Dimensionality, Reliability, Invariance, and Validity of the Multidimensional Social Anxiety Response Inventory-21 (MSARI-21)

Measurement invariance of the Liebowitz Social Anxiety Scale-Self-Report

OBJECTIVE

The Liebowitz Social Anxiety Scale-Self Report (LSAS-SR) is a self-report measure of social anxiety (SA), which has shown adequate psychometric properties across cultures. However, no study has systematically evaluated its measurement invariance (MI) between (a) individuals with and without a diagnosis of social anxiety disorder (SAD) and (b) males and females. The current study addresses this issue.

METHODS

We collected data on 257 (158 females) Italian individuals diagnosed with SAD and 356 (232 females) community-dwelling adults.

RESULTS

We initially found support for the unidimensionality of the Italian LSAS-SR measurement model in all samples. Using the Graded Response Model, we obtained evidence of partial MI and differential item functioning between community-dwelling and SAD-diagnosed individuals and evidence of strong MI between male and female participants.

CONCLUSIONS

The results of this study suggest that the Italian LSAS-SR measures the same trait in the same way across the symptom continuum and sexes, making it a psychometrically sound tool for assessment, screening, and research purposes.

Bifactor exploratory structural equation modeling: A meta-analytic review of model fit

Multivariate behavioral research often focuses on latent constructs-such as motivation, self-concept, or wellbeing-that cannot be directly observed. Typically, these latent constructs are measured with items in standardized instruments. To test the factorial structure and multidimensionality of latent constructs in educational and psychological research, Morin et al. (2016a) proposed bifactor exploratory structural equation modeling (B-ESEM). This meta-analytic review (158 studies, k = 308, N = 778,624) aimed to estimate the extent to which B-ESEM model fit differs from other model representations, including confirmatory factor analysis (CFA), exploratory structural equation modeling (ESEM), hierarchical CFA, hierarchical ESEM, and bifactor-CFA. The study domains included learning and instruction, motivation and emotion, self and identity, depression and wellbeing, and interpersonal relations. The meta-analyzed fit indices were the χ2 /df ratio, the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean squared residual (SRMR). The findings of this meta-analytic review indicate that the B-ESEM model fit is superior to the fit of reference models. Furthermore, the results suggest that model fit is sensitive to sample size, item number, and the number of specific and general factors in a model.

Nomophobia and relationships with latent classes of solitude

Recently, nomophobia (separation anxiety from mobile phone) has become a common phenomenon. The authors' main purpose was to explore latent classes of solitude behaviors and how they are related to nomophobia. Chinese versions of the Nomophobia Scale and the Solitude Behaviour Scale were used in a sample of college students (351 female and 327 male). Latent class analysis, analysis of variance, and regression analysis were employed to classify solitude behaviors and explore the relationship between solitude and nomophobia. A six-class model best fit the data (BIC = 60086.49). Significant differences among the classes were found on nomophobia. Loneliness, social avoidance, and eccentricity significantly predicted nomophobia. Solitude behaviors of college students can be divided into six latent classes. The classes with a high response preference for solitude scored higher on nomophobia, especially the fear of losing an Internet connection. Not self-determined solitude and negative-solitude had a positive effect on nomophobia.