Explore the distribution of naffairs. Does this variable appear to be normally distributed? Why or why not?

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Part 1. Ordinary Least Squares (OLS) Regression 

Use the data <affairs.dta>. You will be using these data to examine the correlates of extra-marital affairs (naffairs). 

 

The predictors will include male (=1 if male), age, yrsmarr (years married) educ (years of education), kids (=1 if have any children, 0 if no children), vryhap (1 if very happy, 0 otherwise), and relig (=5 if a very religious, 1 for anti)

 

Explore the distribution of naffairs. Does this variable appear to be normally distributed? Why or why not? (Provide evidence from Stata)

 

Estimate an OLS (least squares regression) model predicting naffairs using all of the predictors listed above. Paste your regression output below.   

 

Explain the overall fit of the model and note which indicator(s) you used (for model fit).

 

Based on your regression output, which coefficients are statistically significant? Provide substantive interpretations of the coefficients that are statistically significant. 

Part 2. Linear Probability 

 

Generate a dichotomous indicator called “affairs” using the variable naffairs.  The new variable (affairs) should be equal to one if the individual reported any affairs (naffairs > 0), and zero for no affairs. Paste a tabulation of your new variable. 

 

Estimate a linear probability model (LPM) using the new variable (affairs) as the dichotomous outcome and the same predictor variables from part 1. Paste your regression output below. 

 

Explain the overall fit of the model and note which indicator(s) you used (for model fit).

 

Based on your regression output, which coefficients are statistically significant? Provide substantive interpretations of the coefficients that are statistically significant. 

Part 3. Logistic Regression 

 

Estimate a logistic regression using the new variable (affair) as your dichotomous outcome and the same predictor variables. (Important tip: Use the factor notation for dummy variables in order to estimate the predicted probabilities below). Paste your output below.  

 

Explain the overall fit of the model and note which indicator(s) you used (for model fit).

 

Provide a statistical and substantive interpretation of the odds ratios for age and for relig. 

 

Provide an interpretation based on the percent change in the odds for age, and for relig. 

 

Following the logistic regression, use the post-estimation command “margins” to produce the predicted probability of an extra-marital affair for men and women holding everything else at the mean. Describe the results. 

 

Part 4. Comparing the Models 

 

Look at the predictor coefficients across all 3 models. What do we learn in each model? Does it make sense to dichotomize the number of affairs (any affairs / no affairs) to use it in a linear probability or logistic regression model, or does it make more sense to use naffairs as continuous in OLS? Provide justification to support your conclusions.

 

 

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