The key2binary function will convert response pattern data to a
dichotomous format, given a response key.
key2binary(fulldata, key, score_missing = FALSE)an object of class data.frame, matrix, or
table with the response patterns
a vector or matrix consisting of the 'correct' response to the items. Each
value/row corresponds to each column in fulldata. If the input is a matrix, multiple
scoring keys can be supplied for each item. NA values are used to indicate no scoring key (or
in the case of a matrix input, no additional scoring keys)
logical; should missing data elements be returned as incorrect (i.e., 0)?
If FALSE, all missing data terms will be kept as missing
Returns a numeric matrix with all the response patterns in dichotomous format
Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06
data(SAT12)
head(SAT12)
#> Item.1 Item.2 Item.3 Item.4 Item.5 Item.6 Item.7 Item.8 Item.9 Item.10
#> 1 1 4 5 2 3 1 2 1 3 1
#> 2 3 4 2 8 3 3 2 8 3 1
#> 3 1 4 5 4 3 2 2 3 3 2
#> 4 2 4 4 2 3 3 2 4 3 2
#> 5 2 4 5 2 3 2 2 1 1 2
#> 6 1 4 3 1 3 2 2 3 3 1
#> Item.11 Item.12 Item.13 Item.14 Item.15 Item.16 Item.17 Item.18 Item.19
#> 1 2 4 2 1 5 3 4 4 1
#> 2 2 8 2 1 5 2 4 1 1
#> 3 2 1 3 1 5 5 4 1 3
#> 4 2 4 2 1 5 2 4 1 3
#> 5 2 4 2 1 5 4 4 5 1
#> 6 2 3 2 1 5 5 4 4 1
#> Item.20 Item.21 Item.22 Item.23 Item.24 Item.25 Item.26 Item.27 Item.28
#> 1 4 3 3 4 1 3 5 1 3
#> 2 4 3 3 8 1 8 4 1 4
#> 3 4 3 3 1 1 3 4 1 3
#> 4 4 3 1 5 2 5 4 1 3
#> 5 4 3 3 3 1 1 5 1 3
#> 6 4 3 3 4 1 1 4 1 4
#> Item.29 Item.30 Item.31 Item.32
#> 1 1 5 4 5
#> 2 5 8 4 8
#> 3 4 4 4 1
#> 4 4 2 4 2
#> 5 1 2 4 1
#> 6 2 3 4 3
key <- c(1,4,5,2,3,1,2,1,3,1,2,4,2,1,5,3,4,4,1,4,3,3,4,1,3,5,1,3,1,5,4,5)
dicho.SAT12 <- key2binary(SAT12, key)
head(dicho.SAT12)
#> Item.1 Item.2 Item.3 Item.4 Item.5 Item.6 Item.7 Item.8 Item.9 Item.10
#> [1,] 1 1 1 1 1 1 1 1 1 1
#> [2,] 0 1 0 0 1 0 1 0 1 1
#> [3,] 1 1 1 0 1 0 1 0 1 0
#> [4,] 0 1 0 1 1 0 1 0 1 0
#> [5,] 0 1 1 1 1 0 1 1 0 0
#> [6,] 1 1 0 0 1 0 1 0 1 1
#> Item.11 Item.12 Item.13 Item.14 Item.15 Item.16 Item.17 Item.18 Item.19
#> [1,] 1 1 1 1 1 1 1 1 1
#> [2,] 1 0 1 1 1 0 1 0 1
#> [3,] 1 0 0 1 1 0 1 0 0
#> [4,] 1 1 1 1 1 0 1 0 0
#> [5,] 1 1 1 1 1 0 1 0 1
#> [6,] 1 0 1 1 1 0 1 1 1
#> Item.20 Item.21 Item.22 Item.23 Item.24 Item.25 Item.26 Item.27 Item.28
#> [1,] 1 1 1 1 1 1 1 1 1
#> [2,] 1 1 1 0 1 0 0 1 0
#> [3,] 1 1 1 0 1 1 0 1 1
#> [4,] 1 1 0 0 0 0 0 1 1
#> [5,] 1 1 1 0 1 0 1 1 1
#> [6,] 1 1 1 1 1 0 0 1 0
#> Item.29 Item.30 Item.31 Item.32
#> [1,] 1 1 1 1
#> [2,] 0 0 1 0
#> [3,] 0 0 1 0
#> [4,] 0 0 1 0
#> [5,] 1 0 1 0
#> [6,] 0 0 1 0
# multiple scoring keys
key2 <- cbind(c(1,4,5,2,3,1,2,1,3,1,2,4,2,1,5,3,4,4,1,4,3,3,4,1,3,5,1,3,1,5,4,5),
c(2,3,NA,1,rep(NA, 28)))
dicho.SAT12 <- key2binary(SAT12, key2)
# keys from raw character responses
resp <- as.data.frame(matrix(c(
"B","B","D","D","E",
"B","A","D","D","E",
"B","A","D","C","E",
"D","D","D","C","E",
"B","C","A","D","A"), ncol=5, byrow=TRUE))
key <- c("B", "D", "D", "C", "E")
d01 <- key2binary(resp, key)
head(d01)
#> V1 V2 V3 V4 V5
#> [1,] 1 0 1 0 1
#> [2,] 1 0 1 0 1
#> [3,] 1 0 1 1 1
#> [4,] 0 1 1 1 1
#> [5,] 1 0 0 0 0
# score/don't score missing values
resp[1,1] <- NA
d01NA <- key2binary(resp, key) # without scoring
d01NA
#> V1 V2 V3 V4 V5
#> [1,] NA 0 1 0 1
#> [2,] 1 0 1 0 1
#> [3,] 1 0 1 1 1
#> [4,] 0 1 1 1 1
#> [5,] 1 0 0 0 0
d01 <- key2binary(resp, key, score_missing = TRUE) # with scoring
d01
#> V1 V2 V3 V4 V5
#> [1,] 0 0 1 0 1
#> [2,] 1 0 1 0 1
#> [3,] 1 0 1 1 1
#> [4,] 0 1 1 1 1
#> [5,] 1 0 0 0 0