DexterMST

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)
# start a project
db = create_mst_project(":memory:")

items = data.frame(item_id=sprintf("item%02i",1:70), item_score=1, delta=sort(runif(70,-1,1)))

design = data.frame(item_id=sprintf("item%02i",1:70),
                    module_id=rep(c('M4','M2','M5','M1','M6','M3', 'M7'),each=10))

routing_rules = routing_rules = mst_rules(
 `124` = M1[0:5] --+ M2[0:10] --+ M4, 
 `125` = M1[0:5] --+ M2[11:15] --+ M5,
 `136` = M1[6:10] --+ M3[6:15] --+ M6,
 `137` = M1[6:10] --+ M3[16:20] --+ M7)


scoring_rules = data.frame(
  item_id = rep(items$item_id,2), 
  item_score= rep(0:1,each=nrow(items)),
  response= rep(0:1,each=nrow(items))) # dummy respons
  

db = create_mst_project(":memory:")
add_scoring_rules_mst(db, scoring_rules)

create_mst_test(db,
                test_design = design,
                routing_rules = routing_rules,
                test_id = 'sim_test',
                routing = "all")

We can now plot the design

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

We now simulate data:

theta = rnorm(3000)

dat = sim_mst(items, theta, design, routing_rules,'all')
dat$test_id='sim_test'
dat$response=dat$item_score

add_response_data_mst(db, dat)

# IRT, extended nominal response model
f = fit_enorm_mst(db)

head(f)

item_id	item_score	beta	SE_beta
item01	1	-1.0967010	0.0629430
item02	1	-0.9396378	0.0626093
item03	1	-0.9362441	0.0626050
item04	1	-0.9226755	0.0625888
item05	1	-0.7974781	0.0625298
item06	1	-0.8549515	0.0625365

# ability estimates per person
rsp_data = get_responses_mst(db)
abl = ability(rsp_data, parms = f)
head(abl)

booklet_id	person_id	booklet_score	theta
136	1	14	0.1155725
136	10	20	0.9510407
136	100	15	0.2505641
124	1000	11	-1.0122974
125	1001	19	0.3546790
124	1002	15	-0.4465699

# 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	booklet_score	PV1	PV2	PV3	PV4	PV5
136	1	14	-0.3310002	0.1372317	-0.1772800	-0.5183684	-0.0290442
136	10	20	0.8863248	0.8113941	0.6248103	0.8908880	1.0080691
136	100	15	0.8551317	0.3975271	0.4908577	-0.1864416	-0.0545868
136	1003	16	0.6045560	0.1657788	0.5029436	0.3651413	0.5977085
136	1006	24	1.5130258	1.4255989	0.7971183	1.1151426	1.3334579
136	1008	15	0.1326590	-0.2617173	0.0628930	0.6675144	0.2771222

Contributing

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