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	-1.0863339	0.0626345
item02	1	-0.9418913	0.0623325
item03	1	-0.9251972	0.0623113
item04	1	-0.8020044	0.0622434
item05	1	-0.9318730	0.0623195
item06	1	-0.7521299	0.0622601

# 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	19	0.8404993
125	10	19	0.2563194
124	100	9	-1.3259574
136	1000	19	0.8404993
136	1001	14	0.1491514
125	1002	18	0.1129540

# 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	19	0.3839768	0.8766796	1.2262293	0.8659529	1.3278310
136	1000	19	0.4315371	0.4050729	1.0509408	0.5545282	0.9193252
136	1001	14	0.2054455	0.1277102	0.4737698	0.2015225	0.3163110
136	1006	16	0.3514111	-0.0298913	0.6811140	0.0550563	0.2982861
136	1008	14	0.0542015	0.0259179	0.4488831	0.4268412	0.6982703
136	1009	14	0.0898933	0.4605046	0.3446569	0.0130914	-0.1554303

Contributing

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