Estimating an ordered logit model In OLM, a particular b coefficient takes the same value for the logit coefficient for The explication of the OLM is facilitated by each cumulative probability. The model considering an example using the 1997 assumes that the effect of x is the same data. Suppose that the response variable for each cumulative probability. This is health status of children, this is captured cumulative logit model with common by question 302F: effects is often called a"proportional odds F. Health conditions of live births model 2). Basically health Sick but not disabled Congenitally disabled ). Disabled after birth Ordered logit model has the form: We are going to examine the effect on child health of matemal age at childbearing, residence, ethnicity, education, duration of breastfeeding, and child sex We recode the health status variable into 4 categories (1)healthy, (2) basically healthy, (3)sick or disabled P+p and(4)dead, as shown in the following table(we 1-(+2) restrict our sample to children aged 0-5) HEALTH4 ++k ak+ 1-(+2+ sically healthy 9061 d+B+,R=1 Missing system 87 13 • In OLM, a particular b coefficient takes the same value for the logit coefficient for each cumulative probability. The model assumes that the effect of X is the same for each cumulative probability. This cumulative logit model with common effects is often called a “proportional odds” model. 14 8 15 Estimating an ordered logit model • The explication of the OLM is facilitated by considering an example using the 1997 data. Suppose that the response variable is health status of children, this is captured by question 302F: F. Health conditions of live births? 1). Healthy 2). Basically healthy 3). Sick but not disabled 4). Congenitally disabled 5). Disabled after birth 6). Dead 7).N/A 16 HEALTH4 1121 75.8 89.3 89.3 90 6.1 7.2 96.5 15 1.0 1.2 97.7 29 2.0 2.3 100.0 1255 84.9 100.0 224 15.1 1479 100.0 healthy basically healthy sick or disabled dead Total Valid Missing System Total Frequency Percent Valid Percent Cumulative Percent We are going to examine the effect on child health of maternal age at childbearing, residence, ethnicity, education, duration of breastfeeding, and child sex. We recode the health status variable into 4 categories: (1) healthy, (2) basically healthy, (3) sick or disabled, and (4) dead, as shown in the following table (we restrict our sample to children aged 0-5):