In dtkaplan/MultipleChoice: Support for Multiple Choice Questions in Shiny

library(statisticalModeling)
library(mosaic)
library(MultipleChoice)
Scorekeeper <<- list(problem=NULL, attempts=NULL, score=NULL)
C06_M102 <- new.env()

In studies of employment discrimination, several attributes of employees are often relevant: age, sex, race, years of experience, salary, whether promoted, whether laid off, etc.

For each of the following questions, indicate which is the response variable and which is the explanatory variable.

a. Are men paid more than women? - Response variable:

MC_question("C06_M102-ar", hints = TRUE,
            "age", "sex", "race", "years.experience",
            salary = "**RIGHT**", "promoted","laid.off")

- Explanatory variable

MC_question("C06_M102-ax", 
            "age", sex = "**RIGHT**", "race", "years.experience",
            "salary", "promoted", "laid.off")

For many people, it can be confusing at first to decide what is the response variable and what is the explanatory variable.  In this question, there are two variables: salary and sex.  There are two kinds of questions you might ask with these variables: "Can I tell something about how much a person is paid given their sex?" and "Can I tell what sex a person is, given how much they are paid?"  Those questions are different.  

In examining possible employment discrimination, the issue is whether a person's sex influences how much they are paid.  So sex is the explanatory variable and pay is the response variable.

b. On average, how much extra salary is a year of experience worth? - Response variable:

MC_question("C06_M102-br", 
            "age", "sex", "race", "years.experience",
            salary = "**RIGHT**", "promoted","laid.off")

- Explanatory variable

MC_question("C06_M102-bx", 
            "age", "sex", "race", "years.experience" = "**RIGHT**",
            "salary", "promoted", "laid.off")

The response variable is the person's salary.  The explanatory
variable is how much experience they have.

c. Are whites more likely than blacks to be promoted? - Response variable:

MC_question("C06_M102-cr", 
            "age", "sex", "race", "years.experience",
            "salary", "promoted" = "**RIGHT**","laid.off")

- Explanatory variable

MC_question("C06_M102-cx", 
            "age", "sex", "race" = "**RIGHT**", "years.experience",
            "salary", "promoted", "laid.off")

The response variable is whether the person was promoted or not.

d. Are older employees more likely to be laid off than younger ones?
- Response variable:

MC_question("C06_M102-dr", 
            "age", "sex", "race", "years.experience",
            "salary", "promoted","laid.off" = "**RIGHT**")

- Explanatory variable

MC_question("C06_M102-dx",
            "age" = "**RIGHT**", "sex", "race", "years.experience",
            "salary", "promoted", "laid.off")

In this exercise, there is just one explanatory variable being offered in each setting.  However, in real situations you will often be interested in examining **multiple** explanatory variables at the same time.  

[Note: Thanks to George Cobb.]

dtkaplan/MultipleChoice documentation built on May 15, 2019, 4:58 p.m.