First Advisor

Gerasimos Fergadiotis

Date of Award

Fall 2020

Document Type


Degree Name

Bachelor of Arts (B.A.) in Speech and Hearing Sciences and University Honors


Speech and Hearing Sciences




Aphasia -- Diagnosis, Language disorders -- Testing, Neuropsychological tests -- Evaluation, Speech disorders




Background: Confrontation naming tests for the assessment of aphasia are perhaps the most commonly used tests in aphasiology. Recently, such tests have been modeled using item response theory approaches. Despite their advantages, item response theory models require large sample sizes for parameter estimation that are often unrealistic when working with clinical populations. As an alternative approach, Fergadiotis, Kellough & Hula (2015) explored automatic item calibration by regressing item difficulty parameters on word length, age of acquisition (AOA), and lexical frequency as quantified by the Log10CD index. Despite the high predictive utility that they achieved, the model’s performance was far from perfect (R2= .63) which may carry implications for the accuracy of any difficulty parameters derived by the model.

Purpose: This study aims to examine the addition of a fourth psycholinguistic variable to the regression model, multiplex closeness centrality (MCC). It is hypothesized that the ability to capture how well-connected words are in the human lexicon would make MCC a potential indicator of semantic processing which would contribute to the predictive utility of the model.

Method: A multiple regression analysis was carried out with the Philadelphia Naming Test item difficulty parameters as the dependent variable, and lexical frequency, AOA, word length, and MCC as the predictors. Item difficulty parameters were estimated based on a traditional calibration approach.

Results & Conclusions: Our analysis showed a high correlation between MCC and item difficulty and suggested that the addition of MCC has allowed the model to account for more variance. However, the change between the model with three variables and the one with four variables, including MCC, was not statistically significant. In other words, MCC did not add unique information to the regression model despite the high correlation with item difficulty due to the overlapping variance of MCC with other predictors. However, the findings should be interpreted cautiously because of a large number of missing values in MCC. Post hoc analyses indicated that data were missing not at random which might have contributed to the lack of significant findings. Thus, we suggest that future research investigate this type of study using a complete dataset and appropriately apply the missing data theory to their analysis.


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