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Critique each of the following proposed research plans. Your critique should explain any problems with the proposed research and describe how the research plan might be improved. Include a discussion of any additional data that need to be collected and the appropriate statistical techniques for analyzing the data.
(a) A researcher is interested in determining whether a large firm is guilty of gender bias in setting wages. To determine potential bias, the researcher collects salary and gender information for all of the firm’s employees. The researcher then plans to conduct a "difference in means" test to determine whether the average salary for women is significantly different from the average salary for men.
(b) A researcher is interested in determining whether time spent in prison has a permanent effect on a person’s wage rate. He collects data on a random sample of people who have been out of prison for at least fifteen years. He collects similar data on a random sample of people who have never served time in prison. The data set includes information on each person’s current wage, education, age, ethnicity, gender, tenure (time in current job), and occupation, as well as whether the person was ever incarcerated. The researcher plans to estimate the effect of incarceration on wages by regressing wages on an indicator variable for incarceration, including in the regression the other potential determinants of wages (education, tenure, and so on).


Sagot :

The information provided indicates that the means test is too narrow because it excludes factors like the type of engineer, amount of education, and experience. The type of engineer or educational level may be a reflection of the gender with lower earnings.

Additional information on the factors, including gender, education, and the type of engineer, could enhance the research.

Then, it is advised to build a multiple regression where the wage is the dependent variable and the other four variables are independent variables. The "difference in means" test is not appropriate for identifying gender bias in salary setting due to the significance of the omitted variable.

The variables which  are likely useful to add to the regression to control for important omitted​ variables are excessive drug or alcohol use and their gang activity.

To learn more about regression

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