What is problematic about binary questions on gender in health surveys – a missing answer analysis

in Cogent Medicine (2020, December 04)

Background: In many studies, participants who do not state their gender are excluded from the analysis. This may be appropriate if they do not answer the questionnaire seriously. However, some ... [more ▼]

Background: In many studies, participants who do not state their gender are excluded from the analysis. This may be appropriate if they do not answer the questionnaire seriously. However, some participants may have understandable reasons for not reporting their gender, e.g. questioning their gender identity. Research question: How many students and which students do not answer the question on gender? Methods: We analyzed data of the Health Behaviour in School-aged Children study from Ireland, France, Hungary, Scotland, Belgium (Flemish) and Luxembourg (n = 40,053). To explore the reasons for non-response, we divided the participants into 3 groups: 1. Responders answered both socio- demographic questions (age and gender) 2. age non-responders did not answer the question on age. 3. Gender non-responders answered the question on age, but not the one on gender. Results: 311 out of 40,053 (0.8%) pupils aged 11–18 did not report their gender. About 40% of them did not answer the age question either. However, the other 60% belong to the group of gender non-responders and this group is disadvantaged compared to responders: they report lower self-rated health, more health complaints, less family support and more substance use (alcohol, tobacco, cannabis). 1.9% of pupils did not answer the question about age. These age non-responders answered the questionnaire more selectively overall and skipped more questions. Conclusion: The data suggest that the reasons for age non-response and gender non-response are different. For age non-responders, the fear of de-anonymization seems to be the reason for not indicating their age. Not answering the question on gender is rare. If the participants answered the question on age, but not the question on gender, then the variable gender is missing not at random. The health problems of gender non-responders correspond to the health problems of gender non-conforming adolescents. Thus, the question arises if the group of gender non-responders should be included in the analysis and if the question on gender should be asked differently in the future [less ▲]

The influence of well-being, social support, media use and sociodemographic factors on problematic social media sue among Luxembourgish adolescents

in Cogent Medicine (2020), 7(1),

Background: Adolescents spend an increasing amount of time communicating online. Previous research has indicated that electronic media communication has been associated with positive outcomes on ... [more ▼]

Background: Adolescents spend an increasing amount of time communicating online. Previous research has indicated that electronic media communication has been associated with positive outcomes on adolescent well-being and development, however, problematic social media use is on the rise. This study investigates factors that influence problematic social media use (PSMU), based on previous empirical research and the Differential Susceptibility to Media Effects Model by Valkenburg and Peter (2013). Methods: The data used in this study stems from the 2018 Health Behaviour for School-aged Children (HBSC) study in Luxembourg. Data from elementary and secondary school students aged 11 to 18 was used (N = 6164), which was collected through a written survey. A four-stage hierarchical multiple regression analysis was conducted using SPSS, with problematic social media use as the dependent variable. 14 independent variables were included in the model, added in four blocks: sociodemographic factors, social support factors, well-being factors and media use factors.Results: The results indicate that in stage one of the hierarchical regression, the sociodemographic predictors accounted for 3% of the variation in problematic social media use. The addition of the social support factors to the model in stage two explained an additional 7% of the variation in problematic social media use, and the addition of the well-being factors in stage three an additional 5.3%. In stage four of the hierarchical regression media use factors were added to the model, and the four blocks of predictors accounted for 22.2% of the variation in problematic social media use (Adjusted R2 = 0.222). The most important predictors for problematic social media use were preference for online social interaction (β = 0.205, p < .001), the intensity of electronic media communication (β = 0.155, p < .001), psychosomatic complaints (β = 0.136, p < .001), perceived stress (β = 0.122, p < .001) and cyberbullying perpetration (β = 0.117, p < .001). Conclusions: The block of sociodemographic factors contributed minimally to the explanation of the variance in problematic social media use in the model. The most important predictors for problematic social media use were preference for online social interaction, the intensity of electronic media communication, psychosomatic complaints, perceived stress and cyberbullying perpetration. This suggests that there are several starting points for the prevention of problematic social media use among adolescents. [less ▲]