Factor structure and psychometric properties of a Persian translation of the Epworth Sleepiness Scale for Children and Adolescents

Background: Given the high prevalence of excessive daytime disorder (EDS) among children and adolescents, daytime sleepiness should be effectively measured for them to design appropriate intervention program. However, the commonly used instrument Epworth Sleepiness Scale for Children and Adolescents (ESS-CHAD) has little information in its psychometric properties. This study aimed to apply 2 different test theories to examine the psychometric properties of the Persian ESS-CHAD among a large sample of Iranian adolescents and children.Methods: In this methodological study, participants from 8 high schools (n=1371; 700 males), in Qazvin, Iran, completed the ESS-CHAD, a background information sheet, and Insomnia Severity Index (ISI). The ESS-CHAD was translated by using a forward-backward translation method.Two weeks later, the participants completed the ESS-CHAD again. Internal consistency using Cronbach’s alpha, test-retest reliability using intraclass correlation coefficient (ICC), regression analysis testing the correlation between ESS-CHAD and ISI, Confirmatory factor analysis (CFA)with measurement invariance, Rasch analysis with differential item functioning (DIF), and latent class analysis (LCA) were used to examine the psychometric properties of the ESS-CHAD. Results: The internal consistency (α=0.79), test-retest reliability (ICC=0.84), regression findings(β=0.39, P<0.001), CFA (comparative fit index [CFI])=0.974, root-mean square error of approximation [RMSEA]=0.040), supported measurement invariance (∆CFI=-0.009 to 0.007,∆RMSEA=-0.009 to 0.001), Rasch analysis (infit mean square=0.88 to 1.31, outfit mean square=0.68 to 1.19), and no substantial DIF (DIF contrast=-0.43 to 0.38) all indicated that ESSCHAD is a reliable and valid instrument. The LCA further classified the sample into 2 distinctclasses. Conclusion: Persian ESS-CHAD could be used to assess daytime sleepiness for adolescents whoare speaking Persian

Psychometric Properties of the Persian Generalized Trust Scale: Confirmatory Factor Analysis and Rasch Models and Relationship with Quality of Life, Happiness, and Depression

The psychometric properties of the Generalized Trust Scale (GTS) are well established. Furthermore, previous studies have found that the GTS is positively associated with better mental health and lower distress, and the literature finds that trust is good for mental health. However, current literature does not have any psychometric evidence concerning the Persian GTS. This study translated the GTS into Persian and validated its psychometric properties. After translating the GTS into Persian using robust and standardized translation procedure, 1200 Iranians (mean age = 34.83 years; 583 [48.6%] males) completed the GTS, along with the Hospital Anxiety and Depression Scale (HADS), Short Form-12 (SF-12), and Oxford Happiness Questionnaire Short Form (OHQ-SF). The factor structure of Persian GTS was confirmed by a unidimensional model with a method factor (comparative fit index = 0.998; Tucker-Lewis index = 0.992). The unidimensional model was also supported by Rasch analysis (mean square = 0.75 to 1.31). Other properties of the Persian GTS were satisfactory. More specifically, test-retest reliability was good (intraclass correlational coefficient = 0.865), internal consistency was good (α = 0.881), and concurrent validity was supported (standardized β = − 0.086 with depression in the HADS [p = 0.045]; = − 0.162 with anxiety in the HADS [p < 0.001]; = 0.077 with mental component score in the SF-12 [p = 0.044]; = 0.624 with OHQ-SF [p < 0.001]). The six-item Persian GTS has promising psychometric properties and can be an effective measure to assess trust among Iranians

Social media addiction and sexual dysfunction among Iranian women: the mediating role of intimacy and social support

Background and aims: Social media use has become increasingly popular among Internet users. Given the widespread use of social media on smartphones, there is an increasing need for research examining the impact of the use of such technologies on sexual relationships and their constructs such as intimacy, satisfaction, and sexual function. However, little is known about the underlying mechanism why social media addiction impacts on sexual distress. This study investigated whether two constructs (intimacy and perceived social support) were mediators in the association of social media addiction and sexual distress among married women. Methods: A prospective study was conducted where all participants (N = 938; mean age = 36.5 years) completed the Bergen Social Media Addiction Scale to assess social media addiction, the Female Sexual Distress Scale – Revised to assess sexual distress, the Unidimensional Relationship Closeness Scale to assess intimacy, and the Multidimensional Scale of Perceived Social Support to assess perceived social support. Results: The results showed that social media addiction had direct and indirect (via intimacy and perceived social support) effects on sexual function and sexual distress. Discussion and conclusions: The findings of this study facilitate a better understanding of how problematic engaging to social media can affect couples’ intimacy, perceived social support, and constructs of sexual function. Consequently, sexual counseling should be considered an essential element for assessing individual behaviors in the context of social media use

Evaluation of the selfitis behavior scale across two Persian-speaking countries, Iran and Afghanistan: advanced psychometric testing in a large-scale sample

Selfitis—which started off as a hoax but has now been investigated empirically—has been defined as the obsessive–compulsive desire to take photos of oneself and post them on social media. Furthermore, a scale to assess selfitis, the Selfitis Behavior Scale (SBS), has been developed. This study applied advanced psychometric testing methods, including confirmatory factor analysis (utilizing classical test theory) and the Rasch model (utilizing modern test theory), to examine the psychometric properties among Persian speakers (in Iran and Afghanistan). The participants (3163 Iranians and 1100 Afghanistani) completed an online survey posted on Instagram pages. The SBS showed promising properties, including satisfactory reliability (e.g., internal consistency and test–retest reliability), excellent construct validity (e.g., good fit in the CFA and Rasch models), and acceptable measurement invariance across Iranian and Afghan samples. Consequently, the SBS is a valid and reliable instrument for assessing selfitis among Persian-speaking samples

ICA-Based Imagined Conceptual Words Classification on EEG Signals

Independent component analysis (ICA) has been used for detecting and removing the eye artifacts conventionally. However, in this research, it was used not only for detecting the eye artifacts, but also for detecting the brain-produced signals of two conceptual danger and information category words. In this cross-sectional research, electroencephalography (EEG) signals were recorded using Micromed and 19-channel helmet devices in unipolar mode, wherein Cz electrode was selected as the reference electrode. In the first part of this research, the statistical community test case included four men and four women, who were 25–30 years old. In the designed task, three groups of traffic signs were considered, in which two groups referred to the concept of danger, and the third one referred to the concept of information. In the second part, the three volunteers, two men and one woman, who had the best results, were chosen from among eight participants. In the second designed task, direction arrows (up, down, left, and right) were used. For the 2/8 volunteers in the rest times, very high-power alpha waves were observed from the back of the head; however, in the thinking times, they were different. According to this result, alpha waves for changing the task from thinking to rest condition took at least 3 s for the two volunteers, and it was at most 5 s until they went to the absolute rest condition. For the 7/8 volunteers, the danger and information signals were well classified; these differences for the 5/8 volunteers were observed in the right hemisphere, and, for the other three volunteers, the differences were observed in the left hemisphere. For the second task, simulations showed that the best classification accuracies resulted when the time window was 2.5 s. In addition, it also showed that the features of the autoregressive (AR)-15 model coefficients were the best choices for extracting the features. For all the states of neural network except hardlim discriminator function, the classification accuracies were almost the same and not very different. Linear discriminant analysis (LDA) in comparison with the neural network yielded higher classification accuracies. ICA is a suitable algorithm for recognizing of the word’s concept and its place in the brain. Achieved results from this experiment were the same compared with the results from other methods such as functional magnetic resonance imaging and methods based on the brain signals (EEG) in the vowel imagination and covert speech. Herein, the highest classification accuracy was obtained by extracting the target signal from the output of the ICA and extracting the features of coefficients AR model with time interval of 2.5 s. Finally, LDA resulted in the highest classification accuracy more than 60%