Skip to main content

Table 2 Logistic regression with periodical vaccination strong-acceptance as a dependent variable

From: How to boost the boosters? A survey-experiment on the effectiveness of different policies aimed at enhancing acceptance of a “Seasonal” vaccination against COVID-19

  Model 1 Model 2 Model 3
B SE Exp(B) B SE Exp(B) B SE Exp(B)
Constant −2.446*** 0.397 0.087 −5.901*** 1.362 0.003 −5.756*** 1.406 0.003
Gender 0.181 0.176 1.198 − 0.271 0.241 1.011 − 0.246 0.245 0.782
Age 0.028*** 0.006 1.028 0.011 0.008 1.022 0.012 0.008 1.012
Parenthood − 0.132 0.183 0.876 0.022 0.242 1.000 0.027 0.247 1.027
Income 0.128 0.080 1.137 0.000 0.105 0.875 − 0.014 0.107 0.986
Education − 0.087 0.069 0.916 − 0.134 0.091 1.280 − 0.148 0.092 0.862
Sector-UOab 0.047 0.420 1.048 0.247 0.630 2.107 0.089 0.634 1.093
Sector-Arabb 0.147 0.288 1.158 0.745 0.420 0.770 0.716 0.434 2.047
Past COVID vaccination     − 0.261 0.889 2.662 − 0.248 0.925 0.780
Perceived benefits     0.979*** 0.176 0.401 1.027*** 0.180 2.793
Perceived barriers     − 0.914*** 0.144 1.049 − 0.912*** 0.146 0.402
Severity     0.048 0.160 1.382 0.077 0.163 1.080
Susceptibility     0.324* 0.171 1.364 0.280 0.173 1.323
Self-efficacy     0.311* 0.176 0.995 0.311* 0.179 1.365
Trust in Government     − 0.005 0.005 1.021 − 0.005 0.005 0.995
Social norms     0.021** 0.007 1.053 0.023** 0.007 1.023
Had COVID     0.052 0.389 0.917 0.126 0.389 1.134
Relatives had COVID     − 0.087 0.258 1.011 − 0.033 0.262 0.967
Mandate v. negative incentive        − 0.694* 0.324 0.500
Positive Incentive v. negative incentive        − 0.340 0.321 0.712
Information v. negative incentive        −1.113** 0.332 0.328
-2 Log likelihood 809.810    495.106    482.395   
Nagelkerke R2 0.052    0.537    0.552   
  1.  N = 799; *p < 0.05; **p < 0.01; ***p < 0.001; aUO—Ultra-orthodox; bReference group: General Jewish population