Hamermesh and Parker (2005) data on student evaluations of instructors’ beauty and teaching quality for several courses at the University of Texas. See Chapter 10 in Regression and Other Stories.

Hamermesh, D. S., and Parker, A. M. (2005). Beauty in the classroom: Instructors' pulchritude and putative pedagogical productivity. Economics of Education Review, 24:369-376.

Load packages

library("rprojroot")
root<-has_file(".ROS-Examples-root")$make_fix_file()
library("rstanarm")
library("ggplot2")
library("bayesplot")
theme_set(bayesplot::theme_default(base_family = "sans"))

Load data

beauty <- read.csv(root("Beauty/data","beauty.csv"))
head(beauty)
  eval     beauty female age minority nonenglish lower course_id
1  4.3  0.2015666      1  36        1          0     0         3
2  4.5 -0.8260813      0  59        0          0     0         0
3  3.7 -0.6603327      0  51        0          0     0         4
4  4.3 -0.7663125      1  40        0          0     0         2
5  4.4  1.4214450      1  31        0          0     0         0
6  4.2  0.5002196      0  62        0          0     0         0

Do more beautiful profs get higher evaluations?

Make a scatterplot of data

par(mar=c(3,3,1,1), mgp=c(1.7, .5, 0), tck=-.01)
plot(beauty$beauty, beauty$eval)

Fit a linear regression

fit_1 <- stan_glm(eval ~ beauty, data=beauty, refresh=0)
print(fit_1, digits=2)
stan_glm
 family:       gaussian [identity]
 formula:      eval ~ beauty
 observations: 463
 predictors:   2
------
            Median MAD_SD
(Intercept) 4.01   0.03  
beauty      0.13   0.03  

Auxiliary parameter(s):
      Median MAD_SD
sigma 0.55   0.02  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

Make a scatterplot with regression lines

# Labeling the axes
plot(beauty$beauty, beauty$eval, xlab="Beauty", ylab="Average teaching evaluation")
# Display the regression line, added onto the scatterplot (add=TRUE)
coefs <- coef(fit_1)
curve(coefs[1] + coefs[2]*x, add=TRUE)
# Add dotted lines to show +/- 1 standard deviation
sigma <- sigma(fit_1)
curve(coefs[1] + coefs[2]*x + sigma, lty=2, add=TRUE)
curve(coefs[1] + coefs[2]*x - sigma, lty=2, add=TRUE)

ggplot version

ggplot(data=beauty, aes(beauty, eval)) +
  geom_point(size = 2, alpha = 0.75) +
  geom_abline(
    slope = rep(coefs[2], 3),
    intercept = c(coefs[1], coefs[1] - sigma, coefs[1] + sigma),
    linetype = c(1, 2, 2),
    color = "darkgray",
    size = 1
  ) +
  labs(
    x = "Beauty",
    y = "Average teaching evaluation"
  )

Do things differ for male and female profs?

Parallel regression lines

fit_2 <- stan_glm(eval ~ beauty + female, data=beauty, refresh=0)
print(fit_2, digits=2)
stan_glm
 family:       gaussian [identity]
 formula:      eval ~ beauty + female
 observations: 463
 predictors:   3
------
            Median MAD_SD
(Intercept)  4.09   0.03 
beauty       0.15   0.03 
female      -0.20   0.05 

Auxiliary parameter(s):
      Median MAD_SD
sigma 0.54   0.02  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg
coefs2 <- coef(fit_2)

Make several subplots

# Set up a 2x2 grid of plots
par(mfrow=c(2,2))
# Make separate plot for men, ...
plot(beauty$beauty[beauty$female==0], beauty$eval[beauty$female==0], xlim=range(beauty$beauty), ylim=range(beauty$eval),
     xlab="Beauty", ylab="Average teaching evaluation", main="Men")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*0, add=TRUE)
# ... women, ...
plot(beauty$beauty[beauty$female==1], beauty$eval[beauty$female==1], xlim=range(beauty$beauty), ylim=range(beauty$eval),
      xlab="Beauty", ylab="Average teaching evaluation", main="Women")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*1, add=TRUE)
# ... and both sexes on the same plot
# First make the plot with type="n" (which displays axes but does not plot
#   the points), then plot the points and lines separately for each sex
plot(beauty$beauty, beauty$eval, xlab="Beauty", ylab="Average teaching evaluation",
      main="Both sexes", type="n")
points(beauty$beauty[beauty$female==0], beauty$eval[beauty$female==0], col="blue")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*0, add=TRUE, col="blue")
points(beauty$beauty[beauty$female==1], beauty$eval[beauty$female==1], col="red")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*1, add=TRUE, col="red")

ggplot versions

# Men 
gg_male <-
  ggplot(subset(beauty, female == 0), aes(beauty, eval)) +
  geom_point() +
  geom_abline(slope = coefs2[2], intercept = coefs2[1], color = "darkgray")
# Women
gg_female <-
  ggplot(subset(beauty, female == 1), aes(beauty, eval)) +
  geom_point() +
  geom_abline(slope = coefs2[2], intercept = coefs2[1] + coefs2[3], color = "darkgray")
# Both
gg_both <-
  ggplot(data=beauty, aes(beauty, eval)) +
  geom_point(aes(color = factor(female)), show.legend = FALSE) +
  scale_color_manual(values = c("red", "blue")) +
  geom_abline(
    slope = coefs2[2],
    intercept = c(coefs2[1], coefs2[1] + coefs2[3]),
    color = c("blue3", "red3"),
    size = 1
  )
# Put them in a grid
bayesplot_grid(
  gg_male, gg_female, gg_both,
  grid_args = list(ncol = 2),
  xlim = range(beauty$beauty),
  ylim = range(beauty$eval),
  titles = c("Men", "Women", "Both sexes")
)

Do things differ for male and female profs?

Non-parallel regression lines

fit_3 <- stan_glm(eval ~ beauty + female + beauty*female, data=beauty, refresh=0)
print(fit_3, digits=2)
stan_glm
 family:       gaussian [identity]
 formula:      eval ~ beauty + female + beauty * female
 observations: 463
 predictors:   4
------
              Median MAD_SD
(Intercept)    4.11   0.03 
beauty         0.20   0.04 
female        -0.21   0.05 
beauty:female -0.11   0.06 

Auxiliary parameter(s):
      Median MAD_SD
sigma 0.54   0.02  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg
coefs3 <- coef(fit_3)

Make two subplots

# Set up a new 1x2 grid of plots
par(mfrow=c(1,2))
# Display the parallel regression lines in gray and the non-parallel lines
# in heavy black
# Make separate plot for men ...
plot(beauty$beauty[beauty$female==0], beauty$eval[beauty$female==0], xlim=range(beauty$beauty), ylim=range(beauty$eval),
      xlab="Beauty", ylab="Average teaching evaluation", main="Men")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*0,
       lwd=.5, col="gray", add=TRUE)
curve(coefs3[1] + coefs3[2]*x + coefs3[3]*0 + coefs3[4]*x*0,
       lwd=2, col="black", add=TRUE)
# ... and women
plot (beauty$beauty[beauty$female==1], beauty$eval[beauty$female==1], xlim=range(beauty$beauty), ylim=range(beauty$eval),
      xlab="Beauty", ylab="Average teaching evaluation", main="Women")
curve(coefs2[1] +coefs2[2]*x +coefs2[3]*1,
       lwd=.5, col="gray", add=TRUE)
curve(coefs3[1] + coefs3[2]*x + coefs3[3]*1 +coefs3[4]*x*1,
       lwd=2, col="black", add=TRUE)

ggplot version

# we can add to the gg_male and gg_female plots we already made above
gg_male2 <- gg_male + geom_abline(intercept = coefs3[1], slope = coefs3[2], size = 1)
gg_female2 <- gg_female + geom_abline(intercept = coefs3[1] + coefs3[3], slope = coefs3[2] + coefs3[4], size = 1)
# Put them in a grid
bayesplot_grid(
  gg_male2, gg_female2,
  grid_args = list(ncol = 2),
  xlim = range(beauty$beauty),
  ylim = range(beauty$eval),
  titles = c("Men", "Women")
)

More models

Add age

fit_4 <- stan_glm(eval ~ beauty + female + age, data=beauty, refresh=0)
print(fit_4, digits=2)
stan_glm
 family:       gaussian [identity]
 formula:      eval ~ beauty + female + age
 observations: 463
 predictors:   4
------
            Median MAD_SD
(Intercept)  4.22   0.14 
beauty       0.14   0.03 
female      -0.21   0.05 
age          0.00   0.00 

Auxiliary parameter(s):
      Median MAD_SD
sigma 0.54   0.02  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

Add minority

fit_5 <- stan_glm(eval ~ beauty + female + minority, data=beauty, refresh=0)
print(fit_5, digits=2)
stan_glm
 family:       gaussian [identity]
 formula:      eval ~ beauty + female + minority
 observations: 463
 predictors:   4
------
            Median MAD_SD
(Intercept)  4.10   0.03 
beauty       0.15   0.03 
female      -0.19   0.05 
minority    -0.10   0.07 

Auxiliary parameter(s):
      Median MAD_SD
sigma 0.54   0.02  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

Add nonenglish

fit_6 <- stan_glm(eval ~ beauty + female + nonenglish, data=beauty, refresh=0)
print(fit_6, digits=2)
stan_glm
 family:       gaussian [identity]
 formula:      eval ~ beauty + female + nonenglish
 observations: 463
 predictors:   4
------
            Median MAD_SD
(Intercept)  4.12   0.03 
beauty       0.15   0.03 
female      -0.20   0.05 
nonenglish  -0.33   0.10 

Auxiliary parameter(s):
      Median MAD_SD
sigma 0.53   0.02  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

Add nonenglish and lower

fit_7 <- stan_glm(eval ~ beauty + female + nonenglish + lower,
                  data=beauty, refresh=0)
print(fit_7, digits=2)
stan_glm
 family:       gaussian [identity]
 formula:      eval ~ beauty + female + nonenglish + lower
 observations: 463
 predictors:   5
------
            Median MAD_SD
(Intercept)  4.08   0.04 
beauty       0.15   0.03 
female      -0.19   0.05 
nonenglish  -0.31   0.11 
lower        0.09   0.05 

Auxiliary parameter(s):
      Median MAD_SD
sigma 0.53   0.02  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg

Simple model with course indicators

Include course indicators in a regression

fit_8 <- stan_glm(eval ~ beauty + factor(course_id), data=beauty, refresh=0)
print(fit_8, digits=2)
stan_glm
 family:       gaussian [identity]
 formula:      eval ~ beauty + factor(course_id)
 observations: 463
 predictors:   32
------
                    Median MAD_SD
(Intercept)          4.03   0.03 
beauty               0.14   0.03 
factor(course_id)1   0.37   0.23 
factor(course_id)2   0.42   0.38 
factor(course_id)3  -0.17   0.20 
factor(course_id)4  -0.20   0.13 
factor(course_id)5   0.02   0.26 
factor(course_id)6  -0.13   0.22 
factor(course_id)7  -0.32   0.27 
factor(course_id)8  -0.14   0.38 
factor(course_id)9  -0.43   0.19 
factor(course_id)10  0.43   0.23 
factor(course_id)11 -0.08   0.37 
factor(course_id)12  0.03   0.30 
factor(course_id)13 -0.08   0.30 
factor(course_id)14 -0.52   0.30 
factor(course_id)15 -1.44   0.36 
factor(course_id)16  0.18   0.27 
factor(course_id)17  0.34   0.19 
factor(course_id)18  0.27   0.27 
factor(course_id)19 -0.31   0.22 
factor(course_id)20  0.45   0.24 
factor(course_id)21 -0.39   0.15 
factor(course_id)22 -0.29   0.16 
factor(course_id)23  0.37   0.22 
factor(course_id)24 -0.23   0.31 
factor(course_id)25 -0.14   0.31 
factor(course_id)26  0.24   0.30 
factor(course_id)27  0.13   0.37 
factor(course_id)28  0.43   0.28 
factor(course_id)29 -0.08   0.38 
factor(course_id)30  0.30   0.19 

Auxiliary parameter(s):
      Median MAD_SD
sigma 0.53   0.02  

------
* For help interpreting the printed output see ?print.stanreg
* For info on the priors used see ?prior_summary.stanreg
---
title: "Regression and Other Stories: Beauty and Teaching Quality"
author: "Andrew Gelman, Jennifer Hill, Aki Vehtari"
date: "`r format(Sys.Date())`"
output:
  html_document:
    theme: readable
    toc: true
    toc_depth: 2
    toc_float: true
    code_download: true
---
Hamermesh and Parker (2005) data on student evaluations of
instructors’ beauty and teaching quality for several courses at the
University of Texas. See Chapter 10 in Regression and Other
Stories.

Hamermesh, D. S., and Parker, A. M. (2005).  Beauty in the
classroom: Instructors' pulchritude and putative pedagogical
productivity.  Economics of Education Review, 24:369-376.

-------------


```{r setup, include=FALSE}
knitr::opts_chunk$set(message=FALSE, error=FALSE, warning=FALSE, comment=NA)
```

#### Load packages

```{r }
library("rprojroot")
root<-has_file(".ROS-Examples-root")$make_fix_file()
library("rstanarm")
library("ggplot2")
library("bayesplot")
theme_set(bayesplot::theme_default(base_family = "sans"))
```

#### Load data

```{r }
beauty <- read.csv(root("Beauty/data","beauty.csv"))
head(beauty)
```

## Do more beautiful profs get higher evaluations?

#### Make a scatterplot of data

```{r }
par(mar=c(3,3,1,1), mgp=c(1.7, .5, 0), tck=-.01)
plot(beauty$beauty, beauty$eval)
```

#### Fit a linear regression

```{r }
fit_1 <- stan_glm(eval ~ beauty, data=beauty, refresh=0)
print(fit_1, digits=2)
```

#### Make a scatterplot with regression lines

```{r }
# Labeling the axes
plot(beauty$beauty, beauty$eval, xlab="Beauty", ylab="Average teaching evaluation")
# Display the regression line, added onto the scatterplot (add=TRUE)
coefs <- coef(fit_1)
curve(coefs[1] + coefs[2]*x, add=TRUE)
# Add dotted lines to show +/- 1 standard deviation
sigma <- sigma(fit_1)
curve(coefs[1] + coefs[2]*x + sigma, lty=2, add=TRUE)
curve(coefs[1] + coefs[2]*x - sigma, lty=2, add=TRUE)
```

#### ggplot version

```{r }
ggplot(data=beauty, aes(beauty, eval)) +
  geom_point(size = 2, alpha = 0.75) +
  geom_abline(
    slope = rep(coefs[2], 3),
    intercept = c(coefs[1], coefs[1] - sigma, coefs[1] + sigma),
    linetype = c(1, 2, 2),
    color = "darkgray",
    size = 1
  ) +
  labs(
    x = "Beauty",
    y = "Average teaching evaluation"
  )
```

## Do things differ for male and female profs?  
#### Parallel regression lines

```{r }
fit_2 <- stan_glm(eval ~ beauty + female, data=beauty, refresh=0)
print(fit_2, digits=2)
coefs2 <- coef(fit_2)
```

#### Make several subplots

```{r }
# Set up a 2x2 grid of plots
par(mfrow=c(2,2))
# Make separate plot for men, ...
plot(beauty$beauty[beauty$female==0], beauty$eval[beauty$female==0], xlim=range(beauty$beauty), ylim=range(beauty$eval),
     xlab="Beauty", ylab="Average teaching evaluation", main="Men")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*0, add=TRUE)
# ... women, ...
plot(beauty$beauty[beauty$female==1], beauty$eval[beauty$female==1], xlim=range(beauty$beauty), ylim=range(beauty$eval),
      xlab="Beauty", ylab="Average teaching evaluation", main="Women")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*1, add=TRUE)
# ... and both sexes on the same plot
# First make the plot with type="n" (which displays axes but does not plot
#   the points), then plot the points and lines separately for each sex
plot(beauty$beauty, beauty$eval, xlab="Beauty", ylab="Average teaching evaluation",
      main="Both sexes", type="n")
points(beauty$beauty[beauty$female==0], beauty$eval[beauty$female==0], col="blue")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*0, add=TRUE, col="blue")
points(beauty$beauty[beauty$female==1], beauty$eval[beauty$female==1], col="red")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*1, add=TRUE, col="red")
```

#### ggplot versions

```{r }
# Men 
gg_male <-
  ggplot(subset(beauty, female == 0), aes(beauty, eval)) +
  geom_point() +
  geom_abline(slope = coefs2[2], intercept = coefs2[1], color = "darkgray")
# Women
gg_female <-
  ggplot(subset(beauty, female == 1), aes(beauty, eval)) +
  geom_point() +
  geom_abline(slope = coefs2[2], intercept = coefs2[1] + coefs2[3], color = "darkgray")
# Both
gg_both <-
  ggplot(data=beauty, aes(beauty, eval)) +
  geom_point(aes(color = factor(female)), show.legend = FALSE) +
  scale_color_manual(values = c("red", "blue")) +
  geom_abline(
    slope = coefs2[2],
    intercept = c(coefs2[1], coefs2[1] + coefs2[3]),
    color = c("blue3", "red3"),
    size = 1
  )
# Put them in a grid
bayesplot_grid(
  gg_male, gg_female, gg_both,
  grid_args = list(ncol = 2),
  xlim = range(beauty$beauty),
  ylim = range(beauty$eval),
  titles = c("Men", "Women", "Both sexes")
)
```

## Do things differ for male and female profs?  
#### Non-parallel regression lines

```{r }
fit_3 <- stan_glm(eval ~ beauty + female + beauty*female, data=beauty, refresh=0)
print(fit_3, digits=2)
coefs3 <- coef(fit_3)
```

#### Make two subplots

```{r }
# Set up a new 1x2 grid of plots
par(mfrow=c(1,2))
# Display the parallel regression lines in gray and the non-parallel lines
# in heavy black
# Make separate plot for men ...
plot(beauty$beauty[beauty$female==0], beauty$eval[beauty$female==0], xlim=range(beauty$beauty), ylim=range(beauty$eval),
      xlab="Beauty", ylab="Average teaching evaluation", main="Men")
curve(coefs2[1] + coefs2[2]*x + coefs2[3]*0,
       lwd=.5, col="gray", add=TRUE)
curve(coefs3[1] + coefs3[2]*x + coefs3[3]*0 + coefs3[4]*x*0,
       lwd=2, col="black", add=TRUE)
# ... and women
plot (beauty$beauty[beauty$female==1], beauty$eval[beauty$female==1], xlim=range(beauty$beauty), ylim=range(beauty$eval),
      xlab="Beauty", ylab="Average teaching evaluation", main="Women")
curve(coefs2[1] +coefs2[2]*x +coefs2[3]*1,
       lwd=.5, col="gray", add=TRUE)
curve(coefs3[1] + coefs3[2]*x + coefs3[3]*1 +coefs3[4]*x*1,
       lwd=2, col="black", add=TRUE)
```

#### ggplot version

```{r }
# we can add to the gg_male and gg_female plots we already made above
gg_male2 <- gg_male + geom_abline(intercept = coefs3[1], slope = coefs3[2], size = 1)
gg_female2 <- gg_female + geom_abline(intercept = coefs3[1] + coefs3[3], slope = coefs3[2] + coefs3[4], size = 1)
# Put them in a grid
bayesplot_grid(
  gg_male2, gg_female2,
  grid_args = list(ncol = 2),
  xlim = range(beauty$beauty),
  ylim = range(beauty$eval),
  titles = c("Men", "Women")
)
```

## More models
#### Add age

```{r }
fit_4 <- stan_glm(eval ~ beauty + female + age, data=beauty, refresh=0)
print(fit_4, digits=2)
```

#### Add minority

```{r }
fit_5 <- stan_glm(eval ~ beauty + female + minority, data=beauty, refresh=0)
print(fit_5, digits=2)
```

#### Add nonenglish

```{r }
fit_6 <- stan_glm(eval ~ beauty + female + nonenglish, data=beauty, refresh=0)
print(fit_6, digits=2)
```

#### Add nonenglish and lower

```{r }
fit_7 <- stan_glm(eval ~ beauty + female + nonenglish + lower,
                  data=beauty, refresh=0)
print(fit_7, digits=2)
```

## Simple model with course indicators
#### Include course indicators in a regression

```{r }
fit_8 <- stan_glm(eval ~ beauty + factor(course_id), data=beauty, refresh=0)
print(fit_8, digits=2)
```

