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Edward Carney

Researcher at University of Minnesota

Publications -  28
Citations -  693

Edward Carney is an academic researcher from University of Minnesota. The author has contributed to research in topics: Audiogram & Dichotic listening. The author has an hindex of 16, co-authored 28 publications receiving 647 citations. Previous affiliations of Edward Carney include Boys Town & Purdue University.

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Breadth and Depth of Diversity in MinnesotaChallenges to Clinical Competency

TL;DR: The authors considered the breadth or range of economically, racially, ethnically, and linguistically diverse populations represented on caseloads, relative to depth of diversity, to determine if, and how, recent changes in the overall state demographics were reflected on cases of speech-language pathologists.
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Neural coding of formant-exaggerated speech in the infant brain

TL;DR: Results provide the first evidence that formant expansion in infant-directed speech enhances neural activities for phonetic encoding and language learning in the infant brain sensitive to formant exaggeration.
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General Slowing in Language Impairment: Methodological Considerations in Testing the Hypothesis

TL;DR: HLM showed a significant difference in the RT relation between LI and CA groups across studies, indicating that study-specific slowing, rather than general slowing, was present, and this method was compared with another method, hierarchical linear modeling with random coefficients.
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Critical Difference Table for Word Recognition Testing Derived Using Computer Simulation

TL;DR: A table for upper and lower limits of the 95% critical range for changes in word recognition scores obtained with monosyllabic word lists is constructed using newly available methods using computer simulation of the relevant distributions.
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Are False-Positive Rates Leading to an Overestimation of Noise-Induced Hearing Loss?

TL;DR: Computer simulations provide an effective method for estimating false-positive rates for protocols used to identify notched audiograms, and audiometric precision could possibly be improved by eliminating systematic calibration errors.