Predictors of problematic smartphone use among university students

Psicologia: Reflexão e Crítica

Psychology: Research and Review

Table 3 Hierarchical regression analysis predicting problematic smartphone use, their standardized and unstandardized coefficients, the standard error (SE), and tolerance

Predictor	Step 1	Step 2	Step 3	Step 4	b^a	SE^a	Tolerance^a
Predictor	β	β	β	β	b^a	SE^a	Tolerance^a
Intercept	-	-	-	-	43.03	15.99
Demographic characteristics
Age	− 0.19**	− 0.15*	− 0.11	− 0.11	− 0.84	0.47	0.88
Sex (female)	0.19**	0.19**	0.08	0.06	3.94	3.72	0.94
Family monthly income (BRL)
< 2000	− 0.02	− 0.05	− 0.02	0.01	0.32	8.07	0.85
2000–2999	− 0.05	− 0.07	− 0.06	− 0.04	− 4.73	7.63	0.86
3000–3999	− 0.13*	− 0.14*	− 0.02	− 0.02	− 1.54	5.76	0.76
4000–4999	− 0.11	− 0.11	− 0.02	0.02	1.49	6.06	0.79
5000–9999	− 0.22**	− 0.20**	− 0.11	− 0.11	− 6.76	4.41	0.70
> 20,000	− 0.04	− 0.01	− 0.01	− 0.02	− 1.50	4.37	0.67
Loneliness
UCLA-BR		0.28***	0.31***	0.31***	0.65	4.41	0.91
Smartphone social app importance
WhatsApp importance			0.28***	0.26***	9.23	2.36	0.77
Facebook importance			0.10	0.11	2.86	1.80	0.72
Instagram importance			0.20**	0.19**	4.40	1.42	0.83
Smartphone type
Smartphone model
Samsung				− 0.01	− 0.87	4.17	0.79
Others				− 0.17**	− 12.31	4.54	0.80
Adjusted R²	.099***	.171***	.302***	.322***
ΔR²		.072	.131	.020

BRL Brazilian Reais, SAS-BR Brazilian Version of Smartphone Addiction Scale, UCLA-BR Brazilian Version of UCLA Loneliness Scale
*p < .05; **p < .01; ***p < .001
^aMeasure of the last model