CDM (version 2.7-7)

data.timss07.G4.lee: TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011)

Description

TIMSS 2007 (Grade 4) dataset with a subset of 25 mathematics items used in Lee et al. (2011). The dataset includes a sample of 698 Austrian students.

Usage

data(data.timss07.G4.lee)

format

The dataset is a list of item responses (data; information on booklet and gender included), the Q-matrix (q.matrix) and descriptions of the skills (skillinfo) used in Lee et al. (2011). The format is: List of 3 $ data :'data.frame': ..$ idstud : int [1:698] 10110 10111 20105 20106 30203 30204 40106 40107 60111 60112 ... ..$ idbook : int [1:698] 4 5 4 5 4 5 4 5 4 5 ... ..$ girl : int [1:698] 0 0 1 1 0 1 0 1 1 1 ... ..$ M041052 : num [1:698] 1 NA 1 NA 0 NA 1 NA 1 NA ... ..$ M041056 : num [1:698] 1 NA 0 NA 0 NA 0 NA 1 NA ... ..$ M041069 : num [1:698] 0 NA 0 NA 0 NA 0 NA 1 NA ... ..$ M041076 : num [1:698] 1 NA 0 NA 1 NA 1 NA 0 NA ... ..$ M041281 : num [1:698] 1 NA 0 NA 1 NA 1 NA 0 NA ... ..$ M041164 : num [1:698] 1 NA 1 NA 0 NA 1 NA 1 NA ... ..$ M041146 : num [1:698] 0 NA 0 NA 1 NA 1 NA 0 NA ... ..$ M041152 : num [1:698] 1 NA 1 NA 1 NA 0 NA 1 NA ... ..$ M041258A: num [1:698] 0 NA 1 NA 1 NA 0 NA 1 NA ... ..$ M041258B: num [1:698] 1 NA 0 NA 1 NA 0 NA 1 NA ... ..$ M041131 : num [1:698] 0 NA 0 NA 1 NA 1 NA 1 NA ... ..$ M041275 : num [1:698] 1 NA 0 NA 0 NA 1 NA 1 NA ... ..$ M041186 : num [1:698] 1 NA 0 NA 1 NA 1 NA 0 NA ... ..$ M041336 : num [1:698] 1 NA 1 NA 0 NA 1 NA 0 NA ... ..$ M031303 : num [1:698] 1 1 0 1 0 1 1 1 0 0 ... ..$ M031309 : num [1:698] 1 0 1 1 1 1 1 1 0 0 ... ..$ M031245 : num [1:698] 0 0 0 0 0 0 0 0 0 0 ... ..$ M031242A: num [1:698] 1 1 0 1 1 1 1 1 0 0 ... ..$ M031242B: num [1:698] 0 1 0 1 1 1 1 1 1 0 ... ..$ M031242C: num [1:698] 1 1 0 1 1 1 1 1 1 0 ... ..$ M031247 : num [1:698] 0 0 0 0 0 0 0 0 0 0 ... ..$ M031219 : num [1:698] 1 1 1 0 1 1 1 1 1 0 ... ..$ M031173 : num [1:698] 1 1 0 0 0 1 1 1 1 0 ... ..$ M031085 : num [1:698] 1 0 0 1 1 1 0 0 0 1 ... ..$ M031172 : num [1:698] 1 0 0 1 1 1 1 1 1 0 ... $ q.matrix : int [1:25, 1:15] 1 0 0 0 0 0 0 1 0 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:25] "M041052" "M041056" "M041069" "M041076" ... .. ..$ : chr [1:15] "NWN01" "NWN02" "NWN03" "NWN04" ... $ skillinfo:'data.frame': ..$ skillindex : int [1:15] 1 2 3 4 5 6 7 8 9 10 ... ..$ skill : Factor w/ 15 levels "DOR15","DRI13",..: 12 13 14 15 8 9 10 11 4 6 ... ..$ content : Factor w/ 3 levels "D","G","N": 3 3 3 3 3 3 3 3 2 2 ... ..$ content_label : Factor w/ 3 levels "Data Display",..: 3 3 3 3 3 3 3 3 2 2 ... ..$ subcontent : Factor w/ 9 levels "FD","LA","LM",..: 9 9 9 9 1 1 4 6 2 8 ... ..$ subcontent_label: Factor w/ 9 levels "Fractions and Decimals",..: 9 9 9 9 1 1 4 6 2 8 ...

source

TIMSS 2007 study 4th Grade, Austrian sample on booklets 4 and 5

References

Lee, Y. S., Park, Y. S., & Taylan, D. (2011). A cognitive diagnostic modeling of attribute mastery in Massachusetts, Minnesota, and the US national sample using the TIMSS 2007. International Journal of Testing, 11, 144-177.

Examples

data(data.timss07.G4.lee)
dat <- data.timss07.G4.lee$data
q.matrix <- data.timss07.G4.lee$q.matrix
items <- grep( "M0" , colnames(dat) , value=TRUE )

#*** Model 1: estimate DINA model
mod1 <- din( dat[,items] , q.matrix )