Cross-classified item and person effects · Curran-Bauer Analytics

Cross-classified item and person effects

Something that has come up often recently: When participants provide responses to a set of items or prompts, such as words, faces, or pictures, these items may represent a sample from a broader universe of possible items (e.g., a sampling of words from the lexicon). Responses may then best viewed as reflecting two crossed random factors, items and persons. A couple of nice papers on how to fit cross-classified random effects models to this kind of data are Baayen et al (2007) and Locker et al (2007).