Legacy synthetic MFRM datasets inspired by Eckes and Jin (2021)

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). The Eckes & Jin (2021) Method section reports the following design parameters that motivated the synthetic versions shipped here: Study 1 had 307 examinees (149 males, 158 females), 18 raters (4 males, 14 females), and 3 criteria (global impression, task fulfillment, linguistic realization) on a 4-category rating scale (TDN levels rescored 1-4); Study 2 had 206 examinees (66 males, 140 females), 12 raters (1 male, 11 females), and 9 criteria on the same 4-category scale. The packaged datasets reproduce these (examinees, raters, criteria, categories) shapes but use simulated responses, so they are not the real TestDaF data.

Details

Available data objects:

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: legacy synthetic sensitivity variant. These objects can differ in observed rows as well as scores and should not be interpreted as a controlled one-parameter recalibration.

Use load_mfrmr_data() for programmatic selection by key.

Data dimensions

Dataset	Rows	Persons	Raters	Criteria
study1	1842	307	18	3
study2	3287	206	12	9
combined	5129	307	18	12
study1_itercal	1842	307	18	3
study2_itercal	3341	206	12	9
combined_itercal	5183	307	18	12

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

For the combined rows, Persons and Raters count unique raw labels. Treating the two Study labels as distinct namespaces would instead give 513 person labels and 30 rater labels, but would leave two unlinked components.

Provenance and limits

These are legacy synthetic datasets whose stored responses reproduce the dimensions described above. The exact response-generation code and random seed are not available, so the objects must not be used as parameter-recovery evidence or as evidence for a particular generating distribution. The separately stored _itercal objects can differ in observed rows as well as scores; they are legacy variants, not empirical calibration standards or a controlled one-parameter recalibration.

Combined-data caution

The combined objects reuse P001–P206 and R01–R12 across the two study labels. A combined analysis is meaningful only when those labels encode an intended cross-study identity and an explicit anchor or linking design establishes a common scale. Prefixing Person and Rater by Study removes accidental label collisions, but it creates disconnected study components and does not by itself establish a common scale. Analyze the studies separately unless the linking design has been specified and reviewed. The combined datasets are not beginner workflow examples or direct-fit examples.

Interpreting output

The study-specific datasets are in long format and can be passed to fit_mfrm() after confirming column-role mapping. The combined objects require a reviewed identity and anchor/linking design before a joint fit; row-binding or prefixing identifiers alone does not create a common scale.

Typical workflow

Inspect available datasets with list_mfrmr_data().
Load one dataset using load_mfrmr_data().
Fit and diagnose with fit_mfrm() and diagnose_mfrm().

References

Eckes, T., & Jin, K.-Y. (2021). Measuring rater centrality effects in writing assessment: A Bayesian facets modeling approach. Psychological Test and Assessment Modeling, 63(1), 65–94.

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