## Setting up item names for fake data
itemNames <- c('EF1', 'EF2', 'EF3', 'EF4', 'EF5', 'EF6', 'EF7',
'EF8', 'EF9', 'EF10', 'EF11', 'EF12', 'EF13', 'EF14', 'EF15', 'EF16',
'EF17', 'EF18', 'EF19', 'EF20')
set.seed(6375309)
## Generating random item responses for 8 fake respondents
exampleDat <- t(replicate(8, sample(0:4, size = length(itemNames), replace = TRUE)))
## Making half of respondents missing about 10% of items,
## half missing about 50%.
miss10 <- t(replicate(4, sample(c(0, 9), prob = c(0.9, 0.1),
size = length(itemNames), replace = TRUE)))
miss50 <- t(replicate(4, sample(c(0, 9), prob = c(0.5, 0.5),
size = length(itemNames), replace = TRUE)))
missMtx <- rbind(miss10, miss50)
## Using 9 as the code for missing responses
exampleDat[missMtx == 9] <- 9
exampleDat <- as.data.frame(cbind(ID = paste0('ID', 1:8),
as.data.frame(exampleDat)))
names(exampleDat) <- c('ID', itemNames)
## Returns a data frame with scale scores and with original items untouched
scoredDat <- scoreFACT_EF(exampleDat)
names(scoredDat)
scoredDat
## Returns data frame with scale scores, with the appropriate items
## reverse scored, and with item values of 8 and 9 replaced with NA.
## Also illustrates the effect of setting keepNvalid = TRUE.
scoredDat <- scoreFACT_EF(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
names(scoredDat)
## Descriptives of scored scales
summary(scoredDat[, c('EFS')])
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