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Simulated MFRM datasets based on Eckes and Jin (2021)

Source: R/datasets.R
ej2021_data.Rd

Synthetic many-facet rating datasets in long format. All datasets include one row per observed rating.

Format

A data.frame with 5 columns:

Study

Study label ("Study1" or "Study2").

Person

Person/respondent identifier.

Rater

Rater identifier.

Criterion

Criterion facet label.

Score

Observed category score.

Source

Simulated for this package with design settings informed by Eckes and Jin (2021).

Details

Available data objects:

  • mfrmr_example_core

  • mfrmr_example_bias

  • ej2021_study1

  • ej2021_study2

  • ej2021_combined

  • ej2021_study1_itercal

  • ej2021_study2_itercal

  • ej2021_combined_itercal

Naming convention:

  • study1 / study2: separate simulation studies

  • combined: row-bind of study1 and study2

  • _itercal: iterative-calibration variant

Use load_mfrmr_data() for programmatic selection by key.

Data dimensions

DatasetRowsPersonsRatersCriteria
study11842307183
study23287206129
combined51293071812
study1_itercal1842307183
study2_itercal3341206129
combined_itercal51833071812

Score range: 1–4 (four-category rating scale).

Simulation design

Person ability is drawn from N(0, 1). Rater severity effects span approximately -0.5 to +0.5 logits. Criterion difficulty effects span approximately -0.3 to +0.3 logits. Scores are generated from the resulting linear predictor plus Gaussian noise, then discretized into four categories. The _itercal variants use a second iteration of calibrated rater severity parameters.

Interpreting output

Each dataset is already in long format and can be passed directly to fit_mfrm() after confirming column-role mapping.

Typical workflow

  1. Inspect available datasets with list_mfrmr_data().

  2. Load one dataset using load_mfrmr_data().

  3. Fit and diagnose with fit_mfrm() and diagnose_mfrm().

Examples

data("ej2021_study1", package = "mfrmr")
head(ej2021_study1)
#>    Study Person Rater              Criterion Score
#> 1 Study1   P001   R08      Global_Impression     4
#> 2 Study1   P001   R08 Linguistic_Realization     3
#> 3 Study1   P001   R08       Task_Fulfillment     3
#> 4 Study1   P001   R10      Global_Impression     4
#> 5 Study1   P001   R10 Linguistic_Realization     3
#> 6 Study1   P001   R10       Task_Fulfillment     2
table(ej2021_study1$Study)
#> 
#> Study1 
#>   1842