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Generate Item Report for Non-Binary Test Data

Source: R/02_QitemFunctions.R
ItemReport.Rd

Calculates item-level statistics for non-binary test data, including response rates, basic descriptive statistics, and item-total correlations.

Usage

ItemReport(U, na = NULL, Z = NULL, w = NULL)

Arguments

U

Either an object of class "exametrika" or raw data. When raw data is given, it is converted to the exametrika class with the dataFormat function.

na

Values to be treated as missing values

Z

Missing indicator matrix of type matrix or data.frame. Values of 1 indicate observed responses, while 0 indicates missing data.

w

Item weight vector specifying the relative importance of each item

Value

An object of class "exametrika" and "QitemStatistics" containing:

ItemLabel

Labels identifying each item

Obs

Number of valid responses for each item

ObsRatio

Proportion of valid responses for each item (range: 0-1)

ItemMean

Mean score of each item

ItemSD

Standard deviation of each item score

ItemCORR

Item-total correlation coefficients - correlation between item scores and total test scores

ItemCORR_R

Corrected item-total correlation coefficients - correlation between item scores and total test scores excluding the target item

Details

This function is intended for non-binary (ordinal or rated) response data. It provides detailed statistics for each item in the test, focusing on response patterns and the relationship between individual items and overall test performance. If binary data is provided, an error message will be displayed.

Examples

# \donttest{
# Generate item report for sample ordinal data
item_stats <- ItemReport(J15S3810)

# View first few rows of the item report
head(item_stats)
#> $ItemLabel
#>  [1] "V1"  "V2"  "V3"  "V4"  "V5"  "V6"  "V7"  "V8"  "V9"  "V10" "V11" "V12"
#> [13] "V13" "V14" "V15"
#> 
#> $Obs
#>   V1   V2   V3   V4   V5   V6   V7   V8   V9  V10  V11  V12  V13  V14  V15 
#> 3810 3810 3810 2046 2046 2046  985  985  985 1513 1513 1513 3076 3076 3076 
#> 
#> $ObsRatio
#>        V1        V2        V3        V4        V5        V6        V7        V8 
#> 1.0000000 1.0000000 1.0000000 0.5370079 0.5370079 0.5370079 0.2585302 0.2585302 
#>        V9       V10       V11       V12       V13       V14       V15 
#> 0.2585302 0.3971129 0.3971129 0.3971129 0.8073491 0.8073491 0.8073491 
#> 
#> $ItemMean
#>        V1        V2        V3        V4        V5        V6        V7        V8 
#> 1.8230971 1.8149606 2.0582677 1.5948192 1.6544477 1.4745846 1.3908629 0.7096447 
#>        V9       V10       V11       V12       V13       V14       V15 
#> 1.3461929 2.0363516 1.4183741 1.8460013 1.4801691 1.7282185 1.4548114 
#> 
#> $ItemSD
#>        V1        V2        V3        V4        V5        V6        V7        V8 
#> 0.9327474 0.8213917 0.8552853 0.9288219 0.9175956 1.1054337 0.8188873 0.7927068 
#>        V9       V10       V11       V12       V13       V14       V15 
#> 0.9095925 0.8270863 0.9661041 0.8223249 0.9284883 0.8756923 0.7570043 
#> 
#> $ItemCORR
#>        V1        V2        V3        V4        V5        V6        V7        V8 
#> 0.6301239 0.5336564 0.4931032 0.6336335 0.5949090 0.7091589 0.5541948 0.4540134 
#>        V9       V10       V11       V12       V13       V14       V15 
#> 0.5221033 0.4855674 0.5927007 0.3602861 0.6164254 0.3534315 0.5262670 
#> 

# Example with rated data including custom missing value indicator
item_stats2 <- ItemReport(J35S5000, na = -99)
# }