dif_statistic: Differential Item Functioning (DIF) Analysis for Bayesian IRT...
In easyRaschBayes: Bayesian Rasch Analysis Using 'brms'

dif_statistic

R Documentation

Differential Item Functioning (DIF) Analysis for Bayesian IRT Models

Description

Tests for differential item functioning (DIF) in Bayesian Rasch-family models fitted with brms by comparing item parameters across subgroups defined by an exogenous variable. The function fits a DIF model that includes group-by-item interactions and summarizes the posterior distribution of the DIF effects.

Usage

dif_statistic(
  model,
  group_var,
  item_var = item,
  person_var = id,
  data = NULL,
  dif_type = c("uniform", "non-uniform"),
  prob = 0.95,
  rope = 0.5,
  refit = TRUE,
  ...
)

Arguments

`model`	A fitted `brmsfit` object from the baseline (no-DIF) model.
`group_var`	An unquoted variable name identifying the grouping variable for DIF testing (e.g., `gender`). Must be a factor or character variable with exactly 2 levels in the current implementation.
`item_var`	An unquoted variable name identifying the item grouping variable. Default is `item`.
`person_var`	An unquoted variable name identifying the person grouping variable. Default is `id`.
`data`	An optional data frame containing all variables needed for the DIF model, including the group variable. If `NULL` (the default), the function attempts to use `model$data`. Since the baseline model formula typically does not include the group variable, brms will have dropped it from the stored model data. In that case, you must supply the original data frame here.
`dif_type`	Character. For polytomous ordinal models only. `"uniform"` (the default) tests for a uniform location shift per item via a `group:item` fixed-effect interaction. `"non-uniform"` fits group-specific thresholds per item and computes per-threshold DIF effects as the difference between groups. Ignored for dichotomous models.
`prob`	Numeric in `(0, 1)`. Width of the credible intervals. Default is 0.95.
`rope`	Numeric. Half-width of the Region of Practical Equivalence (ROPE) around zero for DIF effects, on the logit scale. Default is 0.5, corresponding to a practically negligible DIF effect. Set to 0 to skip ROPE analysis.
`refit`	Logical. If `TRUE` (the default), the DIF model is fitted automatically by updating the baseline model via `update`, which reuses the compiled Stan code for faster sampling. If `FALSE`, only the DIF model formula is returned (useful for manual fitting with custom settings).
`...`	Additional arguments passed to `update.brmsfit` when refitting the DIF model (e.g., `cores`, `control`).

Details

For polytomous models, two types of DIF can be tested:

Uniform DIF (dif_type = "uniform", default): A single location shift per item across groups, modelled as a group:item fixed-effect interaction. This tests whether the average item difficulty differs between groups.
Non-uniform / threshold-level DIF (dif_type = "non-uniform"): Each item receives group-specific thresholds via thres(gr = interaction(item, group)). DIF effects are computed as the difference in each threshold between groups, revealing whether DIF affects specific response categories.

The function constructs a DIF model by adding a group-by-item interaction to the baseline model:

Dichotomous models (family = bernoulli()): The baseline response ~ 1 + (1 | item) + (1 | id) becomes response ~ 1 + group + (1 + group | item) + (1 | id), where the group slope varying by item captures item-specific DIF.
Polytomous uniform DIF (dif_type = "uniform"): The baseline response | thres(gr = item) ~ 1 + (1 | id) becomes response | thres(gr = item) ~ 1 + group:item + (1 | id).
Polytomous non-uniform DIF (dif_type = "non-uniform"): The baseline becomes response | thres(gr = item_group) ~ 1 + (1 | id), where item_group = interaction(item, group). Each item × group combination gets its own thresholds. DIF effects are the differences between group-specific thresholds for each item, computed draw-by-draw from the posterior.

DIF effects are summarized using:

Probability of Direction (pd): The proportion of the posterior on the dominant side of zero. Values > 0.975 indicate strong directional evidence.
ROPE: The Region of Practical Equivalence (Kruschke, 2018). If > 95\ the DIF effect is practically negligible. If > 95\ outside, the effect is practically significant.
Credible Interval: If the CI excludes zero, there is evidence of DIF at the specified credibility level.

Value

A list with the following elements:

summary: A tibble with one row per item (for uniform DIF) or per item × threshold (for non-uniform DIF) containing: item, optionally threshold, dif_estimate (posterior mean), dif_lower, dif_upper (credible interval), dif_sd (posterior SD), pd (probability of direction), rope_percentage (proportion inside ROPE), and flag (classification).
dif_draws: A matrix of posterior draws for the DIF effects (draws × effects), for further analysis.
dif_model: The fitted DIF brmsfit object (if refit = TRUE), or NULL.
dif_formula: The brmsformula used for the DIF model.
baseline_model: The original baseline model.
plot: A ggplot forest plot of DIF effects with credible intervals and ROPE.

References

Kruschke, J. K. (2018). Rejecting or accepting parameter values in Bayesian estimation. Advances in Methods and Practices in Psychological Science, 1(2), 270–280.

Bürkner, P.-C. (2021). Bayesian Item Response Modeling in R with brms and Stan. Journal of Statistical Software, 100, 1–54. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v100.i05")}

Examples


library(brms)
library(dplyr)
library(tidyr)
library(tibble)

# --- Dichotomous Rasch with DIF testing ---

set.seed(123)
df <- expand.grid(id = 1:200, item = paste0("I", 1:10)) %>%
  mutate(
    gender = rep(sample(c("M", "F"), 200, TRUE), each = 10),
    theta  = rep(rnorm(200), each = 10),
    delta  = rep(seq(-2, 2, length.out = 10), 200),
    dif    = ifelse(item == "I3" & gender == "F", 1.0,
             ifelse(item == "I7" & gender == "F", -0.8, 0)),
    p      = plogis(theta - delta - dif),
    response = rbinom(n(), 1, p)
  )

fit_base <- brm(
  response ~ 1 + (1 | item) + (1 | id),
  data   = df,
  family = bernoulli(),
  chains = 4, 
  cores  = 2, # use more cores if you have
  iter   = 1000 # use at least 2000 
)

dif_result <- dif_statistic(
  model     = fit_base,
  group_var = gender,
  data      = df
)

dif_result$summary
dif_result$plot

# --- Partial Credit Model: uniform DIF ---

df_pcm <- eRm::pcmdat2 %>%
  mutate(across(everything(), ~ .x + 1)) %>%
  rownames_to_column("id") %>%
  mutate(gender = sample(c("M", "F"), n(), TRUE)) %>%
  pivot_longer(!c(id, gender),
               names_to = "item", values_to = "response")

fit_pcm <- brm(
  response | thres(gr = item) ~ 1 + (1 | id),
  data   = df_pcm,
  family = acat,
  chains = 4, 
  cores  = 2, # use more cores if you have
  iter   = 1000 # use at least 2000 
)

# Uniform DIF (default): one shift per item
dif_uni <- dif_statistic(fit_pcm, group_var = gender, data = df_pcm)
dif_uni$plot

# Non-uniform DIF: threshold-level effects
dif_nu <- dif_statistic(fit_pcm, group_var = gender, data = df_pcm,
                         dif_type = "non-uniform")
dif_nu$summary
dif_nu$plot

easyRaschBayes documentation built on March 28, 2026, 5:07 p.m.