Jason's Blog

Absenteeism . Data on 77 employees of the ABX Company have been collected. The dependent variable is absenteeism (ABSENT). The possible explanatory variables are COMPLX = measure of job complexity SENIOR = seniority SATIS = response to "How satisfied are you with your foreman?" In this example, use SENINV = 1/SENIOR, which is the reciprocal of the seniority variable, and COMPLX as two of the explanatory variables. The variable SATIS should be transformed into indicator variables (1 is very dissatisfied, 2 is somewhat dissatisfied, 3 is neither satisfied nor dissatisfied, 4 is somewhat satisfied, 5 is very satisfied). Q1 . Is there a difference in average absenteeism for employees in different supervisor satisfaction groups? Q2 . Using the model chosen, what would be your estimate of the average absenteeism rate for all employees with COMPLX = 60 and SENIOR = 30 who were very dissatisfied with their supervisor? What if they were very satisfied with their supervisor...

In the last post "The Difference of Occupation Choice Among Graduates with Different Majors and Degrees", I built a logistic model with log of salary as one of the independent variable. But we will concern about the endogeneity of it, because the salary may related to other unincluded variables that may affect the probability of working at education institute, and moreover, as a current salary, the occupation itself may affect the salary. So some instruments are needed to solve the possible endogeneity problem in the regression analysis. In the dataset, a variable "satis" measuring the satisfaction of salary seems to be a good possible instrument. For this variable, 4 means very satisfied, 3 means somewhat satisfied, 2 means somewhat unsatisfied, and 1 means very unsatisfied. Let's firstly take a look at the model without using IV. The dependent variable is whether working at education institutes, and independent variables are degree, major, number of years af...