was estimated using bootnet::estimateNetwork with default = “EBICglasso” and corMethod = “cor.” The EBIC hyperparameter (γ) was set to 0.50, and 100 candidate regularization values were evaluated with a minimum λ ratio of 0.01. The model was estimated without refitting or additional thresholding. Pairwise-complete observations and the pairwise-average sample-size rule were specified; however, no values were missing across the 13 variables in the final sample of 1,052 participants.

Second, we examined the nodes and edges, together with expected influence (EI) and bridge expected influence (BEI). The network comprised 13 nodes, of which eight represented Q8PS dimensions and five represented BFI-2 domains. Edges represented regularized partial correlations between two dimensions after controlling for the other 11 dimensions. Positive and negative edges indicated conditional associations in the corresponding directions, and edge thickness represented the absolute edge weight. A spring layout was used to visualize the weight matrix, but the spatial positions of the nodes were not interpreted inferentially.

EI was calculated as the signed sum of all edge weights connected to a node. Unlike strength centrality, EI retains the direction of negative associations and is therefore suitable for networks containing both positive and negative edges (Robinaugh et al., 2016). Higher EI values indicated that a node had relatively stronger overall connections with the other nodes in the network.

One-step BEI was calculated as the signed sum of the edges connecting a node to nodes in the other community. The two communities were defined a priori as the eight Q8PS dimensions and the five BFI-2 domains, respectively, and BEI was calculated using networktools::bridge. No numerical cutoff or statistical threshold was used to classify nodes categorically as bridge nodes. Therefore, BEI was interpreted as a continuous measure, and nodes were described as having relatively high or low BEI based on their estimates, rankings, uncertainty intervals, and bootstrap comparisons (Jones et al., 2021).

Third, we assessed the stability of EI and BEI using 1,000 case-dropping bootstrap samples. Between 5 and 75% of participants were progressively removed across 10 sampling levels. Correlation-stability (CS) coefficients were calculated using a correlation threshold of 0.70. The CS coefficient represents the maximum proportion of cases that can be removed while retaining, with 95% probability, a correlation of at least 0.70 between the centrality estimates obtained from the reduced and original samples. CS coefficients above 0.25 were considered acceptable, whereas values above 0.50 were considered good (Epskamp et al., 2018).

Finally, edge-weight accuracy was evaluated using 1,000 nonparametric bootstrap samples. For each edge, a 95% percentile bootstrap confidence interval was calculated from the 2.5th and 97.5th percentiles of its bootstrap distribution. Narrower confidence intervals indicated greater estimation precision, whereas wider intervals indicated greater uncertainty (Epskamp et al., 2018). Nonparametric bootstrap difference tests were also used to compare edge weights with one another and to compare EI and BEI values across nodes. These tests evaluated whether the bootstrap distribution of the difference between two estimates included zero; they did not test whether an individual edge was statistically different from zero. No multiplicity correction was applied because these comparisons were exploratory, and the difference-test results were interpreted cautiously rather than as confirmatory hypothesis tests.

All network analyses were performed using R version 4.4.1. Zero-order correlations were calculated using Hmisc::rcorr in Hmisc version 5.2–1. Network estimation, bootstrap analyses, and CS coefficients were obtained using bootnet::estimateNetwork, bootnet::bootnet, and bootnet::corStability in bootnet version 1.6. Network visualization was performed using qgraph::qgraph in qgraph version 1.9.8, and BEI was calculated using networktools::bridge in networktools version 1.6.0. A fixed random seed of 20,250,823 was used to ensure reproducibility.

3 Results

3.1 Descriptive statistic

Table 4 presents the descriptive statistics of the study variables, and Figure 2 displays the zero-order Spearman correlations among the 13 dimensions.

Figure 2

Cronbach’s α for the complete 40-item Q8PS was 0.839, and the eight Q8PS subscale coefficients ranged from 0.58 to 0.81. For the BFI-2 domains, α was 0.829 for neuroticism, 0.720 for extraversion, 0.362 for openness, 0.443 for agreeableness, and 0.707 for conscientiousness. The low openness and agreeableness coefficients indicate substantial measurement uncertainty for network associations involving these domains.

3.2 Network analysis

3.2.1 Network model

Figure 3 illustrates the EBICglasso regularized partial correlation network linking the Q8PS and BFI-2 dimensions. Node colors indicate the two prespecified communities. Blue edges represent positive conditional associations, red edges represent negative conditional associations, and thicker edges indicate larger absolute edge weights.

Figure 3

The network contained 78 possible edges, of which 60 were nonzero (76.92%): 47 were positive and 13 were negative. The estimated edge weights ranged from −0.160 to 0.339. Table 5 presents the complete regularized weight matrix.

Table 5

Weight matrix for the EBICglasso regularized partial correlation network.

The largest cross-framework edge connected Q8P Contradiction and BFP neuroticism (weight = 0.215), followed by Q8P Extraversion and BFP extraversion (weight = 0.190).

Q8P Contradiction was also positively connected with BFP agreeableness (weight = 0.161) (tentative). These three positive edges were the most prominent cross-framework associations in the estimated network.

The largest negative cross-framework edge connected Q8P Yanggang and BFP neuroticism (weight = −0.142).

BFP conscientiousness was positively connected with Q8P Yanggang (weight = 0.126), Sensitivity (weight = 0.124), and Introvertion (weight = 0.122).

Additional cross-framework edges were smaller in absolute magnitude, including Q8P Yanggang–BFP extraversion (weight = 0.110) and Q8P Introvertion–BFP extraversion (weight = −0.097).

The remaining cross-framework edge weights were below 0.10 in absolute value.

Although these edges were the largest cross-framework connections relative to the other estimated edges, their absolute magnitudes were small to moderate.

Accordingly, the network findings were interpreted in terms of relative prominence and estimation uncertainty rather than as evidence of large effects.

3.2.2 Expected influence and bridge expected influence

The EI and BEI estimates, their ranks, and 95% bootstrap confidence intervals are presented in Figures 4–6 and Table 6. Q8P Harmoniousness (EI = 0.993, 95% CI [0.867, 1.103]), BFI-2 conscientiousness (EI = 0.993, 95% CI [0.886, 1.088]), and BFI-2 extraversion (EI = 0.976, 95% CI [0.865, 1.081]) showed the highest EI estimates. Q8P Yanggang showed the lowest EI (EI = 0.438, 95% CI [0.334, 0.548]). These rankings were interpreted relatively because several intervals overlapped.

Figure 4

Figure 5

Figure 6

Table 6

Expected influence and bridge expected influence estimates, ranks, and 95% bootstrap confidence intervals.

EI and one-step BEI are reported as unstandardized signed sums of edge weights. Ranks are shown in parentheses, with 1 indicating the highest estimate. Confidence intervals are 95% percentile intervals from 1,000 nonparametric bootstrap samples. Figures 5, 6 present BEI as a continuous measure. Figure 5 shows the node-specific estimates and uncertainty intervals, whereas Figure 6 displays the same BEI information continuously through node-border color within the estimated network.

Under the prespecified two-community definition, BFP conscientiousness had the highest BEI (BEI = 0.302, 95% CI [0.187, 0.399]), followed by Q8P Contradiction (BEI = 0.245, 95% CI [0.175, 0.326]), Q8P Extraversion (BEI = 0.241, 95% CI [0.158, 0.319]), BFP agreeableness (BEI = 0.227, 95% CI [0.134, 0.306]) (tentative), and BFP extraversion (BEI = 0.203, 95% CI [0.119, 0.294]). Bootstrap difference tests did not reliably distinguish BFP conscientiousness from these other relatively high-BEI nodes. Q8P Harmoniousness ranked tenth (BEI = 0.059, 95% CI [−0.018, 0.113]). Therefore, no nodes were classified categorically as statistically identified bridge nodes.

3.2.3 Bootstrap test results

Figure 7 presents the nonparametric bootstrap difference matrix for the nonzero edge weights. Black cells indicate pairs of edges whose bootstrap difference interval excluded zero, whereas grey cells indicate pairs that were not reliably distinguishable. These comparisons concern differences between edge weights and do not establish that an individual edge is statistically significant.

Figure 7

Figure 8 presents the accuracy analysis for all edge weights. The principal cross-framework estimates and 95% bootstrap confidence intervals were: Q8P Contradiction–BFP neuroticism, 0.215 [0.157, 0.268]; Q8P Extraversion–BFP extraversion, 0.190 [0.133, 0.248]; Q8P Contradiction–BFPagreeableness, 0.161 [0.103, 0.224]; and Q8P Yanggang–BFP neuroticism, −0.142 [−0.200, −0.075]. The intervals quantify estimation uncertainty; they should not be interpreted solely from the visual prominence of an edge.

Figure 8

Figures 9, 10 present bootstrap difference tests for EI and BEI, respectively. The separate case-dropping analysis shown in Figure 11 produced CS coefficients of 0.672 for EI and 0.750 for BEI, indicating good stability for both measures. A CS coefficient was not used to assess individual edges because edge accuracy was evaluated using the nonparametric bootstrap confidence intervals in Figure 8.

Figure 9

Figure 10

Figure 11

4 Discussion

This study examined conditional associations between eight Q8P dimensions and five BFP domains. The largest cross-framework edges connected Q8P Contradiction with BFP neuroticism, Q8P Extraversion with BFP extraversion, and Q8P Yanggang with BFP conscientiousness. Although these edges were relatively prominent within the estimated network, their absolute magnitudes were small to moderate and should not be interpreted as evidence that the paired constructs are equivalent.

The positive Q8P Contradiction–BFP neuroticism association is consistent with shared negative-emotional content, whereas the Q8P Extraversion–BFP extraversion and Q8P Yanggang–BFP conscientiousness associations indicate partial correspondence in sociability and goal-directed responsibility. These patterns are descriptive associations among summed domain scores. They do not, by themselves, establish convergent validity, because the present study did not estimate a multitrait–multimethod measurement model or compare the constructs with prespecified external validity criteria.

The remaining Q8P dimensions showed more diffuse profiles across the BFP domains rather than clear one-to-one correspondence. Such patterns may help generate hypotheses about culturally grounded personality content, but they do not demonstrate that these dimensions are unique to the Q8PS or provide incremental validity beyond the BFP. Establishing those claims would require direct comparisons with validated measures and prediction of theoretically relevant external outcomes.

We therefore removed the earlier speculative comparisons linking selected Q8P dimensions to HEXACO honesty–humility or emotional intelligence. Neither HEXACO nor a validated emotional-intelligence measure was administered in this study, so those proposed connections cannot be evaluated empirically with the present data.

Bridge expected influence was interpreted continuously rather than used to classify statistically significant bridge nodes. BFP conscientiousness had the highest estimated BEI, but bootstrap comparisons did not reliably distinguish it from several other relatively high-BEI nodes. Accordingly, the bridge results identify relative patterns of cross-framework connectivity rather than categorical bridge traits.

Taken together, the network provides an exploratory map of how scores from the two frameworks relate in this Chinese adult sample. Interpretation should remain limited by the Q8PS’s provisional factorial validity, the reliability of several subscales, the convenience-sampling design, and the cross-sectional nature of the data (Table 7).

Table 7

Definitions of key constructs.

5 Conclusion

This exploratory network study identified several modest conditional associations between the Q8P and BFP dimensions. The most prominent cross-framework pairings suggest possible areas of content correspondence, but the findings do not establish equivalence, convergent validity, or incremental validity of the Q8PS. The results should be interpreted as exploratory because the factorial validity of the Q8PS remains provisional.

The Q8PS should therefore be regarded as an emerging, culturally grounded descriptive instrument whose eight-domain scoring framework remains provisional. Further item refinement and validation against independent samples, validated comparison measures, and external behavioral criteria are required before broader applied use can be recommended.

However, this study had several limitations. First, the cross-sectional design limited our ability to infer causal relationships between the Q8P and BFP dimensions. Longitudinal or experimental designs are required to explore the developmental trajectories and causal mechanisms underlying these personality constructs.

Second, the sample predominantly comprised young adults from China, which may limit the generalizability of the results to other age groups, cultures, and clinical populations. Future studies should include participants from diverse demographic and cultural backgrounds to validate the cross-cultural applicability of the Q8PS.

Third, although the Q8PS demonstrated good internal consistency and test–retest reliability, further validation is needed in different contexts such as clinical settings or organizational environments to establish its broader utility and predictive validity.

Fourth, while innovative, the network analysis approach relies on partial correlations and may be sensitive to sample-specific variations. Replication of independent samples is necessary to confirm the stability and robustness of the identified network structure and bridge nodes.

Finally, the current study did not examine the behavioral or outcome correlates of the Q8P dimensions (e.g., mental health and job performance). Future research should explore how these culturally grounded traits predict real-world outcomes and enhance the practical relevance of the Q8PS.

Statements

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Ethics statement

The studies involving humans were approved by the Beijing University of Chinese Medicine (Approval No: 2024BZYLL0317). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

QL: Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing. YuM: Data curation, Validation, Visualization, Writing – original draft, Writing – review & editing. YoM: Methodology, Supervision, Writing – review & editing. LL: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by Taiwan Traditional Chinese Medicine Research Base Project of the Taiwan Affairs Office (GTB2017-170).

Acknowledgments

We sincerely thank the volunteers for their participation in the present study.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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References

• 1

  AirhihenbuwaC. O. (1990). A conceptual model for culturally appropriate health education programs in developing countries. Int. Q. Community Health Educ.11, 53–62. doi: 10.2190/LPKH-PMPJ-DBW9-FP6X,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2

  AllikJ.McCraeR. R. (2004). Toward a geography of personality traits: patterns of profiles across 36 cultures. J. Cross-Cult. Psychol.35, 13–28. doi: 10.1177/0022022103260382

  • CrossRef
  • Google Scholar

• 3

  AshtonM. C.LeeK. (2009). The HEXACO–60: a short measure of the major dimensions of personality. J. Pers. Assess.91, 340–345. doi: 10.1080/00223890902935878,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  BarA.OtterbringT. (2021). The role of culture and personality traits in board game habits and attitudes: cross-cultural comparison between Denmark, Germany, and USA. J. Retail. Consum. Serv.61:102506. doi: 10.1016/j.jretconser.2021.102506

  • CrossRef
  • Google Scholar

• 5

  BringmannEronen (2018, 2018). Don’t blame the model: reconsidering the network approach to psychopathology. Psychol. Rev.125, 606–615.

  • Google Scholar

• 6

  BurgerJ.IsvoranuA.-M.LunanskyG.HaslbeckJ. M. B.EpskampS.HoekstraR. H. A.et al. (2023). Reporting standards for psychological network analyses in cross-sectional data. Psychol. Methods. 28, 806–824. doi: 10.1037/met0000471

  • CrossRef
  • Google Scholar

• 7

  CattellR. B. (1945). The description of personality: principles and findings in a factor analysis. Am. J. Psychol.58, 69–90. doi: 10.2307/1417576

  • CrossRef
  • Google Scholar

• 8

  ChenS.CheungA. C. K.ZengZ. (2025). Big five personality traits and university students' academic performance: a meta-analysis. Personal. Individ. Differ.240:113163. doi: 10.1016/j.paid.2025.113163

  • CrossRef
  • Google Scholar

• 9

  CorrP. J.MatthewsG. (2020). The Cambridge Handbook of Personality Psychology. Cambridge, UK: Cambridge University Press.

  • Google Scholar

• 10

  DuQ.RoordaD. L.Koomen HelmaM. Y.JakS.ZeeM. (2025). When personal becomes relational: a meta-analysis and narrative review on personality traits and children's affective relationships with adults. Educ. Res. Rev.48:100688. doi: 10.1016/j.edurev.2025.100688

  • CrossRef
  • Google Scholar

• 11

  Di FabioA.SaklofskeD. H.GoriA.SvicherA. (2023). A network analysis of relationships between the trait emotional intelligence questionnaire short form (TEIQue-SF) and the big five personality traits in Italian workers. Personal. Individ. Differ.210:112228. doi: 10.1016/j.paid.2023.112228

  • CrossRef
  • Google Scholar

• 12

  EpskampS.BorsboomD.FriedE. I. (2018). Estimating psychological networks and their accuracy: a tutorial paper. Behav. Res. Methods50, 195–212. doi: 10.3758/s13428-017-0862-1,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  EpskampSCramerA. O. J.WaldorpL. J.SchmittmannV. D.BorsboomD. (2012). Qgraph: network visu-alizations of relationships in psychometric dataJ. Stat. Softw., 48, 1–18.

  • Google Scholar

• 14

  EvinM.Hidalgo-MunozA.BéquetA. J.MoreauF.TattegrainH.BerthelonC.et al. (2022). Personality trait prediction by machine learning using physiological data and driving behavior. Mach. Learn. Appl.9:100353. doi: 10.1016/j.mlwa.2022.100353

  • CrossRef
  • Google Scholar

• 15

  HanA.KriegerF.KimS.NixonN.GreiffS. (2024). Revisiting the relationship between team members’ personality and their team’s performance: a meta-analysis. J. Res. Personal.112:104526. doi: 10.1016/j.jrp.2024.104526

  • CrossRef
  • Google Scholar

• 16

  HudsonN. W. (2021). “Dynamics and processes in personality change interventions,” in the Handbook of Personality Dynamics and Processes, ed. RauthmannJ. F., vol. 1 (Cambridge, MA, USA: Elsevier Academic Press), 1273–1295.

  • Google Scholar

• 17

  JonasonP. K.WebsterG. D. (2010). The dirty dozen: a concise measure of the dark triad. Psychol. Assess.22, 420–432. doi: 10.1037/a0019265,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 18

  JonesP. J.MaR.McNallyR. J. (2021). Bridge centrality: a network approach to understanding comorbidity. Multivar. Behav. Res.56, 353–367. doi: 10.1080/00273171.2019.1614898,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19

  LeeK.AshtonM. C. (2004). Psychometric properties of the HEXACO personality inventory. Multivar. Behav. Res.39, 329–358. doi: 10.1207/s15327906mbr3902_8,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  LeeY.BerryC. M.ReesR. (2024). Eye of the beholder: a meta-analysis of personality traits' relationships with psychological contract breach and job performance. J. Vocat. Behav.155:104064. doi: 10.1016/j.jvb.2024.104064

  • CrossRef
  • Google Scholar

• 21

  LiL. (2018). Tutorial on MindnaturalologyTheory in Traditional Chinese Medicine. Beijing: China Press of Traditional Chinese Medicine.

  • Google Scholar

• 22

  MaY. C. (2024). Theoretical Construction and Empirical Research on the Dimension Model of Mindnaturalology in Traditional Chinese Medicine. Beijing: Beijing University of Chinese Medicine.

  • Google Scholar

• 23

  McCraeR. R.CostaP. T. (2004). A contemplated revision of the NEO five-factor inventory. Personal. Individ. Differ.36, 587–596. doi: 10.1016/s0191-8869(03)00118-1

  • CrossRef
  • Google Scholar

• 24

  McCraeR. R.JohnO. P. (1992). An introduction to the five-factor model and its applications. J. Pers.60, 175–215. doi: 10.1111/j.1467-6494.1992.tb00970.x,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 25

  MyersI. B.McCaulleyM. H.MostR. (1985). Manual, a Guide to the Development and Use of the Myers–Briggs Type Indicator. London: Consulting Psychologists Press.

  • Google Scholar

• 26

  PadrellM.AmiciF.ÚbedaY.LlorenteM. (2023). Assessing Eysenck’s PEN model to describe personality in chimpanzees. Behav. Process.210:104909. doi: 10.1016/j.beproc.2023.104909,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 27

  PhamD. T.LiuH.DimovS. (2006) An I-ching-TRIZ inspired tool for retrieving conceptual design solutions proceedings of second virtual international conference on intelligent production machines and systems, IPROMS2006 (pp. 381–388).

  • Google Scholar

• 28

  PittengerD. J. (2005). Cautionary comments regarding the Myers-Briggs type indicator. Educ. Psychol. Meas.65, 229–238.

  • Google Scholar

• 29

  SharifibastanF.PallesenS.Enehaug MorkenK. T.HaaveP.ErevikE. K. (2025). Associations between HEXACO ersonality traits, substance use disorders, and behavioral addictions: a systematic review and meta-analysis. Addict. Behav.170:108433. doi: 10.1016/j.addbeh.2025.108433

  • CrossRef
  • Google Scholar

• 30

  RobinaughD. J.MillnerA. J.McNallyR. J. (2016). Identifying highly influential nodes in the complicated grief network. J. Abnorm. Psychol.125, 747–757. doi: 10.1037/abn0000181,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 31

  TenHoutenW. D.WangW. (2001). The eight trigrams of the Chinese I Ching and the eight primary emotions. Asian J. Soc. Psychol.4, 185–199. doi: 10.1111/1467-839x.00085

  • CrossRef
  • Google Scholar

• 32

  TerraccianoA.LuchettiM.KarakoseS.MillerA. A.StephanY.SutinA. R. (2025). Meta-analyses of personality change from the preclinical to the clinical stages of dementia. Ageing Res. Rev.112:102852. doi: 10.1016/j.arr.2025.102852

  • CrossRef
  • Google Scholar

• 33

  ZhangB.LiY. M.LiJ.YeY.YinL.ChenZ.et al. (2022). The big five inventory–2 in China: a comprehensive psychometric evaluation in four diverse samples. Assessment29, 1262–1284. doi: 10.1177/10731911211008245,

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  ZhangL.NiuS.MuF. Q.ZhengY.WuW. X.TianH. T.et al. (2025). Network analysis of first-onset depression and borderline personality traits: results from a 1-year longitudinal study in 7474 community-based residents. J. Affect. Disord.386:119473. doi: 10.1016/j.jad.2025.119473

  • CrossRef
  • Google Scholar

• 35

  ZhangJ.WangT.DuJ.YuanR.FanY.ChenY.et al. (2019). Correlations between five-pattern personality scores from traditional Chinese medicine and autonomic nervous response indicators in healthy female college students. J. Tradit. Chin. Med. Sci.6, 131–137. doi: 10.1016/j.jtcms.2019.03.003

  • CrossRef
  • Google Scholar

Keywords

Big Five personality, cultural differences, network analysis, personality assessment, Quantum-8-personality

Citation

Lin Q, Ma Y, Ma Y and Li L (2026) Bridging eastern and western personality frameworks: a network approach integrating the Quantum-8-personality and Big Five personality traits. Front. Psychol. 17:1704706. doi: 10.3389/fpsyg.2026.1704706

Received

13 September 2025

Revised

01 September 2026

Accepted

17 September 2026

Published

02 October 2026

Volume

17 - 2026

Updates

Copyright

© 2026 Lin, Ma, Ma and Li.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Liangsong Li, 13910174590@139.com

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