RMitemRestscore: Item Restscore Analysis

In easyRasch2: Psychometric Analysis with Rasch Measurement Theory

Item Restscore Analysis

Description

Computes observed and model-expected item-restscore correlations using iarm::item_restscore(), and enriches the output with the absolute difference between observed and expected values, item average locations, and item locations relative to the sample mean person location.

Usage

RMitemRestscore(
  data,
  cutoff = NULL,
  p_value = NULL,
  correction = c("fwer", "fdr_bh", "fdr_by", "none"),
  alpha = 0.05,
  output = "kable",
  sort,
  p_adj = "BH"
)

Arguments

data	A data.frame or matrix of item responses. Items must be scored starting at 0 (non-negative integers). Missing values (NA) are allowed, but at least one complete case (row with no NA) must be present.
cutoff	Optional. Default NULL, which tests each item against the asymptotic normal reference distribution from iarm::item_restscore(), adjusted by p_adj. Can be: The return value of RMitemRestscoreCutoff: the parametric bootstrap null distribution of the observed minus expected gamma. Adds the columns Diff_low and Diff_high (the descriptive interval), and, with p_value resolving to TRUE, p_restscore and padj_restscore. The ⁠$item_cutoffs⁠ data.frame from RMitemRestscoreCutoff directly (columns Item, diff_low, diff_high). Items are then flagged against the interval. With a cutoff, the asymptotic p_adjusted column is dropped and p_adj is ignored.
p_value	Logical or NULL. Whether to compute bootstrap p-values from the simulated null distribution and flag on them. NULL (the default) means use them when they are available: TRUE when cutoff is the full RMitemRestscoreCutoff object, which carries the per-item simulated values in its ⁠$results⁠ element, and FALSE otherwise. Explicit TRUE without the full object is an error rather than a silent downgrade. With FALSE and a cutoff, items are flagged against the interval instead, and a one-time message reports the family-wise error rate that implies.
correction	Character. Multiple-comparison correction applied across items when p_value = TRUE: "fwer" (default) for the Westfall-Young studentised-max step-down (family-wise error rate), "fdr_bh" / "fdr_by" for Benjamini-Hochberg / Benjamini-Yekutieli false discovery rate control, or "none" for uncorrected per-item p-values.
alpha	Numeric in (0, 1). Significance level for the Flagged column when p_value = TRUE. Default 0.05.
output	Character string controlling the return value. Either "kable" (default) for a formatted knitr::kable() table, or "dataframe" for the underlying data.frame.
sort	Optional character string. When sort = "diff", rows are sorted by the absolute magnitude of Difference in descending order, so that both over- and underfitting items appear near the top.
p_adj	Character string specifying the p-value adjustment method passed to iarm::item_restscore(). Default "BH" (Benjamini-Hochberg); use "none" for unadjusted p-values. Run ?stats::p.adjust for the list of available methods. Only used when cutoff = NULL.

Details

Item-restscore correlations using Goodman-Kruskal's gamma (Kreiner, 2011) measure the association between a person's score on a single item and their total score on the remaining items (the "restscore"). Under a correctly fitting Rasch model, observed and model-expected correlations should agree closely.

Item parameters are estimated by conditional maximum likelihood via psychotools::pcmodel() (a dichotomous item is a 2-category PCM); the item-restscore statistic itself comes from iarm::item_restscore() and is conditional on the total score, so it is invariant to the estimation engine. Per-item average locations are the means of the CML thresholds, and the person-location reference is the mean of the Warm WLE estimates.

Relative item location is defined as the item's average location minus the sample mean person location, providing a measure of item targeting.

The iarm package must be installed (it is in Suggests, not Imports).

The asymptotic p-value is miscalibrated. Without a cutoff, each item is tested with (observed - expected) / SE against the standard normal, where the SE is that of the observed gamma alone. The expected gamma is estimated from the same data and correlates with the observed one, so the SE is too large for the difference. The difference is also biased upwards in small samples. Under a true Rasch model the test therefore flags too many items as overfit in small samples and too few as underfit at every sample size, and the adjustment chosen in p_adj does not correct either. In simulation, 20 dichotomous items at n = 150 gave at least one BH-flagged item in 13 percent of datasets (30 percent when 1.5 logits off target), almost all of them overfit, while 9 polytomous items at n = 1000 gave about 1 percent. Pass the object from RMitemRestscoreCutoff() as cutoff for p-values from a parametric bootstrap null instead.

Bootstrap p-values. When p_value = TRUE, each item's observed Difference is compared against its simulated null distribution (from cutoff$results), studentised by the bootstrap mean and SD. Because the bootstrap mean is subtracted, the small-sample upward bias of the difference is removed, and because the bootstrap SD is used, the correlation between observed and expected gamma is accounted for. The marginal p-value is the two-sided Monte-Carlo p-value ⁠(1 + #{|t*| >= |t|}) / (B + 1)⁠, and correction = "fwer" applies the Westfall-Young studentised-max step-down across items (Ferreira, 2024).

The two directions do not get equal shares of alpha. Gamma is bounded at 1, so the null of Difference is left-skewed and a two-sided test on ⁠|t|⁠ rejects more often in the long (underfit) tail. In simulation under a true Rasch model the total rate was nominal, but the per-item marginal rate was about 3 percent for underfit and 2 percent for overfit with 20 dichotomous items at n = 150 and 1.5 logits off target, narrowing toward equal shares with polytomous items and larger samples. With correction = "fwer", over four such conditions, at least one item was flagged as underfit in 3.7 percent of datasets and as overfit in 1.6 percent, 5.0 percent in total. An equal-tailed test, with alpha/2 for each direction, was evaluated and not adopted: it detected 2 to 3 percentage points fewer underfitting items and no more overfitting ones.

Reproducibility. The bootstrap p-values depend on the simulated null, so two analyses of the same data with different seeds can disagree about items near the decision boundary. In simulation, in conditions chosen to include such items, two seeds disagreed about the flag of at least one item in about 10 percent of analyses at 400 iterations and about 6 percent at 1000 with correction = "fwer", and in 13 and 11 percent with correction = "fdr_bh". Set seed in RMitemRestscoreCutoff() for a reproducible analysis, and use 1000 or more iterations for a final one.

The direction in Flagged follows the studentised value, not the sign of Difference. At small sample sizes the null mean of Difference is slightly positive, so an item can be flagged "underfit" while its Difference is still just above zero. This is rare.

Value

• If output = "kable": a knitr_kable object (plain text table via format = "pipe") with columns for item name, observed and expected restscore correlations, the signed difference (observed minus expected), adjusted p-value, the Flagged misfit label, and item location relative to the sample mean person location.

• If output = "dataframe": a data.frame with columns Item, Observed, Expected, Difference, p_adjusted, Flagged, and Relative_location. Flagged is "overfit" (observed above expected, adj. p < .05), "underfit" (below, adj. p < .05), or "" (not flagged).

With a cutoff, p_adjusted is replaced by Diff_low and Diff_high, followed by p_restscore (marginal two-sided bootstrap p-value) and padj_restscore (corrected p-value) when p_value resolves to TRUE. Flagged then reflects padj_restscore < alpha, or the interval when p_value = FALSE.

The Difference column is signed (observed minus expected): positive values indicate that the item correlates more strongly with the rest-score than the Rasch model predicts (over-discrimination / overfit, often associated with local dependence), and negative values indicate weaker-than-expected association (under-discrimination / underfit, often associated with multidimensionality or noise).

Multiple comparisons

The marginal p-value controls the error rate of a single comparison: for one item (or item pair) decided on in advance it is the relevant value. But scanning all k comparisons and flagging whichever fall below alpha tests k hypotheses at once, so the chance of at least one false flag inflates to roughly 1 - (1 - \alpha)^k (e.g. about 34% for k = 8 at alpha = 0.05) – even when every marginal p-value is correctly calibrated. The corrected (adjusted) p-value controls this: correction = "fwer" bounds the probability of any false flag (strict, lower power), while "fdr_bh" / "fdr_by" bound the expected proportion of false flags among those raised (a more lenient middle ground). Rule of thumb: use the marginal p-value for a single pre-specified comparison, and a corrected p-value when screening the whole table – the usual workflow.

Flags depend on the other items

Item infit and item-restscore both judge each item against expectations from a Rasch model fitted to all items, so misfitting items change what is expected of the others. A flag in one direction can therefore produce flags in the other. Overfitting items can push fitting items into the underfit range, and underfitting items, for example from a second dimension, can make fitting items look overfit. How large the effect is depends on how strong the misfit is and how many items share it. In simulation with seven polytomous items, two overfitting items led to about 6 percent of the fitting items being flagged as underfit, and one or two mildly multidimensional items to 1 to 2 percent being flagged as overfit. When items are flagged in both directions, consider removing the clearest misfit and testing again before interpreting the rest.

References

Kreiner, S. (2011). A Note on Item–Restscore Association in Rasch Models. Applied Psychological Measurement, 35(7), 557–561. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0146621611410227")}

Ferreira, J. A. (2024). Methods of testing a 'small' or 'moderate' number of hypotheses simultaneously. Journal of Statistical Theory and Practice, 19(6). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s42519-024-00412-4")}

See Also

RMitemRestscoreCutoff, RMitemRestscorePlot

Examples

if (requireNamespace("iarm", quietly = TRUE)) {
  # Simulate binary item response data (8 items, 200 persons)
  set.seed(42)
  sim_data <- as.data.frame(
    matrix(sample(0:1, 200 * 8, replace = TRUE), nrow = 200, ncol = 8)
  )
  colnames(sim_data) <- paste0("Item", 1:8)

  # Default kable output
  RMitemRestscore(sim_data)

  # Sorted by absolute difference
  RMitemRestscore(sim_data, sort = "diff")

  # Return as data.frame for further processing
  df <- RMitemRestscore(sim_data, output = "dataframe")

  # Bootstrap null distribution, flagging on Westfall-Young corrected
  # p-values (use 1000 or more iterations in a final analysis)
  if (requireNamespace("ggdist", quietly = TRUE)) {
    cutoff_res <- RMitemRestscoreCutoff(sim_data, iterations = 100,
                                        parallel = FALSE, seed = 42)
    RMitemRestscore(sim_data, cutoff = cutoff_res)
  }
}

easyRasch2 documentation built on Oct. 6, 2026, 5:06 p.m.