compute_iota2: Computes Iota and its elements in version 2

Description

Fits a model of Iota2 to the data

Usage

compute_iota2(
  data,
  random_starts = 10,
  max_iterations = 5000,
  cr_rel_change = 1e-12,
  con_step_size = 1e-04,
  con_rel_convergence = 1e-12,
  con_max_iterations = 5000,
  con_random_starts = 5,
  b_min = 0.01,
  fast = TRUE,
  trace = TRUE,
  con_trace = FALSE
)

Value

Returns a list with the following three components: The first component estimates_categorical_level comprises all elements that describe the ratings on a categorical level. The elements are sub-divided into raw estimates and chance-corrected estimates.

raw_estimates

alpha_reliability:: A vector containing the Alpha Reliabilities for each category. These values represent probabilities.
beta_reliability:: A vector containing the Beta Reliabilities for each category. These values represent probabilities.
assignment_error_matrix:: Assignment Error Matrix containing the conditional probabilities for assigning a unit of category i to categories 1 to n.
iota:: A vector containing the Iota values for each category.
iota_error_1:: A vector containing the Iota Error Type I values for each category.
iota_error_2:: A vector containing the Iota Error Type II values for each category.

elements_chance_corrected

alpha_reliability:: A vector containing the chance-corrected Alpha Reliabilities for each category.

beta_reliability:

A vector containing the chance-corrected Beta Reliabilities for each category.

The second component estimates_scale_level contains elements for describing the quality of the ratings on a scale level. It comprises the following elements:

The third component information contains important information regarding the parameter estimation. It comprises the following elements:

Arguments

data: Data for which the elements should be estimated. Data must be an object of type data.frame or matrix with cases in the rows and raters in the columns.
random_starts: An integer for the number of random starts for the EM algorithm.
max_iterations: An integer for the maximum number of iterations within the EM algorithm.
cr_rel_change: Positive numeric value for defining the convergence of the EM algorithm.
con_step_size: Double for specifying the size for increasing or decreasing the probabilities during the conditioning stage of estimation. This value should not be less than 1e-3.
con_rel_convergence: Double for determining the convergence criterion during the conditioning stage. The algorithm stops if the relative change is smaller than this criterion.
con_max_iterations: Integer for the maximum number of iterations during the conditioning stage.
con_random_starts: Integer for the number of random starts within the conditioning stage.
b_min: Value ranging between 0 and 1, determining the minimal size of the categories for checking if boundary values occurred. The algorithm tries to select solutions that are not considered to be boundary values.
fast: Bool If TRUE a fast estimation is applied during the condition stage. This option ignores all parameters beginning with "con_". If FALSE the estimation described in Berding and Pargmann (2022) is used. Default is TRUE.
trace: TRUE for printing progress information on the console. FALSE if this information is not to be printed.
con_trace: TRUE for printing progress information on the console during estimations in the conditioning stage. FALSE if this information is not to be printed.

References

Florian Berding and Julia Pargmann (2022).Iota Reliability Concept of the Second Generation. Measures for Content Analysis Done by Humans or Artificial Intelligences. Berlin: Logos. https://doi.org/10.30819/5581