build_equating_chain: Build a screened linking chain across ordered calibrations

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

Build a screened linking chain across ordered calibrations

Description

Links a series of calibration waves by computing mean offsets between adjacent pairs of fits. Common linking elements (e.g., raters or items that appear in consecutive administrations) are used to estimate the scale shift. Cumulative offsets place all waves on a common metric anchored to the first wave. The procedure is intended as a practical screened linking aid, not as a full general-purpose equating framework.

Usage

build_equating_chain(
  fits,
  anchor_facets = NULL,
  include_person = FALSE,
  drift_threshold = 0.5
)

## S3 method for class 'mfrm_equating_chain'
print(x, ...)

## S3 method for class 'mfrm_equating_chain'
summary(object, ...)

## S3 method for class 'summary.mfrm_equating_chain'
print(x, ...)

Arguments

fits	Named list of mfrm_fit objects in chain order.
anchor_facets	Character vector of facets to use as linking elements.
include_person	Include person estimates in linking.
drift_threshold	Threshold for flagging large residuals in links.
x	An mfrm_equating_chain object.
...	Ignored.
object	An mfrm_equating_chain object (for summary).

Details

The screened linking chain uses a screened link-offset method. For each pair of adjacent waves (A, B), the function:

1. Identifies common linking elements (facet levels present in both fits).

2. Computes per-element differences:

   d_e = \hat{\delta}_{e,B} - \hat{\delta}_{e,A}

3. Computes a preliminary link offset using the inverse-variance weighted mean of these differences when standard errors are available (otherwise an unweighted mean).

4. Screens out elements whose residual from that preliminary offset exceeds drift_threshold, then recomputes the final offset on the retained set.

5. Records Offset_SD (standard deviation of retained residuals) and Max_Residual (maximum absolute deviation from the mean) as indicators of link quality.

6. Flags links with fewer than 5 retained common elements in any linking facet as having thin support.

Cumulative offsets are computed by chaining link offsets from Wave 1 forward, placing all waves onto the metric of the first wave.

Elements whose per-link residual exceeds drift_threshold are flagged in ⁠$element_detail$Flag⁠. A high Offset_SD, many flagged elements, or a thin retained anchor set signals an unstable link that may compromise the resulting scale placement.

Value

Object of class mfrm_equating_chain with components:

links

Tibble of link-level statistics (offset, SD, etc.).

cumulative

Tibble of cumulative offsets per wave.

element_detail

Tibble of element-level linking details.

common_by_facet

Tibble of retained common-element counts by facet.

config

List of analysis configuration.

Which function should I use?

• Use anchor_to_baseline() for a single new wave anchored to a known baseline.

• Use detect_anchor_drift() when you want direct comparison against one reference wave.

• Use build_equating_chain() when no single wave should dominate and you want ordered, adjacent links across the series.

Interpreting output

• ⁠$links⁠: one row per adjacent pair with From, To, N_Common, N_Retained, Offset_Prelim, Offset, Offset_SD, and Max_Residual. Small Offset_SD relative to the offset indicates a consistent shift across elements. LinkSupportAdequate = FALSE means at least one linking facet retained fewer than 5 common elements after screening.

• ⁠$cumulative⁠: one row per wave with its cumulative offset from Wave 1. Wave 1 always has offset 0.

• ⁠$element_detail⁠: per-element linking statistics (estimate in each wave, difference, residual from mean offset, and flag status). Flagged elements may indicate DIF or rater re-training effects.

• ⁠$common_by_facet⁠: retained common-element counts by linking facet for each adjacent link.

• ⁠$config⁠: records wave names and analysis parameters.

• Read links before cumulative: weak adjacent links can make later cumulative offsets less trustworthy.

Typical workflow

1. Fit each administration wave separately: fit_a <- fit_mfrm(...).

2. Combine into an ordered named list: fits <- list(Spring23 = fit_s, Fall23 = fit_f, Spring24 = fit_s2).

3. Call chain <- build_equating_chain(fits).

4. Review summary(chain) for link quality.

5. Visualize with plot_anchor_drift(chain, type = "chain").

6. For problematic links, investigate flagged elements in chain$element_detail and consider removing them from the anchor set.

See Also

detect_anchor_drift(), anchor_to_baseline(), make_anchor_table(), plot_anchor_drift()

Examples

toy <- load_mfrmr_data("example_core")
people <- unique(toy$Person)
d1 <- toy[toy$Person %in% people[1:12], , drop = FALSE]
d2 <- toy[toy$Person %in% people[13:24], , drop = FALSE]
fit1 <- fit_mfrm(d1, "Person", c("Rater", "Criterion"), "Score",
                 method = "JML", maxit = 10)
fit2 <- fit_mfrm(d2, "Person", c("Rater", "Criterion"), "Score",
                 method = "JML", maxit = 10)
chain <- build_equating_chain(list(Form1 = fit1, Form2 = fit2))
summary(chain)
chain$cumulative

mfrmr documentation built on March 31, 2026, 1:06 a.m.