//  Task 1 routine setup
 
    capture log close 
    set more off 
    estimates clear 
   
    log using cda06b-ex6.log, replace text
    version 9.2
    set scheme s2mono 
    
//  pgm:        cda06b-ex6.do
//  task:       6 - Ordinal Outcomes - Excercise
//  project:    CDA Lab Guide
//  author:     your name
//  date:       today's date

    use sci-review, clear
//  Task 2: Since we are familiar with the data set, we will skip the data
//  cleaning step.

//  Task 2a: estimate the ordinal logit model; list the coefficients.


//  Task 2b: estimate the ordinal probit model; list the coefficients.


//  Task 2c: .3 interpret the fully standardized effect of enrol on y* for OLM.

/*  Type your answer here

*/

//  Step 2d: What does a standard deviation change in y* mean?

//  Task 3: Interpretation using the prvalue and prchange commands.

//  Task 3a: run an OLM of jobclass on female, enrol and phd.


//  Task 3b: use prvalue to compute the predicted probabilities for females
//  with other variables held at their means; use the save option to save
//  the predictions.


//  Task 3c: use prvalue to compute the predicted probabilities for males
//  with other variables held at their means; use the dif option to
//  compare predictions to those for females.


//  Task 3d: compute the discrete and marginal change with variables at
//  their means.


//  Task 4 [Optional]: Construct plots of predicted probabilities and
//  cumulative probabilities as phd changes, other variables at their means.

//  Task 4.1: estimate the OPM.


//  Task 4.2: generate predicted probabilities to plot.


//  Task 4.3: plot predicted probabilities:


//  Task 4.4: plot cumulative probabilities:


//  Task 4.5: describe the effects of phd on jobclass.

/*  Type your answer here

*/

//  Task 5 [Optional]: Examine the parallel regression assumption.

//  Task 5.1: create three binary dependent variables.
//
//      dist=1 if jobclass is 4, else 0.
//      strong=1 if jobclass is 4 or 3, else 0.
//      adeq=1 if jobclass is 4, 3, or 2, else 0.


//  Task 5.2: run three binary logits.


//  Task 5.3: run an OLM and compare coefficients to those from the
//  bianry logits. What would you conclude about the parallel regression
//  assumption in the ordered logit model?



//  Task 5.4: use brant to test the parallel regression assumption.


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