data.timss11.G4.AUT.RdThis is the TIMSS 2011 dataset of 4668 Austrian fourth-graders. See George and Robitzsch (2014, 2015, 2018) for publications using the TIMSS 2011 dataset for cognitive diagnosis modeling. The dataset has also been analyzed by Sedat and Arican (2015).
The format of the dataset data.timss11.G4.AUT is:
List of 4 $ data :'data.frame': ..$ uidschool: int [1:4668] 10040001 10040001 10040001 10040001 10040001 10040001 10040001 10040001 10040001 10040001 ... ..$ uidstud : num [1:4668] 1e+13 1e+13 1e+13 1e+13 1e+13 ... ..$ IDCNTRY : int [1:4668] 40 40 40 40 40 40 40 40 40 40 ... ..$ IDBOOK : int [1:4668] 10 12 13 14 1 2 3 4 5 6 ... ..$ IDSCHOOL : int [1:4668] 1 1 1 1 1 1 1 1 1 1 ... ..$ IDCLASS : int [1:4668] 102 102 102 102 102 102 102 102 102 102 ... ..$ IDSTUD : int [1:4668] 10201 10203 10204 10205 10206 10207 10208 10209 10210 10211 ... ..$ TOTWGT : num [1:4668] 17.5 17.5 17.5 17.5 17.5 ... ..$ HOUWGT : num [1:4668] 1.04 1.04 1.04 1.04 1.04 ... ..$ SENWGT : num [1:4668] 0.111 0.111 0.111 0.111 0.111 ... ..$ SCHWGT : num [1:4668] 11.6 11.6 11.6 11.6 11.6 ... ..$ STOTWGTU : num [1:4668] 524 524 524 524 524 ... ..$ WGTADJ1 : int [1:4668] 1 1 1 1 1 1 1 1 1 1 ... ..$ WGTFAC1 : num [1:4668] 11.6 11.6 11.6 11.6 11.6 ... ..$ JKCREP : int [1:4668] 1 1 1 1 1 1 1 1 1 1 ... ..$ JKCZONE : int [1:4668] 1 1 1 1 1 1 1 1 1 1 ... ..$ female : int [1:4668] 1 0 1 1 1 1 1 1 0 0 ... ..$ M031346A : int [1:4668] NA NA NA 1 1 NA NA NA NA NA ... ..$ M031346B : int [1:4668] NA NA NA 0 0 NA NA NA NA NA ... ..$ M031346C : int [1:4668] NA NA NA 1 1 NA NA NA NA NA ... ..$ M031379 : int [1:4668] NA NA NA 0 0 NA NA NA NA NA ... ..$ M031380 : int [1:4668] NA NA NA 0 0 NA NA NA NA NA ... ..$ M031313 : int [1:4668] NA NA NA 1 1 NA NA NA NA NA ... .. [list output truncated] $ q.matrix1:'data.frame': ..$ item : Factor w/ 174 levels "M031004","M031009",..: 29 30 31 32 33 25 8 5 17 163 ... ..$ Co_DA: int [1:174] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_DK: int [1:174] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_DR: int [1:174] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_GA: int [1:174] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_GK: int [1:174] 0 0 0 0 0 0 1 1 0 0 ... ..$ Co_GR: int [1:174] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_NA: int [1:174] 1 0 0 0 0 1 0 0 0 1 ... ..$ Co_NK: int [1:174] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_NR: int [1:174] 0 1 1 1 1 0 0 0 1 0 ... $ q.matrix2:'data.frame': ..$ item : Factor w/ 174 levels "M031004","M031009",..: 29 30 31 32 33 25 8 5 17 163 ... ..$ CONT_D: int [1:174] 0 0 0 0 0 0 0 0 0 0 ... ..$ CONT_G: int [1:174] 0 0 0 0 0 0 1 1 0 0 ... ..$ CONT_N: int [1:174] 1 1 1 1 1 1 0 0 1 1 ... $ q.matrix3:'data.frame': 174 obs. of 4 variables: ..$ item : Factor w/ 174 levels "M031004","M031009",..: 29 30 31 32 33 25 8 5 17 163 ... ..$ COGN_A: int [1:174] 1 0 0 0 0 1 0 0 0 1 ... ..$ COGN_K: int [1:174] 0 0 0 0 0 0 1 1 0 0 ... ..$ COGN_R: int [1:174] 0 1 1 1 1 0 0 0 1 0 ...
The dataset data.timss11.G4.AUT.part is a part of
data.timss11.G4.AUT and contains only the first
three booklets (with N=1010 students). The format is
List of 4 $ data :'data.frame': 1010 obs. of 109 variables: ..$ uidschool: int [1:1010] 10040001 10040001 10040001 10040001 ... ..$ uidstud : num [1:1010] 1e+13 1e+13 1e+13 1e+13 1e+13 ... ..$ IDCNTRY : int [1:1010] 40 40 40 40 40 40 40 40 40 40 ... ..$ IDBOOK : int [1:1010] 1 2 3 1 2 1 2 3 1 2 ... ..$ IDSCHOOL : int [1:1010] 1 1 1 1 1 2 2 2 3 3 ... ..$ IDCLASS : int [1:1010] 102 102 102 102 102 ... ..$ IDSTUD : int [1:1010] 10206 10207 10208 10220 ... ..$ TOTWGT : num [1:1010] 17.5 17.5 17.5 17.5 17.5 ... ..$ HOUWGT : num [1:1010] 1.04 1.04 1.04 1.04 1.04 ... ..$ SENWGT : num [1:1010] 0.111 0.111 0.111 0.111 0.111 ... ..$ SCHWGT : num [1:1010] 11.6 11.6 11.6 11.6 11.6 ... ..$ STOTWGTU : num [1:1010] 524 524 524 524 524 ... ..$ WGTADJ1 : int [1:1010] 1 1 1 1 1 1 1 1 1 1 ... ..$ WGTFAC1 : num [1:1010] 11.6 11.6 11.6 11.6 11.6 ... ..$ JKCREP : int [1:1010] 1 1 1 1 1 0 0 0 0 0 ... ..$ JKCZONE : int [1:1010] 1 1 1 1 1 1 1 1 2 2 ... ..$ female : int [1:1010] 1 1 1 1 0 1 1 1 1 1 ... ..$ M031346A : int [1:1010] 1 NA NA 1 NA 1 NA NA 1 NA ... ..$ M031346B : int [1:1010] 0 NA NA 1 NA 0 NA NA 0 NA ... ..$ M031346C : int [1:1010] 1 NA NA 0 NA 0 NA NA 0 NA ... ..$ M031379 : int [1:1010] 0 NA NA 0 NA 0 NA NA 1 NA ... ..$ M031380 : int [1:1010] 0 NA NA 0 NA 0 NA NA 0 NA ... ..$ M031313 : int [1:1010] 1 NA NA 0 NA 1 NA NA 0 NA ... ..$ M031083 : int [1:1010] 1 NA NA 1 NA 1 NA NA 1 NA ... ..$ M031071 : int [1:1010] 0 NA NA 0 NA 1 NA NA 0 NA ... ..$ M031185 : int [1:1010] 0 NA NA 1 NA 0 NA NA 0 NA ... ..$ M051305 : int [1:1010] 1 1 NA 1 0 0 0 NA 0 1 ... ..$ M051091 : int [1:1010] 1 1 NA 1 1 1 1 NA 1 0 ... .. [list output truncated] $ q.matrix1:'data.frame': 47 obs. of 10 variables: ..$ item : Factor w/ 174 levels "M031004","M031009",..: 29 30 31 32 33 25 8 5 17 163 ... ..$ Co_DA: int [1:47] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_DK: int [1:47] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_DR: int [1:47] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_GA: int [1:47] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_GK: int [1:47] 0 0 0 0 0 0 1 1 0 0 ... ..$ Co_GR: int [1:47] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_NA: int [1:47] 1 0 0 0 0 1 0 0 0 1 ... ..$ Co_NK: int [1:47] 0 0 0 0 0 0 0 0 0 0 ... ..$ Co_NR: int [1:47] 0 1 1 1 1 0 0 0 1 0 ... $ q.matrix2:'data.frame': 47 obs. of 4 variables: ..$ item : Factor w/ 174 levels "M031004","M031009",..: 29 30 31 32 33 25 8 5 17 163 ... ..$ CONT_D: int [1:47] 0 0 0 0 0 0 0 0 0 0 ... ..$ CONT_G: int [1:47] 0 0 0 0 0 0 1 1 0 0 ... ..$ CONT_N: int [1:47] 1 1 1 1 1 1 0 0 1 1 ... $ q.matrix3:'data.frame': 47 obs. of 4 variables: ..$ item : Factor w/ 174 levels "M031004","M031009",..: 29 30 31 32 33 25 8 5 17 163 ... ..$ COGN_A: int [1:47] 1 0 0 0 0 1 0 0 0 1 ... ..$ COGN_K: int [1:47] 0 0 0 0 0 0 1 1 0 0 ... ..$ COGN_R: int [1:47] 0 1 1 1 1 0 0 0 1 0 ...
The dataset data.timss11.G4.sa contains the Q-matrix
used in Sedat and Arican (2015).
List of 2 $ q.matrix:'data.frame': 31 obs. of 13 variables: ..$ N1 : num [1:31] 1 0 0 1 1 0 0 0 0 0 ... ..$ N2 : num [1:31] 1 1 0 0 1 0 0 0 0 0 ... ..$ N3 : num [1:31] 0 0 0 0 1 0 0 0 0 0 ... ..$ A4 : num [1:31] 0 0 1 0 0 1 1 1 0 0 ... ..$ A5 : num [1:31] 0 0 0 0 0 1 0 1 0 0 ... ..$ A6 : num [1:31] 0 0 0 0 0 0 0 0 0 0 ... ..$ A7 : num [1:31] 0 0 1 0 0 0 0 0 0 0 ... ..$ G8 : num [1:31] 0 0 0 0 0 0 0 0 1 1 ... ..$ G9 : num [1:31] 0 0 0 0 0 0 0 0 1 1 ... ..$ G10: num [1:31] 0 0 0 0 0 0 0 0 1 1 ... ..$ G11: num [1:31] 0 0 0 0 0 1 0 0 0 0 ... ..$ D12: num [1:31] 0 0 0 0 0 0 0 0 0 0 ... ..$ D13: num [1:31] 0 0 0 0 0 0 0 0 0 0 ... $ skills : Named chr [1:13] "Possesses understanding of" __truncated__ ... ..- attr(*, "names")=chr [1:13] "N1" "N2" "N3" "A4" ...
George, A. C., & Robitzsch, A. (2014). Multiple group cognitive diagnosis models, with an emphasis on differential item functioning. Psychological Test and Assessment Modeling, 56(4), 405-432.
George, A. C., & Robitzsch, A. (2015) Cognitive diagnosis models in R: A didactic. The Quantitative Methods for Psychology, 11, 189-205.
George, A. C., & Robitzsch, A. (2018). Focusing on interactions between content and cognition: A new perspective on gender differences in mathematical sub-competencies. Applied Measurement in Education, 31(1), 79-97.
Sedat, S. E. N., & Arican, M. (2015). A diagnostic comparison of Turkish and Korean students' Mathematics performances on the TIMSS 2011 assessment. Journal of Measurement and Evaluation in Education and Psychology, 6(2), 238-253.