Table 3.

Impacts of online social capital on Chinese middle-aged and older adults’ depression: based on OLogit model

	Model 1		Model 2		Model 3
	OR	R_SE	OR	R_SE	OR	R_SE
Online interaction	1.069	0.092	1.119	0.096	1.215*	0.109
Online contacts
1–4	0.824	0.224	0.753	0.192	0.743	0.222
5–9	0.786	0.246	0.749	0.225	0.756	0.249
10–19	0.571	0.201	0.584	0.208	0.665	0.254
20–49	0.418*	0.180	0.442^	0.186	0.465^	0.207
≥50	0.596	0.268	0.704	0.327	0.785	0.413
Online closeness of non-specific relationships	0.756**	0.061	0.744***	0.061	0.717***	0.067
Online closeness of specific relationships	0.867^	0.072	0.897	0.075	0.906	0.082
Sex			1.296^	0.205	1.243	0.196
Age			0.977*	0.009	0.969**	0.010
Residence			0.989	0.190	1.386	0.290
Education			0.706***	0.063	0.803*	0.081
Internet age					0.966*	0.015
Online time weekly					1.001	0.006
Internet skill					0.732*	0.094
Intercept
cut point 1	−1.881	0.454	−3.901	0.662	−3.916	0.759
cut point 2	−0.487	0.446	−2.442	0.654	−2.476	0.751
cut point 3	1.443	0.464	−0.462	0.654	−0.363	0.758
cut point 4	3.469	0.562	1.565	0.720	1.612	0.837
Pseudo_R2	0.016		0.037		0.056
n	903		899		776

***p < 0.001, **p < 0.01, *p < 0.05, ^p < 0.1. OR: odds ratio, R_SE: robust standard error