maat (version 1.1.0)

maat: Simulate multi-stage multi-administration adaptive test

Description

maat is the main function for simulating a multi-stage multi-administration adaptive test.

Usage

maat(
  examinee_list = examinee_list,
  assessment_structure = NULL,
  module_list = NULL,
  config = NULL,
  cut_scores = NULL,
  overlap_control_policy = NULL,
  transition_policy = "CI",
  combine_policy = "conditional",
  transition_CI_alpha = NULL,
  transition_percentile_lower = NULL,
  transition_percentile_upper = NULL,
  initial_theta_list = NULL,
  prior_mean_policy = "mean_difficulty",
  prior_mean_user = NULL,
  prior_sd = 1,
  verbose = TRUE
)

Arguments

examinee_list

an examinee list from simExaminees.

assessment_structure

a '>assessment_structure object.

module_list

a module list from loadModules.

config

a '>config_Shadow object. Also accepts a list of '>config_Shadow objects to use separate configurations for each module. Must be from 'TestDesign' 1.3.3 or newer, and its exclude_policy$method slot must be SOFT.

cut_scores

a named list containing cut scores to be used in each grade. Each element must be named in the form G?, where ? is a number.

overlap_control_policy

overlap control is performed by excluding administered items from being administered again within the same examinee.

• all performs overlap control at all module positions.

• within_test performs overlap control only within each test.

• none does not perform overlap control.

transition_policy

• CI uses the confidence interval to perform routing.

• pool_difficulty_percentile uses item difficulty percentiles of all items in the item_pool argument to perform routing.

• pool_difficulty_percentile_exclude_administered uses item difficulty percentiles of all items in the item_pool argument to perform routing, excluding all previous items administered to the examinee.

• on_grade does not permit any transition.

• (default = CI)

combine_policy

• This is only applied when module_position %% 2 == 0 (at Phase 2, which is the end of each test).

• conditional uses the combined theta (using items from the previous module combined with the current module), if the examinee was in the same grade in Phases 1 and 2. If the examinee was in different grades in Phases 1 and 2, then the theta estimate from Phase 2 is used.

• always uses the combined theta.

• never uses the theta estimate from Phase 2.

• (default = conditional)

transition_CI_alpha

the alpha level to use when transition_policy == "CI".

transition_percentile_lower

the percentile value (between 0 and 1) to use for the lower routing when transition_policy == "difficulty_percentile".

transition_percentile_upper

the percentile value (between 0 and 1) to use for the upper routing when transition_policy == "difficulty_percentile".

initial_theta_list

(optional) a list containing initial thetas to use in each module position.

prior_mean_policy

• This is only effective at the beginning of each test. This determines what value is used as the prior mean.

• mean_difficulty uses the mean item difficulty of the current item pool.

• carryover uses the routing theta from the previous module. For Phase 1 of the first test, user supplied values are used if available. Otherwise, the mean item difficulty of the current item pool is used.

• user uses user-supplied values in the prior_mean_user argument.

• (default = mean_difficulty)

prior_mean_user

(optional) user-supplied values for the prior mean. Must be a single value, or a vector for each grade.

prior_sd

user-supplied values for the prior standard deviation. This is only effective at the beginning of each test. This is utilized regardless of prior_mean_policy. Must be a single value, or a vector for each grade. (default = 1)

verbose

if TRUE, print status messages. (default = TRUE)

Value

an '>output_maat object from the simulation.

Examples

# NOT RUN {
library(TestDesign) # >= 1.3.3
config <- createShadowTestConfig(
  final_theta = list(method = "MLE"),
  exclude_policy = list(method = "SOFT", M = 100)
)
# exclude_policy must be SOFT

examinee_list <- maat(
  examinee_list          = examinee_list_math,
  assessment_structure   = assessment_structure_math,
  module_list            = module_list_math,
  overlap_control_policy = "all",
  transition_CI_alpha    = 0.05,
  config                 = config,
  cut_scores             = cut_scores_math
)
# }