emt: EMT

In pmcharrison/emt: Emotion Matching Test

Description

This function defines an emotion matching test (EMT) module for incorporation into a psychTestR timeline. Use this function if you want to include the EMT in a battery of other tests, or if you want to add custom psychTestR pages to your test timeline. For demoing the EMT, consider using demo_emt(). For a standalone implementation of the EMT, consider using standalone_emt().

Usage

emt(num_items = 20L, take_training = TRUE, label = "EMT",
  feedback = emt.feedback.no_score(),
  media_dir = "http://media.gold-msi.org/test_materials/EMT/audio",
  min_response_time = 20, 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, dict = emt::emt_dict)

Arguments

num_items	(Integer scalar) Number of items in the test.
take_training	(Logical scalar) Whether to include the training phase.
label	(Character scalar) Label to give the EMT results in the output file.
feedback	(Function) Defines the feedback to give the participant at the end of the test. By default no feedback is given.
media_dir	(Character scalar) File path to the directory hosting the test's media files (typically a publicly accessible web directory).
min_response_time	Minimum response time for each question, after which response options are activated (seconds).
next_item.criterion	(Character scalar) Criterion for selecting successive items in the adaptive test. See the criterion argument in nextItem for possible values. "bOpt" corresponds to the setting used in the original EMT paper.
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 EMT paper. "WL", weighted likelihood, corresponds to the default setting used in versions <= 1.3.0 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, which corresponds to the setting used in the original EMT paper. 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.
dict	The psychTestR dictionary used for internationalisation.

pmcharrison/emt documentation built on May 26, 2019, 2:31 p.m.