CHECK-IN : Is there a relationship between narcissism and age?

Go into a breakout room and share your screen as you work on the check-in.

Link to Dataset

  • score = narcissism score (40-item scale)
  • age = Entered as a free response. Ages below 14 have been ommited from the dataset.
  • sex = Chosen from a drop down list (1=male, 2=female, 3=other; 0=none was chosen).

code you may need :

head(d) # check the data
hist(d$variable) # graph a numeric variable
plot(d$variable) # graph a categorical variable
plot(d$dv ~ d$iv) # draw a scatterplot
mod <- lm(d$dv ~ d$iv) # define a linear model
abline(mod) # draw the model on the scatterplot
coef(mod) # find the slope and intercept
summary(mod)$r.squared # report R^2

Check-In Spoilers

  • score = narcissism score (40-item scale)
  • age = Entered as a free response. Ages below 14 have been ommited from the dataset.
  • sex = Chosen from a drop down list (1=male, 2=female, 3=other; 0=none was chosen).
d <- read.csv("~/Dropbox/!WHY STATS/Chapter Datasets/Narcissism Data/narcissism_data.csv", stringsAsFactors = T)
head(d)
  score Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20
1    18  2  2  2  2  1  2  1  2  2   2   1   1   2   1   1   1   2   1   1   1
2     6  2  2  2  1  2  2  1  2  1   1   2   2   2   1   2   2   1   1   2   1
3    27  1  2  2  1  2  1  2  1  2   2   2   1   1   1   1   1   2   2   1   1
4    29  1  1  2  2  2  1  2  1  1   2   1   1   1   1   1   1   2   2   1   2
5     6  1  2  1  1  1  2  1  2  1   2   2   2   2   2   1   1   1   1   1   1
6    19  1  2  2  1  2  1  1  1  2   2   1   1   1   2   1   1   1   1   1   1
  Q21 Q22 Q23 Q24 Q25 Q26 Q27 Q28 Q29 Q30 Q31 Q32 Q33 Q34 Q35 Q36 Q37 Q38 Q39
1   1   1   1   2   2   2   1   2   2   2   1   2   1   1   1   2   2   2   1
2   2   2   1   2   2   2   2   1   2   2   2   1   2   2   1   2   2   2   2
3   2   2   2   2   1   2   1   1   2   1   2   2   1   1   2   1   1   2   1
4   1   1   1   2   1   2   1   2   2   1   1   2   1   1   2   1   2   2   1
5   1   2   1   2   2   1   2   1   2   2   2   1   2   2   1   2   2   2   0
6   1   1   1   1   2   1   1   1   2   1   1   2   1   2   1   1   2   2   2
  Q40 elapse gender age
1   2    211      1  50
2   1    149      1  40
3   2    168      1  28
4   1    230      1  37
5   1    389      1  50
6   2    361      1  27
par(mfrow = c(1,2))
hist(d$score)
hist(d$age)

d$age[d$age > 100 | d$age < 14] # outliers
 [1] 366  13 148  13  12 117  13 509  13  13  13  12  13  13 190  13  13  13  11
[20]  13  13  13  13   0  13   0   0  13  13   0  13  13   0   6   0  13   0  13
[39]   0   0  13   0  13  10  13   0   0  13  13   2
d$age[d$age > 100 | d$age < 14] <- NA # they gone.
par(mfrow = c(1,2))
hist(d$score)
hist(d$age)

Defining the Model

plot(d$score ~ d$age)
mod <- lm(d$score ~ d$age)
abline(mod, lwd = 5)

Interpreting The Model

coef(mod)
(Intercept)       d$age 
 18.2149771  -0.1448936 
summary(mod)$r.squared
[1] 0.05512482

Professor Notes Go Here.

Defining the Model

plot(scale(d$score) ~ scale(d$age))
modZ <- lm(scale(d$score) ~ scale(d$age))
abline(modZ, lwd = 5)

Interpreting The Model

round(coef(modZ), 2)
 (Intercept) scale(d$age) 
        0.00        -0.23 
summary(modZ)$r.squared
[1] 0.05512482

Professor Notes Go Here.

Question : Is there a relationship between gender and narcissism?

See Prof. R script and demo.

THE END.