Accuracy of Naming Error Profiles Elicited From Adaptive Short Forms of the Philadelphia Naming Test

The purpose of this study was to evaluate whether a short-form computerized adaptive testing (CAT) version of the Philadelphia Naming Test (PNT) provides error profiles and model-based estimates of semantic and phonological processing that agree with the full test.

Twenty-four persons with aphasia took the PNT-CAT and the full version of the PNT (hereinafter referred to as the "full PNT") at least 2 weeks apart. The PNT-CAT proceeded in two stages: (a) the PNT-CAT30, in which 30 items were selected to match the evolving ability estimate with the goal of producing a 50% error rate, and (b) the PNT-CAT60, in which an additional 30 items were selected to produce a 75% error rate. Agreement was evaluated in terms of the root-mean-square deviation of the response-type proportions and, for individual response types, in terms of agreement coefficients and bias. We also evaluated agreement and bias for estimates of semantic and phonological processing derived from the semantic-phonological interactive two-step model (SP model) of word production.

The results suggested that agreement was poorest for semantic, formal, mixed, and unrelated errors, all of which were underestimated by the short forms. Better agreement was observed for correct and nonword responses. SP model weights estimated by the short forms demonstrated no substantial bias but generally inadequate agreement with the full PNT, which itself showed acceptable test-retest reliability for SP model weights and all response types except for formal errors.

Results suggest that the PNT-CAT30 and the PNT-CAT60 are generally inadequate for generating naming error profiles or model-derived estimates of semantic and phonological processing ability. Post hoc analyses suggested that increasing the number of stimuli available in the CAT item bank may improve the utility of adaptive short forms for generating error profiles, but the underlying theory also suggests that there are limitations to this approach based on a unidimensional measurement model.

https://doi.org/10.23641/asha.22320814.