Predicting confrontation naming item difficulty

Abstract

Background: Item response theory (IRT; Lord & Novick, 1968) is a psychometric framework that can be used to model the likelihood that an individual will respond correctly to an item. Using archival data (Mirman et al., 2010), Fergadiotis, Kellough, and Hula (2015) estimated difficulty parameters for the Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) using the 1-parameter logistic IRT model. Although the use of IRT in test development is advantageous, its reliance on sample sizes exceeding 200 participants make it difficult to implement in aphasiology. Therefore, alternate means of estimating the item difficulty of confrontation naming test items warrant investigation. In a preliminary study aimed at automatic item calibration, Swiderski, Fergadiotis, and Hula (2016) regressed the difficulty parameters from the PNT on word length, age of acquisition (Kuperman et al., 2012), lexical frequency as quantified by the Log10CD index (Brysbaert & New, 2009), and naming latency (Székely et al., 2003). Although this model successfully explained a substantial proportion of variance in the PNT difficulty parameters, a substantial proportion (20%) of the response time data were missing. Further, only 39% of the picture stimuli from Székely and colleagues (2003) were identical to those on the PNT. Given that the IRT sample size requirements limit traditional calibration approaches in aphasiology and that the initial attempts in predicting IRT difficulty parameters in our pilot study were based on incomplete response time data this study has two specific aims. Aims: To estimate naming latencies for the 175 items on the PNT, and assess the utility of psycholinguistic variables and naming latencies for predicting item difficulty. Methods and Procedures: Using a speeded picture naming task we estimated mean naming latencies for the 175 items of the Philadelphia Naming test in 44 cognitively healthy adults. We then re-estimated the model reported by Swiderski et al (2016) with the new naming latency data. Results: The predictor variables described above accounted for a substantial proportion of the variance in the item difficulty parameters (Adj. R2 = .692). Conclusions: In this study we demonstrated that word length, age of acquisition, lexical frequency, and naming latency from neurotypical young adults usefully predict picture naming item difficulty in people with aphasia. These variables are readily available or easily obtained and the regression model reported may be useful for estimating confrontation naming item difficulty without the need for collection of response data from large samples of people with aphasia.

Predicting Confrontation Naming in the Logopenic Variant of Primary Progressive Aphasia

“…The first factor, termed "lexical-semantic usage", received high loadings from AoA, frequency, contextual diversity, SND, and familiarity, such that words that are more frequent, familiar, and contextually diverse had greater SNDs and were acquired earlier in life. The structure of this factor is similar to the "lexical usage" factor reported by Alyahya et al (2020) and is consistent with studies that have reported the considerable inherent intercorrelations between its constituent psycholinguistic properties (Alyahya et al, 2020;Fergadiotis, Swiderski, & Hula, 2019;Lambon Ralph et al, 1998;Nickels & Howard, 1995).…”

Empirical Evaluation of Computer-Adaptive Alternate Short Forms for the Assessment of Anomia Severity

“…Having developed and demonstrated the reliability and validity of the CAT approach in this context, an important next step is to enlarge the item bank beyond the 175 PNT items. To this extent, we have investigated the utility of a predictive model capable of estimating item difficulty of line drawings with lexical predictors (Fergadiotis, Swiderski, & Hula, 2018). Adding new stimulus items and calibrating their difficulty estimates to the same scale will have multiple benefits.…”

Enhancing the Efficiency of Confrontation Naming Assessment for Aphasia Using Computer Adaptive Testing

“…An alternate option is the exploration of predictive modeling for item difficulty estimation (Gierl & Haladyna, 2012) using readily available lexical variables. To this end, we (Fergadiotis, Swiderski, & Hula, 2018) have developed a regression model accounting for a substantial proportion of the variance of the difficulty parameters of the items on the PNT (adj. R 2 = .692).…”