Summary of the Research
The study examined how social media and anxiety affect loneliness among social media users. The quantitative correlational research included 104 active social media users aged 18–65. The Qualtrics survey provided a positive correlation, especially between felt loneliness and social media use, with anxiety predicting higher levels of social media activity and increased loneliness. These studies provide light on social media, anxiety, and loneliness. The study emphasizes social media platforms’ mental health assistance obligations. It also proposes evaluating anxiety and boosting in-person contacts to reduce loneliness among social media users.
Assessment of Research Methods
This study’s quantitative correlational methodology and regression analysis were suitable for investigating the links between social media use, anxiety, and loneliness. Quantitative correlational design examined variable connections and their strength and direction. Regression analysis helped examine anxiety and social media usage’s effects on loneliness (Boursier et al., 2020). The application of quantitative methods enabled the collection of statistical data, organized and tested research questions and hypotheses, allowed population generalization, and could be supplemented by a mixed-methods design to deepen and broaden quantitative analysis. Interviews or open-ended survey questions have helped researchers understand participants’ social media, anxiety, loneliness experiences, attitudes, and motives.
Qualitative data provided valuable contextual information not captured by quantitative metrics. This new data illuminated participants’ subjective feelings, anxiety, and social media usage’s effects on reported loneliness and possible processes. Qualitative data have contextualized quantitative results and improved knowledge of the issue. It has shown how social media usage contributes to loneliness, how people deal with anxiety on social media, and how social support reduces loneliness (Meshi & Ellithorpe, 2021). Mixed-methods studies have triangulated conclusions where quantitative and qualitative data support each other.
The method also would have shown the intricate links between social media use, anxiety, and loneliness (Boursier et al., 2020). Therefore, a quantitative correlational design and regression analysis were acceptable for studying social media use, anxiety, and felt loneliness, although, integrating qualitative data via a mixed-methods approach would have enhanced the research.
Assessment of Data Collection and Analysis Methods
This study’s data collecting and analysis techniques fit its goals and resources. However, sampling and generalizability must be considered. Anonymous Internet surveys were ideal for reaching a large number of participants. It collected data from geographically varied social media users. Qualtrics, a popular survey platform, made data collecting easy and safe. Convenience sampling via social media has drawbacks. Convenience sampling may generate selection bias since participants may vary from non-participants. This may limit generalizability to social media users. Random sampling has improved generalizability. Random sampling from the population of interest increases the chance of a representative sample. This would enable more confident population-wide generalizations regarding social media use, anxiety, and felt loneliness. The valid equipment measured the variables of interest, another research strength. The study’s reliability and comparability with prior studies are improved using validated social media, anxiety, and loneliness measures. Validated tools standardize construct measurement, boosting research internal validity. Regression analysis was suitable for studying social media use, anxiety, and loneliness (Boursier et al., 2020). Regression analysis tested the predicted associations between variables, assessed their strength and significance, and revealed how anxiety and social media use predict subjective loneliness. The study’s goals and resources were satisfied using an anonymous online convenience sampling survey. Convenience sampling decreases generalizability. Future studies may use random sampling to overcome this issue. Validated tools assess variables, improving research dependability.
Examination of the Data Sets
The study data sets are hard to evaluate based on the information supplied. An anonymous online poll obtained data from 104 active social media users 18–65. The variables, response rates, and sample representativeness are not supplied. Examine the data gathering and analysis methods to assess whether the data sets support the researcher’s results. An anonymous online survey may collect data from many participants quickly and easily. Convenience sampling may not be representative. How participants were recruited and if a varied and representative sample was sought is unknown.
The research factors need to be more detailed. Assessing social media use, anxiety, and perceived loneliness requires accurate and reliable assessment methods. Validated instruments’ psychometric qualities, like validity and reliability, provide researchers with a systematic framework to evaluate and ensure data quality, making data quality assessment easier and more accurate. Regression analysis was used to investigate social media use, anxiety, and felt loneliness (O’Day & Heimberg, 2021). Regression analysis evaluates predicted correlations between variables. It lets researchers assess the result variable’s predictability. Correlation does not prove causality. Regression analysis may find connections and predictors but not causal relationships. A correlational research design is needed to analyze the data and examine alternate hypotheses for the observed associations.
The data sets and analytic methodologies can only assess the results’ robustness and validity. The sample’s representativeness, response rate, measuring devices’ validity and reliability, and data processing methods’ suitability affect the results’ dependability. Therefore, with data set details, it is easier to judge their support for the researcher’s results (Meshi & Ellithorpe, 2021). Data quality can only be assessed with information on sample representativeness, response rate, and measuring tools. Future research should be more transparent about data gathering and processing to help evaluate data sets and their support for conclusions.
Assessment Of The Overall Research Design, Data Analysis, Conclusions, And Recommendations
This study’s design and data analysis methodologies addressed the research topics. A quantitative correlational design and regression analysis examined social media use, anxiety, and felt loneliness. These factors showed substantial connections, revealing how social media and anxiety affect loneliness (Boursier et al., 2020). The results and past studies confirm the study’s conclusions. According to research, social media use reduces loneliness, and anxiety predicts loneliness. The research illuminates how these characteristics impact social media users.
The researcher’s advice is realistic. Mental health specialists may help social media companies evaluate users’ well-being and provide services. Social media public service announcements may warn users of the dangers of overuse and encourage better usage. Mental health screenings in regular healthcare may also identify at-risk persons and prompt treatments. The study design, data analysis methodologies, results, and suggestions are well-aligned and provide light on social media use, anxiety, and felt loneliness (O’Day & Heimberg, 2021).
Evaluation of the Report/Dissertation
The methodology section details the study design, data gathering, and analysis. The sample selection, data collecting, and statistical analysis procedures demonstrate the study’s rigor. The report has offered additional information about the data sets and the statistical methods to improve clarity and openness. The results section summarizes the study findings and analyzes the statistical significance and relevance of variables like social media use, anxiety, and loneliness. It also discusses their relationships and effects, illuminating their potentially interconnected and individual contributions to the research outcomes (Boursier et al., 2020). Social networking platform developers and healthcare practitioners are advised under the ramifications section. The results and past studies confirm the study’s conclusions. The report/dissertation clearly explains the study’s aims, methodology, results, and consequences.
Suggested follow-on Research
Based on this study, future studies should examine social media use, anxiety, and reported loneliness across populations with greater sample numbers and a broader age range. This would help explain how these factors affect different age groups. The reciprocal link between loneliness, anxiety, and social media use may reveal how these characteristics change over time (O’Day & Heimberg, 2021). Qualitative data collected from participants in mixed-methods study would deepen comprehension.
References
Boursier, V., Gioia, F., Musetti, A., & Schimmenti, A. (2020). Facing loneliness and anxiety during the COVID-19 isolation: the role of excessive social media use in a sample of Italian adults. Frontiers in Psychiatry, 11, 586222. https://www.frontiersin.org/articles/10.3389/fpsyt.2020.586222/full
Meshi, D., & Ellithorpe, M. E. (2021). Problematic social media use and social support received in real-life versus on social media: Associations with depression, anxiety, and social isolation. Addictive Behaviors, 119, 106949. https://www.sciencedirect.com/science/article/pii/S0306460321001349
O’Day, E. B., & Heimberg, R. G. (2021). Social media use, social anxiety, and loneliness: A systematic review. Computers in Human Behavior Reports, 3, 100070. https://www.sciencedirect.com/science/article/pii/S245195882100018X
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