Abstract
As technology advances, social media has become a widespread tool for people to build connections and foster entertainment, which may also impact their mental health. This study aims to investigate how different types and ways of using social media affect mental health across demographics.The study used correlation and linear regression analysis to assess the association between age, gender, socioeconomic status, types of social media usage and mental health symptoms. High school students (ages 13 to 18) completed an online anonymous multiple-choice survey. These questions measured the participants’ mental health symptoms, demographics, and use of social media. As a result, time spent on social media was positively associated with anxiety, loneliness, and depression, and negatively associated with self-esteem. Covariate findings within this study showed that creating posts and sharing content on social media (rather than browsing or “liking” posts) was associated with higher depressive symptom severity. This research study suggests that social media as well as the types and ways of using correlate with adolescent mental health. Future research should further investigate how public use of social media affects the mental health of adolescents. Understanding this relationship will lead to better knowledge of the effects of social media on mental health, ultimately fostering a healthier social media experience for adolescents.Keywords include: Social Media, Mental Health, and Adolescents.
Introduction
With the advancement of technology in modern society, social media has become popular due to the flexibility it offers in facilitating connections, building community, and fostering entertainment1. People worldwide engage with different types of social media including YouTube, Instagram, and Facebook among others, resulting in social media outlets quickly becoming the most frequently downloaded and used applications on individual’s devices2.
Studies have shown that this immense amount of social media usage negatively impacts people’s mental health, contributing to depression and anxiety symptoms3. The study refers to self-discrepancy theory, suggesting social media often present idealized images; therefore, it contributes to the likelihood of social comparison and feelings of self-discrepancy. Research has also shown that greater social media use, such as Snapchat and TikTok, is also related to increased loneliness and low self-esteem4. The study aligns itself with social comparison theory, where people are frequently exposed to others’ highlights and positive experiences with social media, leading to a perception that others’ lives are more successful or fulfilling that contributes to lower loneliness and self-esteem. Moreover, research has displayed that increased social media usage, including Twitter, Instagram, and Facebook, is associated with moderate levels of loneliness and lower self-esteem5. However, high heterogeneity across studies was also found, highlighting substantial variation in results. This suggests that other unmeasured factors may act as covariates, influencing the strength of the relationship between social media use and depression. In summary, while social media use is correlated with lower mental health, other factors likely contribute to the variability in this relationship.
Studies have identified that an individual’s demographics play a factor in how their mental health is affected by social media. One study about the association of age reveals a stronger association between social media and poor mental health among younger adolescents, aged 10 to 14, than older adolescents, aged 15 to 206. Another study regarding the correlation of gender shows that girls have a higher tendency to develop mental health problems when using social media compared to boys7. These studies highlight disparities in how social media affects one’s mental health, depending on factors such as one’s age and gender.
Types of social media usage correlate with mental health differently. Researchers found that private social media use, which facilitates more intimate and personal communication, is associated with fewer mental health problems8. In contrast, public social media use—characterized by content sharing, public interactions, and participation in broader discussions—along with social comparison focused on appearances and lifestyles, tends to exacerbate mental health issues8. This highlights the complex dynamics of social media on mental health, illustrating that both demographics and individual usage patterns play a significant role.
This creates a rationale for further research that effectively synthesizes these factors and how they play a role in individuals’ mental health when using social media. The evidence collected will help people to adeptly adjust their social media usage based on their backgrounds, which protects their mental health, promotes positivity, and increases happiness in their daily lives. The proposed study will determine how each factor affects the association between social media and mental health, which provides insights that help people manage their time and way of using social media to promote positive mental health.
This study investigates how different types and ways of using social media affect mental health across varying demographics. The current investigation is composed of an anonymous survey including multiple-choice questions. These questions aimed to measure the participants’ mental health, demographics, and use of social media.
The hypothesis is that more time spent on social media will positively correlate with poor mental health symptoms, such as anxiety, loneliness, and depression, while negatively correlating with self-esteem. Social media that are focused on public and comparison use will have a more severe effect than private ones. Demographics play a role, with girls facing greater effects than boys. Moreover, younger individuals and those from lower socioeconomic backgrounds are associated with more mental health symptoms from social media exposure.
Methods:
Research Design
This is a cross-sectional study, which used linear regression analysis to assess the association of how age, gender, socioeconomic status, and types of social media usage correlate with mental health when using social media. Each participant participated in the survey anonymously and answered the same sets of questions.
Participants
In total, 40 participants participated due to the limited funding and resource available for the study. The participants were adolescents, ranging from ages 13-18 with a mean age of 15.7. Their socio-economic status ranged from 4 to 10, with a mean score of 7.03 on a scale of 10, indicating a moderate to high socio-economic status. Additionally, 40% of the participants were male, while 60% were female. The only inclusion criteria for participants was that they must be adolescent girls and boys who age from 13-18 and not diagnosed with any mental health conditions or disabilities.
Data Collection
40 local high school students were recruited to complete an anonymous Google Form survey with 25 multiple choice questions. For recruitment, these participants were sent out the survey through email and school announcements. At the beginning of the survey, participants were informed of a general outline of my research, but not a detailed description of the specific factors being analyzed or the purpose of my study to avoid producing biases in the results. Participants were not compensated.
Variables and Measurements
Age. The survey asked the participants their age to determine if age affects mental health when using social media.
Gender. The survey also asked the participants, “What is your gender?” with two different answer choices: female and male.
Socioeconomic status. Socioeconomic status was also collected. The survey incorporated the MacArthur Scale of Subjective Social Status – Youth Version9 to assess socioeconomic status. The survey presented an image of a ladder with each rung of the ladder corresponding to a number from 1 through 10. The participant then chose the number that corresponds to the place on the ladder that matches their perception of their socioeconomic status. The following question was posed: “Imagine that this ladder pictures how American society is set up. At the top of the ladder are the people who are the best off — those who have the most money, the highest amount of schooling, and the jobs that bring the most respect. At the bottom are people who are the worst off — those who have the least money, little or no education, no job, or jobs that no one wants or respects. Now think about your family. Where do you think your family would be on this ladder?” with a Likert scale of 1-10.
Time using social media every day. The average time using social media every day was collected to analyze the association between social media use and mental health. The survey asked the participants, “What is the average time you spend on social media every day?” with six different answer choices: less than 1 hour, 1 hour, 2 hours, 3 hours, 4 to 5 hours, and more than 5 hours.
Frequency of using specific social media platforms. The survey asked the respondents about how frequently they use each social media platform, specifically Instagram, Snapchat, Discord, Pinterest, YouTube, TikTok, Whatsapp, and Reddit. The survey had seven different choices for each social media type: never, less often [than every few weeks], every few weeks, 1–2 times a week, 3–6 times a week, about once a day, and several times a day. When analyzing the data, Snapchat and WhatsApp were categorized as social private given that these platforms primarily involves direct messaging to friends and families. Discord and Reddit as public social because they mainly focus on large online communities and public forums. Lastly, Instagram, Pinterest, YouTube, and TikTok as social comparisons as they mostly include sharing personal lifestyle to a broader audience that encourages users to present a curated version of their lives.
Frequency of ways of using social media. Participants answered how frequently they use social media in certain ways, including direct messaging or contacting close families and friends, creating posts or content, following celebrities or influencers, watching videos or content, engaging in public discussions, and staying up to date with current events. It will ask, “How frequently do you use social media for each of the following purposes?” with seven answer choices for each: never, less often [than every few weeks], every few weeks, 1–2 times a week, 3–6 times a week, about once a day, and several times a day. When analyzing the data, direct messaging or contacting close families and friends was categorized as social private, creating posts or videos and engaging in public discussions as public social, and following celebrities or influencers, watching videos or content, as well as staying up to date with current events, as social comparison.
Mental health. In the survey, participants were presented with a series of statements that are related to mental health, such as anxiety, depression, and confidence. Each statement allowed the participant to rate on a scale of 1 to 4, with 1 signifying that it does not apply to the participant and 4 denoting that the statement completely applies to them. This means that the higher the number selected, the more relevant the statement is to the participant.
Anxiety. The survey included items from the General Anxiety Disorder-7 (GAD-7) Anxiety to measure anxiety levels10. The survey displayed five statements to rate from 1 to 4 to determine the participant’s anxiety levels: (item 1) I do not constantly feel nervous or anxious; (item 2) I am not easily irritable or annoyed; (item 3) I worry about different things; and (item 4) I feel afraid as if something awful might happen. Items 1 and 2 are reverse coded, employing an inverted scale in which higher scores indicate lower anxiety symptom severity.
Loneliness. The survey included items from the UCLA Loneliness Scale to measure loneliness levels11. The survey presented four statements for the participant to rate from 1 to 4: (item 1) I have rarely experienced any feelings of loneliness or isolation recently; (item 2) I feel as if nobody really understands me; (item 3) No one really knows me well; and (item 4) I feel included and supported by others. Items 1 and 4 are reverse coded where higher scores indicate lower loneliness severity.
Depression. The survey included items from the Patient Health Questionnaire-812. The survey provided four statements to rate from 1 to 4 to determine the participants’ depression level: (item 1) I have trouble concentrating; (item 2) I don’t feel tired; (item 3) I have interest or pleasure in doing things; and (item 4) I have trouble falling or staying asleep, or sleeping too much. Items 2 and 3 are reverse coded where higher scores indicate lower depression severity.
Self-esteem. The survey used items from the Rosenberg Self-Esteem Scale to measure self-esteem13. The survey presented four statements to rate from 1 to 4 to determine the participant’s confidence level: (item 1) I feel that I have a number of good qualities; (item 2) I certainly feel useless at times; (item 3) I feel I do not have much to be proud of; and (item 4) I take a positive attitude toward myself. Items 2 and 3 are reverse coded where higher scores indicate higher self-esteem.
Procedure
Each participant was instructed to complete this study independently. They used a smartphone, laptop, or any other electronic device to complete the survey, which involved answering 25 questions about their mental health, age, gender, social media usage, and socioeconomic status. Participants were not informed of the intent of this study. This study was intended to take approximately 5-10 minutes per participant.
Data Analysis
Descriptive statistics (mean, SD, frequency, and range) are presented from the data collected, including information on demographics, social media, and mental health. Bivariate associations between all studied variables were assessed using Pearson correlations.
For the main hypothesis tests, four regression analyses were conducted to examine the associations between time using social media (independent variable) and each of the mental health dependent variables (anxiety, depression, loneliness, and self-esteem). If the main hypothesis is supported, a linear regression analysis will be conducted to examine the second part of the hypothesis. This analysis will assess whether covariates, including demographic factors and types of social media usage, are associated with the impact of mental health through social media.
Ethical Considerations
Confidentiality was maintained through an anonymous survey. Participants completed a consent form prior to engaging in the survey, which outlined the study’s purpose, procedures, potential risks, and benefits; parents completed the consent for participants under the age of 18. The study received formal approval from the high school’s Institutional Review Board (IRB), ensuring adherence to ethical standards and protection of the rights of all participants.
Results
Social Media Time and Mental Health
Direct correlations between time using social media and mental health were found to be significant for all four variables (anxiety, loneliness, depression, and self-esteem).
Specifically, as shown in Table 1, social media time is found to be positively correlated with anxiety (r=0.41, p=0.009, α=0.153), denoting that more time spent on social media is associated with increased anxiety. Social media is also found to be positively correlated with both loneliness (r=0.323, p=0.042, α=0.752) and depression (r=0.494, p=0.001, α=0.655), indicating that increased social media usage is associated with higher levels of both loneliness and depression. Lastly, social media is negatively correlated with self-esteem (r=-0.37, p=0.019, α=0.838), displaying that with the increase of use of social media, an individual’s self-esteem lowers. The medium effect sizes present in the results suggests a difference or relationship that is moderately significant. It indicates that the observed effect is not due to chance but is a noticeable change or association.
Linear Regression Analysis
Predictors of Anxiety, Loneliness, and Self-esteem
Age, gender, and socioeconomic status, types of social media use were not found to be significant predictors for anxiety, depression, and self-esteem based on using linear regression analysis, as shown in Tables 2, 3, and 5. Moreover, after conducting linear regression analysis, no significant correlation between social media usage and anxiety (r=0.25, p=0.29) or loneliness was observed (r=0.30, p=0.16), indicating that the covariates studied may play a critical role in influencing these mental health outcomes.
Predictors of Depression
A linear regression analysis significantly predicted depression symptom severity with social media use. Table 4 shows that creating posts and content was found as a covariate that positively predicts depression (r=0.50, p=0.033). This suggests that the public use of social media contributes to the strength of the positive association between social media usage and depression severity.
Discussion:
This research showed that time using social media is positively correlated with anxiety, depression, and loneliness, while negatively correlated with self-esteem, indicating that the main hypothesis is supported. The findings from this study also support my hypothesis that certain public social use of social media, like creating posts or content, serve as covariates that play a role in the correlation between social media and depression. However, it revealed that while public social media use did not correlate with anxiety, loneliness, or self-esteem. The study did not support the hypothesis regarding demographics, as factors like age, gender, and socioeconomic status were not found to be associated with mental health outcomes.
Social Media & Anxiety
My research builds on past studies that investigated social media and mental health for adolescents. One study suggests that the increased anxiety from social media could be due to social and peer pressure. Specifically, adolescents often feel compelled to present a perfect version of themselves online, which can lead to heightened stress and anxiety as they navigate expectations from peers and society14. Another study also showed a positive association for anxiety and social media use, indicating that frequent engagement with social media platforms can exacerbate feelings of inadequacy, contributing to a cycle of anxiety that can be difficult to escape15. Together, these findings support the current study’s findings and illustrate that social media impacts levels of anxiety in adolescents. However, the present study highlights that it is crucial to acknowledge that the correlation between social media time and anxiety was no longer significant, suggesting that the covariates included (i.e., age, gender, SES) contribute to this relationship.
Social Media & Loneliness
In addition, one study showed that more intensive use of social media is associated with higher levels of loneliness for adolescents16. The authors note that social media contributes to individuals becoming isolated from actual social environments, increasing one’s loneliness. Another study presents similar findings, showing that increased social media usage is associated with increased loneliness. The study suggests that social media may reduce face-to-face interactions, which creates difficulties with socialization and causes a preference for being alone17. The authors state that the lonelier an individual is, the more social media they will use, creating a cycle of constant scrolling and isolation. These reinforces my research findings about the positive correlation between social media and loneliness. However, following linear regression analysis, this relationship was no longer significant, suggesting that the covariates studied in this research is associated with the relationship between social media usage and loneliness.
Social Media & Depression
Past studies also suggest that social media is positively associated with depression for adolescents. One study reveals that more time spent on social media is generally associated with greater symptoms of depression18. The authors suggest that social media could instigate stress or reinforce negative self-evaluations when individuals receive undesirable feedback from others or engage in negative social comparisons, leading individuals to develop depressive symptoms. Similarly, another study also found a positive link between social media use and depression, suggesting that excessive emotional investment in online interactions can evoke feelings of inadequacy and despair19. These findings align with my research results, demonstrating that as adolescents become more immersed in social media, their risk of developing depression rises, likely due to negative social comparisons and excessive emotional investment.
Social Media & Self-Esteem
Prior research has also shown that social media is negatively associated with self-esteem. One study found that social media is associated with lower self-esteem, as well as body image plays a significant role. Specifically, when faced with unrealistic beauty standards perpetuated by social media, many struggle to maintain confidence and accept themselves20. Similar study also underscores the relationship between social media and lower self-esteem21. Their research highlights that as individuals scroll through their social media feeds, they often encounter idealized images and lifestyles. This exposure can lead to feelings of inadequacy, drawing unfavorable comparisons with others. These findings support my research results, suggesting that social media fosters unrealistic comparisons and beauty standards, undermining self-esteem among adolescents.
Other contributing factors
Additionally, covariate findings of variables among public social media use provide further insights into mental health dynamics. The act of creating posts or content on social media represents a complex interplay between self-expression and overall well-being. While these activities can facilitate creativity and foster connections, they may also lead to adverse mental health outcomes. The positive correlation identified in the current study indicates that adolescents who frequently engage in content creation may experience elevated depressive symptoms. This may arise from the pressures associated with curating an idealized online persona, which invites scrutiny and fosters self-doubt. Ultimately, the act of sharing personal experiences can increase depressive symptoms, as individuals grapple with the fear of negative judgment from their peers.
Taken together, this pattern of findings suggests that social media, as well as types and ways of using it, could become a key factor that is associated with increased mental health symptoms. However, it is important to acknowledge that other confounding variables that were not taken into account in this study such as participants’ personality traits and offline social factors could potentially affect their mental health as well. This shows the underlying complexity behind mental health and highlights that social media should not be considered as the only factor that contributes to it.
Broader Implications:
This research enhances our understanding of social media’s impact on mental health, empowering individuals to make informed choices about their online behaviors. By illuminating the connections between social media use and emotional well-being, it encourages adolescents to foster healthier habits while using social media. Increased awareness can also lead to meaningful discussions within families and educational settings, promoting transparency about potential risks and benefits. Specifically, parents and educators can provide better guidance toward appropriate uses of social media to protect the mental health of adolescents. Policy makers can also implement the information to educate parents and adolescents, highlighting potential strategies and ways that best protect mental health when using social media. As people become more informed, they may advocate for social media features that enhance mental well-being. Ultimately, this knowledge paves the way for a more mindful online experience, prioritizing emotional health and meaningful connections.
Strengths and Limitations:
These findings should be contextualized with consideration of the current study’s strengths and weaknesses. Strengths include a clear and systematic research design. This structured approach ensures that the research process is well-defined and logically sequenced, leading to more targeted results. However, there are some limitations in this study. One major limitation is the small sample size collected due to limited resources and funding provided. The limited number of participants does not meet the “10 events per variable” rule and can restrict the generalizability of the findings. Further, many non-significant role of demographic variables found in this study despite significant findings from established research suggests the low statistical power present due to small sample size. Consequently, the results have limited ability to accurately reflect the broader population’s attitudes or behaviors and can lead to Type I and II errors. The incorporation of adapted scales will contribute to the bias in this study as the different wording applied can potentially affect the validity of the scales. The limited acknowledgement of certain confounding variables such as offline social support can also render discrepancy in the finding. Furthermore, the recruitment of participants via email oversampled more motivated and higher SES adolescents (mean SES = 7.03/10); therefore, the result may not equally represent the whole population. The reliance on self-reported data poses another challenge where participants may have provided unauthentic responses. This can lead to skewed data, ultimately affecting the study’s conclusions. The classification of social media platforms into rigid categories such as “public” and “private” oversimplifies platform usage patterns, resulting the categories to become ambiguous and contributing to biased result. Additionally, the study did not adjust for family-wise error rate, which could potentially increase the probability of false positives in findings.
Future Directions:
Future research should focus on conducting studies with larger sample and more diverse participant to provide a more reliable analysis to better understand the complexities surrounding social media and mental health in adolescents. Research should further investigate the effects of covariates as many were prematurely dismissed. Specifically, it should delve deeper into the impact of public social media engagement. Investigating how activities like posting content or participating in online communities can shed light on the dynamics of social validation and peer pressure. Furthermore, focusing more on the interplay between different platforms and the types of content shared can yield more information about social media and mental health. By employing these approaches, future studies can deepen our understanding of the intricate relationship between social media and mental health, ultimately contributing to more informed discussions and understanding in the field.
Appendix
| Table 1 (Time on Social Media and Mental Health Bivariate Analysis – Significant Findings on All Variables) | ||
| Time on social media | ||
| Anxiety | Pearson’s r | 0.41 |
| df | 38 | |
| p-value | 0.009 | |
| Cronbach’s α | 0.153 | |
| Loneliness | Pearson’s r | 0.323 |
| df | 38 | |
| p-value | 0.042 | |
| Cronbach’s α | 0.752 | |
| Depression | Pearson’s r | 0.494 |
| df | 38 | |
| p-value | 0.001 | |
| Cronbach’s α | 0.655 | |
| Self Esteem | Pearson’s r | -0.37 |
| df | 38 | |
| p-value | 0.019 | |
| Cronbach’s α | 0.838 | |
| Data Table 2 (Anxiety Linear Regression Analysis – No Significant Finding) | |||||
| 95% Confidence Interval | |||||
| Predictor | SE | p | Stand. Estimate | Lower | Upper |
| Intercept | 9.754 | 0.198 | |||
| Time on social media | 0.473 | 0.29 | 0.25359 | -0.233 | 0.74 |
| Age | 0.475 | 0.617 | -0.124 | -0.634 | 0.386 |
| Gender | 1.298 | 0.863 | -0.0438 | -0.564 | 0.477 |
| Socioeconomic Status | 0.489 | 0.589 | -0.1281 | -0.614 | 0.358 |
| 0.386 | 0.655 | 0.10776 | -0.388 | 0.604 | |
| Snapchat | 0.569 | 0.162 | -0.4063 | -0.99 | 0.177 |
| Bereal | 2.822 | 0.736 | -0.0825 | -0.586 | 0.421 |
| Discord | 0.386 | 0.936 | -0.0291 | -0.771 | 0.713 |
| 0.533 | 0.652 | 0.13386 | -0.476 | 0.743 | |
| YouTube | 0.284 | 0.476 | 0.16795 | -0.315 | 0.651 |
| TikTok | 0.302 | 0.814 | -0.0565 | -0.55 | 0.437 |
| 0.414 | 0.949 | -0.0144 | -0.481 | 0.452 | |
| 0.445 | 0.422 | -0.1918 | -0.68 | 0.296 | |
| Direct messaging | 0.787 | 0.988 | 0.00408 | -0.571 | 0.58 |
| Creating posts/content | 0.539 | 0.472 | 0.20273 | -0.374 | 0.78 |
| Following Celebrities | 0.321 | 0.903 | 0.03082 | -0.491 | 0.553 |
| Watching Contents | 0.593 | 0.758 | -0.0979 | -0.751 | 0.555 |
| Engage in Public Discussions | 0.574 | 0.905 | 0.03358 | -0.544 | 0.611 |
| Staying Up to Date with Current events | 0.421 | 0.405 | 0.24567 | -0.356 | 0.848 |
| Data Table 3 (Loneliness Linear Regression Analysis – No Significant Finding) | |||||
| 95% Confidence Interval | |||||
| Predictor | SE | p | Stand. Estimate | Lower | Upper |
| Intercept | 9.067 | 0.105 | |||
| Time on social media | 0.44 | 0.161 | 0.2961 | -0.128 | 0.72 |
| Age | 0.441 | 0.853 | -0.04 | -0.485 | 0.404 |
| Gender | 1.207 | 0.936 | 0.0178 | -0.436 | 0.472 |
| Socioeconomic Status | 0.454 | 0.216 | -0.2594 | -0.683 | 0.164 |
| 0.359 | 0.906 | -0.0247 | -0.457 | 0.408 | |
| Snapchat | 0.529 | 0.269 | -0.2773 | -0.786 | 0.231 |
| Bereal | 2.623 | 0.161 | -0.3061 | -0.745 | 0.133 |
| Discord | 0.359 | 0.961 | 0.0154 | -0.632 | 0.663 |
| 0.495 | 0.468 | 0.1883 | -0.343 | 0.72 | |
| YouTube | 0.264 | 0.579 | 0.1139 | -0.307 | 0.535 |
| TikTok | 0.281 | 0.873 | 0.0335 | -0.397 | 0.464 |
| 0.385 | 0.864 | 0.0337 | -0.373 | 0.441 | |
| 0.414 | 0.644 | 0.0956 | -0.33 | 0.521 | |
| Direct messaging | 0.731 | 0.689 | -0.0977 | -0.599 | 0.404 |
| Creating posts/content | 0.501 | 0.647 | 0.112 | -0.391 | 0.615 |
| Following Celebrities | 0.298 | 0.12 | -0.354 | -0.809 | 0.101 |
| Watching Contents | 0.551 | 0.617 | -0.1386 | -0.708 | 0.431 |
| Engage in Public Discussions | 0.534 | 0.663 | 0.1069 | -0.397 | 0.611 |
| Staying Up to Date with Current events | 0.391 | 0.62 | 0.1266 | -0.398 | 0.651 |
| Data Table 4 (Depression Linear Regression Analysis – Significant Findings on Time on Social Media & Creating Posts/Contents) | |||||
| 95% Confidence Interval | |||||
| Predictor | SE | p | Stand. Estimate | Lower | Upper |
| Intercept | 7.912 | 0.162 | |||
| Time on social media | 0.384 | 0.013 | 0.49465 | 0.11477 | 0.875 |
| Age | 0.385 | 0.437 | -0.1513 | -0.5495 | 0.247 |
| Gender | 1.053 | 0.324 | 0.19695 | -0.2097 | 0.604 |
| Socioeconomic Status | 0.396 | 0.504 | -0.1237 | -0.5032 | 0.256 |
| 0.313 | 0.463 | 0.13889 | -0.2484 | 0.526 | |
| Snapchat | 0.461 | 0.986 | 0.00398 | -0.4516 | 0.46 |
| Bereal | 2.289 | 0.431 | -0.1514 | -0.5447 | 0.242 |
| Discord | 0.313 | 0.054 | 0.56985 | -0.0099 | 1.15 |
| 0.432 | 0.387 | 0.20193 | -0.2741 | 0.678 | |
| YouTube | 0.231 | 0.719 | -0.066 | -0.4429 | 0.311 |
| TikTok | 0.245 | 0.864 | -0.032 | -0.4176 | 0.354 |
| 0.336 | 0.249 | 0.20726 | -0.1572 | 0.572 | |
| 0.361 | 0.612 | 0.09407 | -0.2868 | 0.475 | |
| Direct messaging | 0.638 | 0.559 | -0.128 | -0.5773 | 0.321 |
| Creating posts/content | 0.437 | 0.033 | 0.4944 | 0.04379 | 0.945 |
| Following Celebrities | 0.26 | 0.339 | -0.1915 | -0.5991 | 0.216 |
| Watching Contents | 0.481 | 0.624 | -0.1216 | -0.6318 | 0.389 |
| Engaging in Public Discussions | 0.466 | 0.621 | -0.1086 | -0.5598 | 0.343 |
| Staying Up to Date with Current events | 0.341 | 0.528 | -0.1449 | -0.615 | 0.325 |
| Data Table 5 (Self-esteem Linear Regression Analysis – Significant Finding on Time on Social Media) | |||||
| 95% Confidence Interval | |||||
| Predictor | SE | p | Stand. Estimate | Lower | Upper |
| Intercept | 10.246 | 0.459 | |||
| Time on social media | 0.497 | 0.038 | -0.423 | -0.82 | -0.03 |
| Age | 0.499 | 0.716 | 0.0734 | -0.342 | 0.489 |
| Gender | 1.363 | 0.388 | -0.1796 | -0.604 | 0.245 |
| Socioeconomic Status | 0.513 | 0.209 | 0.2464 | -0.15 | 0.643 |
| 0.406 | 0.382 | 0.1733 | -0.231 | 0.578 | |
| Snapchat | 0.597 | 0.678 | 0.0959 | -0.38 | 0.571 |
| Bereal | 2.964 | 0.626 | 0.0974 | -0.313 | 0.508 |
| Discord | 0.406 | 0.934 | -0.0242 | -0.629 | 0.581 |
| 0.56 | 0.174 | -0.336 | -0.833 | 0.161 | |
| YouTube | 0.299 | 0.887 | -0.0271 | -0.421 | 0.366 |
| TikTok | 0.317 | 0.257 | -0.2253 | -0.628 | 0.177 |
| 0.435 | 0.119 | -0.2973 | -0.678 | 0.083 | |
| 0.468 | 0.666 | -0.0835 | -0.481 | 0.314 | |
| Direct messaging | 0.827 | 0.962 | -0.0109 | -0.48 | 0.458 |
| Creating posts/content | 0.566 | 0.673 | 0.0964 | -0.374 | 0.567 |
| Following Celebrities | 0.337 | 0.424 | 0.1663 | -0.259 | 0.592 |
| Watching Contents | 0.623 | 0.773 | 0.0747 | -0.458 | 0.607 |
| Engaging in Public Discussions | 0.603 | 0.229 | -0.2803 | -0.751 | 0.191 |
| Staying Up to Date Current events | 0.442 | 0.352 | 0.2241 | -0.267 | 0.715 |
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