View source: R/item.correlations.R
item.correlations | R Documentation |
Calculate item correlations, item-rest correlations and correlations between items and exogenous variables
item.correlations(do=NULL,resp=NULL,items=1:do$recursive.structure[1],exo=(do$recursive.structure[1]+1):do$recursive.structure[2])
do |
an object of class |
resp |
A data.frame or matrix of recoded data (only used if |
items |
A vector of columns from the recoded data to include as items in the analysis or a character vector of variable labels |
do.testlets |
Bolean. If TRUE, testlets are combined to superitems. |
do.split |
Bolean. If TRUE, items coded as split are split. |
exo |
A vector of columns from the recoded data to include as exogenous variables in the analysis or a character vector of variable labels |
max.name.length |
Maximum length of item names (to be printed in tables) |
accept.na |
A boolean. Include cases with missing values in responses |
verbose |
Print results |
extra.verbose |
Print warnings in PDF and HTML-output |
caption.items |
Caption of items table |
caption.rest |
Caption of item-rest table |
caption.exo |
Caption of exogenous variables table |
First step in item screening: Analysis of consistency (Positive correlations)
Y_i
and Y_j
are positively correlated for all pairs of items
Y_a
is positively monotonically related to the rest-score R_a
and all subscores S_B
where Y_a
\not\in
B
If X
is positively related to \theta
, then X
will also be positively related to S
, to all subscores, S_A
, and all item responses Y_i
Returns a list of correlations
Jeppe Bundsgaard jebu@edu.au.dk
Kreiner, S. & Christensen, K.B. (2011). Item Screening in Graphical Loglinear Rasch Models. Psychometrika, vol. 76, no. 2, pp. 228-256. DOI: 10.1007/s11336-9203-Y
library(iarm)
do<-digram.object(project = "desc2",data = desc2,variables = c(5:14,2:4,1),recursive.structure = c(10,13))
item.correlations(do)
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