EDT: EDT

In klausfrieler/EDT: Emotion Discrimination Test

EDT

Description

This function defines a EDT module for incorporation into a psychTestR timeline. Use this function if you want to include the EDT in a battery of other tests, or if you want to add custom psychTestR pages to your test timeline.

Usage

EDT(
  num_items = 18L,
  with_welcome = TRUE,
  take_training = FALSE,
  with_finish = TRUE,
  label = "EDT",
  feedback = EDT_feedback_with_score(),
  dict = EDT::EDT_dict,
  adaptive = TRUE,
  autoplay = TRUE,
  next_item.criterion = "bOpt",
  next_item.estimator = "BM",
  next_item.prior_dist = "norm",
  next_item.prior_par = c(0, 1),
  final_ability.estimator = "WL",
  constrain_answers = FALSE
)

Arguments

num_items	(Integer scalar) Number of items in the test.
with_welcome	(Scalar boolean) Indicates, if a welcome page shall be displayed. Defaults to TRUE
take_training	(Logical scalar) Whether to include the training phase. Defaults to FALSE
with_finish	(Scalar boolean) Indicates, if a finish (not final!) page shall be displayed. Defaults to TRUE
label	(Character scalar) Label to give the EDT results in the output file.
feedback	(Function) Defines the feedback to give the participant at the end of the test.
dict	The psychTestR dictionary used for internationalisation.
adaptive	(Scalar boolean) Indicates whether you want to use the adaptive EDT2 (TRUE) or the non-adaptive EDT (FASLE). Default is adaptive = TRUE.
autoplay	(Scalar boolean) Indicates whether you want to have autoplay for item pages (instruction pages always not-autoplay)
next_item.criterion	(Character scalar) Criterion for selecting successive items in the adaptive test. See the criterion argument in nextItem for possible values. Defaults to "bOpt".
next_item.estimator	(Character scalar) Ability estimation method used for selecting successive items in the adaptive test. See the method argument in thetaEst for possible values. "BM", Bayes modal, corresponds to the setting used in the original MPT paper. "WL", weighted likelihood, corresponds to the default setting used in versions <= 0.2.0 of this package.
next_item.prior_dist	(Character scalar) The type of prior distribution to use when calculating ability estimates for item selection. Ignored if next_item.estimator is not a Bayesian method. Defaults to "norm" for a normal distribution. See the priorDist argument in thetaEst for possible values.
next_item.prior_par	(Numeric vector, length 2) Parameters for the prior distribution; see the priorPar argument in thetaEst for details. Ignored if next_item.estimator is not a Bayesian method. The dfeault is c(0, 1).
final_ability.estimator	Estimation method used for the final ability estimate. See the method argument in thetaEst for possible values. The default is "WL", weighted likelihood. #' If a Bayesian method is chosen, its prior distribution will be defined by the next_item.prior_dist and next_item.prior_par arguments.
constrain_answers	(Logical scalar) If TRUE, then item selection will be constrained so that the correct answers are distributed as evenly as possible over the course of the test. We recommend leaving this option disabled.

Details

For demoing the EDT, consider using EDT_demo(). For a standalone implementation of the EDT, consider using EDT_standalone().

klausfrieler/EDT documentation built on April 9, 2024, 1:07 a.m.