東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Psychometric Characteristics of Acad...

Claar, Courtney Cici B.

Linked to FindBook

Google Book

Amazon

博客來

Psychometric Characteristics of Academic Language Discourse Analysis Tools.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Psychometric Characteristics of Academic Language Discourse Analysis Tools./
Author:	Claar, Courtney Cici B.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	95 p.
Notes:	Source: Masters Abstracts International, Volume: 84-02.
Contained By:	Masters Abstracts International84-02.
Subject:	Language. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29256744
ISBN:	9798841700555

Psychometric Characteristics of Academic Language Discourse Analysis Tools.
Claar, Courtney Cici B.

Psychometric Characteristics of Academic Language Discourse Analysis Tools. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 95 p.

Source: Masters Abstracts International, Volume: 84-02.

Thesis (Ed.S.)--University of South Florida, 2022.

This item must not be sold to any third party vendors.

Academic language plays a key role in students' educational success, yet its development in primary grades is poorly understood and often neglected (Snow & Uccelli, 2008). Academic language skills may enhance overall academic performance if targeted early and intensively. However, current methods of assessment are not sufficient to understanding the construct well enough to develop evidence-based intervention strategies. This investigation examined the psychometric properties of two discourse analysis tools designed to directly measure students' comprehension and production of academic language. Academic language samples (n = 7,887) from a previous cohort-design study (n = 1,040; Kindergarten through third grade participants) were scored using the Narrative Language Measure (NLM) Flowchart and the Expository Language Measure (ELM) Flowchart. A confirmatory factor analysis was used to test two-factor models for both flowcharts. The total scores and subscale scores of the NLM Flowchart demonstrated moderate to strong interrater reliability, moderate convergent validity, and approximate fit with the proposed model (generation χ²(46) = 743.85, p < .001, SRMR = .06, RMSEA = .08, CFI = .88, and TLI = .86; retell χ²(46) = 784.80, p < .001, SRMR = .05, RMSEA = .09, CFI = .91, and TLI = .90). One subscale (i.e., Narrative Structure) showed adequate internal consistency via Cronbach's alpha. This study found mixed evidence of interrater reliability for the ELM Flowchart, with weak agreement on one subscale (i.e., Passage Structure) and substantial to strong agreement on the other (i.e., Language Complexity). The ELM Flowchart demonstrated moderate convergent validity, but neither subscale reached acceptable levels of internal consistency via Cronbach's alpha. The appropriateness of using reflective indicator tools to evaluate constructs that may be better suited to a formative model is discussed. Other implications of the findings also are discussed.

ISBN: 9798841700555Subjects--Topical Terms:

643551
Language.
Subjects--Index Terms:

Academic language

Psychometric Characteristics of Academic Language Discourse Analysis Tools.
LDR:03238nmm a2200409 4500 001 2323142
005 20231010062915.5
006 m o d
007 cr#unu||||||||
008 231204s2022 ||||||||||||||||| ||eng d
020 $a 9798841700555
035 $a (MiAaPQ)AAI29256744
035 $a AAI29256744
040 $a MiAaPQ $c MiAaPQ
100 1 $a Claar, Courtney Cici B. $3 3642126
245 1 0 $a Psychometric Characteristics of Academic Language Discourse Analysis Tools.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 95 p.
500 $a Source: Masters Abstracts International, Volume: 84-02.
500 $a Advisor: Spencer, Trina D.;Castillo, Jose.
502 $a Thesis (Ed.S.)--University of South Florida, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Academic language plays a key role in students' educational success, yet its development in primary grades is poorly understood and often neglected (Snow & Uccelli, 2008). Academic language skills may enhance overall academic performance if targeted early and intensively. However, current methods of assessment are not sufficient to understanding the construct well enough to develop evidence-based intervention strategies. This investigation examined the psychometric properties of two discourse analysis tools designed to directly measure students' comprehension and production of academic language. Academic language samples (n = 7,887) from a previous cohort-design study (n = 1,040; Kindergarten through third grade participants) were scored using the Narrative Language Measure (NLM) Flowchart and the Expository Language Measure (ELM) Flowchart. A confirmatory factor analysis was used to test two-factor models for both flowcharts. The total scores and subscale scores of the NLM Flowchart demonstrated moderate to strong interrater reliability, moderate convergent validity, and approximate fit with the proposed model (generation χ²(46) = 743.85, p < .001, SRMR = .06, RMSEA = .08, CFI = .88, and TLI = .86; retell χ²(46) = 784.80, p < .001, SRMR = .05, RMSEA = .09, CFI = .91, and TLI = .90). One subscale (i.e., Narrative Structure) showed adequate internal consistency via Cronbach's alpha. This study found mixed evidence of interrater reliability for the ELM Flowchart, with weak agreement on one subscale (i.e., Passage Structure) and substantial to strong agreement on the other (i.e., Language Complexity). The ELM Flowchart demonstrated moderate convergent validity, but neither subscale reached acceptable levels of internal consistency via Cronbach's alpha. The appropriateness of using reflective indicator tools to evaluate constructs that may be better suited to a formative model is discussed. Other implications of the findings also are discussed.
590 $a School code: 0206.
650 4 $a Language. $3 643551
650 4 $a Elementary education. $3 641385
650 4 $a Educational tests & measurements. $3 3168483
653 $a Academic language
653 $a Language comprehension
653 $a Language measures
653 $a Language sampling
653 $a Oral language
653 $a Structural assessment
690 $a 0679
690 $a 0524
690 $a 0288
710 2 $a University of South Florida. $b Educational Measurement and Research. $3 1681700
773 0 $t Masters Abstracts International $g 84-02.
790 $a 0206
791 $a Ed.S.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29256744