Reverse score specific items given empirical range or specific scoring range.
Arguments
- data
an object of class
data.frame,matrix, ortablewith the response patterns- which
names of items or column integer location in
datathat should be rescored. If missing the all columns indatawill be reverse scored- range
(optional) a named
listto specify the low and high score ranges. Specified names must match the names found indata, and each element of this list should contain only two values. If items specified inwhichare omitted from this list then the empirical min/max information will be used instead- append
character vector indicating what to append to the item names that have been rescored
Value
returns the original data object with the specified
items reverse scored replacing the original scoring scheme
References
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
Author
Phil Chalmers rphilip.chalmers@gmail.com
Examples
a <- rlnorm(20)
a[c(1,5,10)] <- -a[c(1,5,10)]
diffs <- t(apply(matrix(runif(20*4, .3, 1), 20), 1, cumsum))
diffs <- -(diffs - rowMeans(diffs))
d <- diffs + rnorm(20)
dat <- simdata(a,d,itemtype='graded', N=300)
head(dat)
#> Item_1 Item_2 Item_3 Item_4 Item_5 Item_6 Item_7 Item_8 Item_9 Item_10
#> [1,] 4 4 2 4 4 4 2 4 4 0
#> [2,] 4 4 1 2 4 0 4 0 4 0
#> [3,] 3 0 4 0 4 0 4 0 0 0
#> [4,] 0 4 3 4 4 4 4 4 4 0
#> [5,] 0 0 0 1 4 4 0 1 1 1
#> [6,] 1 0 0 0 3 1 3 0 0 0
#> Item_11 Item_12 Item_13 Item_14 Item_15 Item_16 Item_17 Item_18 Item_19
#> [1,] 4 4 0 4 4 4 1 4 1
#> [2,] 4 0 2 4 4 2 0 1 4
#> [3,] 4 2 0 0 0 2 0 1 4
#> [4,] 4 4 3 4 4 4 4 4 4
#> [5,] 4 2 1 0 4 2 2 4 3
#> [6,] 0 4 3 3 1 1 0 1 2
#> Item_20
#> [1,] 4
#> [2,] 4
#> [3,] 3
#> [4,] 4
#> [5,] 0
#> [6,] 4
# \donttest{
# fitted model has negative slopes due to flipped scoring
mod <- mirt(dat)
#>
Iteration: 1, Log-Lik: -7522.936, Max-Change: 2.37242
Iteration: 2, Log-Lik: -6865.484, Max-Change: 2.22393
Iteration: 3, Log-Lik: -6756.486, Max-Change: 0.90324
Iteration: 4, Log-Lik: -6737.275, Max-Change: 0.54260
Iteration: 5, Log-Lik: -6730.503, Max-Change: 0.46765
Iteration: 6, Log-Lik: -6728.606, Max-Change: 0.36291
Iteration: 7, Log-Lik: -6727.507, Max-Change: 0.21885
Iteration: 8, Log-Lik: -6727.128, Max-Change: 0.25701
Iteration: 9, Log-Lik: -6726.949, Max-Change: 0.16799
Iteration: 10, Log-Lik: -6726.796, Max-Change: 0.00910
Iteration: 11, Log-Lik: -6726.780, Max-Change: 0.07955
Iteration: 12, Log-Lik: -6726.747, Max-Change: 0.05852
Iteration: 13, Log-Lik: -6726.719, Max-Change: 0.00357
Iteration: 14, Log-Lik: -6726.715, Max-Change: 0.00154
Iteration: 15, Log-Lik: -6726.714, Max-Change: 0.00394
Iteration: 16, Log-Lik: -6726.712, Max-Change: 0.00262
Iteration: 17, Log-Lik: -6726.711, Max-Change: 0.00511
Iteration: 18, Log-Lik: -6726.709, Max-Change: 0.00012
Iteration: 19, Log-Lik: -6726.709, Max-Change: 0.00012
Iteration: 20, Log-Lik: -6726.709, Max-Change: 0.00247
Iteration: 21, Log-Lik: -6726.708, Max-Change: 0.00065
Iteration: 22, Log-Lik: -6726.708, Max-Change: 0.00022
Iteration: 23, Log-Lik: -6726.708, Max-Change: 0.00032
Iteration: 24, Log-Lik: -6726.708, Max-Change: 0.00010
coef(mod, simplify=TRUE)$items
#> a1 d1 d2 d3 d4
#> Item_1 -1.1171924 2.4296469 1.73288248 1.26067403 0.2297665
#> Item_2 7.7979262 0.6344815 0.10842378 -0.40343108 -0.5373447
#> Item_3 0.2629820 1.0227374 0.40406181 0.05877758 -0.7416570
#> Item_4 4.7612348 0.4181758 0.09659425 -0.39620617 -0.8081206
#> Item_5 -0.2508314 1.7270595 1.44413339 1.00569169 0.4374845
#> Item_6 0.2825226 0.6987403 0.33984035 -0.10978202 -0.5318410
#> Item_7 0.5483014 1.1687726 0.61066779 -0.17876210 -0.8428922
#> Item_8 0.2589735 0.1645560 -0.50937611 -0.97348674 -1.6301845
#> Item_9 4.7482498 0.4760300 -0.17069300 -1.25756806 -2.0364066
#> Item_10 -3.4544141 -0.9539374 -1.70812723 -1.99858134 -2.6710047
#> Item_11 4.5041025 1.3508597 0.82245138 0.38868246 -0.6469871
#> Item_12 0.6126098 2.0281560 1.23506017 0.35720032 -0.2322765
#> Item_13 0.9980961 -0.4148028 -1.06332972 -1.72305387 -2.4443652
#> Item_14 2.9404701 1.4499665 0.65492514 0.16151658 -0.1173663
#> Item_15 3.0592768 1.0422137 0.65828309 0.23004194 -0.8853085
#> Item_16 2.1588771 1.2437703 0.88064791 -0.61804921 -1.1362945
#> Item_17 0.9028578 1.2465837 0.38556807 -0.45153380 -0.9196150
#> Item_18 0.2863930 2.4301695 1.84214734 1.30810063 0.9140620
#> Item_19 1.3145920 1.6329817 0.90542889 -0.07961438 -0.6340161
#> Item_20 1.0298438 2.3208002 1.75183674 0.90002111 0.6407807
plot(mod, type = 'itemscore')
# }
# reverse the scoring for items 1, 5, and 10 only using empirical min/max
revdat <- reverse.score(dat, c('Item_1', 'Item_5', 'Item_10'))
head(revdat)
#> Item_1.RS Item_2 Item_3 Item_4 Item_5.RS Item_6 Item_7 Item_8 Item_9
#> [1,] 0 4 2 4 0 4 2 4 4
#> [2,] 0 4 1 2 0 0 4 0 4
#> [3,] 1 0 4 0 0 0 4 0 0
#> [4,] 4 4 3 4 0 4 4 4 4
#> [5,] 4 0 0 1 0 4 0 1 1
#> [6,] 3 0 0 0 1 1 3 0 0
#> Item_10.RS Item_11 Item_12 Item_13 Item_14 Item_15 Item_16 Item_17 Item_18
#> [1,] 4 4 4 0 4 4 4 1 4
#> [2,] 4 4 0 2 4 4 2 0 1
#> [3,] 4 4 2 0 0 0 2 0 1
#> [4,] 4 4 4 3 4 4 4 4 4
#> [5,] 3 4 2 1 0 4 2 2 4
#> [6,] 4 0 4 3 3 1 1 0 1
#> Item_19 Item_20
#> [1,] 1 4
#> [2,] 4 4
#> [3,] 4 3
#> [4,] 4 4
#> [5,] 3 0
#> [6,] 2 4
# compare
apply(dat[,c(1,5,10)], 2, table)
#> Item_1 Item_5 Item_10
#> 0 35 46 179
#> 1 23 12 23
#> 2 21 23 8
#> 3 57 37 17
#> 4 164 182 73
apply(revdat[,c(1,5,10)], 2, table)
#> Item_1.RS Item_5.RS Item_10.RS
#> 0 164 182 73
#> 1 57 37 17
#> 2 21 23 8
#> 3 23 12 23
#> 4 35 46 179
# \donttest{
# slopes all positive now
mod2 <- mirt(revdat)
#>
Iteration: 1, Log-Lik: -7198.364, Max-Change: 1.41175
Iteration: 2, Log-Lik: -6841.463, Max-Change: 2.02941
Iteration: 3, Log-Lik: -6762.380, Max-Change: 0.90238
Iteration: 4, Log-Lik: -6740.393, Max-Change: 0.58760
Iteration: 5, Log-Lik: -6733.319, Max-Change: 0.45932
Iteration: 6, Log-Lik: -6730.644, Max-Change: 0.33337
Iteration: 7, Log-Lik: -6728.083, Max-Change: 0.21976
Iteration: 8, Log-Lik: -6727.348, Max-Change: 0.26777
Iteration: 9, Log-Lik: -6727.076, Max-Change: 0.19258
Iteration: 10, Log-Lik: -6726.812, Max-Change: 0.05129
Iteration: 11, Log-Lik: -6726.773, Max-Change: 0.04236
Iteration: 12, Log-Lik: -6726.754, Max-Change: 0.02718
Iteration: 13, Log-Lik: -6726.728, Max-Change: 0.00540
Iteration: 14, Log-Lik: -6726.724, Max-Change: 0.00281
Iteration: 15, Log-Lik: -6726.722, Max-Change: 0.00454
Iteration: 16, Log-Lik: -6726.718, Max-Change: 0.00108
Iteration: 17, Log-Lik: -6726.718, Max-Change: 0.00757
Iteration: 18, Log-Lik: -6726.716, Max-Change: 0.00049
Iteration: 19, Log-Lik: -6726.716, Max-Change: 0.00656
Iteration: 20, Log-Lik: -6726.714, Max-Change: 0.00121
Iteration: 21, Log-Lik: -6726.714, Max-Change: 0.00597
Iteration: 22, Log-Lik: -6726.712, Max-Change: 0.00044
Iteration: 23, Log-Lik: -6726.712, Max-Change: 0.00013
Iteration: 24, Log-Lik: -6726.712, Max-Change: 0.00028
Iteration: 25, Log-Lik: -6726.711, Max-Change: 0.00010
Iteration: 26, Log-Lik: -6726.711, Max-Change: 0.00028
Iteration: 27, Log-Lik: -6726.711, Max-Change: 0.00035
Iteration: 28, Log-Lik: -6726.711, Max-Change: 0.00017
Iteration: 29, Log-Lik: -6726.711, Max-Change: 0.00028
Iteration: 30, Log-Lik: -6726.711, Max-Change: 0.00011
Iteration: 31, Log-Lik: -6726.711, Max-Change: 0.00043
Iteration: 32, Log-Lik: -6726.711, Max-Change: 0.00023
Iteration: 33, Log-Lik: -6726.711, Max-Change: 0.00027
Iteration: 34, Log-Lik: -6726.711, Max-Change: 0.00041
Iteration: 35, Log-Lik: -6726.711, Max-Change: 0.00011
Iteration: 36, Log-Lik: -6726.711, Max-Change: 0.00027
Iteration: 37, Log-Lik: -6726.711, Max-Change: 0.00043
Iteration: 38, Log-Lik: -6726.711, Max-Change: 0.00027
Iteration: 39, Log-Lik: -6726.711, Max-Change: 0.00029
Iteration: 40, Log-Lik: -6726.711, Max-Change: 0.00025
Iteration: 41, Log-Lik: -6726.711, Max-Change: 0.00034
Iteration: 42, Log-Lik: -6726.711, Max-Change: 0.00016
Iteration: 43, Log-Lik: -6726.711, Max-Change: 0.00012
Iteration: 44, Log-Lik: -6726.711, Max-Change: 0.00027
Iteration: 45, Log-Lik: -6726.710, Max-Change: 0.00036
Iteration: 46, Log-Lik: -6726.710, Max-Change: 0.00018
Iteration: 47, Log-Lik: -6726.710, Max-Change: 0.00027
Iteration: 48, Log-Lik: -6726.710, Max-Change: 0.00011
Iteration: 49, Log-Lik: -6726.710, Max-Change: 0.00041
Iteration: 50, Log-Lik: -6726.710, Max-Change: 0.00026
Iteration: 51, Log-Lik: -6726.710, Max-Change: 0.00028
Iteration: 52, Log-Lik: -6726.710, Max-Change: 0.00024
Iteration: 53, Log-Lik: -6726.710, Max-Change: 0.00031
Iteration: 54, Log-Lik: -6726.710, Max-Change: 0.00015
Iteration: 55, Log-Lik: -6726.710, Max-Change: 0.00012
Iteration: 56, Log-Lik: -6726.710, Max-Change: 0.00026
Iteration: 57, Log-Lik: -6726.710, Max-Change: 0.00034
Iteration: 58, Log-Lik: -6726.710, Max-Change: 0.00018
Iteration: 59, Log-Lik: -6726.710, Max-Change: 0.00026
Iteration: 60, Log-Lik: -6726.710, Max-Change: 0.00010
Iteration: 61, Log-Lik: -6726.710, Max-Change: 0.00008
coef(mod2, simplify=TRUE)$items
#> a1 d1 d2 d3 d4
#> Item_1.RS 1.1182344 -0.2276345 -1.2585807 -1.73084542 -2.4277274
#> Item_2 7.8235698 0.6524445 0.1261525 -0.38602040 -0.5201021
#> Item_3 0.2632045 1.0232150 0.4045218 0.05926287 -0.7411866
#> Item_4 4.7761244 0.4286916 0.1068883 -0.38632467 -0.7986421
#> Item_5.RS 0.2510760 -0.4370332 -1.0052289 -1.44369998 -1.7266003
#> Item_6 0.2827955 0.6992590 0.3403556 -0.10926752 -0.5313230
#> Item_7 0.5487299 1.1697835 0.6116507 -0.17771403 -0.8418820
#> Item_8 0.2591667 0.1650238 -0.5089098 -0.97301720 -1.6297131
#> Item_9 4.7606067 0.4861014 -0.1609173 -1.24853436 -2.0280968
#> Item_10.RS 3.4634667 2.6802518 2.0073365 1.71669942 0.9620659
#> Item_11 4.5179204 1.3616041 0.8328228 0.39869560 -0.6378781
#> Item_12 0.6132529 2.0293027 1.2362469 0.35825699 -0.2310679
#> Item_13 0.9990765 -0.4129181 -1.0614558 -1.72125802 -2.4426937
#> Item_14 2.9461576 1.4563648 0.6611071 0.16754860 -0.1114038
#> Item_15 3.0649176 1.0485816 0.6646115 0.23628993 -0.8793653
#> Item_16 2.1617219 1.2479992 0.8848828 -0.61395520 -1.1322718
#> Item_17 0.9038909 1.2483193 0.3872151 -0.44980307 -0.9180791
#> Item_18 0.2866437 2.4306786 1.8426855 1.30858320 0.9146260
#> Item_19 1.3159613 1.6354544 0.9078839 -0.07718882 -0.6315840
#> Item_20 1.0310717 2.3228460 1.7538611 0.90199444 0.6427890
plot(mod2, type = 'itemscore')
# }
# use different empirical scoring information due to options not used
# 0 score not observed for item 1, though should have been rescored to a 4
dat[dat[,1] == 0, 1] <- 1
table(dat[,1])
#>
#> 1 2 3 4
#> 58 21 57 164
# 4 score not observed for item 5, though should have been rescored to a 0
dat[dat[,5] == 4, 5] <- 3
table(dat[,5])
#>
#> 0 1 2 3
#> 46 12 23 219
# specify theoretical scoring values in the range list
revdat2 <- reverse.score(dat, c('Item_1', 'Item_5', 'Item_10'),
range = list(Item_1 = c(0,4), Item_5 = c(0,4)))
head(revdat2)
#> Item_1.RS Item_2 Item_3 Item_4 Item_5.RS Item_6 Item_7 Item_8 Item_9
#> [1,] 1 4 2 4 1 4 2 4 4
#> [2,] 1 4 1 2 1 0 4 0 4
#> [3,] 2 0 4 0 1 0 4 0 0
#> [4,] 4 4 3 4 1 4 4 4 4
#> [5,] 4 0 0 1 1 4 0 1 1
#> [6,] 4 0 0 0 1 1 3 0 0
#> Item_10.RS Item_11 Item_12 Item_13 Item_14 Item_15 Item_16 Item_17 Item_18
#> [1,] 4 4 4 0 4 4 4 1 4
#> [2,] 4 4 0 2 4 4 2 0 1
#> [3,] 4 4 2 0 0 0 2 0 1
#> [4,] 4 4 4 3 4 4 4 4 4
#> [5,] 3 4 2 1 0 4 2 2 4
#> [6,] 4 0 4 3 3 1 1 0 1
#> Item_19 Item_20
#> [1,] 1 4
#> [2,] 4 4
#> [3,] 4 3
#> [4,] 4 4
#> [5,] 3 0
#> [6,] 2 4
table(dat[,1])
#>
#> 1 2 3 4
#> 58 21 57 164
table(revdat2[,1])
#>
#> 1 2 3 4
#> 164 57 21 58
table(dat[,5])
#>
#> 0 1 2 3
#> 46 12 23 219
table(revdat2[,5])
#>
#> 1 2 3 4
#> 219 23 12 46