LINGUIST List 15.20

Mon Jan 12 2004

Diss: Applied Ling: Moore: 'Procedure for...'

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  1. pmh_kc, Procedure for the Design of a Readability Index...

Message 1: Procedure for the Design of a Readability Index...

Date: Wed, 7 Jan 2004 12:38:27 -0500 (EST)
From: pmh_kc <pmh_kcyahoo.co.uk>
Subject: Procedure for the Design of a Readability Index...

Institution: Universidad Nacional Aut�noma de M�xico
Program: Masters in Applied Linguistics
Dissertation Status: Completed
Degree Date: 2003

Author: Pauline Moore

Dissertation Title: Procedure for the Design of a Readability Index
for English Texts as read by Hispanic Readers

Linguistic Field: Applied Linguistics

Subject Language: English (code: ENG), Spanish (code: SPN)

Dissertation Director 1: Roland Terborg
Dissertation Director 2: Natalia Ignatieva
Dissertation Director 3: Marilyn Buck
Dissertation Director 4: Laura Garc�a Landa

Dissertation Abstract:

This dissertation used verbal report techniques to research reading
difficulties encountered in English text by two Hispanic readers.
Think-aloud and recall protocols were collected from two readers, who
were interviewed to discover their beliefs about and attitude to
reading in English, with three different texts, which had been
previously measured for difficulty on four dimensions; lexical
complexity, syntactic complexity, discourse pattern and cultural load.
The resulting transcripts were then analyzed for the frequency of
problems presented by each text. The frequency and type of problems
were compared to the predictions of order and degree of difficulty
resulting from the analysis of the four textual dimensions. It was
found that, while there were significant differences in the reading
ability and style of the two subjects, there was also considerable
similarity in the difficulties they encountered. The results suggest
that text difficulty is susceptible to measurement which would prove
useful for selection of examination materials. It would also seem
clear that no one dimension is sufficient on its own to predict text
difficulty.
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