reporting_checklist: Build an auto-filled MFRM reporting checklist

In mfrmr: Estimation and Diagnostics for Many-Facet Measurement Models

Build an auto-filled MFRM reporting checklist

Description

Build an auto-filled MFRM reporting checklist

Usage

reporting_checklist(
  fit,
  diagnostics = NULL,
  bias_results = NULL,
  hierarchical_structure = NULL,
  include_references = TRUE
)

Arguments

fit	Output from fit_mfrm().
diagnostics	Optional output from diagnose_mfrm(). When NULL, diagnostics are computed with residual_pca = "none".
bias_results	Optional output from estimate_bias() or a named list of such outputs.
hierarchical_structure	Optional output from analyze_hierarchical_structure(). When supplied, the "Hierarchical structure review" checklist item is flipped to DraftReady = TRUE and its Detail column surfaces the number of nested / crossed facet pairs and whether the ICC table is available.
include_references	If TRUE, include a compact reference table in the returned bundle.

Details

This helper ports the app-level reporting checklist into a package-native bundle. It does not try to judge substantive reporting quality; instead, it checks whether the fitted object and related diagnostics contain the evidence typically reported in MFRM write-ups.

Checklist items are grouped into seven core sections:

• Method section

• Global fit

• Facet-level statistics

• Element-level statistics

• Rating scale diagnostics

• Bias/interaction analysis

• Visual displays

When a fit uses the latent-regression population-model branch, the checklist also adds a ⁠Population Model⁠ section covering coefficient reporting, categorical model-matrix coding, complete-case omissions, posterior-basis wording, and ConQuest scope wording.

The output is designed for manuscript preparation, reproducibility records, and reproducible reporting workflows.

Value

A named list with checklist tables. Class: mfrm_reporting_checklist.

What this checklist means

reporting_checklist() is a manuscript-preparation guide. It tells you which reporting elements are already present in the current analysis objects and which still need to be generated or documented. The primary draft-status column is DraftReady; ReadyForAPA is retained as a backward-compatible alias.

What this checklist does not justify

• It is not a single run-level pass/fail decision for publication.

• DraftReady = TRUE / ReadyForAPA = TRUE does not certify formal inferential adequacy.

• Missing bias rows may simply mean bias_results were not supplied.

Interpreting output

• checklist: one row per reporting item with Available = TRUE/FALSE. DraftReady = TRUE means the item can be drafted into a report with the package's documented caveats. ReadyForAPA is a backward-compatible alias of the same flag; neither field certifies formal inferential adequacy.

• section_summary: available items by section.

• The Global Fit section includes a "Fit/separation reporting boundary" row that points to precision_review_report(), fit_measures_table(), and facets_fit_review() before users phrase fit, ZSTD, separation, or reliability claims.

• software_scope: external-software relationship summary for mfrmr, FACETS, ConQuest, and SPSS-style tabular handoffs.

• facets_positioning: report-ready wording that states mfrmr is not a FACETS numerical clone and separates native estimation from FACETS-style handoff or external-table review.

• visual_scope: plotting-route summary that separates report-default 2D figures from exploratory surface/3D-ready data handoffs, including a short InterpretationCheck for the main user-facing caveat.

• references: core background references when requested.

Recommended next step

Review the rows with Available = FALSE or DraftReady = FALSE, then add the missing diagnostics, bias results, or narrative context before calling build_apa_outputs() for draft text generation. For RSM / PCM reporting runs, the preferred route is an MML fit plus diagnose_mfrm(..., diagnostic_mode = "both") so the checklist can see the legacy and strict marginal screens together.

How this differs from operational review

reporting_checklist() is the manuscript/reporting branch of the package. Use it when the question is "what is still missing from the report?" rather than "which observations or links need follow-up?" For operational review:

• Use build_misfit_casebook() after diagnose_mfrm() when you need ranked misfit cases and grouping views for local follow-up.

• Use build_linking_review() after anchor/drift/chain helpers when you need operational linking triage rather than manuscript-oriented reporting tables.

Typical workflow

1. Fit with fit_mfrm(). For RSM / PCM reporting runs, prefer method = "MML".

2. Compute diagnostics with diagnose_mfrm(). For RSM / PCM, prefer diagnostic_mode = "both".

3. Run reporting_checklist() to see which reporting elements are already available from the current analysis objects.

4. If the issue is operational rather than manuscript-facing, branch to build_misfit_casebook() or build_linking_review() instead of treating reporting_checklist() as the single review hub.

See Also

build_apa_outputs(), build_visual_summaries(), specifications_report(), data_quality_report(), build_misfit_casebook(), build_linking_review()

Examples

# Fast smoke run: a JML fit + legacy-only diagnostic produces a
# populated checklist in well under a second.
toy <- load_mfrmr_data("example_core")
fit_quick <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                      method = "JML", maxit = 30)
diag_quick <- diagnose_mfrm(fit_quick, residual_pca = "none",
                             diagnostic_mode = "legacy")
chk_quick <- reporting_checklist(fit_quick, diagnostics = diag_quick)
head(chk_quick$checklist[, c("Section", "Item", "DraftReady")])


fit <- fit_mfrm(toy, "Person", c("Rater", "Criterion"), "Score",
                method = "MML", quad_points = 7, maxit = 30)
diag <- diagnose_mfrm(fit, residual_pca = "both", diagnostic_mode = "both")
chk <- reporting_checklist(fit, diagnostics = diag)
summary(chk)
# Look for: a high `Ready` / `Total` ratio in the summary block.
#   Sections with `Ready = 0` need follow-up before submitting
#   (typically diagnostic_mode = "both" or a residual-PCA pass).
apa <- build_apa_outputs(fit, diag)
head(chk$checklist[, c("Section", "Item", "DraftReady", "NextAction")])
# Look for: every row where `DraftReady = "yes"` is ready to paste
#   into the manuscript. `"no"` rows include a concrete `NextAction`
#   step (e.g. "run plot_qc_dashboard()") so the gap can be closed
#   without re-reading the methodology guide.
nchar(apa$report_text)

mfrmr documentation built on June 13, 2026, 1:07 a.m.