Skip to main content

Personality inventory for DSM-5 brief form(PID-5-BF) in Chinese students and patients: evaluating the five-factor model and a culturally informed six-factor model

Abstract

Background

The Personality Inventory for DSM-5 Brief Form (PID-5-BF) is a 25-item measuring tool evaluating maladaptive personality traits for the diagnosis of personality disorders(PDs). As a promising scale, its impressive psychometric properties have been verified in some countries, however, there have been no studies about the utility of the PID-5-BF in Chinese settings. The current study aimed to explore the maladaptive personality factor model which was culturally adapted to China and to examine psychometric properties of the PID-5-BF among Chinese undergraduate students and clinical patients.

Methods

Seven thousand one hundred fifty-five undergraduate students and 451 clinical patients completed the Chinese version of the PID-5-BF. Two hundered twenty-eight students were chosen randomly for test-retest reliability at a 4-week interval. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted to discover the most suitable factor structure in China, measurement invariance(MI), internal consistency, and external validity were also calculated.

Results

The theoretical five-factor model was acceptable, but the exploratory six-factor model was more applicable in both samples (Undergraduate sample: CFI = 0.905, TLI = 0.888, RMSEA = 0.044, SRMR = 0.039; Clinical sample: CFI = 0.904, TLI = 0.886, RMSEA = 0.047, SRMR = 0.060). In the Chinese six-factor model, the Negative Affect domain was divided into two factors and the new factor was named “Interpersonal Relationships”, which was in line with the Big-Six Personality model in Chinese. Measurement invariance across non-clinical and clinical sample was established (configural, weak, strong MI, and partial strict MI). Aside from acceptable internal consistency (Undergraduate sample: alpha = 0.84, MIC = 0.21; Clinical sample: alpha = 0.86, MIC = 0.19) and test-retest reliability(0.73), the correlation between the 25-item PID-5-BF and the 220-item PID-5 was significant(p < 0.01). The six PDs measured by Personality diagnostic questionnaire-4+ (PDQ-4+) were associated with and predicted by expected domains of PID-5-BF.

Conclusions

Both the theoretical five-factor model and the exploratory six-factor model of the PID-5-BF were acceptable to the Chinese population. The five-factor model could allow for comparison and integration with other work on the original theoretical model. However, the Chinese six-factor structure may be more culturally informed in East Asian settings. In sum, the PID-5-BF is a convenient and useful screening tool for personality disorders.

Peer Review reports

Background

Personality disorders (PDs) are common psychiatric conditions that are disruptive to everyday functioning. Because PDs are often misdiagnosed or missed entirely, reliable and valid clinical diagnostic tools for PDs are needed [1]. In light of the numerous weaknesses of the traditional PD taxonomic diagnostic system, including a high comorbidity rate, arbitrary cutoff scores, and substantial heterogeneity within PDs in Section III of the DSM-5 [2], the APA (2013) proposed an Alternative Model of Personality Disorder (AMPD) diagnosis for six PDs (antisocial, avoidant, borderline, narcissistic, obsessive-compulsive and schizotypal), wherein 25 maladaptive personality trait facets are organized into five domains (Criterion B) after measuring personality functioning (Criterion A) [3]. The AMPD provides theoretical trait domains across six specified PDs, thus converting PD diagnosis from a categorical to a dimensional scheme.

The Personality Inventory for DSM-5 (PID-5) is a 220-item self-report measurement that was developed specifically to evaluate hierarchically organized personality traits in accordance with the AMPD. The reliability and validity of the PID-5 have been confirmed in multiple studies, which have yielded internal consistency values above 0.8 for most domains [4,5,6,7]. The five broad domains affirmed in prior studies [8, 9] have been described as comparable to maladaptive variants of the Big-Five Model [10], which would be expected given that the development of the associated dimensional model of personality pathology was informed by the normal personality taxonomy [11]. Although the PID-5 has satisfactory psychometric properties, its utility is limited due to its having a large number of items, and thus its taking substantial time to complete [12]. With the aim of screening for PDs quickly and accurately, the PID-5 Brief Form (PID-5-BF) was developed by extracting core items from the five domains (Negative Affect, Detachment, Antagonism, Disinhibition, and Psychoticism) of the PID-5 [13]. Correlation coefficients between the five PID-5-BF domains and the original PID-5 domains have been reported to be very good, with Bach et al. (2016) reporting a mean correlation coefficient value of 0.90 [14], and Debast et al. (2017) reporting correlation coefficients in the range of 0.81–0.87 [15].

PID-5-BF results provide an overall assessment of degree of personality maladjustment and point to potential PDs [16]. The PID-5-BF has been shown to be reliable and valid in a number of countries in Europe [14,15,16,17,18], North America [19,20,21], and Asia [22]. In all but two cases, internal consistency coefficients above 0.8 were obtained; internal consistency coefficients for Belgium [15] and Denmark [14] were between 0.66 and 0.78. The PID-5-BF has been shown to have satisfactory discriminant validity (effect size of mean domain level = 0.46, p < .05) between individuals with and without PDs [14]. In terms of criterion validity, each PD measured by Personality Diagnostic Questionnaire-4(PDQ-4) was associated with and predicted by theoretical PID-5-BF domains. Regarding internalizing and externalizing criteria, the PID-5-BF Negative Affect domain sub-score is a robust predictor of Inventory for Depression and Anxiety Symptoms-2 scores. Meanwhile, the PID-5-BF Disinhibition and Antagonism domain sub-scores are specifically predictive of Externalizing Spectrum Inventory scale scores. Somewhat unexpectedly, PID-5-BF Psychoticism domain subscores were found to be significantly predictive of scores for the aforementioned three scales [20]. PID-5-BF domain sub-scores and total scores correlated significantly with Personality Assessment Screener scores [21].

Although the PID-5 domains (Negative Affect, Detachment, Antagonism, Disinhibition, and Psychoticism) have been understood as putative maladaptive variants of Big-Five Model dimensions (Neuroticism, Extraversion, Agreeableness, Conscientiousness, Openness) respectively, the appropriateness of the Big-Five Model differs across cultures. For example, the Big-Five Model was found not well-fitted in India [23], the Philippines [24], Korea [25], and Japan [26]. In consideration of cultural dissociations of personality constructs, it is also of interest to note that the replicability of the Openness domain of the NEO Personality Inventory was also found to be poor in Asian countries in a cross-cultural study that included 24 cultures [27]. In addition, a six-factor personality model has been discovered to be more suitable than the five-factor structure in Chinese settings [28,29,30,31,32,33,34]. As one dimension of Big Six personality, Interpersonal Relationship was raised based on the Chinese context [28, 35], which reflected the humanistic ethic spirit of Chinese culture. Song et al. [31] developed the Chinese personality assessment inventory(CPAI) to measure the six-factor model, and in Cheung’s research, joint factor analysis on CPAI and NEO-PI-R confirmed the unique factor (Interpersonal Relatedness) loaded only by CPAI and none of the NEO-PI-R facets, which encompasses the traditional Chinese concepts of relationship orientation (Ren Qing; i.e. reciprocity of favors, affections, etc.), Harmony (e.g. lack of conflict, balance), and Face (as in “saving face” or reputation) [36, 37]. Similarly, the five-factor model of the PID-5-BF did not achieve an adequate fit in a study of Filipino college students [22]. Given that the five-factor model of the PID-5-BF has been supported principally in studies with Western samples, it is likely that cultural differences may underlie differing factor structure findings in the literature. Accordingly, the most suitable factor structure of the PID-5-BF in Chinese respondents remains to be clarified. In addition, Measurement invariance (MI) studies of the PID-5-BF should be conducted to determine whether factor structure differs between populations constituted by individuals of different cultures [38, 39]. Although MI of the PID-5 has been examined across cultures, sexes, and across clinical and non-clinical samples [40,41,42], the MI of the PID-5-BF is unknown.

The current study was the first to examine the psychometric properties of the Chinese version of the PID-5-BF. The aims of this study were threefold. First, we set out to determine the most suitable factor structure of the PID-5-BF in a Chinese population sample. Second, we assessed MI of the Chinese version of PID-5-BF across undergraduate students and clinical patient samples, which is important for the generalization of the PID-5-BF in both research and clinical settings. Third, to investigate criterion-related validity, we analyzed how well PID-5-BF scores correlate with scores obtained on the 220-item PID-5 and PDQ-4+, also conducted multiple regression analysis.

Methods

Participants

Seven thousand nine hundred eighty-five university students were recruited from two Chinese universities in Hunan Province from 2015 to 2017. After omitting subjects with missing data values, we retained a final undergraduate sample of 7155 subjects (3713 male, 51.9%, and 3436 female, 48.0%) with a mean age of 18.26 [standard deviation (SD), 1.33; range 18–23] years.

The clinical sample was collected from 2018 to 2020, including 451 outpatients (205 men, 45.5%, and 246 women, 54.5%), with a mean age of 24.62 (SD, 8.49; range, 18–57) years, who had been referred to the psychological clinic in hospital for assessment and treatment. Each patient was diagnosed based on the Structured Clinical Interview for DSM-IV system by two experienced psychiatrists (WX, LXW). The distribution of diagnoses in the clinical sample was as follows: PD, 43.2%; major depressive disorder, 22.6%; anxiety disorder, 4.9%; bipolar disorder, 7.1%; obsessive-compulsive disorder, 4.9%; schizophrenia, 3.1%; and other mental disorders (substance use disorders, somatic symptom disorder, eating disorder, Post-traumatic stress disorder), 14.2%.

All study procedures were approved by the Ethics Committee of Second Xiangya Hospital, Central South University. All participants signed informed consent.

Instruments

Chinese PID-5 and PID-5-BF

The full-length PID-5 [43] is a 220-item self-report scale developed in the USA to index 25 lower-order trait facets (Cronbach’s α, 0.72–0.96) organized into five higher-order trait domains of personality pathology (Cronbach’s α, 0.84–0.96) [43]. We invited two psychologists to translate the PID-5 scale from American English into Chinese; and then it was translated back into English by a bilingual teacher, with repeated revisions to ensure translation accuracy. We evaluated the scale in 10 undergraduate students, after the final adjustment of items, this version of PID-5 was set down (concrete steps were shown in Fig. 1). The PID-5-BF [44] was developed by extracting 25 items from the original PID-5, representing 21 of the 25 trait facets (facets not included: Restricted Affectivity, Rigid Perfectionism, Submissiveness, and Suspiciousness). Items are rated on a 0–3 Likert-type scale, with higher scores representing greater dysfunction. Each of the five higher-order domains is represented by five items (Negative Affect: Items 8, 9, 10, 11, and 15; Detachment: Items 4, 13, 14, 16, and 18; Antagonism: Items 17, 19, 20, 22, and 25; Disinhibition: Items 1, 2, 3, 5, and 6; and Psychoticism: Items 7, 12, 21, 23, and 24).

Fig. 1
figure1

Flowchart of procedures for translation of the Personality Inventory for the DSM-5-Brief Form (PID-5-BF)

PDQ-4+

The PDQ-4+ [45] is a self-report PD assessment scale based on the DSM-4. Items are answered as “yes” (scored as 1) or “no” (scored as 0). The Chinese version of the PDQ-4+ [46] used in the current study contains 107 items constituting 12 PD-type sub-scales. The PDQ-4+ has been used reliably in PD studies in China [47, 48], with Cronbach’s α values ranging from 0.49 (Passive-Aggressive) to 0.72 (Depressive).

Model testing procedures

In the undergraduate sample, 3985 students finished the 220-item PID-5, and 7155 students completed the PID-5-BF and PDQ-4+. For the clinical sample, we obtained 451 valid PID-5-BF, 395 valid PID-5, and 231 valid PDQ-4+ questionnaires. For evaluation of test-retest reliability, 228 undergraduate sample participants (93 male, 135 female) were chosen randomly for a PID-5-BF re-test taken 4 weeks after the initial test. Construct validity of the Chinese PID-5-BF was assessed with exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) after randomly (roughly) halving the undergraduate sample randomly into an EFA subsample (N = 3633) and a CFA subsample (N = 3522). CFA was conducted to test the theoretical model suggested being the best fitting model in the EFA.

MI tests across the population included four nested models. Model 1 (configural invariance) tested the factor structure of latent variables across our two population samples with all parameters freely estimated. Model 2 (weak invariance) was based on the configural results with factor loadings equalized across groups. Next, Model 3 (strong invariance) had the consistency of variable intercepts; achieving this model indicates that latent factor scores have the same meaning across groups, and thus that group comparisons are tenable. Model 4 (strict invariance) requires equalized error variance on the basis of the previous three models [49], which is rarely achieved [50].

Data analysis

Data analysis was performed in IBM SPSS Statistics 23.0 and Mplus 7.4. To examine construct validity, we first conducted EFA to identify the most suitable factor model of the PID-5-BF. Oblique rotation was used to allow correlation among factors. Each item with a factor loading ≥0.3 was accepted as a factor component [51]. For EFA and CFA, model fit indices applied included the comparative fit index (CFI), the Tucker-Lewis index (TLI), the standard root mean square residual (SRMR), and the root mean square error of approximation (RMSEA) with a 90% confidence interval (CI). Acceptable fit values were: CFI ≥ 0.90, TLI ≥ 0.90, SRMR ≤0.08, and RMSEA ≤0.08 [52]. Model modifications were made on the basis of item correlations and MI index values.

MI was estimated based on three indices, namely CFI, TLI, and RMSEA, wherein represents the difference between two adjacent models. Invariance was verified when CFI and TLI were ≤ 0.01 and RMSEA was < 0.015 [53]. In cases where these criteria were not met, the largest-value modification indices were selected to determine the parameters of which item(s) should be released to be free across groups iteratively until the CFI was ≤0.01, demonstrating potential partial invariance [40].

Internal consistency was determined by calculating Cronbach’s α and mean inter-item correlation (MIC) values. Cronbach’s α > 0.8 and > 0.9 signified acceptable and good reliability, respectively; a MIC > 0.15 was considered acceptable [54]. Test-retest reliability was estimated by calculating a Pearson’s correlation coefficient (r). To examine criterion validity, Pearson’s r index values were also calculated between the PID-5-BF and the original PID-5, as well as between the PID-5-BF and the PDQ-4+ (representing the six DSM-4 PDs retained in DSM-5, Section III). Pearson r values > 0.30 and > 0.50 indicated medium and large effect sizes, respectively [55]. Finally, multiple regression analysis was performed to evaluate multivariate models in which the PID-5-BF domains predicted PDQ-4+ scores using a step-by-step method. R2 was used to measure the effect size of each variate, higher value represents a stronger predictive effect; the variance inflation factor (VIF) was calculated in order to assess collinearity [56], < 5 represents non-collinearity among variables.

Results

Descriptive statistics

The mean (SD) PID-5-BF total score obtained for the full university student sample (N = 7155) was 16.4 (8.20), with females having a significantly greater (t = − 6.80, p < 0.01) mean score, at 17.10 (7.96), than males, at 15.79 (8.32). The mean (SD) PID-5-BF total score obtained for the clinical sample (N = 451) was 29.78 (10.70), with females again showing a significantly greater (t = − 4.37, p < 0.01) mean score, at 31.75 (10.98), than males, at 27.41 (9.86). The mean obtained for the clinical sample was significantly greater (t = 26.04, p < 0.01) than that obtained for undergraduate student sample. The mean (SD) PID-5-BF total score obtained for the sub-sample used in the EFA, at 16.29 (8.24) (N = 3633), was statistically similar (t = − 1.34, p = 0.18) to that obtained for the sub-sample used in the CFA, at 16.55 (8.11) (N = 3522).

EFA and CFA

EFA supported an exploratory five-factor model and an exploratory six-factor model. The factor designations of the items are compared across these two exploratory models and Krueger et al.’s (2013) previously reported five-factor model [44] in Table 1. In our exploratory five-factor model, five items (8, 9, 11, 15, and 20) did not load into any factor; item 10 was the only item to reach a 0.3 loading weight in the Negative Affect domain, and it loaded with item 19, which had belonged to the Antagonism factor in the previously published model. Our exploratory six-factor model had higher fit indices (CFI = 0.969, TLI = 0.944) than the exploratory five-factor model (CFI = 0.952, TLI = 0.922), with fewer items failing to load on any factor (items 8 and 11). A new factor named Interpersonal Relationships was added to the original five factors. The factor loading of each item in the exploratory six-factor model are reported in Table 2.

Table 1 Item comparison among three models
Table 2 Factor loading for the exploratory six-factor model (N = 3633)

We chose to pursue an analysis of our exploratory six-factor model because it had better model fit indices and fewer items that failed to load than our exploratory five-factor model and because of the significant differences in the Negative Affect domain between our exploratory five-factor model and the theoretical five-factor model. On the whole, both the theoretical five-factor model and exploratory six-factor model were acceptable. However, as shown in Table 3, CFA results showed that the six-factor model has achieved better indices when compared with the theoretical five-factor model, which indicated that the six-factor model would be more culturally-adapted in Chinese settings. As the theoretical five-factor model failed to meet the precondition of measurement invariance, we chose the six-factor model to conduct further analysis.

Table 3 Goodness of fit index values for the compared models

MI across populations

As shown in Table 4, we established configural, weak, and strong MI across the undergraduate and clinical samples. However, the acceptable index criteria were not met for strict MI. We allowed the residual variances of items with the largest modification indices to be freely estimated until the CFI of the last model was ≤0.01. Parameter constraints of items 15, 4, 14, 18, 17, 20, 7, 12, 24 and 5 were released in this process. Subsequently, partial strict MI of our modified six-factor model was supported. Hence, ultimately, our modified six-factor PID-5-BF model achieved configural MI, weak MI, strong MI, and partial strict MI across our undergraduate and clinical samples.

Table 4 Fit indexes of the PID-5-BF for MI across population

Reliability

In the undergraduate sample, we obtained a Cronbach’s α of 0.84, a MIC of 0.21 for the PID-5-BF total scale, and domain MICs in the range of 0.29–0.46. Domain sub-scores correlated significantly with total scores (r = 0.38–0.80, all p < 0.01). With respect to test-retest reliability over a 4-week interval, we obtained a Pearson correlation coefficient of 0.73 for the total scale, with domain correlation coefficients in the range of 0.50–0.67.

For the clinical sample, we obtained a Cronbach’s α of 0.86, a MIC of 0.19 for the total scale, and domain MICs in the range of 0.27–0.58. Similar to our results with the undergraduate sample, we observed significant correlations between the domain sub-scores and the PID-5-BF total score (r = 0.26–0.78).

Criterion validity

Correlation coefficients for each domain were in the range of 0.64–0.86 in the undergraduate student sample and in the range of 0.69–0.91 in the clinical patient sample. As shown in Table 5, the Interpersonal Relationships domain showed the greatest correlation coefficient with the Negative Affect domain in both samples. Finally, as shown in Table 6, PID-5-BF domain scores correlated significantly with the six PDs in Section III of the DSM-5 (schizotypal, antisocial, borderline, narcissistic, avoidant, and obsessive-compulsive).

Table 5 Correlations between the PID-5-BF and full-length PID-5
Table 6 Correlations between PID-5-BF domains and the six DSM-4 PDs retained in Section III of the DSM-5 and measured by PDQ-4 +

The results of multiple regression analysis were shown in Table 7. VIF values did not suggest any collinearity problem (all< 5) for the PID-5-BF scales. In summary, the PID-5-BF scales predicted a significant amount of variance in the PDQ-4+. Psychoticism was the best predictor in almost all of the six PDs except for the antisocial personality disorder, and the Interpersonal Relationships domain was the second-best predictor of the NPD, AVPD, and OCPD.

Table 7 Regression analysis results for PID-5-BF domains’ predictive effect of six SCID-II PDs in undergraduate students

Discussion

Similar to most previous studies of the PID-5-BF in other countries [14,15,16, 19, 20], the theoretical five-factor model was acceptable, but an exploratory six-factor model was more suitable and culturally-adapted in our Chinese sample. Following modifications, the Chinese six-factor model of PID-5-BF achieved configural MI, weak MI, strong MI, and partial strict MI across undergraduate and clinical samples. In agreement with prior studies [14, 20], we found that the PID-5-BF was highly correlated with the original 220-item PID-5, and the domains generally correlated with and predicted the six PDs retained in Section III of the DSM-5. We obtained acceptable Cronbach’s α and MIC values for the PID-5-BF [54], revealing a good internal consistency of PID-5-BF, similar to prior studies [16, 17, 20]. Furthermore, our finding of good test-retest reliability over a 4-week interval is in agreement with prior work showing similarly good test-retest reliability of the PID-5-BF over a 2-week interval in a sample of high school students [16].

Factor structure

Both the theoretical five-factor model and exploratory six-factor model were acceptable, while our results demonstrated that the six-factor structure was more culturally informed. Interestingly, a prior study conducted with Filipino college students also showed a relatively poor fit of the theoretical five-factor model for the PID-5-BF [22]. Thus, we speculate that the discrepancy may reflect differences in the way people from Western versus Eastern cultures understand personality constructs and thus interpret items of the PID-5-BF.

Four of the factors in our exploratory six-factor model were consistent with the theoretical five-factor structure. Only the Negative Affect domain failed to align, and items 10 (fear being alone) and 19 (I crave attention) were placed in the newly added factor called Interpersonal Relationships. According to traditional personality theories in Western cultures, which focus on internal characteristics of the individual [57], item 10 may remind people of loneliness, which is considered as the experience of negative feelings about missing or lacking relationships, emphasis of which is on emotion. On the contrary, in Eastern cultures, which are generally more collectivist, individuals are often considered to be inherently closely connected with others [58]. Accordingly, being alone may be viewed as social isolation, referring to the absence of relationships with others [59]. In Chinese culture, item 10 may express one kind of fear of being socially isolated, which failed to achieve interpersonal harmony. Item 19, which is associated with the Antagonism domain of the original PID-5-BF, refers to behaviors that put the individual at odds with others, including an exaggerated sense of self-importance and an expectation of special treatment [10]. However, in a collectivist society, one’s sense of belonging is an important aspect of his or her personality constitution [60]. Consequently, item 19 is likely to be understood as one’s desire to fit into a certain group and to communicate with others. In the Chines culture, item 19 is correlated with Face, the concept of which is more interpersonally connected [32]. Being focused on by others could increase one’s face and enhance confidence in social settings. Prior studies have explored the influences of collectivist versus individualist cultures on personality [60,61,62]. In summary, cultural differences in how one understands and interprets Items 10 and 19 may affect the factor loading of these two items.

The unique structure of the DSM-5 personality trait model in Chinese respondents, compared to respondents from most other examined countries, may be related to cultural differences in general personality models [43, 63]. Although the five-factor model has been widely used globally, it may not be fully applicable in a variety of cultural contexts due to its Western-central derivation. Consistent with this supposition, the five-factor model was not well-fitted when the NEO Personality Inventory was examined in the Philippines [24], Korea [25], and Japan [26]. Hence, it appears that the Big-Five Model does not fully explain personality traits in collectivist society contexts [36]. Prior studies have proposed a six-factor hypothesis of Chinese personality traits, with the addition of Interpersonal Relationships [28]. The importance of this sixth dimension for Chinese personality analysis has been affirmed in the Chinese Personality Assessment Inventory (CPAI) standardization studies [37], which contributed to the Big Six personality model. A recent study examining the relationships between Big Six personality and entrepreneurial intention discovered the decisive effect of the Interpersonal Relationship dimension in the Chinese settings for the first time [35], which was a shred of strong evidence to support the standpoint that the five-factor model was acceptable while the six-factor structure had more cultural applicability in East-Asian contexts. Moreover, this cultural-specific factor in PID-5-BF would help us to comprehend personality disorders from a cultural perspective.

MI

To the best of our knowledge, this study is the first to explore MI of the PID-5-BF. The establishment of MI provides evidence of a consistent underlying structure across groups and thus enables group means to be compared [38]. When performing nested MI modeling, as was done here with configural, weak, strong, and strict MI, MI must be established sequentially from lower- to higher-level MI analyses [64]. We were able to achieve MI fully with respect to factor structure (configural MI), metric (weak MI), and intercept (strong MI) equivalences for the PID-5-BF in both samples. Strict invariance was partially satisfied.

Because our modification index analyses led us to release constraints on Items 15, 4, 14, 18, 17, 20, 7, 12, 24, and 5 to better achieve strict invariance, it can be deduced that the residual variances of these items were not equivalent across our two sample groups. Notwithstanding, upon achieving strong MI, we were able to conclude that our finding of higher PID-5-BF scores in our clinical sample, compared to our undergraduate sample, could be considered a reliable finding. Moreover, these data affirm a satisfactory discriminant validity of the PID-5-BF for differentiating between nonclinical and clinical individuals.

External validity and clinical value

Although the 220-item PID-5 has many merits for personality diagnosis—such as close relations with clinical symptoms, the ability to be combined with various psychotherapy methods, and good stability over time [12]—its length hinders its clinical utility. Pires et al. (2018) examined the psychometric properties of the 220-item (original), 100-item (short form), and 25-item (brief form) PID-5 versions in a sample of Portuguese university students and concluded that any of the three could be used to assess maladaptive personality traits reliably and validly [18]. Bach et al. (2016) compared the three forms in a Danish population and showed that the three scales were highly similar with respect to internal consistency, factor structure, discriminant validity, and correlation with DSM-4 PD dimensions [14]. The present findings of very strong correlation coefficients between the PID-5-BF and the 220-item PID-5 in both of our samples indicated that in addition to saving time, reducing the burden upon participants, and being generally more clinic friendly, the PID-5-BF maintains the validity of the original instrument to a remarkable degree.

Moreover, the factors of the PID-5-BF in our six-factor model showed good alignment with the six PDs in Section III of the DSM-5. Similar to a previous study [20], the Psychoticism domain correlated highly with all of the PDs in the undergraduate sample and we speculated that as items in the Psychoticism domain were much different from daily life, most college students would be sensitive to abnormal behaviors, which thereby limiting and attenuating the specificity of Psychoticism. All domains had significant relations with one or more PDs, which was consistent with the previous study [14], and each PD was significantly associated with expected domains of PID-5-BF. However, the discriminant validity was not impressive, which may due to the range restriction of the sample. The significant but unspecific correlation between PID-5-BF and PDQ-4+ embodied the consistency and diversity between DSM-5 and DSM-4. Furthermore, it may reflect the trend from categorical scheme to dimensional frame in PD diagnosis, which reminders us that PID-5- traits might all relate to a general factor of maladaptivity [3, 14].

Multiple regression analysis revealed that the Interpersonal Relationships domain was the second-best predictor of the NPD, AVPD, and OCPD, which could make sense in theory. For example, NPDs always attempt at regulation through attention and approval seeking from others, relationships are largely superficial. AVPDs avoid social situations and inhibit interpersonal relationships. OCPDs have difficulties in establishing and sustaining close relationships [10]. All of the three PDs have a high proportion of maladjustment in interpersonal relationships. The reason why the IR factor correlated weakly with BPD may be that IR was more closely related to interpersonal sensitivity and withdrawal but BPD was relevant to a long-standing pattern of instability in interpersonal relationships. Except for the Psychoticism domain, nearly every PD’s sorting of predictors was conformed to the theoretical hypothesis or clinical observation. In summary, the PID-5-BF retained satisfactory psychometric properties, despite its extensive omission of items relative to the 220-item PID-5, affirming its suitability as a preliminary clinical PD screening tool.

The PID-5-BF can be used to differentiate between psychologically healthy and troubled respondents, at least preliminarily. Bach et al. (2016) reported that the PID-5-BF has very good discriminant validity between psychiatric outpatients and community-dwelling individuals [14], consistent with our findings of significantly higher PID-5-BF scores in our clinical patients than in our undergraduate students’ sample. Clinicians can administer the PID-5-BF to acquire a rough estimation of one’s personality functioning, laying the foundation of further treatment planning, and then judge the need for additional assessments. Although the PID-5-BF may not provide unique clinical information regarding specific symptoms, it can describe personality traits through the assessment of dimensions, embodying differences in degree rather than in a category, contributing to individualized therapy development.

Limitations and future directions

The present study had some noteworthy limitations. First, the retested sample and clinical population were relatively small due to practical limitations. Second, the clinical sample was heterogeneous, including patients diagnosed with various psychological disorders. Third, the current study was cross-sectional, and cross-sectional studies cannot demonstrate predictive validity with the robustness of longitudinal studies. Hence, there is a need for larger longitudinal and clinical-sample studies of the PID-5-BF, particularly with samples constituted by patients with PDs.

Finally, the current study explored a novel six-factor model for the PID-5-BF in Chinese samples, the validity of which should be tested in 220-item PID-5 in further studies. This Chinese six-factor model was supported by previous studies on personality traits in Chinese samples [36, 37]. However, the six-factor structure limited the possibility to compare with or generalize to some advanced researches using the original PID-5-BF operationalization. For instance, recently the combination of DSM-5 and ICD-11 in the diagnosis of personality disorders has aroused increasing attention [65,66,67,68,69,70,71]. Researchers developed some novel algorithms to assess ICD-11 personality domains from the five-factor structure and relevant sub-scores of PID-5 [70, 71]. It’s worth noting that in 2018 Oltmanns et al. developed a new instrument named the personality inventory for ICD-11(piCD) to assess five trait dimensions of ICD-11 [72], which indicated that combination of PID-5-BF and piCD for personality disorder study would become a more direct and reasonable way to explore the relationship between these two diagnostic systems in the Chinese sample.

On the other hand, because dimensions represent continua from normal to abnormal, actionable score ranges need to be established based on ample empirical data collected in clinical practice rather than developed from theoretical hypotheses. Further correlational analyses between the PID-5-BF and other psychological scales are also needed. MI of the PID-5-BF should also be further examined across genders, age bands, and cultures, particularly in Asia and the Pacific Islands.

Conclusion

Psychometric properties of the Chinese version of PID-5-BF were partially supported as the factor of Negative Affect was not replicated in its entirety. It was divided into two domains and we named the novel domain Interpersonal Relationships. Based on the current study, both the theoretical five-factor model and the exploratory six-factor model of PID-5-BF were acceptable for the Chinese population. The five-factor model allows for comparison and integration with other work on the original theoretical model. However, the Chinese six-factor structure may be more culturally informed in East Asian settings. In general, the PID-5-BF is suitable for assessing personality traits and clinical screening for PDs quickly.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to no permission from participants to share anonymized participant data publicly but are available from the corresponding author on reasonable request.

Abbreviations

PID-5-BF:

Personality Inventory for DSM-5 Brief Form

PD:

Personality disorder

EFA:

Exploratory factor analysis

CFA:

Confirmatory factor analysis

MI:

Measurement invariance

PDQ-4 + :

Personality diagnostic questionnaire-4+

References

  1. 1.

    Tyrer P, Reed GM, Crawford MJ. Classification, assessment, prevalence, and effect of personality disorder. Lancet. 2015;385(9969):717–26.

    Article  Google Scholar 

  2. 2.

    Al-Dajani N, Gralnick TM, Bagby RM. A psychometric review of the personality inventory for DSM-5 (PID-5): current status and future directions. J Pers Assess. 2018;100(4):448.

    Article  Google Scholar 

  3. 3.

    Wright AG, Thomas KM, Hopwood CJ, Markon KE, Pincus AL, Krueger RF. The hierarchical structure of DSM-5 pathological personality traits. J Abnorm Psychol. 2012;121(4):951–7.

    Article  Google Scholar 

  4. 4.

    Anderson, J. L., Sellbom, M., Bagby, R. M., Quilty, L. C., Veltri, C. O., Markon, K. E., & Krueger, R.F On the convergence between PSY-5 domains and PID-5 domains and facets: implications for assessment of DSM-5 personality traits. Assessment 2013, 20(3), 286–294.

  5. 5.

    Ashton MC, Lee K, de Vries RE, Hendrickse J, Born MP. The maladaptive personality traits of the personality inventory for DSM-5 (PID-5) in relation to the HEXACO personality factors and SCHIZOTYPY/dissociation. J Personal Disord. 2012;26(5):641–59.

    Article  Google Scholar 

  6. 6.

    Fossati A, Krueger RF, Markon KE, Borroni S, Maffei C. Reliability and validity of the personality inventory for DSM-5 (PID-5): predicting DSM-IV personality disorders and psychopathy in community-dwelling Italian adults. Assessment. 2013;20(6):689–708.

    Article  Google Scholar 

  7. 7.

    Jopp AM, South SC. Investigating the personality inventory for DSM-5 using self and spouse reports. J Personal Disord. 2015;29(2):193–214.

    Article  Google Scholar 

  8. 8.

    Bastiaens T, Claes L, Smits D, De Clercq B, De Fruyt F, Rossi G, Vanwalleghem D, Vermote R, Lowyck B, Claes S, De Hert M. The construct validity of the Dutch personality inventory for DSM-5 personality disorders (PID-5) in a clinical sample. Assessment. 2016;23(1):42–51.

    Article  Google Scholar 

  9. 9.

    Gore, W. L., & Widiger, T.A The DSM-5 dimensional trait model and five-factor models of general personality. J Abnorm Psychol. 2013;122(3):816–21.

  10. 10.

    Association AP. Diagnostic and statistical manual of mental disorders (DSM-5®). Washington, DC: American Psychiatric Pub; 2013.

  11. 11.

    Kotov R, Krueger RF, Watson D, Achenbach TM, Althoff RR, Bagby RM, Brown TA, Carpenter WT, Caspi A, Clark LA, Eaton NR, Forbes MK, Forbush KT, Goldberg D, Hasin D, Hyman SE, Ivanova MY, Lynam DR, Markon K, Miller JD, Moffitt TE, Morey LC, Mullins-Sweatt SN, Ormel J, Patrick CJ, Regier DA, Rescorla L, Ruggero CJ, Samuel DB, Sellbom M, Simms LJ, Skodol AE, Slade T, South SC, Tackett JL, Waldman ID, Waszczuk MA, Widiger TA, Wright AGC, Zimmerman M. The Hierarchical Taxonomy of Psychopathology (HiTOP): A Dimensional Alternative to Traditional Nosologies. J Abnormal Psychol. 2017;126(4):454–77.

    Article  Google Scholar 

  12. 12.

    Rui D, Tunong C, Qinging S. Relationship between PID-5 personality questionnaire and big five model and its application. Med Recapitulate. 2018;24(24):4894–8.

    Google Scholar 

  13. 13.

    American Psychiatric Association. The Personality Inventory for DSM-5—Brief Form (PID-5-BF)-Adult. 2013c. Retrieved from http://www.psychiatry.org/practice/dsm/dsm5/online-assessment-measures#Personality

    Google Scholar 

  14. 14.

    Bach B, Maples-Keller JL, Bo S, Simonsen E. The alternative DSM-5 personality disorder traits criterion: a comparative examination of three self-report forms in a Danish population. Personal Disord Theory Res Treatment. 2016;7(2):124–35.

    Article  Google Scholar 

  15. 15.

    Debast I, Rossi G, van Alphen SP. Construct validity of the DSM-5 SECTION iii maladaptive trait domains in older adults. J Personal Disord. 2017;31(5):671–88.

    Article  Google Scholar 

  16. 16.

    Fossati, A., Somma, A., Borroni, S., Markon, K. E., & Krueger, R.F The personality inventory for DSM-5 brief form: evidence for reliability and construct validity in a sample of community-dwelling Italian adolescents. Assessment 2017, 24(5), 615–631.

  17. 17.

    Combaluzier S, Gouvernet B, Menant F, Rezrazi A. Validation of a French translation of Krueger's personality inventory for DSM-5 in its brief form (PID-5 BF). Encephale. 2018;44(1):9–13.

    CAS  Article  Google Scholar 

  18. 18.

    Pires R, Ferreira AS, Guedes D, Goncalves B, Henriques-Calado J. A study of the psychometric qualities of the Portuguese version of the personality inventory for DSM-5 (PID-5): full version, reduced form and brief form. Revista Iberoamericana De Diagnostico Y Evaluacion-E Avaliacao Psicologica. 2018;47(2):197–212.

    Google Scholar 

  19. 19.

    Hyatt CS, Maples-Keller JL, Crowe ML, Sleep CE, Carter ST, Michopoulos V, Stevens JS, Jovanovic T, Bradley B, Miller JD, Powers A. Psychometric Properties of the Personality Inventory for DSM-5-Brief Form in a Community Sample with High Rates of Trauma Exposure. J Pers Assess. 2020;1–10.

  20. 20.

    Anderson JL, Sellbom M, Salekin R. T: utility of the personality inventory for DSM-5-brief form (PID-5-BF) in the measurement of maladaptive personality and psychopathology. Assessment. 2018;25(5):596–607.

    Article  Google Scholar 

  21. 21.

    Porcerelli JH, Hopwood CJ, Jones JR. Convergent and discriminant validity of personality inventory for DSM-5 -BF in a primary care sample. J Personal Disord. 2018;33(6):1–11.

    Google Scholar 

  22. 22.

    Untalan J. Examining the Reliability, Factor Structure, and Convergent Validity of the Personality Inventory for DSM-5-Brief Form (PID-5-BF) in a Filipino College Sample. San Beda College: ResearchGate. Manuscript submitted for publication; 2015.

  23. 23.

    Kunnel John R, Xavier B, Waldmeier A, Meyer A, Gaab J. Psychometric evaluation of the BFI-10 and the NEO-FFI-3 in Indian adolescents. Front Psychol. 2019;10:1057.

  24. 24.

    Huang CD, Church AT, Katigbak MS. Identifying cultural differences in items and traits: differential item functioning in the NEO personality inventory. J Cross-Cult Psychol. 1997;28(2):192–218.

  25. 25.

    McCrae RR, Zonderman AB, Costa PT Jr, Bond MH, Paunonen SV. Evaluating replicability of factors in the revised NEO personality inventory: confirmatory factor analysis versus Procrustes rotation. J Pers Soc Psychol. 1996;70(3):552.

  26. 26.

    Gondo Y, Shimonaka Y, Nakazato K, Ishihara Q, Imuta H. Preliminary study for the standardization of the Japanese version of NEO-PI-R. Tokyo: The 57th Meeting of the Japanese Psychological Association; 1993.

  27. 27.

    De Fruyt F, De Bolle M, McCrae RR, Terracciano A, Costa PT Jr, Collaborators of the Adolescent Personality Profiles of Cultures, P. Assessing the universal structure of personality in early adolescence: the NEO-PI-R and NEO-PI-3 in 24 cultures. Assessment. 2009;16(3):301–11.

  28. 28.

    Jianxin Z, Mingjie Z. Searching for a personality structure of Chinese: a theoretical hypothesis of a six factor model of personality traits. Adv Psychol Sci. 2006;14(4):574–85.

  29. 29.

    Wang DF, Cui H. The Chinese personality traits: interpersonal relationship. Psychol Explor. 2008;4:41–5.

  30. 30.

    Feng DB, Xi YH. The theoretical exploration and philosophy reflection on personality trait in Chinese context. Wuhan Univ Technol. 2013;26:381–6.

  31. 31.

    Song WZ, Zhang JX, Zhang MQ, Liang J. Procedure for development of the Chinese personality assessment inventory and its meaning (CPAI). Acta Psychol Sin. 1993;4:400–7.

  32. 32.

    Cheung FM, Leung K, Fan RM, Song WZ, Zhang JX, Zhang JP. Development of the Chinese personality assessment inventory (CPAI). Cross-Cult Psychol. 1996;27:181–99.

  33. 33.

    Zhang JX, Zhang MQ, Liang J. Clinical utility of the Big Six personality-the relation mode of the Chinese Personality Assessment Inventory (CPAI), the NEO Five-factor Inventory and the clinical scale MMPI-2. Hangzhou: Proceedings of the Fourth Academic Conference of China Mental Health Association; 2003.

  34. 34.

    Yang T. The ERP study of interpersonal relationship as a personal trait. Shanghai: Shanghai Normal University; 2005.

  35. 35.

    Mei H, Ma Z, Jiao S, Chen X, Lv X, Zhan Z. The sustainable personality in entrepreneurship: the relationship between big six personality, entrepreneurial self-efficacy, and entrepreneurial intention in the Chinese context. Sustainability. 2017;9(9):1649.

  36. 36.

    Cheung FM, Leung K, Zhang JX, Sun HF, Gan YQ, Song WZ, Xie D. Indigenous Chinese personality constructs - is the five-factor model complete? J Cross-Cult Psychol. 2001;32(4):407–33.

  37. 37.

    Cheung FM, Cheung SF, Leung K, Ward C, Leong F. The English version of the Chinese personality assessment inventory. J Cross-Cult Psychol. 2003;34(4):433–52.

  38. 38.

    Han K, Colarelli SM, Weed NC. Methodological and statistical advances in the consideration of cultural diversity in assessment: a critical review of group classification and measurement invariance testing. Psychol Assess. 2019;31(12):1481–96.

    Article  Google Scholar 

  39. 39.

    Vandenberg RJ, Lance CE. A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. Organ Res Methods. 2000;3(1):4–70.

    Article  Google Scholar 

  40. 40.

    Bach B, Sellbom M, Simonsen E. Personality inventory for DSM-5 (PID-5) in clinical versus nonclinical individuals: generalizability of psychometric features. Assessment. 2018;25(7):815–25.

    Article  Google Scholar 

  41. 41.

    Suzuki, T., South, S. C., Samuel, D. B., Wright, A. G. C., Yalch, M. M., Hopwood, C. J., & Thomas, K.M Measurement invariance of the DSM-5 Section III pathological personality trait model across sex. Personal Disord 2019, 10(2), 114–122.

  42. 42.

    Thimm JC, Jordan S, Bach B. Hierarchical structure and cross-cultural measurement invariance of the Norwegian version of the personality inventory for DSM-5. J Pers Assess. 2017;99(2):204–10.

    Article  Google Scholar 

  43. 43.

    Krueger, R. F., Derringer, J., Markon, K. E., Watson, D., & Skodol, A.E Initial construction of a maladaptive personality trait model and inventory for DSM-5. Psychol Med 2011, 42(9), 1879–1890.

  44. 44.

    Krueger R, Derringer J, Markon K, Watson D, Skodol A. The personality inventory for DSM-5—brief form (PID-5-BF)—adult. Washington, DC: American Psychiatric Association; 2013.

  45. 45.

    Hyler S. PDQ-4: Personality diagnostic questionnaire-DSM-IV version. New York: New York State Psychiatric Institute; 1996.

  46. 46.

    Yang J, McCrae RR, Costa PT, Yao SQ, Dai XY, Cai TS, Gao BL. The cross-cultural generalizability of axis-II constructs: an evaluation of two personality disorder assessment instruments in the People's Republic of China. J Pers Disord. 2000;14(3):249–63.

  47. 47.

    Ling H, Qian MY, Yang BJ. Reliability and validity of the chinese version of the personality diagnostic questionnaire-4+: a study with chinese college students. Soc Behav Pers. 2010;38(3):311–20.

  48. 48.

    Huang XT, Ling H, Yang BJ, Dou G. Screening of personality disorders among Chinese college students by personality diagnostic questionnaire-4+. J Personal Disord. 2007;21(4):448–54.

  49. 49.

    He JY, Zhong X, Yao SQ. Factor structure of the geriatric depression scale and measurement invariance across gender among Chinese elders. J Affect Disord. 2018;238:136–41.

  50. 50.

    Rens VDS, Peter S, Alain DB, et al. Editorial: Measurement Invariance. Front Psychol. 2015;6(883):1064.

  51. 51.

    Nunnally JC, Bernstein IH. Psychometric Theory. 3rd ed. New York: McGraw-Hill; 1994.

  52. 52.

    Hu L-T, Bentler PM. Evaluating model fit. 1995.

  53. 53.

    Cheung GW, Rensvold RB. Testing factorial invariance across groups: a reconceptualization and proposed new method. J Manag. 1999;25(1):1–27.

  54. 54.

    Clark LA, Watson D. Constructing validity: Basic issues in objective scale development. Psychol Assessment. 1995;7(3):309.

  55. 55.

    Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale: Erlbaum; 1988.

  56. 56.

    Cohen J, Cohen P, West SG, Aiken LS. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. Hillsdale: Lawrence Erlbaum; 2003.

  57. 57.

    Markus HR, Kitayama S. Culture and the self: Implications for cognition, emotion, and motivation. Psychol Rev. 1991;98(2):224.

  58. 58.

    Markus HR, Kitayama S. The cultural psychology of personality. J Cross-Cult Psychol. 1998;29(1):63–87.

  59. 59.

    Jenny DJG, Havens B. Cross-national comparisons of social isolation and loneliness: introduction and overview. Can J Aging. 2004;23(02):109–13.

  60. 60.

    Heine SJ, Buchtel EE. Personality: the universal and the culturally specific. Annu Rev Psycholb. 2009;60:369–94.

  61. 61.

    Ip GWM, Bond MH. Culture, values, and the spontaneous self-concept. Asian J Psychol. 1995;1(1):29–35.

  62. 62.

    Kuhn MH, McPartland TS. An empirical investigation of self-attitudes. Am Sociol Rev. 1954;19(1):68–76.

  63. 63.

    De Fruyt F, De Clercq B, De Bolle M, Wille B, Markon K, Krueger RF. General and maladaptive traits in a five-factor framework for DSM-5 in a university student sample. Assessment. 2013;20(3):295–307.

  64. 64.

    Wang MC, Armour C, Wu Y, Ren F, Zhu XZ, Yao SQ. Factor structure of the CES-D and measurement invariance across gender in mainland Chinese adolescents. J Clin Psychol. 2013;69(9):966–79.

  65. 65.

    Bach B, El Abiddine FZ. Empirical structure of DSM-5 and ICD-11 personality disorder traits in Arabic-speaking Algerian culture. Int J Ment Health. 2020;49(2):186–200.

  66. 66.

    Bach B, Sellbom M, Kongerslev M, Simonsen E, Krueger RF, Mulder R. Deriving ICD-11 personality disorder domains from dsm-5 traits: initial attempt to harmonize two diagnostic systems. Acta Psychiatr Scand. 2017;136(1):108–17.

  67. 67.

    Lotfi M, Bach B, Amini M, Simonsen E. Structure of DSM-5 and ICD-11 personality domains in Iranian community sample. Personal Ment Health. 2018;12(2):155–69.

  68. 68.

    Lugo V, Silva de Oliveira SE, Hessel CR, Monteiro RT, Pasche NL, Pavan G, Motta LS, Pacheco MA, Spanemberg L. Evaluation of DSM-5 and ICD-11 personality traits using the personality inventory for DSM-5 (PID-5) in a Brazilian sample of psychiatric inpatients. Personal Ment Health. 2019;13(1):24–39.

  69. 69.

    Sellbom M, Solomon-Krakus S, Bach B, Bagby RM. Validation of personality inventory for DSM-5 (PID-5) algorithms to assess ICD-11 personality trait domains in a psychiatric sample. Psychol Assess. 2020;32(1):40–9.

  70. 70.

    Kerber A, Schultze M, Müller S, Wright AGC, Spitzer C, Krueger RF, Knaevelsrud C, Zimmermann J. Development of a short and ICD-11 compatible measure for DSM-5 maladaptive personality traits using ant Colony optimization algorithms. Psychol Assess. 2020.

  71. 71.

    Bach B, Kerber A, Aluja A, Bastiaens T, Claes L, Keeley JW, et al. International Assessment of DSM-5 and ICD-11 Personality Disorder Traits: Toward a Common Nosology in DSM-5.1. Psychopathology. 2020;53:179–88.

  72. 72.

    Oltmanns JR, Widiger TA. A self-report measure for the icd-11 dimensional trait model proposal: the personality inventory for icd-11. Psychol Assess. 2018:30(2).

Download references

Acknowledgements

We thank all co-authors without whom the research wouldn’t be completed, also we appreciate every participant in our study.

Funding

This work was supported by Chinese Ministry of Education’s Humanities and Social Science Research Project (Grant No. 13YJA190015), the National Natural Science Foundation of China (Grant No. 31671144), and Education Reform Project of General Colleges and Universities in Hunan Province (Grant No. 2017jy77) in the design of the study and the data collection. This work was also supported by the Hunan Provincial Natural Science Foundation of China (Grant No. 2019JJ40362) and the Fundamental Research Funds for the Central Universities of Central South University(grant No. 2020zzts284) in analysis and interpretation of data and in writing the manuscript.

Author information

Affiliations

Authors

Contributions

XW and YS supervised the study. PZ and ZO performed the data analysis and wrote the paper, SF, JH, LF, and XL contributed to the data collection. JZ, YX, FL, and XW provided resources of the sample. All authors revised and approved the final manuscript.

Corresponding author

Correspondence to Xiang Wang.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the ethics committee of Second Xiangya Hospital, Central South University. All participants were over 18 years old and had written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, P., Ouyang, Z., Fang, S. et al. Personality inventory for DSM-5 brief form(PID-5-BF) in Chinese students and patients: evaluating the five-factor model and a culturally informed six-factor model. BMC Psychiatry 21, 107 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12888-021-03080-x

Download citation

Keywords

  • Personality inventory for DSM-5-brief form (PID-5-BF)
  • Factor structure
  • Reliability
  • Validity
  • Measurement invariance
  • Personality diagnostic questionnaire-4+ (PDQ-4+)