We report the development and validation of a scale to measure online shopping addiction. Inspired by previous theories and research on behavioral addiction, the Griffiths’s widely accepted six-factor component model was referred to and an 18-item scale was constructed, with each component measured by three items. The results of exploratory factor analysis, based on Sample 1 (999 college students) and confirmatory factor analysis, based on Sample 2 (854 college students) showed the Griffiths’s substantive six-factor structure underlay the online shopping addiction scale. Cronbach’s alpha suggested that the resulting scale was highly reliable. Concurrent validity, based on Sample 3 (328 college students), was also satisfactory as indicated by correlations between the scale and measures of similar constructs. Finally, self-perceived online shopping addiction can be predicted to a relatively high degree. The present 18-item scale is a solid theory-based instrument to empirically measure online shopping addiction and can be used for understanding the phenomena among young adults.

Social media fatigue (SMF), which refers to social media users’ tendency to withdraw from social media because of feeling overwhelmed, is closely related to individuals’ social life and well-being. Many studies focused on understanding SMF and exploring its enablers and influences. However, few pieces of research administered a standard measurement of SMF. This study aimed to develop and validate a measure of SMF, and a cross-sectional survey was conducted among 1599 participants in total. Semi-structured interviews of 30 participants were firstly conducted as a pilot study, and an initial version of the social media fatigue scale (SMFS) with 24 items was generated. Then, both exploratory factor analysis (N = 509) and confirmatory factor analysis (N = 552) as well as reliability and validity analysis (N = 508) were conducted and a 15-item SMFS was finally developed. The results demonstrated that: 1) SMF was a multi-dimension concept including a cognitive aspect, an emotional aspect and a behavioral aspect; 2) the three-dimensional structure of the SMFS (cognitive-behavioral-emotional structure) fitted the data well; 3) the McDonald’s Omega coefficients for the SMFS was 0.83, suggesting that the SMFS was reliable; 4) criterion validity was satisfactory as indicated by both the significant correlations between self-rated scores of fatigue and total SMFS scores and the significant regression model of SMF on social media privacy, social media confidence, and negative feeling after comparison. Based on the Limited Capacity Model, the present study expanded SMF from a unidimensional model to a three-dimension model, and developed a 15-item SMFS. The study enriched the existing knowledge of SMF, and coined a reliable and valid tool for measuring it. Besides, concluding the typical characteristics of SMF, the study may provide some inspiration for both researchers and social media managers and operators in mitigating SMF.

The fit of cognitive diagnostic models (CDMs) to response data needs to be evaluated, since CDMs might yield misleading results when they do not fit the data well. Limited-information statistic M₂ and the associated root mean square error of approximation (RMSEA₂) in item factor analysis were extended to evaluate the fit of CDMs. The findings suggested that the M₂ statistic has proper empirical Type I error rates and good statistical power, and it could be used as a general statistical tool. More importantly, we found that there was a strong linear relationship between mean marginal misclassification rates and RMSEA₂ when there was model–data misfit. The evidence demonstrated that .030 and .045 could be reasonable thresholds for excellent and good fit, respectively, under the saturated log-linear cognitive diagnosis model.

This study investigated the effects of social-emotional competency on pupils’ academic achievement, academic emotions and attitudes, and interpersonal relationships. Participants were 7106 fourth-grade and fifth-grade students in western China. The results were: (1) social-emotional competency positively predicted pupils’ academic achievement (including reading, mathematics, and science); (2) social-emotional competency predicted pupils’ academic emotions and attitudes, including learning anxiety and interest, and academic emotions and attitudes played a mediating role in the relation between social-emotional competency and academic achievement; and (3) social-emotional competency positively predicted pupils’ interpersonal relationships, including peer relationships and teacher–student relationships, and interpersonal relationships played a mediating role in the relation between social-emotional competency and academic achievement. These findings highlighted the importance of social-emotional competency to child development in western China, where many children might lack their parents’ company.

Background:The Geriatric Anxiety Inventory (GAI) was developed to assess anxiety in older adults. The objectives of this work were as follows: (a) to analyze the psychometric properties of the Chinese version of the GAI (GAI-CV), and (b) to explore the extent of anxiety and related factors in the elderly Chinese residents of Beijing.Methods:Participants in this study included 1,047 people (59.4% female) more than 60 years old who were living in the community. They were randomly selected from 15 communities in Beijing. Basic information was collected. Anxiety was measured using the GAI-CV, the Self-Rating Anxiety Scale (SAS), and the Beck Anxiety Inventory (BAI).Results:The GAI-CV exhibited good internal consistency (Cronbach's α = 0.94) and demonstrated good concurrent validity against the SAS (r = 0.52, p = 0.018) and the BAI (r = 0.560, p = 0.000). Item response theory (IRT) analyses showed that the items of the GAI-CV exhibited high difficulty (0.97–2) and discrimination parameters (1.91–5.33). The items exhibited information parameters greater than 1.25 with the exceptions of items 2, 12, and 18. The GAI-CV scores were significantly associated with gender, age, and chronic disease. However, no significant differences due to marriage or education were found.Conclusions:The GAI is a new scale that was specifically designed to measure anxiety in older people. The results of this study suggest that the GAI-CV had good psychometric properties, but some items need to be modified. IRT analyses indicated that the GAI-CV provided good measures of anxiety across the moderately high to very high levels. The GAI-CV may be a useful instrument for further research studies aimed at analyzing high-level anxiety among older adults in China.