triplets: Triplets of Pairwise Comparisons

In thurstonianIRT: Thurstonian IRT Models

Triplets of Pairwise Comparisons

Description

This data set contains synthetic data of 200 participants on 4 triplets. In each triplet, participants had to rank the three alternative items according to their preference. Reponses were then converted into a set of dichotomous pairwise responses between all the three alternatives. More details can be found in Brown and Maydeu-Olivares (2011).

Usage

triplets

Format

A data frame of 200 observations containing information on 12 variables. Overall, the 12 items measure 3 different traits. Items 1, 4, 7, and 10 load on trait 1, items 2, 5, 8, and 11 load on trait 2, and items 3, 6, 9, and 12 load on trait 3. Moreover, items 4, 9, and 11 are inverted.

i1i2

Response preferences between item 1 and 2.

i1i3

Response preferences between item 1 and 3.

i2i3

Response preferences between item 2 and 3.

i4i5

Response preferences between item 4 and 5.

i4i6

Response preferences between item 4 and 6.

i5i6

Response preferences between item 5 and 6.

i7i8

Response preferences between item 7 and 8.

i7i9

Response preferences between item 7 and 9.

i8i9

Response preferences between item 8 and 9.

i10i11

Response preferences between item 10 and 11.

i10i12

Response preferences between item 10 and 12.

i11i12

Response preferences between item 11 and 12.

Source

Brown, A., & Maydeu-Olivares, A. (2011). Item response modeling of forced-choice questionnaires. Educational and Psychological Measurement, 71(3), 460-502. doi:10.1177/0013164410375112

Examples

# load the data
data("triplets")

# define the blocks of items
blocks <-
  set_block(c("i1", "i2", "i3"), traits = c("t1", "t2", "t3"),
          signs = c(1, 1, 1)) +
  set_block(c("i4", "i5", "i6"), traits = c("t1", "t2", "t3"),
            signs = c(-1, 1, 1)) +
  set_block(c("i7", "i8", "i9"), traits = c("t1", "t2", "t3"),
            signs = c(1, 1, -1)) +
  set_block(c("i10", "i11", "i12"), traits = c("t1", "t2", "t3"),
            signs = c(1, -1, 1))

# generate the data to be understood by 'thurstonianIRT'
tdat <- make_TIRT_data(
  triplets, blocks, direction = "larger",
  format = "pairwise", family = "bernoulli", range = c(0, 1)
)


# fit the data using Stan
fit <- fit_TIRT_stan(tdat, chains = 1)
print(fit)
predict(fit)

thurstonianIRT documentation built on Aug. 22, 2023, 5:08 p.m.