compute_facet_design_effect: Compute Kish design effects for each facet

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

Compute Kish design effects for each facet

Description

Combines per-facet average cluster size with ICC estimates to return the Kish (1965) design effect Deff = 1 + (m - 1) * rho, where m is the average number of observations per facet element and rho is the ICC.

Usage

compute_facet_design_effect(
  data,
  facets,
  icc_table = NULL,
  score = NULL,
  person = NULL
)

Arguments

data	Data frame in long format.
facets	Character vector of facet column names.
icc_table	Output from compute_facet_icc() (optional; will be computed on the fly when NULL).
score	Score column name; required when icc_table is NULL.
person	Person column; passed through to compute_facet_icc().

Value

A data.frame of class mfrm_facet_design_effect with columns Facet, AvgClusterSize, ICC, DesignEffect, and EffectiveN.

Interpreting output

• Deff = 1: facet behaves like simple random sampling; no clustering-induced variance inflation.

• Deff > 1: variance of the mean estimate is inflated by a factor of Deff relative to SRS. EffectiveN = N / Deff is the sample size one would need under SRS to achieve the same precision. For rater-mediated designs, Deff well above 1 on the Rater facet means rater-level clustering is noticeable; consider whether rater generalisation is warranted.

• Reported ICC is pulled from icc_table$ICC (the variance share); interpretation is the same as in compute_facet_icc().

Typical workflow

1. Run compute_facet_icc() to get the variance-component shares.

2. Feed the result and the data into compute_facet_design_effect(data, facets, icc_table = icc).

3. Use Deff as part of the Methods discussion when generalising over raters or sites. Large Deff values argue for reporting robust SEs or moving to a hierarchical model.

References

Kish, L. (1965). Survey Sampling. New York: Wiley.

Park, I., & Lee, H. (2001). The design effect: Do we know all about it? In Proceedings of the American Statistical Association, Survey Research Methods Section (pp. 143-148).

See Also

compute_facet_icc(), analyze_hierarchical_structure().

Examples

toy <- load_mfrmr_data("example_core")
if (requireNamespace("lme4", quietly = TRUE)) {
  icc <- compute_facet_icc(toy, facets = c("Rater", "Criterion"),
                           score = "Score", person = "Person")
  deff <- compute_facet_design_effect(toy,
                                      facets = c("Rater", "Criterion"),
                                      icc_table = icc)
  print(deff)
  # Large DesignEffect -> modest EffectiveN relative to raw N.
}

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