Random-effects meta-regression of each item's DIF estimate (`d - c`) on item features (e.g. idioms, cultural referents, measurement units, vocabulary load), weighting by `1 / (se_d^2 + tau^2)`. The between-item variance `tau^2` not explained by the features is estimated by the method of moments. Coefficients are logits of DIF per unit of the feature. That is guidance a translation team can act on.
Value
Data frame of coefficients (`term`, `estimate`, `se`, `z`, `p_value`) with attribute `tau` (residual DIF SD). Features with no variation across items are dropped with a warning.
Examples
sim <- td_simulate(n_ref = 400, n_focal = 120, n_items = 20, seed = 5)
dif <- td_dif(td_calibrate(sim$responses, sim$group))
td_features(dif, sim$features)
#> term estimate se z p_value
#> 1 (Intercept) -0.05714491 0.08564583 -0.6672235 0.504629368
#> 2 idiom 0.21161373 0.19391980 1.0912435 0.275165749
#> 3 cultural 0.54969875 0.20130565 2.7306673 0.006320625
#> 4 units -0.46375958 0.14599001 -3.1766528 0.001489853
#> 5 vocabulary 0.25843348 0.13175854 1.9614172 0.049830371