Social Media vs Anxiety

Results and Analysis Demographic and Descriptive Statisctics Analysis Demographic Characteristics The descriptive statistics for age in Appendix I show that the age of the study participants ranged from 18 to 25 years, with a mean age of 21.43 and a standard deviation of 2.313. The skewness of 0.026 indicates a nearly symmetrical distribution, meaning the data had lighter tails than a normal distribution. Overall, the sample represented a relatively young population with a broad age range that is evenly spread out. The distribution of the gender variable was nearly balanced, with 51% male (102 individuals) and 49% female (98 individuals). Regarding the year of study, the sample comprised 17% first-year students (34 individuals), 21% second-year students (42 individuals), 27.5% third-year students (55 individuals), and 34.5% fourth-year students (69 individuals). This distribution shows a progressive increase in the number of participants with each advancing year of study, with the largest proportion being in their fourth year, suggesting that older students were more represented in the current study. Descriptive Statistics of the Independent and Dependent Variables According to the descriptive statistics for the independent variable, Social Media Use (hours/day), show a range of 11 hours, with a minimum of 3 hours/day and a maximum of 14 hours/day. The mean social media use is 9.54 hours/day, with a standard deviation of 2.508, indicating a moderate spread around the mean. The skewness value of -0.249 (standard error 0.172) suggests a slight negative skew, meaning more individuals tend to use social media for fewer hours than the average. For the dependent variable, Anxiety Level, the range is 14, with a minimum of 5 and a maximum of 19. The mean anxiety level is 14.22, with a standard deviation of 3.026, indicating some variability in anxiety levels among the participants. The skewness value of -0.526 (standard error 0.172) shows a moderate negative skewness, suggesting that more individuals have anxiety levels below the mean. These statistics suggest that while there is variability in social media use and anxiety levels, there is a slight tendency towards lower usage and anxiety levels within the sample. Independent T-test (Differences in Social Media Use and Anxiety Levels between Male and Females) Table 5 of Appendix II, the “Group Statistics,” show that Males (N=102) have a mean social media use of 9.46 hours/day (SD=2.440) and a mean anxiety level of 14.04 (SD=2.955). Females (N=98) have a mean social media use of 9.61 hours/day (SD=2.587) and a mean anxiety level of 14.41 (SD=3.102). This close similarity in means and standard deviations between males and females suggests only slight differences in both social media use and anxiety levels between the genders. The "Independent Samples Test" table no significant difference in variances for both social media use (F=0.287, p=0.592) and anxiety levels (F=0.070, p=0.792). For social media use, the t-test (t=-0.426, df=198, p=0.671) suggests no significant difference between males and females, with a mean difference of -0.151 hours/day. Similarly, for anxiety levels, the t-test (t=-0.861, df=198, p=0.390) also shows no significant difference, with a mean difference of -0.369. The 95% confidence intervals for both variables include zero, reinforcing the conclusion that there is no significant difference. One-way ANOVA analysis (Differences in Social Media Use and Anxiety Levels across Years of Study) According to the descriptives table of Appendix III, first-year students report a mean social media use of 7.35 hours/day, which increases to 11.20 hours/day by the fourth year. The anxiety levels also show an increasing trend from 10.94 in the first year to 16.58 in the fourth year. The progressive increase in both social media use and anxiety levels suggests a potential correlation between year of study and these variables. The ANOVA table shows that the F-value for social media usage is 38.862 (p-value of < 0.001), indicating a significant difference between groups. Similarly, for anxiety levels, the F-value is 67.812 (p-value of < 0.001), also indicating significant differences across year of studies. Therefore, the year of study significantly affects both social media use and anxiety levels. These trends are graphically represented in Figures 1 and 2 below. Figure SEQ Figure \* ARABIC 1: Social Media Usage against Year of Study Figure SEQ Figure \* ARABIC 2: Anxiety Levels against Year of Study As shown in Figure 1 and 2, both social media use and anxiety levels increase significantly as students progress through their years of study. These observations are further proved by post hoc tests of Appendix III. Accoding to the tests, significant differences are found between social media use and almost all pairs, particularly between first-year and third-year, first-year and fourth-year, and so forth, with p-values <0.001 as shown in Table 11. Similarly, for anxiety levels, significant differences are also observed across various pairs, especially between first-year and fourth-year, second-year and fourth-year, with p-values <0.001. One-way ANOVA analysis (Differences in Social Media Use and Anxiety Levels across SNS Categories) Based on the descriptives in Appendix IV, Facebook and Instagram users recorded almost similar means for social media use (10.64 and 10.73 hours/day), while X-Twitter users have a significantly lower mean (7.03 hours/day). On the other hand, Anxiety levels follow a similar pattern, with Facebook and Instagram users showing higher mean anxiety levels (15.67 and 15.74) compared to X-Twitter users (10.98). These differences indicate that the nature of SNS used has an impact on the levels of social media use and anxiety. These differences are depicted by the ANOVA output. According to Table 13, the F-value for social media use is 84.112, with a significance level of <0.001, indicating significant differences in social media use across the platforms. Similarly, for anxiety levels, the F-value is 110.426 (sig. value of < 0.001), suggesting significant differences in anxiety levels among the different SNS platforms. These results confirm that the type of social media platform significantly impacts both social media use (hours) and anxiety levels. The post hoc tests in Appendix IV affirm significant differences between X-Twitter and both Facebook and Instagram in regard to social media use, with mean differences of -3.609 and -3.694, respectively (p < 0.001). Similarly, for anxiety levels, there are significant differences between X-Twitter and both Facebook and Instagram, with mean differences of -4.688 and -4.756, respectively (p < 0.001). Figure 3 and 4 elaborate these observations. Figure SEQ Figure \* ARABIC 3:Social Media Use vs SNS Figure SEQ Figure \* ARABIC 4: Anxiety Level vs SNS Figure 3 affirms that X-Twitter users spend significantly less time on social media compared to users of Facebook and Instagram. On the othert hand, Figure 4 indicates that X-Twitter users experience significantly lower anxiety levels compared to users of Facebook and Instagram. Correlation Analysis The correlation analysis output in Appendix V show that all correlations between age, social media use, and anxiety levels are significant at the 0.01 level (2-tailed), indicating strong and statistically significant relationships among the variables. In particular, the Pearson correlation between age and social media use is 0.824, with a p-value of <0.001. This indicates a strong positive correlation, suggesting that as age increases, social media use also increases. Similarly, the correlation between age and anxiety levels is 0.854, with a p-value of <0.001, indicating a strong positive relationship, affirming that that older individuals in the sample tend to have higher anxiety levels. On the other hand, the correlation coefficinet between social media use and anxiety levels is the strongest at 0.952, with a p-value of <0.001. This extremely high correlation suggests that higher social media use is strongly associated with higher anxiety levels. The strength of this correlation underscores the significant relationship between the amount of time spent on social media and the anxiety levels among the participants. Hypothesis 1 test: Null hypothesis: There is a strong correlation between time spent on social media and anxiety levels among college students Alternative hypothesis: There is no correlation between time spent on social media and anxiety levels among learners. Since the correlation analysis affirms statistcially significant positive correlation between hours spent on social media and levels of anxiety, the researcher failed to reject the null hypothesis, concluding that a strong positive correlation exists between social media usage and levels of anxiety among university students. Regression Analyses Regression Analysis : Anxiety Level, Social Media Use, Gender The model summary in Appendix VI shows an R value of 0.952, indicating a very high correlation between the predictors (social media use and gender) and the dependent variable (anxiety level). These R Square suggests that approximately 90.7% of the variance in anxiety levels can be explained by social media use and gender. The Adjusted R Square, which adjusts for the number of predictors in the model, is very close at 0.906, indicating a strong explanatory power of the model. Table 18, the ANOVA table, shows the a sum of squares for the regression to be 1652.618, a residual sum of 169.702, and an F-value of 959.228, which is highly significant (p < 0.001). This indicates that the model significantly predicts the dependent variable, anxiety levels. The high F-value shows that the predictors (social media use and gender) together explain a significant amount of the variance in anxiety levels, confirming the robustness of the model. The coefficients table highlights the unstandardized coefficient for the constant of 3.186, which is the predicted value of anxiety level when social media use is zero, and the individual is male (the reference category). The coefficient for social media use is 1.147, indicating that for each additional hour of social media use, the anxiety level increases by approximately 1.147 units, holding gender constant. This coefficient is highly significant (t = 43.710, p < 0.001), demonstrating a strong impact of social media use on anxiety levels. On the other hand, the gender variable, with females coded as 1 and males as the reference category, has a coefficient of 0.195. This suggests that females have slightly higher anxiety levels than males, though this difference is not statistically significant as shown by a p-values of 0.139. This indicates that, while gender shows a slight positive effect on anxiety levels, it is not a significant predictor when controlling for social media use. Regression Analysis 2: Anxiety Level, Social Media Use, Nature of SNS The model summary in Appendix VII indicates an R value of 0.958, insinuating that the regression model has a very strong positive relationship between the predictors (social media use, Instagram, and X-Twitter, with Facebook as the reference category) and the dependent variable (anxiety level). The R Square value of 0.918 indicates that approximately 91.8% of the variance in anxiety levels can be explained by the predictors included in the model. The adjusted R Square of 0.917 is very close to the R Square value, suggesting that the model does not suffer from overfitting. The standard error of the estimate (0.873) is relatively low, indicating that the model's predictions are close to the actual anxiety levels. The ANOVA table highlights a sum of squares of 1672.972, which is considerably larger than the residual sum of squares of 149.348. This results in a mean square for the regression of 557.657, compared to the mean square for the residuals of 0.762. The F-value of 731.851, with a significance level of <0.001, indicates that the model is statistically significant. This high F-value and the low p-value suggest that the predictors, as a whole, significantly explain the variation in anxiety levels. Table 22, the regression coefficients, provides detailed information about the contribution of each predictor to the model. According to the table, the unstandardized coefficient for social media use is 1.025, indicating that for each additional hour of social media use, the anxiety level increases by 1.025 units, holding other variables constant. This predictor has a high t-value (30.521) and a p-value of <0.001, making it a highly significant contributor to the model. The Instagram variable has a very small negative unstandardized coefficient (-0.020) and is not statistically significant (p = 0.895), suggesting that being an Instagram user does not significantly affect anxiety levels compared to Facebook users. Conversely, the X-Twitter variable has a larger negative unstandardized coefficient (-0.988) and is statistically significant (t = -5.020, p < 0.001), indicating that X-Twitter users tend to have significantly lower anxiety levels compared to Facebook users, controlling for other factors. Hypothesis 2 test: Null hypothesis: While the overall anxiety score is higher for image-based media (Instagram, Facebook), text-based media presents lower anxiety levels (X—Twitter). Alternative hypothesis: There is no difference in anxiety levels between image-based media and text-based media. Since one-way ANOVA analysis and Regression analysis affirm that Twitter users are linked to lower levels of anxiety compared to Instagram and Facebook users, the null hypothesis is accepted, concluding that anxiety scores are higher for image-based media (Facebook, Instagram), while text-based media (Twitter) presents lower anxiety levels. Discussion, limitations and recommendations for future studies Discussion The current study investigated the impact of social media use on anxiety levels among student. Primarily, the research examined the nature of correlation between social media use (hours/day) and levels of anxiety, and whether social media use significantly impacted anxiety levels. Also, the study explored whether varius SNS had different impacts in terms of anxiety levels among learners. The impact and correlation between social media usage and anxiety levels The current study established strong positive correlations between social media usage and levels of anxiety. The more hours spend by students in social media, the more likely are they to report higher levels of anxiety, and vice versa. This association was affirmed by the statistically significant Pearson correlation coefficient obtained through correlation analysis. Furthermore, through regression analyses, the findings affirmed that social media usage significantly impacts anaxiety levels among college students. These findings concur with existing literature with affirms positive associations between social media use and level of anxiety (Kolhar et al., 2021; Liu & Ma, 2020). For instance, according to Kolhar et al. (2020), there exists a positive association between overall social media use and the presence of anxiety, depression, and psychological distress symptoms among younger people. Similarly, Liu and Ma (2020) established a positive relationship between narcissism, diminished self-esteem and social networking site dependancy. Therefore, both previous studies and the current paper show that social media usage is positively correlated with anxiety level in younger populations. The impact of SNS on anxiety levels Also, the current paper established statistically significant differences in anaxiety levels between image-based media (Instagram, Facebook) and text-based media (X_Twitter) usage. This was affirmed by One-way ANOVA, which showed significance differences in anaxiety levels between Twitter users and Facebook/Instagram users. However, there were no significant differences in the anxiety levels between image-based media usage, that is, Facebook and Instagram. These observations were further proven by the regression analysis involving anxiety levels (outcome variable) and social media use and SNS (Facebook, Instagram, and Twitter) as the explanatory variables. As illustrated, while there are no significant impacts on anxiety levels for Instagram users with Facebook as the reference category, individuals who use Twitter are likely to have lower levels of anxiety compared to Facebook users. This is perhaps because Facebook is image-based while Twitter ia a text-based SNS. Limitations Like other studies, this research was not without limitations. One major limitation of this study is the reliance on self-reported measures to estimate social media usage and anxiety levels. Such perception-based data can be subject to biases, including underreporting or overreporting by participants. These inaccuracies may affect the validity of the findings, as they do not provide an objective measure of social media usage or precisely capture the true anxiety levels of the respondents. Another limitation is the potential lack of generalizability due to the sample characteristics. The study's participants, aged 18-25, were all drawn from a single university. This specific demographic and institutional context may not adequately represent the broader population. Consequently, the findings may not be applicable to individuals of different ages, from other universities, or from diverse geographic and cultural backgrounds, thereby limiting the study's generalizability. Recommendations for future studies To address the limitations highlighted above, future studies should incorporate objective data collection methods. Utilizing digital tracking tools to measure actual social media usage and validated clinical assessments for anxiety levels would enhance the accuracy of the findings. This approach will provide a more reliable understanding of the relationship between social media use and anxiety, minimizing the biases inherent in self-reported data. Also, future research should aim to include a more diverse sample. This can be achieved by recruiting participants from multiple universities, different age groups, and varied geographic and cultural backgrounds. By broadening the demographic scope, future studies will provide more comprehensive insights that are applicable to a wider population, thereby strengthening the validity and applicability of the findings.  References Kolhar, M., Kazi, R. N. A., & Alameen, A. (2021). Effects of social media use on learning, social interactions, and sleep duration among university students. Saudi journal of biological sciences, 28(4), 2216–2222. Liu, C., & Ma, J. (2020). Social media addiction and burnout: The mediating roles of envy and social media use anxiety. Current Psychology, 39(6), 1883–1891.  Appencices Appendix I: Demographic and Descriptive Statisctics Analysis Table 1: Descriptive Statistics N Minimum Maximum Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error Age 200 18 25 21.43 2.313 .026 .172 -1.246 .342 Valid N (listwise) 200 Table 2: Gender Frequency Percent Valid Percent Cumulative Percent Valid Male 102 51.0 51.0 51.0 Female 98 49.0 49.0 100.0 Total 200 100.0 100.0 Table 3: Year of Study Frequency Percent Valid Percent Cumulative Percent Valid First Year 34 17.0 17.0 17.0 Second Year 42 21.0 21.0 38.0 Third Year 55 27.5 27.5 65.5 Fourth Year 69 34.5 34.5 100.0 Total 200 100.0 100.0 Table 4: Descriptive Statistics N Range Minimum Maximum Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Statistic Statistic Statistic Statistic Std. Error Statistic Std. Error Social Media Use (Hours/Day) 200 11 3 14 9.54 2.508 -.249 .172 -.788 .342 Anxiety Level 200 14 5 19 14.22 3.026 -.526 .172 -.410 .342 Valid N (listwise) 200 Appendix II: Independent T-test (Differences in Social Media Use and Anxiety Levels between Male and Females) Table 5: Group Statistics Gender N Mean Std. Deviation Std. Error Mean Social Media Use (Hours/Day) Male 102 9.46 2.440 .242 Female 98 9.61 2.587 .261 Anxiety Level Male 102 14.04 2.955 .293 Female 98 14.41 3.102 .313 Table 6: Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Significance Mean Difference Std. Error Difference 95% Confidence Interval of the Difference One-Sided p Two-Sided p Lower Upper Social Media Use (Hours/Day) Equal variances assumed .287 .592 -.426 198 .335 .671 -.151 .355 -.852 .550 Equal variances not assumed -.426 196.093 .335 .671 -.151 .356 -.853 .550 Anxiety Level Equal variances assumed .070 .792 -.861 198 .195 .390 -.369 .428 -1.214 .476 Equal variances not assumed -.861 196.448 .195 .391 -.369 .429 -1.214 .477 Table 7: Independent Samples Effect Sizes Standardizera Point Estimate 95% Confidence Interval Lower Upper Social Media Use (Hours/Day) Cohen's d 2.513 -.060 -.337 .217 Hedges' correction 2.523 -.060 -.336 .216 Glass's delta 2.587 -.059 -.336 .219 Anxiety Level Cohen's d 3.028 -.122 -.399 .156 Hedges' correction 3.040 -.121 -.398 .155 Glass's delta 3.102 -.119 -.396 .159 a. The denominator used in estimating the effect sizes. Cohen's d uses the pooled standard deviation. Hedges' correction uses the pooled standard deviation, plus a correction factor. Glass's delta uses the sample standard deviation of the control (i.e., the second) group. Appendix III: One-way ANOVA analysis (Differences in Social Media Use and Anxiety Levels across Years of Study) Table 8: Descriptives N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Social Media Use (Hours/Day) First Year 34 7.35 2.360 .405 6.53 8.18 3 12 Second Year 42 7.98 2.030 .313 7.34 8.61 5 13 Third Year 55 9.98 1.910 .258 9.47 10.50 5 13 Fourth Year 69 11.20 1.860 .224 10.76 11.65 7 14 Total 200 9.54 2.508 .177 9.19 9.88 3 14 Anxiety Level First Year 34 10.94 2.651 .455 10.02 11.87 5 15 Second Year 42 12.26 2.338 .361 11.53 12.99 9 18 Third Year 55 14.78 1.988 .268 14.24 15.32 10 18 Fourth Year 69 16.58 1.818 .219 16.14 17.02 11 19 Total 200 14.22 3.026 .214 13.80 14.64 5 19 Table 9: ANOVA Sum of Squares df Mean Square F Sig. Social Media Use (Hours/Day) Between Groups 466.873 3 155.624 38.862 <.001 Within Groups 784.882 196 4.005 Total 1251.755 199 Anxiety Level Between Groups 928.125 3 309.375 67.812 <.001 Within Groups 894.195 196 4.562 Total 1822.320 199 Table 10: ANOVA Effect Sizesa Point Estimate 95% Confidence Interval Lower Upper Social Media Use (Hours/Day) Eta-squared .373 .263 .456 Epsilon-squared .363 .252 .447 Omega-squared Fixed-effect .362 .251 .446 Omega-squared Random-effect .159 .100 .212 Anxiety Level Eta-squared .509 .409 .580 Epsilon-squared .502 .400 .573 Omega-squared Fixed-effect .501 .398 .572 Omega-squared Random-effect .250 .181 .308 a. Eta-squared and Epsilon-squared are estimated based on the fixed-effect model. Post Hoc Tests Table 11: Multiple Comparisons Bonferroni Dependent Variable (I) Year of Study (J) Year of Study Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Social Media Use (Hours/Day) First Year Second Year -.623 .462 1.000 -1.85 .61 Third Year -2.629* .437 <.001 -3.79 -1.47 Fourth Year -3.850* .419 <.001 -4.97 -2.73 Second Year First Year .623 .462 1.000 -.61 1.85 Third Year -2.006* .410 <.001 -3.10 -.91 Fourth Year -3.227* .392 <.001 -4.27 -2.18 Third Year First Year 2.629* .437 <.001 1.47 3.79 Second Year 2.006* .410 <.001 .91 3.10 Fourth Year -1.221* .362 .005 -2.19 -.26 Fourth Year First Year 3.850* .419 <.001 2.73 4.97 Second Year 3.227* .392 <.001 2.18 4.27 Third Year 1.221* .362 .005 .26 2.19 Anxiety Level First Year Second Year -1.321* .493 .048 -2.63 -.01 Third Year -3.841* .466 <.001 -5.08 -2.60 Fourth Year -5.639* .448 <.001 -6.83 -4.45 Second Year First Year 1.321* .493 .048 .01 2.63 Third Year -2.520* .438 <.001 -3.69 -1.35 Fourth Year -4.318* .418 <.001 -5.43 -3.20 Third Year First Year 3.841* .466 <.001 2.60 5.08 Second Year 2.520* .438 <.001 1.35 3.69 Fourth Year -1.798* .386 <.001 -2.83 -.77 Fourth Year First Year 5.639* .448 <.001 4.45 6.83 Second Year 4.318* .418 <.001 3.20 5.43 Third Year 1.798* .386 <.001 .77 2.83 *. The mean difference is significant at the 0.05 level. Appendix IV: One-way ANOVA analysis (Differences in Social Media Use and Anxiety Levels across SNS Categories) Table 12: Descriptives N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum Lower Bound Upper Bound Social Media Use (Hours/Day) Facebook 64 10.64 2.027 .253 10.13 11.15 5 14 Instagram 73 10.73 1.758 .206 10.32 11.14 7 14 X-Twitter 63 7.03 1.769 .223 6.59 7.48 3 12 Total 200 9.54 2.508 .177 9.19 9.88 3 14 Anxiety Level Facebook 64 15.67 2.016 .252 15.17 16.18 11 19 Instagram 73 15.74 1.908 .223 15.29 16.18 12 19 X-Twitter 63 10.98 2.345 .295 10.39 11.57 5 18 Total 200 14.22 3.026 .214 13.80 14.64 5 19 Table 13: ANOVA Sum of Squares df Mean Square F Sig. Social Media Use (Hours/Day) Between Groups 576.564 2 288.282 84.112 <.001 Within Groups 675.191 197 3.427 Total 1251.755 199 Anxiety Level Between Groups 963.172 2 481.586 110.426 <.001 Within Groups 859.148 197 4.361 Total 1822.320 199 Table 14: ANOVA Effect Sizesa Point Estimate 95% Confidence Interval Lower Upper Social Media Use (Hours/Day) Eta-squared .461 .359 .538 Epsilon-squared .455 .352 .533 Omega-squared Fixed-effect .454 .351 .532 Omega-squared Random-effect .294 .213 .363 Anxiety Level Eta-squared .529 .433 .598 Epsilon-squared .524 .427 .594 Omega-squared Fixed-effect .523 .426 .593 Omega-squared Random-effect .354 .271 .422 a. Eta-squared and Epsilon-squared are estimated based on the fixed-effect model. Post Hoc Tests Table 15: Multiple Comparisons Bonferroni Dependent Variable (I) Nature of SNS (J) Nature of SNS Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Social Media Use (Hours/Day) Facebook Instagram -.085 .317 1.000 -.85 .68 X-Twitter 3.609* .329 <.001 2.82 4.40 Instagram Facebook .085 .317 1.000 -.68 .85 X-Twitter 3.694* .318 <.001 2.93 4.46 X-Twitter Facebook -3.609* .329 <.001 -4.40 -2.82 Instagram -3.694* .318 <.001 -4.46 -2.93 Anxiety Level Facebook Instagram -.068 .358 1.000 -.93 .80 X-Twitter 4.688* .371 <.001 3.79 5.58 Instagram Facebook .068 .358 1.000 -.80 .93 X-Twitter 4.756* .359 <.001 3.89 5.62 X-Twitter Facebook -4.688* .371 <.001 -5.58 -3.79 Instagram -4.756* .359 <.001 -5.62 -3.89 *. The mean difference is significant at the 0.05 level. Appendix V: Correlation Analysis Table 16: Correlations Age Social Media Use (Hours/Day) Anxiety Level Age Pearson Correlation 1 .824** .854** Sig. (2-tailed) <.001 <.001 N 200 200 200 Social Media Use (Hours/Day) Pearson Correlation .824** 1 .952** Sig. (2-tailed) <.001 <.001 N 200 200 200 Anxiety Level Pearson Correlation .854** .952** 1 Sig. (2-tailed) <.001 <.001 N 200 200 200 **. Correlation is significant at the 0.01 level (2-tailed).  Appendix VI: Regression Analysis (Anxiety Level, Social Media Use, Gender) Table 17: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .952a .907 .906 .928 a. Predictors: (Constant), Female, Social Media Use (Hours/Day) Table 18: ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 1652.618 2 826.309 959.228 <.001b Residual 169.702 197 .861 Total 1822.320 199 a. Dependent Variable: Anxiety Level b. Predictors: (Constant), Female, Social Media Use (Hours/Day) Table 19: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3.186 .265 12.034 <.001 Social Media Use (Hours/Day) 1.147 .026 .951 43.710 <.001 Female .195 .131 .032 1.486 .139 a. Dependent Variable: Anxiety Level Appendix VII: Regression Analysis (Anxiety Level, Social Media Use, Nature of SNS) Table 20: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .958a .918 .917 .873 a. Predictors: (Constant), X-Twitter, Instagram, Social Media Use (Hours/Day) Table 21: ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 1672.972 3 557.657 731.851 <.001b Residual 149.348 196 .762 Total 1822.320 199 a. Dependent Variable: Anxiety Level b. Predictors: (Constant), X-Twitter, Instagram, Social Media Use (Hours/Day) Table 22: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 4.762 .374 12.741 <.001 Social Media Use (Hours/Day) 1.025 .034 .850 30.521 <.001 Instagram -.020 .150 -.003 -.132 .895 X-Twitter -.988 .197 -.152 -5.020 <.001 a. Dependent Variable: Anxiety Level