Computing correlation indices for subscores and the total score.

This function computes Cronbach's Alpha and Stratified Alpha (Cronbach et al., 1965) <doi: 10.1177/001316446502500201>. Disattenuated correlations are also provided.

Usage

subscore.corr(test.data)

Arguments

test.data: A list that contains item responses of all subtests and the entire test, which can be obtained using function ’data.prep’.

Value

summary: Summary of obtained subscores (e.g., mean, sd).
correlation: Correlation indices as indicated above.

References

Cronbach, L., Schonenman, P., & McKie, D. (1965). "Alpha coefficients for stratified-parallel tests." Educational and Psychological Measurement, 25, 291-282. doi: 10.1177/001316446502500201.

Examples

# Transferring scored response data to the required list format
test.data<-data.prep(scored.data,c(3,15,15,20),
                     c("Algebra","Geometry","Measurement", "Math"))
  
#Estimate true subscores using Haberman's method based on observed subscores     
subscore.corr(test.data) 
#> $Correlation
#>               Algebra  Geometry Measurement      Math
#> Algebra     1.0000000 0.8143313   0.8336757 0.9357667
#> Geometry    0.8143313 1.0000000   0.8198970 0.9295419
#> Measurement 0.8336757 0.8198970   1.0000000 0.9507132
#> Math        0.9357667 0.9295419   0.9507132 1.0000000
#> 
#> $Disattenuated.correlation
#>               Algebra  Geometry Measurement
#> Algebra     0.8169114 1.0081315   1.0096427
#> Geometry    0.8143313 0.7987169   1.0042016
#> Measurement 0.8336757 0.8198970   0.8346106
#> 
#> $Reliabilities
#>                 alpha stratefied.alpha
#> Algebra     0.8169114               NA
#> Geometry    0.7987169               NA
#> Measurement 0.8346106               NA
#> Math        0.9311719         0.931094
#> 
       
subscore.s(test.data)$summary
#>             Orig.mean  Orig.sd subscore.s.mean subscore.s.sd
#> Algebra      9.773333 3.662153        9.773333      2.991654
#> Geometry     9.100000 3.626432        9.100000      2.896492
#> Measurement 13.060000 4.539506       13.060000      3.788720
subscore.s(test.data)$correlation
#> NULL