Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims ...Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims to validate the 21-item SocialNetwork Addiction Scale-Chinese(SNAS-C)in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population.Methods:A crosssectional survey was conducted,recruiting 3387 college students.Latent profile analysis(LPA)and receiver operating characteristic(ROC)curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C.Results:Three profile models were selected based on multiple statistical criteria,classifying participants into low-risk,moderate-risk,and high-risk groups.The highest-risk group was defined as“positive”for SNA,while the remaining groups were considered“negative”,serving as the reference standard for ROC analysis.The optimal cutoff score was determined to be 72(sensitivity:98.2%,specificity:96.86%),with an overall classification accuracy of 97.0%.The“positive”group reported significantly higher frequency of social network usage,greater digitalmedia dependence scores,and a higher incidence of network addiction.Conclusion:This study identified the optimal cutoff score for the SNAS-C as≥72,demonstrating high sensitivity,specificity,and diagnostic accuracy.This threshold effectively distinguishes between high-risk and low-risk SNA.展开更多
Purpose:The core component theory of addiction behavior provides a multidimensional theoretical model for measuring social networking addiction.Based on this theoretical model,the Social Networking Addiction Scale(SNA...Purpose:The core component theory of addiction behavior provides a multidimensional theoretical model for measuring social networking addiction.Based on this theoretical model,the Social Networking Addiction Scale(SNAS)was developed.The aim of this study was to test the psychometric properties of the Chinese version of the SNAS(SNAS-C).Methods:This study used a sample of 3383 Chinese university students to conduct confirmatory factor analysis(CFA)to explore the structural validity of the SNAS-C.This study examined the Pearson correlations between the six subscales of the SNAS-C(i.e.,salience,mood modification,tolerance,withdrawal symptoms,conflict,and relapse)and“social appearance anxiety(SAA)”,and“parent and peer attachment(PPA)”to test the construct validity of the SNAS-C.Cronbach’s alpha coefficient was used to evaluate the internal consistency of both the total and subscale scores.Results:The results of the CFA indicated that the six-factor model had an acceptable modelfit(χ²/df=10.954,p<0.001;GFI=0.946;AGFI=0.928;IFI=0.964;TLI=0.955;CFI=0.964;RMR=0.041;SRMR=0.037;RMSEA=0.054[90%CI[0.052–0.056]).Additionally,the total score of the SNAS-C was significantly positively correlated with the total score of SAA(r=0.43,p<0.01),and the subscales of the SNAS-C were significantly positively correlated with the total score of SAA to a medium degree(salience,r=0.31,mood modification,r=0.27,tolerance,r=0.34,withdrawal symptoms,r=0.39,conflict,r=0.41,relapse,r=0.40,p<0.01).The results of the SNAS-C and PPA were somewhat different,as the SNAS-C was significantly negatively correlated with the PPA(r=-0.12,p<0.01),and the four subscales were significantly negatively correlated with the PPA to a low degree(tolerance,r=-0.11,withdrawal symptoms,r=-0.15,conflict,r=-0.21,relapse,r=-0.15,p<0.01).Finally,the SNAS-C also exhibited good internal consistency(Cronbach’sα>0.808).Conclusion:The results indicate that the six-factor model of the SNAS-C is acceptable,and its validity can meet the tendencies of social networking addiction among Chinese college students.In summary,the current translation and validation study of the SNAS-C provides an auxiliary tool for future research on social networking addiction in the Chinese context,but the generalizability of this scale in different populations requires further investigation.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.72364006).
文摘Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims to validate the 21-item SocialNetwork Addiction Scale-Chinese(SNAS-C)in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population.Methods:A crosssectional survey was conducted,recruiting 3387 college students.Latent profile analysis(LPA)and receiver operating characteristic(ROC)curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C.Results:Three profile models were selected based on multiple statistical criteria,classifying participants into low-risk,moderate-risk,and high-risk groups.The highest-risk group was defined as“positive”for SNA,while the remaining groups were considered“negative”,serving as the reference standard for ROC analysis.The optimal cutoff score was determined to be 72(sensitivity:98.2%,specificity:96.86%),with an overall classification accuracy of 97.0%.The“positive”group reported significantly higher frequency of social network usage,greater digitalmedia dependence scores,and a higher incidence of network addiction.Conclusion:This study identified the optimal cutoff score for the SNAS-C as≥72,demonstrating high sensitivity,specificity,and diagnostic accuracy.This threshold effectively distinguishes between high-risk and low-risk SNA.
基金supported by the Guizhou Provincial Basic Research Program(Natural Science)Project(Qiankehe Fund for Basic Research-ZK[2023]General 251).
文摘Purpose:The core component theory of addiction behavior provides a multidimensional theoretical model for measuring social networking addiction.Based on this theoretical model,the Social Networking Addiction Scale(SNAS)was developed.The aim of this study was to test the psychometric properties of the Chinese version of the SNAS(SNAS-C).Methods:This study used a sample of 3383 Chinese university students to conduct confirmatory factor analysis(CFA)to explore the structural validity of the SNAS-C.This study examined the Pearson correlations between the six subscales of the SNAS-C(i.e.,salience,mood modification,tolerance,withdrawal symptoms,conflict,and relapse)and“social appearance anxiety(SAA)”,and“parent and peer attachment(PPA)”to test the construct validity of the SNAS-C.Cronbach’s alpha coefficient was used to evaluate the internal consistency of both the total and subscale scores.Results:The results of the CFA indicated that the six-factor model had an acceptable modelfit(χ²/df=10.954,p<0.001;GFI=0.946;AGFI=0.928;IFI=0.964;TLI=0.955;CFI=0.964;RMR=0.041;SRMR=0.037;RMSEA=0.054[90%CI[0.052–0.056]).Additionally,the total score of the SNAS-C was significantly positively correlated with the total score of SAA(r=0.43,p<0.01),and the subscales of the SNAS-C were significantly positively correlated with the total score of SAA to a medium degree(salience,r=0.31,mood modification,r=0.27,tolerance,r=0.34,withdrawal symptoms,r=0.39,conflict,r=0.41,relapse,r=0.40,p<0.01).The results of the SNAS-C and PPA were somewhat different,as the SNAS-C was significantly negatively correlated with the PPA(r=-0.12,p<0.01),and the four subscales were significantly negatively correlated with the PPA to a low degree(tolerance,r=-0.11,withdrawal symptoms,r=-0.15,conflict,r=-0.21,relapse,r=-0.15,p<0.01).Finally,the SNAS-C also exhibited good internal consistency(Cronbach’sα>0.808).Conclusion:The results indicate that the six-factor model of the SNAS-C is acceptable,and its validity can meet the tendencies of social networking addiction among Chinese college students.In summary,the current translation and validation study of the SNAS-C provides an auxiliary tool for future research on social networking addiction in the Chinese context,but the generalizability of this scale in different populations requires further investigation.
