psych (version 1.6.12)

score.multiple.choice: Score multiple choice items and provide basic test statistics

Description

Ability tests are typically multiple choice with one right answer. score.multiple.choice takes a scoring key and a data matrix (or data.frame) and finds total or average number right for each participant. Basic test statistics (alpha, average r, item means, item-whole correlations) are also reported.

Usage

score.multiple.choice(key, data, score = TRUE, totals = FALSE, ilabels = NULL, missing = TRUE, impute = "median", digits = 2,short=TRUE,skew=FALSE)

Arguments

key
A vector of the correct item alternatives
data
a matrix or data frame of items to be scored.
score
score=FALSE, just convert to right (1) or wrong (0). score=TRUE, find the totals or average scores and do item analysis
totals
total=FALSE: find the average number correct total=TRUE: find the total number correct
ilabels
item labels
missing
missing=TRUE: missing values are replaced with means or medians missing=FALSE missing values are not scored
impute
impute="median", replace missing items with the median score impute="mean": replace missing values with the item mean
digits
How many digits of output
short
short=TRUE, just report the item statistics, short=FALSE, report item statistics and subject scores as well
skew
Should the skews and kurtosi of the raw data be reported? Defaults to FALSE because what is the meaning of skew for a multiple choice item?

Value

Details

Basically combines score.items with a conversion from multiple choice to right/wrong.

The item-whole correlation is inflated because of item overlap.

The example data set is taken from the Synthetic Aperture Personality Assessment personality and ability test at http://test.personality-project.org.

See Also

score.items, omega

Examples

Run this code
data(iqitems)
iq.keys <- c(4,4,4, 6,6,3,4,4,  5,2,2,4,  3,2,6,7)
score.multiple.choice(iq.keys,iqitems)
#just convert the items to true or false 
iq.tf <- score.multiple.choice(iq.keys,iqitems,score=FALSE)
describe(iq.tf)  #compare to previous results

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