klausur.mufo: Evaluate multiple choice tests with several test forms

Description

This function can be used to evaluate tests that have several test forms. Please be aware that its results only make sense if each test form uses the same items, only in a different order.

Usage

klausur.mufo(
  data,
  marks = NULL,
  mark.labels = NULL,
  items = NULL,
  wght = NULL,
  score = "solved",
  matn = NULL,
  na.rm = TRUE,
  cronbach = TRUE,
  item.analysis = TRUE
)

Arguments

data

An object of class klausuR.answ-class.

marks

A vector assigning marks to points achieved (see details). Alternatively, set it to "suggest" to let klausur.gen.marks calculate suggestions under the assumption of normal distribution. If NULL, this value must be set in the data object.

mark.labels

If marks="suggest", use these as the marks you want to give.

items

Indices of a subset of variables in answ to be taken as items.

wght

A vector with weights for each item (named also according to Item###). If NULL, the value from the data object will be used.

score

Specify the scoring policy, must be one of "solved" (default), "partial", "liberal", "NR", "ET", "NRET", or "NRET+".

matn

A matriculation number of a subject, to receive detailed results for that subject.

na.rm

Logical, whether cases with NAs should be ignored in data. Defaults to TRUE.

cronbach

Logical. If TRUE, Cronbach's alpha will be calculated.

item.analysis

Logical. If TRUE, some usual item statistics like difficulty and discriminatory power will be calculated. If cronbach is TRUE, too, it will include the alpha values if each item was deleted.

Value

An object of class klausuR.mult-class with the following slots.

forms

A character vector naming all test forms

results.part

A list of objects of class klausuR, holding all partial results

results.glob

An object of class klausuR with the global results

Not all slots are shown by default (refer to show).

Details

Firstly, klausur.mufo will compute partial results for each parallel form, and in the end combine these to global results. Cronbach alpha and item analysis will be calculated for all subjects accordingly, therefore the test items of all tests will be re-ordered to fit the order of the first given test form (this does not apply to the partial results).

The parameters are mostly the same as those for klausur. However, in the data object the slot corr must also contain corr.key, to communicate the order of items in each test form, and the slot id needs one additional variable called Form.

An example: You have prepared a test in two different parallel forms "A" an "B", So in addition to the variables in data@id you need to create a variable called Form, to document which test subject was given which test form. Since form "B" holds the same items as form "A", only in a different order, we only need to define these positions and we're done. Therefore corr.key must be a matrix or data.frame, again with a column called "Form", one column for each item, and one row of data for each test form. That is, you'd need one row for test form "A" and one for test form "B", giving an index for each item where it is placed in the form. For "A" this is simply ascending numbers from 1 to how many questions you asked, but for row "B" each number indicates at which position an item of "A" is to be found. See the example below.

Examples

Run this code

# NOT RUN {
# this will create the data.frame "antworten.mufo"
# and the matrix "corr.key"
data(antworten.mufo)

# vector with correct answers:
richtig <- c(Item01=3, Item02=2, Item03=2, Item04=2, Item05=4,
 Item06=3, Item07=4, Item08=1, Item09=2, Item10=2, Item11=4,
 Item12=4, Item13=2, Item14=3, Item15=2, Item16=3, Item17=4,
 Item18=4, Item19=3, Item20=5, Item21=3, Item22=3, Item23=1,
 Item24=3, Item25=1, Item26=3, Item27=5, Item28=3, Item29=4,
 Item30=4, Item31=13, Item32=234)

# vector with assignement of marks:
notenschluessel <- c()
# scheme of assignments: marks[points_from:to] <- mark
notenschluessel[0:12]  <- 5.0
notenschluessel[13:15] <- 4.0
notenschluessel[16:18] <- 3.7
notenschluessel[19:20] <- 3.3
notenschluessel[21]    <- 3.0
notenschluessel[22]    <- 2.7
notenschluessel[23]    <- 2.3
notenschluessel[24]    <- 2.0
notenschluessel[25:26] <- 1.7
notenschluessel[27:29] <- 1.3
notenschluessel[30:32] <- 1.0

# now combine all test data into one object of class klausur.answ
mufo.data.obj <- klausur.data(answ=antworten.mufo, corr=richtig, marks=notenschluessel,
      corr.key=corr.key)
# expect some warnings here, because some items have no variance
# in their subtest results, hence item analysis fails on them
klsr.mufo.obj <- klausur.mufo(mufo.data.obj)
# }