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Table 2 Panel ordered Logit regression analysis of subjective well-being

From: Analysis of influence of physical health factors on subjective wellbeing of middle-aged and elderly women in China

variable Coef Std. Err P value [95% Conf. Interval]
Age
 45–59 0.748 0.130  < 0.001 0.493 ~ 1.003
 60–74 0.212 0.053  < 0.001 0.108 ~ 0.317
 ≥ 75     
Registered residence
 urban 0.171 0.054 0.001 0.065 ~ 0.276
 rural     
Education status
 college or university degree and above -0.063 0.090 0.483 -0.240 ~ 0.113
 Senior high school -0.144 0.087 0.100 -0.317 ~ 0.027
 junior high school -0.017 0.065 0.783 -0.145 ~ 0.109
 primary school and below     
Marital status
 Married 0.521 0.076  < 0.001 0.371 ~ 0.670
 unmarried     
Working situation
 Yes -0.314 0.054  < 0.001 -0.421 ~ -0.208
 No     
SRH
 very bad -1.155 0.098  < 0.001 -1.347 ~ -0.962
 bad -1.032 0.096  < 0.001 -1.221 ~ -0.843
 acceptable -0.835 0.088  < 0.001 -1.007 ~ -0.663
 good -0.391 0.100  < 0.001 -0.588 ~ -0.195
 very good     
chronic disease
 Yes -0.001 0.053 0.991 -0.104 ~ 0.103
 No     
Hospitalization
 Yes 0.046 0.062 0.454 -0.075 ~ 0.169
 No     
two-week morbidity
 Yes -0.158 0.049 0.001 -0.255 ~ -0.062
 No     
Physical exercise
 Yes 0.229 0.046  < 0.001 0.135 ~ 0.317
 No     
Drinking status
 Yes 0.003 0.123 0.978 -0.238 ~ 0.245
 No     
Smoking status
 Yes 0.224 0.119 0.060 -0.009 ~ 0.458
 No     
BMI
 obesity 0.437 0.118  < 0.001 0.205 ~ 0.670
 overweight 0.357 0.098  < 0.001 0.164 ~ 0.549
 normal weight 0.256 0.092 0.006 0.074 ~ 0.438
 underweight     
 Income status 0.123 0.022  < 0.001 0.079 ~ 0.167
 Social status 0.327 0.023  < 0.001 0.281 ~ 0.374
Cut1 -5.249 0.236   -5.711 ~ -4.785
Cut2 -4.216 0.196   -4.600 ~ -3.831
Cut3 -3.305 0.180   -3.658 ~ -2.951
Cut4 -2.506 0.173   -2.845 ~ -2.165
Cut5 -2.091 0.171   -2.426 ~ -1.756
Cut6 -0.252 0.167   -0.579 ~ 0.075
Cut7 0.254 0.167   -0.072 ~ 0.581
Cut8 0.735 0.167   0.407 ~ 1.062
Cut9 2.005 0.169   1.674 ~ 2.334
Cut10 2.506 0.169   2.174 ~ 2.838
  1. Model Wald chi2 = 846.74; Log likelihood = -17,990.021; The number of ‘cut’ in the regression is related to the number of dependent variable classifications. As an auxiliary parameter, the parameter value of ‘cut’ could be interpreted as which value is needed to enter the corresponding dependent variable category. The SWB of MAEW was measured from 0 to 10 as an ordinal categorical variable. Therefore, this study had 10 auxiliary parameters (cut1–10)