Mind Over Media Dataset
Description
The research hypothesizes that a growth mindset positively influences well-being, while perceived peer pressure, fear of missing out, perceived cybervictimization, and social comparison orientation negatively affect well-being. A structural equation modeling approach tests these hypotheses using data from a survey of 612 undergraduate students aged 18–24, exploring relationships between social media use factors, growth mindset, and well-being. The final sample consisted of 612 respondents. These participants’ age range from 18 to 24 years old (Mage = 19.54, SDage = 1.24; 67.5% females, 27.8% males, 4.7% alternatives). The data was collected through a questionnaire comprised of seven sections including 1) Personal Information (participants provided basic details such as gender, age, and year of study, social media spending time (respondents shared insights into their daily social media usage, including the average time spent on various platforms), 2) PERMA Well-being (this section assessed participants' overall well-being across five dimensions: Positive Emotions, Engagement, Relationships, Meaning, and Accomplishment), 3) Growth Mindset (participants evaluated their beliefs about challenges, obstacles, effort, criticism, and success of others), 4) Social Comparison Orientation (Respondents indicated their tendency to compare themselves to others regarding to abilities and opinions, 5) Perceived peer pressure (participants rated the extent to which they felt pressured by their peers to use social media for various activities), 6) Fear of missing out (respondents assessed their levels of missing out on three aspects of fear of missing out, on social events, news, and commercial), and 7) Perceived cybervictimization (participants reported how often participants experienced four types of cybervictimization: visual cybervictimization, verbal cybervictimization, social exclusion, and impersonation. Notable findings shows a growth mindset strongly correlates with enhanced well-being, substantiating the hypothesis that personal belief that is malleable promotes better mental health. Social comparison orientation negatively impacts well-being, indicating that unfavorable comparisons on social platforms reduce mental health. However, social comparison’s negative impact on well-being is mitigated by a growth mindset, emphasizing cognitive buffers against social media induced ill-being. In addition, fear of missing out demonstrated a positive association with well-being. This contrasts with its traditionally negative portrayal, suggesting that under certain conditions, staying informed via social media fosters connection and satisfaction.
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Participants were recruited from three universities in Chiang Mai, selected from a pool of seven universities in the region. These universities were chosen to ensure a diverse range of academic fields, including human and social sciences, science and technology, and health sciences. This approach aimed to capture a broad spectrum of perspectives on social media use across disciplines. The participants were randomly selected by proportional stratified sampling and participated by completing a set of questionnaires. We excluded cases with any missing responses across all questions. Twenty-two students provided invalid responses. The final sample consisted of 612 respondents. To be eligible, participants had to be enrolled in the 2023 academic year and actively use social media platforms (e.g., Facebook, Instagram, Line, TikTok) at least once daily for the past four weeks. Researchers visited each university to directly approach students in common areas. After providing an overview of the study and emphasizing voluntary participation and confidentiality, students who agreed were asked to sign consent forms and complete questionnaires. The reliability of all measures was assessed using Cronbach's alpha, demonstrating satisfactory internal consistency. Pearson correlation analysis was conducted to examine the relationships between the six latent variables and the twenty observed variables. Structural Equation Modeling (SEM) analysis, implemented using LISREL 12.4.3.0, was employed to test the research hypotheses. The results indicated that the proposed model exhibited adequate fit to the data.