CML Calibration of Multi Stage Tests

Conditional Maximum Likelihood Calibration and data management of multistage tests. Functions for calibration of the Extended Nominal Response and the Interaction models, DIF and profile analysis. See Robert J. Zwitser and Gunter Maris (2015).


DexterMST is an R package acting as a companion to dexter and adding facilities to manage and analyze data from multistage tests (MST). It includes functions for importing and managing test data, assessing and improving the quality of data through basic test and item analysis, and fitting an IRT model, all adapted to the peculiarities of MST designs. DexterMST typically works with project database files saved on disk.

Installation

install.packages('dexterMST')

If you encounter a bug, please post a minimal reproducible example on github. We post news and examples on a blog, it's also the place for general questions.

Example

Here is an example for a simple two-stage test.

library(dexterMST)
library(dplyr)
db = create_mst_project(":memory:")
 
# define dummy item scoring rules (i.e. response==score)
scoring_rules = data.frame(item_id = rep(sprintf("item%02.0f",1:50), each=2),
                            response = rep(0:1,times=50),
                            item_score = rep(0:1,times=50))
 
add_scoring_rules_mst(db, scoring_rules)
 
# define routing rules
routing_rules = mst_rules(
  easy = Mod_1[0:5] --+ Mod_2, 
  hard = Mod_1[6:10] --+ Mod_3)
 
# define a module design (i.e., specifify which items belong to which modules)
design = data.frame(module_id = rep(c('Mod_2','Mod_1','Mod_3'), times=c(20,10,20)),
                   item_id = paste0("item",sprintf("%02.0f",1:50)),
                   item_position = c(1:20,1:10,1:20))
 
# create/define an mst test
create_mst_test(db,
                test_design = design,
                routing_rules = routing_rules,
                test_id = 'ZwitserMaris')

We can now plot the design

# plot test designs for all tests in the project
design_plot(db)

We now simulate data:

sim_RM = function(theta,delta)
{
  nP=length(theta)
  dat=matrix(0,nP,length(delta))
  for (i in 1:length(delta)) dat[,i]=1*(rlogis(nP,0,1)<=(theta-delta[i]))
  return(dat)
}
a = rep(1,50)
delta = c(runif(20,-2.3,0),runif(10,-0.6,2),runif(20,1.2,2.4)) # M2, M1, M3
b=exp(-delta)
c = rep(0,50)
nP = 10000
# simulate theta from a mixture of two normals
grp = sample(2, nP, replace = TRUE, prob = c(.6,.4))
theta = rnorm(nP, mean = c(0,1)[grp], sd = c(1.5,0.5)[grp])
 
data = data.frame(sim_RM(theta, delta))
colnames(data) = sprintf("item%02.0f",1:50)
 
# add person id to the data
data$person_id = 1:nrow(data)
 
# extract two booklets from the complete data, based on the sum score on the first module
bk1 = data[rowSums(data[,21:30])<=5,] %>% select(person_id, item01:item30)
bk2 = data[rowSums(data[,21:30])>5,] %>% select(person_id, item21:item30, item31:item50)
 
# add response data to the project
add_booklet_mst(db, bk1, test_id = 'ZwitserMaris', booklet_id = 'easy')
add_booklet_mst(db, bk2, test_id = 'ZwitserMaris', booklet_id = 'hard')
# IRT, extended nominal response model
f = fit_enorm_mst(db)
 
head(f)
item_id item_score beta SE_beta
item01 1 -0.9378481 0.0306780
item02 1 -1.8104684 0.0335670
item03 1 -1.4555312 0.0320611
item04 1 -1.4145089 0.0319168
item05 1 -2.1264352 0.0353192
item06 1 -1.7498811 0.0332764
# ability estimates per person
rsp_data = get_responses_mst(db)
abl = ability(rsp_data, parms = f)
head(abl)
booklet_id person_id sumScore theta
easy 1 9 -1.0288607
easy 10 23 1.5102358
easy 100 23 1.5102358
easy 1000 7 -1.4308794
easy 10000 16 0.1809514
easy 1001 13 -0.3232153
# ability estimates without item Item01
abl2 = ability(rsp_data, parms = f, item_id != "item01")
 
# plausible values
pv = plausible_values(rsp_data, parms = f, nPV = 5)
head(pv)
booklet_id person_id sumScore PV1 PV2 PV3 PV4 PV5
easy 1 9 -0.4409709 -0.2709257 -0.8330791 -1.0715047 -0.4009712
easy 10 23 1.5483993 1.0318135 1.3107129 0.4420585 1.6680941
easy 100 23 1.7134969 1.5736628 1.5000658 1.8495170 0.3622951
easy 1000 7 -1.0344610 -1.7829133 -1.3854931 -0.7899523 -1.4050426
easy 10000 16 0.0497648 0.1811482 -0.1966962 0.5819318 0.4640787
easy 1001 13 -0.0745779 -0.8048086 0.2805427 0.4067189 -0.2027418

Contributing

Contributions are welcome but please check with us first about what you would like to contribute.

News

dexterMST 0.1.1

  • This release mostly to prepare release of new dexter version and maintain competability

dexterMST 0.1.0

  • You may want to take a look at the companion packages, dexter and dextergui

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("dexterMST")

0.1.2 by Timo Bechger, 3 months ago


http://dexterities.netlify.com


Report a bug at https://github.com/jessekps/dexter/issues


Browse source code at https://github.com/cran/dexterMST


Authors: Timo Bechger [aut, cre] , Jesse Koops [aut] , Ivailo Partchev [aut] , Gunter Maris [aut] , Robert Zwitser [ctb]


Documentation:   PDF Manual  


Task views: Psychometric Models and Methods


GPL (>= 2) license


Imports Rcpp, dexter, dplyr, RSQLite, rlang, dbplyr, fastmatch, igraph, tidyr, tibble, DBI, crayon, graphics, methods, stats, utils

Suggests knitr, testthat, mirt, ggplot2, Cairo

Linking to Rcpp


Suggested by tmt.


See at CRAN