What protects us against the COVID-19 threat? Cultural tightness matters

BMC Public Health

Table 2 Spearman’s correlations among the main variables

	1	2	3	4	5	6	7	8	9	10	11	12	13
1 Age	1
2 Sex	0.04	1
3 School type	0.89**	0.05*	1
4 Location 1	0.02	0.04	0.05*	1
5 Location 2	0.16**	−0.07**	0.18**	−0.33**	1
6 Province 1	0.40	−0.02	0.47**	0.07**	0.12**	1
7 Province 2	−0.57**	0.05*	−0.61**	0.05*	−0.04	−0.30**	1
8 Province 3	−0.44**	−0.11**	−0.51**	−0.11**	−0.20**	−0.28**	−0.28**	1
9 Province 4	0.33**	−0.04	0.33**	−0.04	0.16**	−0.24**	−0.25**	−0.23**	1
10 Cultural tightness	−0.08**	−0.10**	−0.08**	−0.01	0.06*	−0.06**	0.12*	−0.02	−0.05	1
11 Risk perception of COVID-19	0.01	0.07**	0.01	−0.02	0.01	−0.08**	−0.07**	0.06*	0.06*	−0.04	1
12 Perceived protection efficacy	−0.01	−0.04	−0.02	0.03	−0.04	0.07**	0.06**	−0.08**	−0.08**	0.28**	−0.25**	1
13 Anxiety	0.04	0.01	0.03	0.05*	0.02	−0.06*	−0.03	−0.02	−0.05*	−0.07**	0.27**	−0.20**	1
14 Depression	0.08*	0.05*	0.09**	0.04	0.02	0.00	−0.09*	−0.03	−0.09*	−0.11**	0.28**	−0.24**	0.73**

Note. Sex was coded as 1 = male, 2 = female. School type was coded as 1 = high school, 2 = college. Dummy variable location 1 was coded as 0 = city, 1 = town and location 2 was coded as 0 = city, 1 = village. Dummy variable province 1 was coded as 0 = Anhui, 1 = Jiangsu; province 2 was coded as 0 = Anhui, 1 = Liaoning; province 3 was coded as 0 = Anhui, 1 = Inner Mongolia; province 4 was coded as 0 = Anhui, 1 = others. *p < .05, **p < .01

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