psychometric (version 2.2)

item.exam: Item Analysis

Description

Conducts an item level analysis. Provides item-total correlations, Standard deviation in items, difficulty, discrimination, and reliability and validity indices.

Usage

item.exam(x, y = NULL, discrim = FALSE)

Arguments

x

matrix or data.frame of items

y

Criterion variable

discrim

Whether or not the discrimination of item is to be computed

Value

A table with rows representing each item and columns repsenting :

Sample.SD

Standard deviation of the item

Item.total

Correlation of the item with the total test score

Item.Tot.woi

Correlation of item with total test score (scored without item)

Difficulty

Mean of the item (p)

Discrimination

Discrimination of the item (u-l)/n

Item.Criterion

Correlation of the item with the Criterion (y)

Item.Reliab

Item reliability index

Item.Rel.woi

Item reliability index (scored without item)

Item.Validity

Item validity index

Warning

Be cautious when using data with missing values or small data sets. Listwise deletion is employed for both X (matrix of items to be analyzed) and Y (criterion). When the datasets are small, such listwise deletion can make a big impact. Further, since the upper and lower groups are defined as the upper and lower 1/3, the stability of this division of examinees is greatly increased with larger N.

Details

If someone is interested in examining the items of a dataset contained in data.frame x, and the criterion measure is also in data.frame x, one must parse the matrix or data.frame and specify each part into the function. See example below. Otherwise, one must be sure that x and y are properly merged/matched. If one is not interested in assessing item-criterion relationships, simply leave out that portion of the call. The function does not check whether the items are dichotomously coded, this is user specified. As such, one can specify that items are binary when in fact they are not. This has the effect of computing the discrimination index for continuously coded variables. The difficulty index (p) is simply the mean of the item. When dichotomously coded, p reflects the proportion endorsing the item. However, when continuously coded, p has a different interpretation.

References

Allen, M. J. & Yen, W. M. (1979). Introduction to measurement theory. Monterey, CA: Brooks/Cole.

See Also

alpha, discrim

Examples

data(TestScores)
# Look at the data
TestScores
# Examine the items
item.exam(TestScores[,1:10], y = TestScores[,11], discrim=TRUE)