data.immer: Some Example Datasets for the immer Package

Description

Some example rating datasets for the immer package.

Usage

data(data.immer01a)
data(data.immer01b)
data(data.immer02)
data(data.immer03)
data(data.immer04a)
data(data.immer04b)
data(data.immer05)
data(data.immer06)
data(data.immer07)
data(data.immer08)
data(data.immer09)
data(data.immer10)

Arguments

Format

The format of the dataset data.immer01a is:
'data.frame': 23904 obs. of 8 variables: $ idstud: int 10001 10001 10003 10003 10003 10004 10004 10005 10005 10006 ... $ type : Factor w/ 2 levels "E","I": 1 2 1 1 2 1 2 1 2 1 ... $ rater : Factor w/ 57 levels "R101","R102",..: 1 36 33 20 21 57 36 9 31 21 ... $ k1 : int 2 1 0 0 0 2 2 1 2 0 ... $ k2 : int 1 1 0 0 0 1 1 1 2 0 ... $ k3 : int 1 1 0 0 0 1 1 1 2 1 ... $ k4 : int 2 2 1 0 0 1 1 1 2 1 ... $ k5 : int 1 2 0 0 0 2 1 2 3 2 ...
The format of the dataset data.immer01b is:
'data.frame': 4244 obs. of 8 variables: $ idstud: int 10001 10003 10005 10007 10009 10016 10018 10022 10024 10029 ... $ type : Factor w/ 1 level "E": 1 1 1 1 1 1 1 1 1 1 ... $ rater : Factor w/ 20 levels "R101","R102",..: 1 20 9 5 14 19 20 6 10 10 ... $ k1 : int 2 0 1 2 2 2 3 1 3 2 ... $ k2 : int 1 0 1 2 2 1 3 2 2 1 ... $ k3 : int 1 0 1 1 3 2 2 1 3 1 ... $ k4 : int 2 0 1 2 3 2 2 2 3 2 ... $ k5 : int 1 0 2 1 3 1 2 3 3 1 ...

This dataset is a subset of data.immer01a.
The format of the dataset data.immer02 is:
'data.frame': 6105 obs. of 6 variables: $ idstud: int 10002 10004 10005 10006 10007 10008 10009 10010 10013 10014 ... $ rater : Factor w/ 44 levels "DR101","DR102",..: 43 15 12 21 9 3 35 24 11 17 ... $ a1 : int 3 1 2 1 0 2 1 2 1 1 ... $ a2 : int 3 0 3 1 0 3 0 2 2 1 ... $ a3 : int 1 2 0 1 2 3 2 2 1 1 ... $ a4 : int 2 1 2 1 1 3 1 2 2 1 ...
The format of the dataset data.immer03 is:
'data.frame': 6466 obs. of 6 variables: $ idstud: int 10001 10002 10003 10004 10005 10006 10007 10009 10010 10012 ... $ rater : Factor w/ 44 levels "R101","R102",..: 18 10 8 25 19 31 16 22 29 6 ... $ b1 : int 1 2 1 3 3 2 3 2 2 1 ... $ b2 : int 2 1 0 3 3 1 1 2 2 1 ... $ b3 : int 2 3 1 2 3 1 2 2 2 2 ... $ b4 : int 1 2 0 2 2 2 3 2 3 1 ...
The format of the dataset data.immer04a is:
'data.frame': 25578 obs. of 7 variables: $ idstud: int 10001 10001 10001 10002 10002 10002 10003 10003 10004 10004 ... $ task : Factor w/ 4 levels "l1","l2","s1",..: 1 4 4 1 1 3 1 3 2 2 ... $ rater : Factor w/ 43 levels "R101","R102",..: 14 31 25 39 35 19 43 27 12 4 ... $ TA : int 5 2 4 0 0 0 2 6 5 3 ... $ CC : int 4 1 3 1 0 0 2 6 4 3 ... $ GR : int 4 1 2 1 0 0 1 7 5 2 ... $ VOC : int 4 2 3 1 0 0 1 6 5 3 ...
The format of the dataset data.immer04b is:
'data.frame': 2975 obs. of 7 variables: $ idstud: int 10002 10004 10010 10013 10015 10016 10024 10025 10027 10033 ... $ task : Factor w/ 1 level "s1": 1 1 1 1 1 1 1 1 1 1 ... $ rater : Factor w/ 20 levels "R101","R102",..: 19 1 5 16 13 13 8 10 19 5 ... $ TA : int 0 3 5 5 3 2 3 6 4 5 ... $ CC : int 0 3 4 5 4 1 4 7 3 3 ... $ GR : int 0 3 3 6 5 2 3 6 3 2 ... $ VOC : int 0 2 4 6 5 2 3 6 3 2 ...

This dataset is a subset of data.immer04a.
The format of the dataset data.immer05 is:
'data.frame': 21398 obs. of 9 variables: $ idstud : int 10001 10001 10002 10002 10003 10003 10004 10004 10005 10005 ... $ type : Factor w/ 2 levels "l","s": 2 1 2 1 2 1 2 1 2 1 ... $ task : Factor w/ 6 levels "l1","l4","l5",..: 5 2 6 3 5 1 5 1 5 2 ... $ rater : Factor w/ 41 levels "ER101","ER102",..: 1 40 38 23 37 33 2 33 21 27 ... $ idstud_task: Factor w/ 19484 levels "10001l4","10001s3",..: 2 1 4 3 6 5 8 7 10 9 ... $ TA : int 3 4 6 6 4 2 0 3 1 3 ... $ CC : int 5 4 5 5 3 3 0 2 5 3 ... $ GR : int 4 4 5 6 5 3 0 4 5 4 ... $ VO : int 6 4 6 6 4 3 0 3 4 3 ...
The dataset data.immer06 is a string containing an input syntax for the FACETS program.
The format of the dataset data.immer07 is:
'data.frame': 1500 obs. of 6 variables: $ pid : int 1 1 1 2 2 2 3 3 3 4 ... $ rater: chr "R1" "R2" "R3" "R1" ... $ I1 : num 1 1 2 1 1 1 0 1 1 2 ... $ I2 : num 0 1 1 2 1 2 1 1 2 1 ... $ I3 : num 1 1 2 0 0 1 1 0 2 1 ... $ I4 : num 0 0 1 0 0 1 0 1 2 0 ...
The format of the dataset data.immer08 is
'data.frame': 16 obs. of 3 variables: $ Facility: int 1 1 1 1 2 2 2 2 3 3 ... $ Research: int 1 2 3 4 1 2 3 4 1 2 ... $ weights : int 40 6 4 15 4 25 1 5 4 2 ...
The dataset data.immer09 contains reviewer ratings for conference papers (Kuhlisch et al., 2016):
'data.frame': 128 obs. of 3 variables: $ idpaper : int 1 1 1 2 2 3 3 3 4 4 ... $ idreviewer: int 11 15 20 1 10 11 15 20 13 16 ... $ score : num 7 7 7 7 7 7 7 7 7 7 ...
Dataset data.immer10 contain standard setting ratings of 13 raters on 61 items.
'data.frame': 61 obs. of 15 variables: $ item : chr "I01" "I02" "I03" "I04" ... $ itemdiff: num 380 388 397 400 416 425 427 434 446 459 ... $ R01 : int 1 3 2 2 1 3 2 2 3 1 ... $ R02 : int 1 1 1 1 1 2 1 2 2 1 ... $ R03 : int 1 1 1 1 1 1 2 2 3 1 ... $ R04 : int 1 2 1 3 2 2 2 2 3 2 ... $ R05 : int 1 1 2 1 1 1 2 2 3 2 ... $ R06 : int 1 2 1 1 1 2 2 2 3 2 ... $ R07 : int 1 2 1 2 1 1 2 1 3 1 ... $ R08 : int 2 2 1 2 1 1 2 2 3 2 ... $ R09 : int 2 1 1 2 1 2 1 2 3 1 ... $ R10 : int 2 2 2 2 1 2 2 3 3 2 ... $ R11 : int 2 2 1 2 1 2 2 2 3 2 ... $ R12 : int 2 2 1 3 1 2 2 2 3 2 ... $ R13 : int 1 1 1 1 1 1 1 1 2 1 ...

References

Kuhlisch, W., Roos, M., Rothe, J., Rudolph, J., Scheuermann, B., & Stoyan, D. (2016). A statistical approach to calibrating the scores of biased reviewers of scientific papers. Metrika, 79, 37-57.