psychotools.ipynb

Kernel: R (R-Project)

Are there cursing students on CoCalc?

Sure, we have it all.

Item Response Theory dataset, fed into a Rasch model, and plotted.

Excerpt taken out of R's CRAN package "psychotools":

The 24 items are constructed by factorial combination of four different frustrating situations (see below), three possible verbally aggressive responses (curse, scold, shout), and two behavioural models (want, do). The four situations are

• S1: A bus fails to stop for me.

• S2: I miss a train because a clerk gave me faulty information.

• S3: The grocery store closes just as I am about to enter.

• S4: The operator disconnects me when I used up my last 10 cents for a call.

Note that the first two situations are other-to-blame situations, and the latter two are self-to-blame situations. The subjects were 316 first-year psychology students from a university in the Dutch speaking part of Belgium.

require("psychotools")

Loading required package: psychotools

data("VerbalAggression", package = "psychotools")

nrow(VerbalAggression)

316

head(VerbalAggression$resp2, n=10)

S1WantCurse	S1DoCurse	S1WantScold	S1DoScold	S1WantShout	S1DoShout	S2WantCurse	S2DoCurse	S2WantScold	S2DoScold	⋯	S3WantScold	S3DoScold	S3WantShout	S3DoShout	S4WantCurse	S4DoCurse	S4WantScold	S4DoScold	S4WantShout	S4DoShout
0	1	0	0	0	1	0	1	0	0	⋯	0	0	1	0	1	1	0	1	0	1
0	0	0	0	0	0	0	0	0	0	⋯	0	0	0	0	0	0	0	0	0	0
1	0	1	1	1	1	1	0	0	0	⋯	0	0	0	0	0	1	0	0	0	0
1	1	1	1	1	1	1	1	1	1	⋯	0	0	0	0	0	0	0	0	0	0
1	1	0	1	1	0	1	1	0	0	⋯	0	0	0	0	1	1	0	0	0	0
1	1	1	0	0	0	1	1	0	0	⋯	1	0	1	0	0	1	0	1	0	0
1	1	1	1	1	1	1	1	1	1	⋯	0	0	1	0	0	0	0	0	0	0
0	1	0	0	0	0	0	1	0	0	⋯	0	0	0	0	0	1	0	0	0	0
0	1	0	0	0	0	1	1	1	0	⋯	0	0	1	1	1	0	0	1	0	0
1	1	1	1	1	1	1	1	1	1	⋯	0	0	1	0	1	1	1	1	1	1

m <- raschmodel(VerbalAggression$resp2[, 1:12])
summary(m)

Rasch model

Difficulty parameters:
              Estimate Std. Error z value Pr(>|z|)    
S1DoCurse   -1.556e-08  2.042e-01   0.000  1.00000    
S1WantScold  6.857e-01  1.995e-01   3.436  0.00059 ***
S1DoScold    8.727e-01  1.994e-01   4.376 1.21e-05 ***
S1WantShout  1.208e+00  2.003e-01   6.032 1.62e-09 ***
S1DoShout    2.294e+00  2.131e-01  10.766  < 2e-16 ***
S2WantCurse -5.393e-01  2.135e-01  -2.527  0.01152 *  
S2DoCurse    3.614e-01  2.009e-01   1.799  0.07200 .  
S2WantScold  5.345e-01  2.000e-01   2.673  0.00753 ** 
S2DoScold    1.359e+00  2.012e-01   6.755 1.42e-11 ***
S2WantShout  1.283e+00  2.007e-01   6.395 1.61e-10 ***
S2DoShout    3.067e+00  2.343e-01  13.088  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Log-likelihood: -1255 (df = 11) 
Number of iterations in BFGS optimization: 19 

## visualizations
plot(m, type = "profile")
plot(m, type = "regions")
plot(m, type = "curves")
plot(m, type = "information")
plot(m, type = "piplot")