Build a data quality summary report (preferred alias)

Build a data quality summary report (preferred alias)

Usage

data_quality_report(
  fit,
  data = NULL,
  person = NULL,
  facets = NULL,
  score = NULL,
  weight = NULL,
  min_category_count = 10,
  dominant_category_cutoff = 0.95,
  include_fixed = FALSE
)

Arguments

fit

Output from fit_mfrm().

data

Optional raw data frame used for row-level review.

person

Optional person column name in data.

facets

Optional facet column names in data.

score

Optional score column name in data.

weight

Optional weight column name in data.

min_category_count

Minimum raw or weighted count used to label a non-zero facet-level score category as sparse. Default 10.

dominant_category_cutoff

Proportion in (0, 1] used to flag a facet level whose responses are dominated by one score category. Default 0.95.

include_fixed

If TRUE, include a legacy-compatible fixed-width text block.

Value

A named list with data-quality report components. Class: mfrm_data_quality.

Details

summary(out) is supported through summary(). plot(out) is dispatched through plot() for class mfrm_data_quality (type = "dashboard", "quality_flags", "row_review", "category_counts", "score_support", "facet_category_usage", "facet_response_patterns", "score_map", "missing_rows").

Interpreting output

• summary: retained/dropped row overview.

• quality_overview: area-level QC status for rows, score support, facet-category use, and design matching.

• quality_flags: prioritized QC flags with counts and recommended next actions. This is not an item/person/rater table.

• row_review: reason-level breakdown for data issues.

• category_counts: post-filter category usage, including retained zero-count score-support categories.

• score_support_review: quick view of zero-count boundary/intermediate categories and their threshold-functioning caveats.

• category_usage_by_facet: facet-level category counts over the retained score support.

• category_usage_summary: per-facet-level zero/sparse category summary.

• facet_response_patterns: facet-level response-pattern summaries, including single-category and dominant-category use.

• caveats: user-facing score-support warnings, including cases where non-consecutive original labels such as 1, 2, 4, 5 were recoded because keep_original = FALSE.

• score_map: original-to-internal score mapping used when labels are recoded.

• unknown_elements: facet levels in raw data but not in fitted design.

Typical workflow

1. Run data_quality_report(...) with raw data.

2. Check summary(out) and plot(out, type = "dashboard"), then inspect quality_flags, score-support, score-map, facet-response-pattern, and missing/unknown element sections as needed.

3. Resolve missing values, score-support gaps, and sparse categories before final estimation/reporting.

See also

fit_mfrm(), describe_mfrm_data(), specifications_report(), mfrmr_reports_and_tables, mfrmr_compatibility_layer

Examples

toy <- load_mfrmr_data("example_operational")
fit <- fit_mfrm(
  toy, "Person", c("Rater", "Criterion"), "Score",
  method = "MML", quad_points = 7, maxit = 30
)
out <- data_quality_report(
  fit,
  data = toy, person = "Person",
  facets = c("Rater", "Criterion"), score = "Score"
)
summary(out)
#> mfrmr Data Quality Summary 
#>   Class: mfrm_data_quality
#>   Components: 14
#> 
#> Data quality overview
#>  TotalLinesInData TotalDataLines TotalNonBlankResponsesFound MissingScoreRows
#>               282            282                         282                0
#>  MissingFacetRows MissingPersonRows InvalidWeightRows OutOfRangeScoreRows
#>                 0                 0                 0                   0
#>  ValidResponsesUsedForEstimation ZeroCountScoreCategories
#>                              282                        0
#>  IntermediateZeroCountScoreCategories FacetLevelsWithZeroCategories
#>                                     0                             0
#>  FacetLevelsWithIntermediateZeroCategories FacetLevelsWithSparseCategories
#>                                          0                               6
#>  FacetLevelsWithSingleCategoryUse FacetLevelsWithDominantCategoryUse
#>                                 0                                  0
#>  FacetLevelsWithBoundaryOnlyUse ScoreSupportCaveats
#>                               0                   0
#> 
#> Review rows: quality_flags
#>                Area Severity                                  Flag Count
#>  Facet category use   review Facet levels have sparse category use     6
#>          Unit PercentOfData
#>  facet levels            NA
#>                                                               Action
#>  Interpret category-functioning evidence for these levels as sparse.
#> 
#> Settings
#>                   Setting Value
#>        min_category_count    10
#>  dominant_category_cutoff  0.95
#> 
#> Notes
#>  - Data quality summary for missingness, row status, score support, and
#>    category usage.
#>  - QC overview: 0 high-priority area(s), 1 review area(s).
#>  - Priority QC flags: 1 flag(s), including 0 high-severity flag(s).
#>  - Facet-level category use: 0 level(s) have zero-count categories; 0 have
#>    zero-count intermediate categories; 6 have sparse non-zero categories.
p_dq <- plot(out, draw = FALSE)
p_dq$data$plot
#> [1] "dashboard"