5. The log likelihood ratio chi-square test statistic = 197.05, < .001, which indicates LR p χ2(6) that the full model with four predictor variables provides a better fit than the null model with no independent variables in predicting the ordinal response variable. 6. The estimated logit coefficients for are .011 and for each binary model. In educ –.095 Model 1, the Wald test = .63, =.526, and the 95% CI is [–.022, .043]; in Model 2, z p Wald = < .001, and the 95% CI is [–.136, z p –4.44, –.053]. For the predictor, the coefficients are .713 and .932 for each binary model. satfin In Model 1, the Wald test = .990, < .001, and the 95% CI is [.572, .854]; in Model 2, z p Wald = < .001, and the 95% CI is [.737, 1.126]. z p –9.37, 7. ORs for are 1.011 and .910 across two binary models; in the first binary model, educ which compares categories 2 and 3 with category 1, OR for is not significant; in educ the second binary model, which compares category 3 with categories 1 and 2, OR = .910, < .001, which indicates that a higher educational level decreases the odds of being not p too happy versus being pretty happy and very happy. In other words, a higher educational level increases the odds of being pretty happy and very happy. ORs for are 2.041 and 2.538 across two binary models, which are both satfin significant. They indicate that the increase in satisfaction with financial situation is associated with the increase in the odds of being pretty happy and very happy versus being not too happy and the odds of being very happy versus being pretty happy and not too happy.
5. The log likelihood ratio chi-square test statistic LR χ2(6) = 197.05, p < .001, which indicates that the full model with four predictor variables provides a better fit than the null model with no independent variables in predicting the ordinal response variable. 6. The estimated logit coefficients for educ are .011 and –.095 for each binary model. In Model 1, the Wald z test = .63, p =.526, and the 95% CI is [–.022, .043]; in Model 2, Wald z = –4.44, p < .001, and the 95% CI is [–.136, –.053]. For the satfin predictor, the coefficients are .713 and .932 for each binary model. In Model 1, the Wald z test = .990, p < .001, and the 95% CI is [.572, .854]; in Model 2, Wald z = –9.37, p < .001, and the 95% CI is [.737, 1.126]. 7. ORs for educ are 1.011 and .910 across two binary models; in the first binary model, which compares categories 2 and 3 with category 1, OR for educ is not significant; in the second binary model, which compares category 3 with categories 1 and 2, OR = .910, p < .001, which indicates that a higher educational level decreases the odds of being not too happy versus being pretty happy and very happy. In other words, a higher educational level increases the odds of being pretty happy and very happy. ORs for satfin are 2.041 and 2.538 across two binary models, which are both significant. They indicate that the increase in satisfaction with financial situation is associated with the increase in the odds of being pretty happy and very happy versus being not too happy and the odds of being very happy versus being pretty happy and not too happy.