dualScale (version 1.0.0)

ds_mcf: Forced multiple choice data analysis

Description

Forced multiple choice data analysis

Usage

ds_mcf(input, crit, solutions = NULL, mode = c("rad", "act"))

Value

call

Call with all of the specified arguments are specified by their full names

orig_data

Initial data

crit_item

The criterion item for forced classification

item_op_lbl

Item options labels

sub_lbl

Subjects options labels

solutions_mcf

Maximum possible solutions for forced multiple choice

solutions_mc

Maximum possible solutions for multiple choice

info_x

Distribution of component information according to output

out_x

Results obtained according to output

item_stat_x

Item statistics according to output (Not type C)

rij_x

Inter item correlation according to output (Not type C)

proj_opt_x

Projected option weights according to output

proj_sub_x

Projected subject scores according to output

norm_opt_x

Normed option weights according to output

norm_sub_x

Normed subject scores according to output

match_missmatch

Match-mismatch tables

predict

Percentage of correct classification

comp_cont

Component contamination

tot_cont

Total contamination

Arguments

input

A data set with valid data

crit

Used to determine a criterion item for forced classification analysis

solutions

Optional argument. A number of intended solutions

mode

Correction mode to incorrect data.

Details

There are three types of outputs: Forced classification of the criterion item (type A); dual scaling of non-criterion items by ignoring the criterion item (type B); dual scaling of non-criterion items after eliminating the influence of the criterion item (type C). These three types correspond to, respectively, dual scaling of data projected onto the subspace of the criterion item, dual scaling of non-criterion items, and dual scaling of data in the complementary space of the criterion item.

See Also

ds_mc_check()

Examples

ds_mcf(singaporean, crit = 1)