Table 3.

Multiple Linear Regression for Predictors of Social Media Usage from the Adolescent Perspective Using LASSO-penalized Variable Selection

	Bootstrapped LASSO Parameter Estimates
Model Outcome and Predictor Variables*	Mean Estimate	SD	95% CI	Standardized Estimate	Adjusted R2
Social Media Usage (overall sample)					0.277
Intercept	10.789	1.094	8.720 to 12.879	0
QIDS-C Total	0.326	0.0888	0.154 to 0.483	0.368
Social Media Usage (MDD sample)					0.217
Intercept	8.202	4.909	0.583 to 22.681	0
Sex (female vs. male)	3.893	1.449	1.573 to 7.033	0.372
Social Media Usage (Healthy Control Sample)					–
Intercept	11.155	0.728	9.750 to 12.633	0

Note. The LASSO estimates were based on 10,000 bootstrap samples of the model; Mean Estimate = bootstrap parameter estimate (regression coefficient); SD = standard deviation of the mean parameter estimate; 95% CI for the mean parameter estimate; For the 95% CI that does not contain zero (0), the respective mean parameter estimate is statistically significant at alpha = 0.05 (two-tailed); Standardized Estimate = bootstrap standardized regression coefficient; Adjusted R-squared is the model R-squared based on the LASSO-penalized variable selection; Observed sample: N=60 for the overall sample, n = 30 for the MDD subgroup and n = 30 for the normal control subgroup.

Social media usage was assessed using the Bergen Social Media Addiction Scale (BSMAS), which is 6-item scale that measures risk of social media addiction over the past year. Total score on the BSMAS ranges from 6 to 30, with higher scores representing greater risk of social media addiction.

^*Predictor variables were selected from a pool of 9 potential predictor variables via the LASSO-penalized variable selection method (which performs simultaneous variable selection and parameter estimation) in the context of a multiple linear regression model that was based on 10,000 bootstrap samples.

No significant predictors emerged in the normal control sample.