The relationship between fear of negative evaluation and social anxiety: a systematic review and meta-analysis

Songhao Peng; Min Zhou; Longyan Yue; Xiaojing Hei; Zihan Li; Fanxue Kong; Tao Zhang; Xiaoli Ren; Yan Song

doi:10.1186/s40359-026-05211-z

. 2026 Oct 8;14:1458. doi: 10.1186/s40359-026-05211-z

The relationship between fear of negative evaluation and social anxiety: a systematic review and meta-analysis

Songhao Peng ^1,^2,^#, Min Zhou ^1,^✉,^#, Longyan Yue ¹, Xiaojing Hei ¹, Zihan Li ¹, Fanxue Kong ¹, Tao Zhang ^1,³, Xiaoli Ren ⁴, Yan Song ⁵

PMCID: PMC13651285 PMID: 42850663

Abstract

Background

Fear of negative evaluation (FNE) is widely regarded as a central cognitive component of social anxiety (SA). However, the strength and stability of the association between FNE and SA, as well as potential moderating factors, remain unclear. This study aimed to systematically synthesize existing evidence on the relationship between FNE and SA and to examine potential moderators.

Methods

This systematic review and meta-analysis followed PRISMA 2020 guidelines and was registered in PROSPERO (CRD42024619488). A systematic search of major databases was conducted for studies published between January 2010 and April 2025. Eligible studies examined the association between FNE and SA. Two reviewers independently performed study selection, data extraction, and quality assessment. Random-effects models were used to estimate pooled effect sizes, with heterogeneity assessed using Q and I² statistics. Moderator analyses were conducted to explore sources of variability.

Results

A total of 143 studies comprising 146 independent samples (N = 80,285) were included. The pooled correlation between FNE and SA was r = 0.53 (95% CI: 0.50–0.55; I² = 88.62%), indicating a moderate positive association. Significant heterogeneity was observed across studies. Moderator analyses suggested that social anxiety measurement tools and cultural background were associated with variability in effect sizes, with stronger correlations observed in Western samples (r = 0.59) compared to Eastern samples (r = 0.52). In contrast, gender, age, publication year, and FNE measurement tools were not significant moderators. Sensitivity analyses confirmed the robustness of the findings, and no evidence of substantial publication bias was detected.

Conclusion

This meta-analysis provides robust evidence for a moderate positive association between FNE and SA, suggesting that FNE is an important cognitive component of social anxiety. The strength of this association varies across cultural contexts and measurement approaches, highlighting the importance of contextual and methodological factors in shaping observed effects. However, the substantial heterogeneity and predominance of cross-sectional designs limit causal interpretation. Future longitudinal and cross-cultural studies are needed to clarify the developmental and contextual dynamics of this relationship.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s40359-026-05211-z.

Keywords: Fear of Negative Evaluation, Social Anxiety, Meta-Analysis, Moderator Analysis

Introduction

Social anxiety (SA) represents a persistent fear of being scrutinized or negatively evaluated in social or performance situations, often leading to significant avoidance of social interactions [1]. When persistent and impairing, SA may develop into Social Anxiety Disorder (SAD), also known as Social Phobia, a clinical condition characterized by significant functional impairment or distress, which can have a substantial impact on both physical and mental health, as well as social functioning. According to the World Mental Health Survey, approximately 4% of the global population experiences lifetime SAD, with notable cross-national variation [2]. SA significantly limits individuals’ functioning across academic, occupational, and interpersonal domains, and is closely linked to a range of mental health issues, including depression, substance use, and diminished self-esteem [3, 4]. From a theoretical perspective, sensitivity to social evaluation has been identified as a core cognitive mechanism underlying SA, whereby individuals with excessive concern about negative evaluation tend to experience elevated anxiety in social situations, leading to avoidance behaviors and secondary psychological problems [5].

As a specific cognitive-emotional component of this broader construct, fear of negative evaluation (FNE) refers to an individual’s excessive worry and apprehension about being unfavorably evaluated by others [6]. Although FNE was initially conceptualized as sensitivity to negative social feedback and is widely regarded as a key component of SA, the two constructs differ in their underlying psychological characteristics. While FNE primarily captures an emotional–cognitive reaction to potential criticism, SA encompasses broader cognitive, behavioral, and functional domains [7]. Specifically, SA involves maladaptive self-focused attention, anticipatory worry, safety and avoidance behaviors, and disruptions in daily functioning that extend beyond evaluative fear [8]. According to the cognitive model of social anxiety, heightened anticipation of negative evaluation not only triggers anxiety but also sustains avoidance behaviors and self-focused attention [9]. Subsequent research has highlighted additional cognitive vulnerabilities such as anxiety sensitivity and intolerance of uncertainty [10]. According to clinical diagnostic criteria, the DSM-5 definition of social anxiety disorder explicitly embeds FNE as a core symptom, but empirical evidence suggests that SA can also emerge in the absence of pronounced FNE, for instance among individuals characterized by high shame proneness or low social self-efficacy [11]. Collectively, these perspectives indicate that FNE functions as a cognitive vulnerability factor embedded within, but not equivalent to, the broader syndrome of SA. Clarifying this hierarchical relationship is therefore essential for refining theoretical models and accurately interpreting the strength of their association.

A substantial body of empirical research has examined the relationship between FNE and SA, generally supporting a positive association. Cross-sectional studies have demonstrated parallel trends between FNE and SA across diverse populations [12, 13], while longitudinal studies provide preliminary evidence for a prospective association. For instance, Fredrick and Luebbe found that FNE reliably predicted subsequent SA across different populations, suggesting that FNE may predict changes in subsequent SA [14]. However, despite existing broad support for this association, variability in effect sizes across studies points to the influence of moderating factors that warrant further investigation.

Among the factors contributing to these variations, demographic, temporal, methodological, and cultural domains may serve as potential moderators. First, continuous variables such as age, gender composition (e.g., proportion of male participants), and publication year may systematically influence observed effect sizes. Developmentally, early adolescence has been identified as a period of heightened vulnerability to SA, characterized by increased sensitivity to social evaluation and elevated levels of both FNE and SA, which may in turn strengthen their association [15, 16]. Gender composition may also contribute to variability, as women generally report higher levels of both FNE and SA, which may be related to greater sensitivity to social evaluation, gendered coping styles, and internalized social role expectations [17]. In addition, publication year may reflect temporal shifts in diagnostic criteria, measurement approaches, and sociocultural conceptualizations of SA [18]. Second, categorical factors such as cultural background and measurement tools represent important sources of heterogeneity. Cultural background has been shown to influence the strength of the FNE-SA relationship. Cross-cultural studies suggest that differing conceptions of social evaluation and self-identity in Eastern and Western cultures may lead to divergent patterns of association [19, 20]. Importantly, these cultural differences may not only alter the degree of evaluative fear but also its psychological meaning, for example whether negative evaluation threatens one’s personal worth or the maintenance of face and relational harmony. Methodological factors, namely variation in measurement instruments, may contribute to discrepancies. For example, Carleton et al. reported correlations ranging from 0.49 to 0.65 between the BFNE-II and widely used SA scales (e.g., SPS, SIAS) [21], whereas Joogoolsingh et al. found a substantially lower correlation of 0.28 [22]. Importantly, some studies have directly treated FNE scale scores as proxies for SA levels [23, 24]; however, this practice has been criticized for conflating two theoretically distinct constructs and undermining their conceptual differentiation [25]. In summary, although the positive association between FNE and SA is well established, substantial heterogeneity in this association has been observed across studies. This variability is likely attributable to a complex interplay of continuous and categorical moderators; however, the extent to which these factors systematically account for differences in effect sizes remains unclear. Accordingly, the present meta-analysis aims to (1) quantify the overall association between FNE and SA, and (2) examine potential moderators, including demographic, cultural, and methodological factors, in order to explain observed heterogeneity.

Method

This study was conducted in accordance with the PRISMA 2020 guidelines [26]. Additionally, the study protocol has been prospectively registered on the PROSPERO platform (Registration No. CRD42024619488).

Literature search

A comprehensive literature search was conducted for studies published in Chinese or English between January 1, 2010, and April 1, 2025. The final search was conducted on April 1, 2025. Chinese-language studies were retrieved from the China National Knowledge Infrastructure (CNKI) and Wanfang Data using search terms related to fear of negative evaluation (e.g., “负面评价恐惧”, “惧怕否定评价”) and social anxiety (e.g., “社交回避”, “社交苦恼”, “社交恐怖”, “交往焦虑”, “社会焦虑”, “人际焦虑”). English-language literature was searched in PubMed, ProQuest, and Web of Science using combinations of keywords associated with fear of negative evaluation (“fear of negative evaluation”) and social anxiety (e.g., “interaction anxiousness”, “interaction anxiety”, “social avoidance distress”, “SAD”, “social anxi*”, “social phobi*”, “social avoid*”, “social inhib*”, “interaction anxi*”, “communication anxi*”, “social distress”, “social fear”, and “social worry”). The asterisk (*) denotes truncation to capture all variations of the root term; for example, “social anxi” includes both “social anxiety” and “social anxiousness”. Detailed search strategies for each database are provided in Supplementary Appendix S1.

Literature selection

Inclusion Criteria: (1) studies must be quantitative empirical investigations reporting numerical outcomes; (2) studies must clearly describe the measurement instruments used; (3) studies must report Pearson correlation coefficients between variables of interest or provide sufficient data to convert to a correlation-based effect size; (4) if multiple publications reported on the same dataset, only one was retained; (5) participants must be adolescents or adults (including individuals diagnosed with Social Anxiety Disorder), without major physical illnesses or psychiatric disorders; (6) studies must report a clear sample size, with a minimum of 20 valid participants; (7) publications must be written in either Chinese or English.

Exclusion Criteria: (1) theoretical papers or review articles without empirical data were excluded; (2) intervention or experimental studies that did not allow the calculation of correlation coefficients (r) were excluded; (3) Studies involving clinical populations primarily characterized by other psychiatric disorders (e.g., depression, PTSD, psychosis) were excluded; (4) studies with low methodological quality (i.e., < 3 points for cross-sectional studies or < 6 points for longitudinal studies) were excluded; (5) studies without access to full texts or complete effect size data were excluded. The study selection process is presented in Fig. 1.

Two independent reviewers screened all titles, abstracts, and full-text articles based on predefined inclusion and exclusion criteria. Disagreements were resolved through discussion until consensus was reached. EndNote X9 was used for duplicate removal and reference management.

Literature coding

The following information was extracted and coded from each article: study title, first author, publication year, type of publication, language, quality assessment score, sample size, participants’ mean age, proportion of male participants, cultural background, measurement instruments for FNE and SA, and the effect size. Cultural background was coded based on the country or region from which the sample was drawn. Samples from collectivistic cultures were coded as Eastern, whereas those from individualistic cultures were coded as Western. The correlation coefficient (r) was used as the effect size index, with all coefficients Fisher’s z-transformed for analysis to stabilize variance and normalize the distribution, and the results back-transformed to Pearson’s r for interpretation. If the original study did not report r directly but instead provided alternative statistics(e.g., t, F, χ², or β), these were converted into r using the following formulas: Inline graphic ; ; ; ;]), and subsequently coded (Card, 2012; Rosenthal & DiMatteo, 2001; Peterson & Brown, 2005). If a study reported only the correlations between subdimensions, the overall correlation between total scores was computed using the following formula: [27, 28]. During the coding process, if a study reported multiple independent samples, each sample was coded separately. When multiple effect sizes were reported within a single study, a weighted average of correlation coefficients was calculated for the primary meta-analysis to obtain a single effect size per study, thereby ensuring a more representative estimate of the overall effect. For moderator analyses, all available effect sizes were retained to preserve study-level variability, and RVE was applied to account for potential dependence among effect sizes. For longitudinal studies, only baseline effect sizes were extracted to ensure comparability across studies and avoid dependence from repeated measurements within the same sample. No missing data were present because only studies reporting complete correlation and sample sizes were included in the meta-analysis. Data extraction was independently performed by two trained coders. Any discrepancies in coding were resolved through discussion after re-examining the original articles until consensus was reached. The bibliographic information and quality assessment tables of the included studies are provided in Supplementary Material.

Quality assessment

This study adopted the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies developed by the National Heart, Lung, and Blood Institute to systematically evaluate the quality of the included studies [29]. Each item was scored 1 if the criterion was met, and 0 otherwise. The total score ranged from 0 to 8 for cross-sectional studies and from 0 to 14 for longitudinal studies, with higher scores indicating better methodological quality. Inter-rater reliability was assessed using Cohen’s kappa coefficient, with values above 0.80 indicating high agreement [30]. Two researchers independently conducted the quality assessments, and the resulting Kappa value was 0.876, demonstrating a high level of consistency.

Sensitivity analysis and heterogeneity

To assess the robustness of the results, sensitivity analyses were conducted using the leave-one-out method and externally standardized residuals [31]. Specifically, each study was removed sequentially from the dataset using the leave-one-out method, and the pooled effect size was recalculated to assess whether any single study disproportionately influenced the overall results. Studies with externally standardized residuals greater than 3 were identified as potential outliers, further enhancing the robustness of the analysis. After excluding these outliers, sensitivity analyses were repeated to confirm the stability of the findings.

To evaluate heterogeneity, we employed Cochran’s Q test, the I² statistic (with 95% confidence intervals), and prediction intervals (PIs) [32]. Q test was used to distinguish between sampling error and true between-study heterogeneity, with a significant result (p < 0.05) indicating substantial heterogeneity. The I² statistic quantifies the percentage of variability in effect sizes that is not caused by sampling error, with higher values indicating greater heterogeneity. Specifically, I² values below 25% indicate low heterogeneity, between 25% and 75% moderate heterogeneity, and above 75% high heterogeneity.

However, I² is influenced by the precision of the included studies, and when study sample sizes are large, I² values may approach 100%, which does not necessarily reflect true heterogeneity. Therefore, we used PIs as a more intuitive and practically meaningful measure of heterogeneity. PIs provide an estimate of the likely distribution range of true effect sizes in future studies [33]. By calculating PIs, we can not only assess the heterogeneity present in the current data but also predict the potential variation in effect sizes across future research, offering a more comprehensive understanding of the stability and generalizability of the results.

Publication bias

Publication bias refers to the tendency for studies with statistically significant results to be more likely published [34]. This study included not only journal articles but also unpublished dissertations, thereby reducing the risk of bias to some extent. In addition, the moderating effect of publication status was examined to assess the risk of publication bias. Furthermore, publication bias was assessed using a funnel plot, Egger’s regression test, and the Fail-safe N method [35–37]. The funnel plot was inspected for symmetry to determine potential bias; Egger’s regression was used to test the significance of the intercept; a non-significant result indicated no publication bias. The fail-safe N method estimated how many null-effect studies would be needed to reduce the overall meta-analytic result to non-significance; if the Fail-safe N exceeded the threshold of 5n + 10, the result was considered robust and unlikely to be affected by publication bias. Trim-and-fill analysis was used to estimate the potential impact of potentially missing studies on the pooled effect size [38].

Model selection

There are two analytic models in meta-analysis: the fixed-effects model and the random-effects model. The fixed-effects model assumes that all included studies estimate the same true effect size, and any observed variation is due solely to sampling error. In contrast, the random-effects model assumes that the true effect size may vary across studies, and that observed differences may be influenced by potential moderators [18].

For model selection, we considered that the included studies were conceptually and methodologically diverse. Conceptually, variations existed in the operationalization and theoretical understanding of fear of negative evaluation and social anxiety across studies. Methodologically, the studies differed in participants (age, gender), publication year, measurement tools, and cultural context (Eastern and Western). Given this diversity, substantial variability in effect sizes was expected. Therefore, we adopted a random-effects model as the primary analytic framework a priori.

Statistical analysis

All statistical analyses were conducted in R (version 4.2.2) using the meta, metafor and clubSandwich packages. A meta-analysis was first performed to estimate the overall effect size between FNE and SA. To explore potential sources of heterogeneity, moderator analyses were conducted using subgroup analysis for categorical variables and meta-regression for continuous variables. Subgroup analysis was used to examine between-group differences in effect sizes for categorical variables (e.g., cultural background, FNE measurement tool, SA measurement tool) and to account for study-level characteristics, while meta-regression was employed to explore the impact of continuous variables (e.g., mean age, male proportion, publication year) on effect sizes. Subgroup analyses assessed between-group differences in effect sizes using the Q statistic. Analyses were considered valid when each subgroup included more than three effect sizes [39]. Meta-regression was performed using the restricted maximum likelihood method to explore the impact of continuous variables on effect sizes. RVE with CR2 small-sample corrections was additionally conducted as a robustness check to account for within-study dependence in moderator analyses [40].

Results

Study characteristics

A total of 143 studies, including 146 independent samples and 163 effect sizes with 80,285 participants, were included. Of these, 76 were Chinese-language and 67 English-language, and 37 were theses or dissertations, while 106 were journal articles. Participants’ mean age ranged from 12.39 to 47.31 years (overall mean = 21.89, SD = 6.64), and males made up 39.59% of the total sample. The 134 cross-sectional studies had quality scores ranging from 4 to 7 (average = 5.23), while the 9 longitudinal studies had scores between 9 and 12 (average = 10.00). Overall, the study quality was high, exceeding the recommended cutoffs for both study types (4 for cross-sectional, 7 for longitudinal). Item-level inspection suggested that methodological limitations were most commonly related to participant selection/reporting procedures and the assessment of exposure levels, whereas most studies met criteria related to outcome measurement and statistical analysis. Detailed quality assessment ratings are provided in Supplementary Table S3.

Sensitivity analysis

Sensitivity analysis showed that removing each study sequentially resulted in correlation coefficients between 0.521 and 0.527, indicating no single study significantly influenced the overall estimate. One study was identified as an outlier based on externally standardized residuals. After its exclusion, the correlation coefficient ranged from 0.524 to 0.529, closely matching the original results.

Main effect testing

The pooled correlation between FNE and SA was r = 0.527 (p < 0.001), with the 95% confidence interval [0.50, 0.55] and a PI of [0.29, 0.76], indicating a moderate positive correlation. Significant heterogeneity was found (Q = 1338.28, p < 0.001, I² = 88.62%), indicating substantial variability across studies and the need for further moderator analyses.

Publication bias assessment

The funnel plot is presented in Fig. 2. The correlation coefficients between FNE and SA appear relatively symmetrical around the pooled effect size; Egger’s regression test also indicated a non-significant effect (t = 0.670, p = 0.504). The Fail-safe N was 77,078, suggesting that many unpublished null-effect studies would be needed to overturn the findings. Additionally, moderation analysis showed no significant difference between published and unpublished studies (p = 0.058). A trim-and-fill analysis imputed 31 potentially missing studies on the left side of the funnel plot. After adjustment, the pooled effect size decreased from r = 0.527 to r = 0.483 (95% CI [0.460, 0.506]) but remained statistically significant.

Moderator analysis

Meta-regression and subgroup analyses were performed to examine sources of heterogeneity in the FNE–SA association (see Table 1). Continuous variables (age, male proportion, and publication year) were analyzed via meta-regression, and categorical variables (SA and FNE measurement tools, cultural background) via subgroup analysis. Gender (b = -0.101, 95% CI [-0.22, 0.02], p = 0.097), age (b = 0.003, 95% CI [-0.001, 0.006], p = 0.097), and publication year (b = 0.001, 95% CI [-0.005, 0.006], p = 0.823) were not significant moderators. SA measurement tools significantly moderated the relationship (Q = 55.52, df = 6, p < 0.001), with the strongest correlation using the SIAS (r = 0.601) and the weakest using the SAD scale (r = 0.414). FNE measurement tools had no significant moderating effect (Q = 3.52, df = 3, p = 0.318). Cultural background significantly moderated the relationship (Q = 10.89, df = 1, p = 0.001), with stronger associations in Western (r = 0.592) than Eastern cultures (r = 0.524).

Table 1.

Moderator analyses for categorical variables

Moderator	heterogeneity			k	r	95% CI		p
Moderator	Q_−Between	df	p	k	r	Lower	Upper	p
SA measurement	55.52	6	< 0.001
IAS				36	0.512	0.48	0.54	< 0.001
SIAS				36	0.601	0.56	0.64	< 0.001
LSAS				18	0.596	0.41	0.57	< 0.001
SAD				16	0.414	0.36	0.46	< 0.001
SPS				13	0.580	0.51	0.65	< 0.001
SAS-A				13	0.540	0.44	0.63	< 0.001
Others				32	0.611	0.57	0.65	< 0.001
FNE measurement	3.52	3	0.318
BFNE				95	0.551	0.52	0.58	< 0.001
BFNE-S				36	0.571	0.53	0.61	< 0.001
BFNE-II				22	0.524	0.48	0.57	< 0.001
FNE				11	0.504	0.42	0.58	< 0.001
Culture	10.89	1	0.001
Eastern				103	0.524	0.50	0.55	< 0.001
Western				61	0.592	0.56	0.62	< 0.001

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*Scales with fewer than 10 uses were grouped into the “Other” category. k = number of effect sizes

To examine whether cultural moderation was confounded by measurement preference, a mixed-effects meta-regression was conducted including both cultural background and measurement tool type. The model was significant (QM = 44.89, df = 10, p < 0.001), explaining 20.08% of between-study heterogeneity. After controlling for measurement type, the cultural effect became marginally significant (β = 0.07, SE = 0.04, p = 0.064), suggesting that measurement differences may partially account for the observed cultural differences. RVE-adjusted moderator analyses yielded results largely consistent with the primary moderator analyses, with cultural background and SA measurement type remaining significant moderators, whereas FNE measurement type and continuous moderators (age, gender, and publication year) remained non-significant (Supplementary Table S4).

Discussion

This meta-analysis of 143 studies (N = 80,285) confirmed a moderate and robust correlation between FNE and SA. This finding aligns with the majority of previous research [41–44], providing empirical support for theoretical models that conceptualize FNE as a core cognitive–emotional feature of SA [9, 45]. Evaluation apprehension theory posits that FNE and SA represent overlapping yet distinct constructs: FNE reflects a specific fear of negative judgment, whereas SA encompasses a broader syndrome [46]. The moderate correlation observed in this study suggests that FNE is closely associated with SA and may represent an important cognitive component within its broader framework. This association is consistent with cognitive-behavioral models, in which FNE has been conceptualized as a maintaining cognitive bias within the frameworks of Clark and Wells (1995) and Rapee and Heimberg (1997), rather than as a mere symptom of SA. In summary, the association between FNE and SA was robustly positive across studies. However, the substantial heterogeneity observed and the relatively wide prediction interval indicate that the strength of this association may vary across populations and research contexts. Therefore, given the substantial residual heterogeneity, the pooled effect size should be interpreted as a context-averaged association across diverse samples and settings rather than as a uniform estimate applicable across all populations.

From the perspective of participant characteristics, this study found that neither gender nor age significantly moderated the FNE–SA relationship, consistent with prior research [47, 48]. This pattern is in line with cognitive-behavioral accounts, which emphasize FNE as a central cognitive component underlying SA [49], and may reflect its socially adaptive function, rendering it less susceptible to demographic variations [10]. This pattern may partly reflect the predominance of adolescent and student samples, which restricted between-study variability. In addition, because age and gender were examined at the study level, these findings should not be interpreted as evidence that individual-level age or gender differences are absent.

In terms of study characteristics, this study found that the year of publication did not significantly moderate the FNE–SA relationship, suggesting that despite changes in the social environment, the association has remained stable over time, consistent with previous research [50]. This temporal stability may be attributed to the persistent evaluative criteria underlying SA, with FNE operating as a core cognitive mechanism shaping emotional responses in social contexts. Furthermore, from a methodological standpoint, despite the increasing diversity of measurement tools, widely adopted instruments continue to demonstrate strong reliability and validity, supporting the robustness and consistency of this relationship over time [51, 52]. However, since most studies were cross-sectional, future research should explore whether this relationship shifts in rapidly evolving digital environments and on social media platforms.

In terms of measurement characteristics, the type of SA measurement tool significantly moderated the FNE–SA relationship, likely due to differences in the specific content assessed by various instruments. FNE integrates both cognitive and emotional components, so scales emphasizing emotional experience tend to show stronger associations. For instance, the SIAS emphasizes emotional experiences that align more closely with the emotional nature of FNE and may therefore be associated with stronger correlations, whereas the SAD focuses on behavioral responses, reflecting avoidance rather than the emotional experience itself, which may be associated with a weaker correlation [53]. Furthermore, variations in the theoretical framework of the instruments, such as short-term versus long-term assessments, may influence the aspects of SA captured. According to the state–trait anxiety framework, these assessments may elicit distinct cognitive and affective responses, contributing to minor measurement-related variability [54]. However, these variations were modest (r = 0.41–0.60), suggesting that the instruments largely assess the same overarching construct of social anxiety. Although SA measurement type emerged as a significant moderator, the magnitude of the differences was relatively small and may primarily reflect measurement emphasis rather than meaningful differences in the strength of the FNE–SA association. Therefore, the theoretical implications of this moderation effect should be interpreted cautiously, and future studies may benefit from using multiple instruments to enhance the robustness of findings.

Secondly, the type of FNE measurement tool did not significantly moderate the relationship, likely due to the high convergence among the four scales included. Although the studies used different versions of FNE instruments, most were derived from the original FNE scale and are based on a shared theoretical framework. These tools consistently measure the emotional experiences and self-appraisal processes associated with external negative evaluation, and they capture individual sensitivity to negative social feedback in a conceptually coherent manner [55]. Given the strong conceptual and structural overlap among these scales, statistical differences between them were minimal, thus reducing the likelihood of detecting significant moderation effects. An additional consideration concerns the conceptual overlap between FNE and SA measures. Although concerns about social evaluation are relevant to both constructs, this overlap may have contributed, at least partially, to the magnitude of the observed association. Nevertheless, the moderate effect size (r = 0.53) is consistent with the view that FNE and SA are related yet distinguishable constructs. As noted earlier, FNE primarily reflects an emotional–cognitive reaction to potential criticism, whereas SA encompasses broader cognitive, behavioral, and functional domains [7]. These findings support the view that FNE is an important cognitive component of social anxiety rather than an equivalent construct.

Cultural background was found to significantly moderate the relationship between FNE and SA, with a stronger correlation observed in Western cultural contexts compared to Eastern ones. This difference may be related to varying cultural interpretations of social evaluation. Eastern cultures emphasize collectivism, prioritizing group harmony, and encouraging individuals to avoid standing out, whereas Western cultures place greater value on individualism and personal expression. These broad cultural tendencies may contribute to differences in how negative evaluation is interpreted and experienced [56]. Moreover, cultural background also affects not only the cognitive representation of FNE but also emotional expression and social attribution. Some studies suggest that the concept of “face” in Eastern cultures may contribute to differences in the expression of SA, thereby weakening the observable association between the two constructs [57]. Consistent with this view, FNE in Western societies is often linked to concerns about self-worth and personal adequacy, whereas in Eastern collectivistic cultures it is more closely related to maintaining social face and relational harmony [58]. However, after controlling for measurement type, the cultural effect became marginally significant, suggesting that cultural factors and measurement methods may overlap to some extent. Different cultural groups may preferentially use measurement instruments that better align with their values and social norms, which could affect how FNE and SA are assessed [59]. In addition, the Eastern–Western distinction represents a broad cultural categorization and may not fully capture heterogeneity within each group. Countries classified within the same category can differ substantially in social norms, values, and self-construals [60]. Therefore, the observed cultural differences should be interpreted as reflecting broad cultural tendencies rather than homogeneous cultural patterns. Given the relatively small effect size and the potential overlap between cultural and methodological influences, further research is needed to disentangle cultural effects from measurement-related factors.

This study employed a meta-analytic approach to comprehensively examine the overall strength of the association between FNE and SA, as well as its potential moderating factors, thereby providing updated empirical confirmation for this well-established relationship. First, the findings offer quantitative support for cognitive-behavioral models of SA, underscoring the salient role of FNE and the robustness of the overall positive association across the included studies, while recognizing that its magnitude varies across populations and contexts. Second, this study systematically integrated the moderating effects of sample characteristics, cultural backgrounds and different measurement tools, addressing the issues of fragmentation, high heterogeneity, and inconsistency in previous research findings. Finally, the results may have implications for clinical interventions, suggesting that cognitive restructuring and emotion regulation strategies targeting FNE could be relevant considerations in interventions addressing SA. Moreover, although trim-and-fill analysis suggested the possibility of some missing studies, the adjusted effect size remained statistically significant and substantial in magnitude, indicating that potential publication bias is unlikely to fully account for the observed association.

Despite the empirical advances made in this study, several limitations remain. First, most included studies were based on adolescent and college student samples, with limited representation from other age groups or occupational populations, which may affect the generalizability of the findings. Second, most of the primary studies adopted a cross-sectional design, which only allows for the identification of linear associations and restricts the ability to infer causal relationships between FNE and SA. Third, in the cultural analysis, the binary classification of Eastern and Western cultures may have been too broad to fully capture within-group cultural heterogeneity (e.g., the hybrid characteristics of Japanese culture). In addition, a considerable proportion of heterogeneity remained unexplained even after conducting moderator analyses, indicating that the pooled estimate should be interpreted with caution and may not be equally applicable across all populations and contexts. Fourth, many included studies were published in Chinese, and some English-language studies were also based in China, which may limit the generalizability of the findings.

Conclusions

This meta-analysis demonstrates a moderate positive association between FNE and SA, supporting the view that FNE represents an important cognitive component of social anxiety. The findings further indicate that this relationship is influenced by social anxiety measurement tools and cultural context, while remaining relatively stable across demographic factors and over time. However, given the substantial heterogeneity across studies and the predominance of cross-sectional designs, these findings should be interpreted with caution.

Supplementary Information

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Acknowledgements

Not applicable.

Abbreviations

FNE: Fear of Negative Evaluation
SA: Social Anxiety
SAD: Social Anxiety Disorder
SIAS: Social Interaction Anxiety Scale
IAS: Interaction Anxiousness Scale
LSAS: Liebowitz Social Anxiety Scale
SPS: Social Phobia Scale
SAS-A: Social Anxiety Scale for Adolescents
BFNE: Brief Fear of Negative Evaluation Scale
BFNE-S: Brief Fear of Negative Evaluation Scale – Straightforward version
BFNE-II: Brief Fear of Negative Evaluation Scale II
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PROSPERO: International Prospective Register of Systematic Reviews
REML: Restricted Maximum Likelihood
PIs: Prediction Intervals
RVE: Robust variance estimation

Authors’ contributions

**Songhao Peng** conceptualized the study, conducted the initial literature search and data collection, performed data analysis, and drafted the original manuscript. **Min Zhou** revised the initial draft, contributed to the theoretical framework, and performed substantial edits throughout the manuscript. **Xiaojing Hei** and **Longyan Yue** assisted in data collection, quality assessment, and coding of included studies. **Zihan Li** contributed to the translation of literature and the review of documents. **Fanxue Kong** and **Tao Zhang** contributed to the supplementary data analysis and performed final revisions of the manuscript. **Xiaoli Ren** and **Yan Song** provided critical feedback on methodological integrity and helped improve the clarity and structure of the final manuscript. All authors have read and approved the final version of the manuscript.

Funding

This work was supported by the Postgraduate Education Innovation Program of Shanxi Province, Grant No. 2025SJ249, awarded to Songhao Peng, and the 2024 Shanxi Provincial Higher Education Teaching Reform and Innovation Project, Grant No. J20240607, awarded to Min Zhou.

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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Songhao Peng and Min Zhou contributed equally to this work and should be considered as co–first authors.

References

1.Morrison AS, Heimberg RG. Social anxiety and social anxiety disorder. Ann Rev Clin Psychol. 2013;9(1):249–74. [DOI] [PubMed] [Google Scholar]
2.Stein DJ, Lim CC, Roest AM, De Jonge P, Aguilar-Gaxiola S, Al-Hamzawi A, et al. The cross-national epidemiology of social anxiety disorder: Data from the World Mental Health Survey Initiative. BMC Med. 2017;15:1–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Fares K, Haddad C, Malaeb D, Sacre H, Akel M, Salameh P, et al. Social anxiety and its correlates among Lebanese adults: role of self-esteem, depression, alcohol use disorder, alexithymia, and work fatigue. Prim care companion CNS disorders. 2022;24(1):39101. [DOI] [PubMed] [Google Scholar]
4.Villarama JA, Ayeras SRF, Bumanlag JB, De Guzman BFD, Reyes BMB, Ruiz AKF. Too Anxious to Speak: Assessing the Impact of Social Anxiety on High School Students’ Self-Esteem, Academic Performance, and Coping Strategies. J Interdisciplinary Perspect. 2024;2(7):1–1. [Google Scholar]
5.Khakpoor S, Saed O, Shahsavar A. The concept of Anxiety sensitivity in social anxiety disorder presentations, symptomatology, and treatment: A theoretical perspective. Cogent Psychol. 2019;6(1):1617658. [Google Scholar]
6.Watson D, Friend R. Measurement of social-evaluative anxiety. J Consult Clin Psychol. 1969;33(4):448. [DOI] [PubMed] [Google Scholar]
7.Liu Y, Zhang D. On Theory of Fear of Evaluation and Its Relative Research. Adv Psychol Sci. 2010;18(01):106–13. [Google Scholar]
8.Heimberg RG. Social phobia: Diagnosis, assessment, and treatment. Guilford Press; 1995.
9.Clark DM, Crozier W, Alden L. A cognitive perspective on social phobia. Essent Handb social anxiety Clin. 2005;193–218.
10.Teale Sapach MJ, Carleton RN, Mulvogue MK, Weeks JW, Heimberg RG. Cognitive constructs and social anxiety disorder: Beyond fearing negative evaluation. Cogn Behav Ther. 2015;44(1):63–73. [DOI] [PubMed] [Google Scholar]
11.Edition F. others. Diagnostic and statistical manual of mental disorders. Am Psychiatric Assoc. 2013;21(21):591–643. [Google Scholar]
12.Shomali Ahmadabadi M, Zabihi M, Khodarahmi A, Barkhordari Ahmadabadi A. The Mediating Role of Self-Criticism in the Relationship between Fear of Negative Evaluation and Social Anxiety in Medical Science Students. Tolooebehdasht. 2024;23(4):44–56. [Google Scholar]
13.Sigurvinsdottir R, Soring K, Kristinsdottir K, Halfdanarson SG, Johannsdottir KR, Vilhjalmsson HH, et al. Social anxiety, fear of negative evaluation, and distress in a virtual reality environment. Behav Change. 2021;38(2):109–18. [Google Scholar]
14.Fredrick JW, Luebbe AM. Prospective associations between fears of negative evaluation, fears of positive evaluation, and social anxiety symptoms in adolescence. Child Psychiatry Hum Dev. 2024;55(1):195–205. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Haller SP, Kadosh KC, Scerif G, Lau JY. Social anxiety disorder in adolescence: How developmental cognitive neuroscience findings may shape understanding and interventions for psychopathology. Dev Cogn Neurosci. 2015;13:11–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Rapee RM, Oar EL, Johnco CJ, Forbes MK, Fardouly J, Magson NR, et al. Adolescent development and risk for the onset of social-emotional disorders: A review and conceptual model. Behav Res Ther. 2019;123:103501. [DOI] [PubMed] [Google Scholar]
17.Asher M, Asnaani A, Aderka IM. Gender differences in social anxiety disorder: A review. Clin Psychol Rev. 2017;56:1–12. [DOI] [PubMed] [Google Scholar]
18.Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis. Wiley; 2021.
19.Jefferies P, Ungar M. Social anxiety in young people: A prevalence study in seven countries. PLoS ONE. 2020;15(9):e0239133. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Woody SR, Miao S, Kellman-McFarlane K. Cultural differences in social anxiety: A meta-analysis of Asian and European heritage samples. Asian Am J Psychol. 2015;6(1):47. [Google Scholar]
21.Carleton RN, Collimore KC, McCabe RE, Antony MM. Addressing revisions to the Brief Fear of Negative Evaluation scale: Measuring fear of negative evaluation across anxiety and mood disorders. J Anxiety Disord. 2011;25(6):822–8. [DOI] [PubMed] [Google Scholar]
22.Joogoolsingh N, Wu MS, Lewin AB, Storch EA. Symptom accommodation related to social anxiety symptoms in adults: phenomenology, correlates, and impairment. J Cogn Psychother. 2015;29(1):3–19. [DOI] [PubMed] [Google Scholar]
23.Gray E, Beierl ET, Clark DM. Sub-types of safety behaviours and their effects on social anxiety disorder. PLoS ONE. 2019;14(10):e0223165. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Modini M, Abbott MJ, Hunt C. A systematic review of the psychometric properties of trait social anxiety self-report measures. J Psychopathol Behav Assess. 2015;37:645–62. [Google Scholar]
25.Dryman MT, Heimberg RG. Examining the relationships among social anxiety, fears of evaluation, and interpretation bias. Cogn Therapy Res. 2015;39:646–57. [Google Scholar]
26.Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. 10.1136/bmj.n71. [DOI] [PMC free article] [PubMed]
27.Hunter JE, Schmidt FL. Methods of meta-analysis: Correcting error and bias in research findings. Sage; 2004.
28.Zhang Y, Li S, Yu G. The relationship between loneliness and mobile phone addiction: A meta-analysis. Adv Psychol Sci. 2020;28(11):1836–52. [Google Scholar]
29.National Heart, Lung, and Blood Institute. Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. 2021. Available from: https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools.
30.McHugh ML. Interrater reliability: the kappa statistic. Biochemia Med. 2012;22(3):276–82. [PMC free article] [PubMed] [Google Scholar]
31.Viechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Res synthesis methods. 2010;1(2):112–25. [DOI] [PubMed] [Google Scholar]
32.Harrer M, Cuijpers P, Ebert AFT. DD. Doing Meta-Analysis With R: A Hands-On Guide. 1st ed. Boca Raton, FL and London: Chapman & Hall/CRC; 2021. [Google Scholar]
33.IntHout J, Ioannidis JP, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ open. 2016;6(7):e010247. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Thornton A, Lee P. Publication bias in meta-analysis: its causes and consequences. J Clin Epidemiol. 2000;53(2):207–16. [DOI] [PubMed] [Google Scholar]
35.Rosenthal R. The file drawer problem and tolerance for null results. Psychol Bull. 1979;86(3):638. [Google Scholar]
36.Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Becker BJ. Failsafe N or file-drawer number. In: Rothstein HR, Sutton AJ, Borenstein M, editors. Publication bias in meta-analysis: prevention, assessment and adjustments. Chichester: Wiley; 2005. pp. 111–25. 10.1002/0470870168.ch7. [DOI]
38.Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–63. [DOI] [PubMed] [Google Scholar]
39.Song H, Zmyslinski-Seelig A, Kim J, Drent A, Victor A, Omori K, et al. Does Facebook make you lonely? A meta analysis. Comput Hum Behav. 2014;36:446–52. [Google Scholar]
40.Tipton E. Small sample adjustments for robust variance estimation with meta-regression. Psychol Methods. 2015;20(3):375. [DOI] [PubMed] [Google Scholar]
41.Cheng G, Zhang D, Ding F. Self-esteem and fear of negative evaluation as mediators between family socioeconomic status and social anxiety in Chinese emerging adults. Int J Soc Psychiatry. 2015;61(6):569–76. [DOI] [PubMed] [Google Scholar]
42.Peker M, Akkuş K. Fear of positive evaluation differentially predicts social anxiety: a six-month longitudinal panel study. Curr Psychol. 2024;43(4):3621–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Tuo A, Liang H, ZHOU H, Li B, LIU Y, YANG X, et al. Relationship between evaluation fear of online bullying and negative and social anxiety among college students in Fujian province. J Guizhou Med Univ. 2022;47(09):1052–6. [Google Scholar]
44.Xu Y, LIU H, LIU H. Parallel mediating role of technology intrusion and fear of negative evaluation inthe relationship between social anxiety and smartphone addiction of college students. China J Health Psychol. 2024;32(06):926–33. [Google Scholar]
45.Rapee RM, Heimberg RG. A cognitive-behavioral model of anxiety in social phobia. Behav Res Ther. 1997;35(8):741–56. [DOI] [PubMed] [Google Scholar]
46.Weeks JW, Howell AN. The bivalent fear of evaluation model of social anxiety: Further integrating findings on fears of positive and negative evaluation. Cogn Behav Ther. 2012;41(2):83–95. [DOI] [PubMed] [Google Scholar]
47.Bates GW, Apputhurai P, Knowles SR. Bivalent Fears of Evaluation in Social Anxiety: Evaluation of an Extended Psychoevolutionary Model. Eur J Invest Health Psychol Educ. 2024;14(11):2918–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Jia Y, Yue Y. Influencing Factors of Social Anxiety of Undergraduate Nursing Students Based on Random Forest Model: A Cross-Sectional Study. Nurs Open. 2025;12(1):e70137. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Heimberg RG, Brozovich FA, Rapee RM. A cognitive behavioral model of social anxiety disorder: Update and extension. Social anxiety. Elsevier; 2010. pp. 395–422.
50.Zhang Y, Chen J, Gao W, Chen W, Xiao Z, Qi Y, et al. From fears of evaluation to social anxiety: The longitudinal relationships and neural basis in healthy young adults. Int J Clin health Psychol. 2023;23(2):100345. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Krieg A, Xu Y, Cicero DC. Comparing social anxiety between Asian Americans and European Americans: An examination of measurement invariance. Assessment. 2018;25(5):564–77. [DOI] [PubMed] [Google Scholar]
52.Merguie JL. Measurement Invariance of the Brief Fear of Negative Evaluation Scale Across Sexual and Gender Identity Groups [Master’s Thesis]. Sam Houston State University; 2023.
53.Caballo VE, Salazar IC, Irurtia MJ, Arias B, Nobre L. The assessment of social anxiety through five self-report measures, LSAS-SR, SPAI, SPIN, SPS, and SIAS: A critical analysis of their factor structure. Behav Psychology/Psicología Conductual. 2013;21(3).
54.Spielberger CD, Gorsuch RL, Lushene RE, Vagg PR, Jacobs GA. Manual for the State-Trait Anxiety Inventory, Form Y. Palo Alto: Consulting Psychologists Press; 1983.
55.Carleton RN, Collimore KC, McCabe RE, Antony MM. Addressing revisions to the Brief Fear of Negative Evaluation scale: Measuring fear of negative evaluation across anxiety and mood disorders. J Anxiety Disord. 2011;25(6):822–8. [DOI] [PubMed] [Google Scholar]
56.Okawa S, Arai H, Sasagawa S, Ishikawa S, ichi, Norberg MM, Schmidt NB, et al. A cross-cultural comparison of the bivalent fear of evaluation model for social anxiety. J Behav Cogn Therapy. 2021;31(3):205–13. [Google Scholar]
57.Zane N, Yeh M. The use of culturally-based variables in assessment: Studies on loss of face. Asian American mental health: Assessment theories and methods. Springer; 2002. pp. 123–38.
58.Markus HR, Kitayama S. Culture and the self: Implications for cognition, emotion, and motivation. College student development and academic life. Routledge; 2014. pp. 264–93.
59.Van de Vijver FJ, Leung K. Methods and data analysis for cross-cultural research. Volume 116. Cambridge University Press; 2021.
60.Oyserman D, Coon HM, Kemmelmeier M. Rethinking individualism and collectivism: evaluation of theoretical assumptions and meta-analyses. Psychol Bull. 2002;128(1):3. [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1.^{(31.9KB, docx)}

Supplementary Material 2.^{(34.6KB, xlsx)}

Supplementary Material 3.^{(820.1KB, docx)}

Supplementary Material 4.^{(815.1KB, docx)}

Supplementary Material 5.^{(34.5KB, xlsx)}

Supplementary Material 6.^{(18.6KB, docx)}

Data Availability Statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

[CR1] 1.Morrison AS, Heimberg RG. Social anxiety and social anxiety disorder. Ann Rev Clin Psychol. 2013;9(1):249–74. [DOI] [PubMed] [Google Scholar]

[CR2] 2.Stein DJ, Lim CC, Roest AM, De Jonge P, Aguilar-Gaxiola S, Al-Hamzawi A, et al. The cross-national epidemiology of social anxiety disorder: Data from the World Mental Health Survey Initiative. BMC Med. 2017;15:1–21. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Fares K, Haddad C, Malaeb D, Sacre H, Akel M, Salameh P, et al. Social anxiety and its correlates among Lebanese adults: role of self-esteem, depression, alcohol use disorder, alexithymia, and work fatigue. Prim care companion CNS disorders. 2022;24(1):39101. [DOI] [PubMed] [Google Scholar]

[CR4] 4.Villarama JA, Ayeras SRF, Bumanlag JB, De Guzman BFD, Reyes BMB, Ruiz AKF. Too Anxious to Speak: Assessing the Impact of Social Anxiety on High School Students’ Self-Esteem, Academic Performance, and Coping Strategies. J Interdisciplinary Perspect. 2024;2(7):1–1. [Google Scholar]

[CR5] 5.Khakpoor S, Saed O, Shahsavar A. The concept of Anxiety sensitivity in social anxiety disorder presentations, symptomatology, and treatment: A theoretical perspective. Cogent Psychol. 2019;6(1):1617658. [Google Scholar]

[CR6] 6.Watson D, Friend R. Measurement of social-evaluative anxiety. J Consult Clin Psychol. 1969;33(4):448. [DOI] [PubMed] [Google Scholar]

[CR7] 7.Liu Y, Zhang D. On Theory of Fear of Evaluation and Its Relative Research. Adv Psychol Sci. 2010;18(01):106–13. [Google Scholar]

[CR8] 8.Heimberg RG. Social phobia: Diagnosis, assessment, and treatment. Guilford Press; 1995.

[CR9] 9.Clark DM, Crozier W, Alden L. A cognitive perspective on social phobia. Essent Handb social anxiety Clin. 2005;193–218.

[CR10] 10.Teale Sapach MJ, Carleton RN, Mulvogue MK, Weeks JW, Heimberg RG. Cognitive constructs and social anxiety disorder: Beyond fearing negative evaluation. Cogn Behav Ther. 2015;44(1):63–73. [DOI] [PubMed] [Google Scholar]

[CR11] 11.Edition F. others. Diagnostic and statistical manual of mental disorders. Am Psychiatric Assoc. 2013;21(21):591–643. [Google Scholar]

[CR12] 12.Shomali Ahmadabadi M, Zabihi M, Khodarahmi A, Barkhordari Ahmadabadi A. The Mediating Role of Self-Criticism in the Relationship between Fear of Negative Evaluation and Social Anxiety in Medical Science Students. Tolooebehdasht. 2024;23(4):44–56. [Google Scholar]

[CR13] 13.Sigurvinsdottir R, Soring K, Kristinsdottir K, Halfdanarson SG, Johannsdottir KR, Vilhjalmsson HH, et al. Social anxiety, fear of negative evaluation, and distress in a virtual reality environment. Behav Change. 2021;38(2):109–18. [Google Scholar]

[CR14] 14.Fredrick JW, Luebbe AM. Prospective associations between fears of negative evaluation, fears of positive evaluation, and social anxiety symptoms in adolescence. Child Psychiatry Hum Dev. 2024;55(1):195–205. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR15] 15.Haller SP, Kadosh KC, Scerif G, Lau JY. Social anxiety disorder in adolescence: How developmental cognitive neuroscience findings may shape understanding and interventions for psychopathology. Dev Cogn Neurosci. 2015;13:11–20. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR16] 16.Rapee RM, Oar EL, Johnco CJ, Forbes MK, Fardouly J, Magson NR, et al. Adolescent development and risk for the onset of social-emotional disorders: A review and conceptual model. Behav Res Ther. 2019;123:103501. [DOI] [PubMed] [Google Scholar]

[CR17] 17.Asher M, Asnaani A, Aderka IM. Gender differences in social anxiety disorder: A review. Clin Psychol Rev. 2017;56:1–12. [DOI] [PubMed] [Google Scholar]

[CR18] 18.Borenstein M, Hedges LV, Higgins JP, Rothstein HR. Introduction to meta-analysis. Wiley; 2021.

[CR19] 19.Jefferies P, Ungar M. Social anxiety in young people: A prevalence study in seven countries. PLoS ONE. 2020;15(9):e0239133. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR20] 20.Woody SR, Miao S, Kellman-McFarlane K. Cultural differences in social anxiety: A meta-analysis of Asian and European heritage samples. Asian Am J Psychol. 2015;6(1):47. [Google Scholar]

[CR21] 21.Carleton RN, Collimore KC, McCabe RE, Antony MM. Addressing revisions to the Brief Fear of Negative Evaluation scale: Measuring fear of negative evaluation across anxiety and mood disorders. J Anxiety Disord. 2011;25(6):822–8. [DOI] [PubMed] [Google Scholar]

[CR22] 22.Joogoolsingh N, Wu MS, Lewin AB, Storch EA. Symptom accommodation related to social anxiety symptoms in adults: phenomenology, correlates, and impairment. J Cogn Psychother. 2015;29(1):3–19. [DOI] [PubMed] [Google Scholar]

[CR23] 23.Gray E, Beierl ET, Clark DM. Sub-types of safety behaviours and their effects on social anxiety disorder. PLoS ONE. 2019;14(10):e0223165. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR24] 24.Modini M, Abbott MJ, Hunt C. A systematic review of the psychometric properties of trait social anxiety self-report measures. J Psychopathol Behav Assess. 2015;37:645–62. [Google Scholar]

[CR25] 25.Dryman MT, Heimberg RG. Examining the relationships among social anxiety, fears of evaluation, and interpretation bias. Cogn Therapy Res. 2015;39:646–57. [Google Scholar]

[CR26] 26.Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. 10.1136/bmj.n71. [DOI] [PMC free article] [PubMed]

[CR27] 27.Hunter JE, Schmidt FL. Methods of meta-analysis: Correcting error and bias in research findings. Sage; 2004.

[CR28] 28.Zhang Y, Li S, Yu G. The relationship between loneliness and mobile phone addiction: A meta-analysis. Adv Psychol Sci. 2020;28(11):1836–52. [Google Scholar]

[CR29] 29.National Heart, Lung, and Blood Institute. Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. 2021. Available from: https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools.

[CR30] 30.McHugh ML. Interrater reliability: the kappa statistic. Biochemia Med. 2012;22(3):276–82. [PMC free article] [PubMed] [Google Scholar]

[CR31] 31.Viechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Res synthesis methods. 2010;1(2):112–25. [DOI] [PubMed] [Google Scholar]

[CR32] 32.Harrer M, Cuijpers P, Ebert AFT. DD. Doing Meta-Analysis With R: A Hands-On Guide. 1st ed. Boca Raton, FL and London: Chapman & Hall/CRC; 2021. [Google Scholar]

[CR33] 33.IntHout J, Ioannidis JP, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ open. 2016;6(7):e010247. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Thornton A, Lee P. Publication bias in meta-analysis: its causes and consequences. J Clin Epidemiol. 2000;53(2):207–16. [DOI] [PubMed] [Google Scholar]

[CR35] 35.Rosenthal R. The file drawer problem and tolerance for null results. Psychol Bull. 1979;86(3):638. [Google Scholar]

[CR36] 36.Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR37] 37.Becker BJ. Failsafe N or file-drawer number. In: Rothstein HR, Sutton AJ, Borenstein M, editors. Publication bias in meta-analysis: prevention, assessment and adjustments. Chichester: Wiley; 2005. pp. 111–25. 10.1002/0470870168.ch7. [DOI]

[CR38] 38.Duval S, Tweedie R. Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–63. [DOI] [PubMed] [Google Scholar]

[CR39] 39.Song H, Zmyslinski-Seelig A, Kim J, Drent A, Victor A, Omori K, et al. Does Facebook make you lonely? A meta analysis. Comput Hum Behav. 2014;36:446–52. [Google Scholar]

[CR40] 40.Tipton E. Small sample adjustments for robust variance estimation with meta-regression. Psychol Methods. 2015;20(3):375. [DOI] [PubMed] [Google Scholar]

[CR41] 41.Cheng G, Zhang D, Ding F. Self-esteem and fear of negative evaluation as mediators between family socioeconomic status and social anxiety in Chinese emerging adults. Int J Soc Psychiatry. 2015;61(6):569–76. [DOI] [PubMed] [Google Scholar]

[CR42] 42.Peker M, Akkuş K. Fear of positive evaluation differentially predicts social anxiety: a six-month longitudinal panel study. Curr Psychol. 2024;43(4):3621–31. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 43.Tuo A, Liang H, ZHOU H, Li B, LIU Y, YANG X, et al. Relationship between evaluation fear of online bullying and negative and social anxiety among college students in Fujian province. J Guizhou Med Univ. 2022;47(09):1052–6. [Google Scholar]

[CR44] 44.Xu Y, LIU H, LIU H. Parallel mediating role of technology intrusion and fear of negative evaluation inthe relationship between social anxiety and smartphone addiction of college students. China J Health Psychol. 2024;32(06):926–33. [Google Scholar]

[CR45] 45.Rapee RM, Heimberg RG. A cognitive-behavioral model of anxiety in social phobia. Behav Res Ther. 1997;35(8):741–56. [DOI] [PubMed] [Google Scholar]

[CR46] 46.Weeks JW, Howell AN. The bivalent fear of evaluation model of social anxiety: Further integrating findings on fears of positive and negative evaluation. Cogn Behav Ther. 2012;41(2):83–95. [DOI] [PubMed] [Google Scholar]

[CR47] 47.Bates GW, Apputhurai P, Knowles SR. Bivalent Fears of Evaluation in Social Anxiety: Evaluation of an Extended Psychoevolutionary Model. Eur J Invest Health Psychol Educ. 2024;14(11):2918–31. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR48] 48.Jia Y, Yue Y. Influencing Factors of Social Anxiety of Undergraduate Nursing Students Based on Random Forest Model: A Cross-Sectional Study. Nurs Open. 2025;12(1):e70137. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR49] 49.Heimberg RG, Brozovich FA, Rapee RM. A cognitive behavioral model of social anxiety disorder: Update and extension. Social anxiety. Elsevier; 2010. pp. 395–422.

[CR50] 50.Zhang Y, Chen J, Gao W, Chen W, Xiao Z, Qi Y, et al. From fears of evaluation to social anxiety: The longitudinal relationships and neural basis in healthy young adults. Int J Clin health Psychol. 2023;23(2):100345. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR51] 51.Krieg A, Xu Y, Cicero DC. Comparing social anxiety between Asian Americans and European Americans: An examination of measurement invariance. Assessment. 2018;25(5):564–77. [DOI] [PubMed] [Google Scholar]

[CR52] 52.Merguie JL. Measurement Invariance of the Brief Fear of Negative Evaluation Scale Across Sexual and Gender Identity Groups [Master’s Thesis]. Sam Houston State University; 2023.

[CR53] 53.Caballo VE, Salazar IC, Irurtia MJ, Arias B, Nobre L. The assessment of social anxiety through five self-report measures, LSAS-SR, SPAI, SPIN, SPS, and SIAS: A critical analysis of their factor structure. Behav Psychology/Psicología Conductual. 2013;21(3).

[CR54] 54.Spielberger CD, Gorsuch RL, Lushene RE, Vagg PR, Jacobs GA. Manual for the State-Trait Anxiety Inventory, Form Y. Palo Alto: Consulting Psychologists Press; 1983.

[CR55] 55.Carleton RN, Collimore KC, McCabe RE, Antony MM. Addressing revisions to the Brief Fear of Negative Evaluation scale: Measuring fear of negative evaluation across anxiety and mood disorders. J Anxiety Disord. 2011;25(6):822–8. [DOI] [PubMed] [Google Scholar]

[CR56] 56.Okawa S, Arai H, Sasagawa S, Ishikawa S, ichi, Norberg MM, Schmidt NB, et al. A cross-cultural comparison of the bivalent fear of evaluation model for social anxiety. J Behav Cogn Therapy. 2021;31(3):205–13. [Google Scholar]

[CR57] 57.Zane N, Yeh M. The use of culturally-based variables in assessment: Studies on loss of face. Asian American mental health: Assessment theories and methods. Springer; 2002. pp. 123–38.

[CR58] 58.Markus HR, Kitayama S. Culture and the self: Implications for cognition, emotion, and motivation. College student development and academic life. Routledge; 2014. pp. 264–93.

[CR59] 59.Van de Vijver FJ, Leung K. Methods and data analysis for cross-cultural research. Volume 116. Cambridge University Press; 2021.

[CR60] 60.Oyserman D, Coon HM, Kemmelmeier M. Rethinking individualism and collectivism: evaluation of theoretical assumptions and meta-analyses. Psychol Bull. 2002;128(1):3. [PubMed] [Google Scholar]

PERMALINK

The relationship between fear of negative evaluation and social anxiety: a systematic review and meta-analysis

Songhao Peng

Min Zhou

Longyan Yue

Xiaojing Hei

Zihan Li

Fanxue Kong

Tao Zhang

Xiaoli Ren

Yan Song

Abstract

Background

Methods

Results

Conclusion

Supplementary Information

Introduction

Method

Literature search

Literature selection

Fig. 1.

Literature coding

Quality assessment

Sensitivity analysis and heterogeneity

Publication bias

Model selection

Statistical analysis

Results

Study characteristics

Sensitivity analysis

Main effect testing

Publication bias assessment

Fig. 2.

Moderator analysis

Table 1.

Discussion

Conclusions

Supplementary Information

Acknowledgements

Abbreviations

Authors’ contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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