Dynamic Norming for Systematic Analysis of Language Transcripts

Autor/inn/en	Tucci, Alexander; Plante, Elena; Heilmann, John J.; Miller, Jon F.
Titel	Dynamic Norming for Systematic Analysis of Language Transcripts
Quelle	In: Journal of Speech, Language, and Hearing Research, 65 (2022) 1, S.320-333 (14 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Tucci, Alexander) ORCID (Plante, Elena) ORCID (Heilmann, John J.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
Schlagwörter	Norms; Psychometrics; Databases; Error of Measurement; Transcripts (Written Records); Sample Size; Children; Child Language; Language Impairments + Suchen Sie Ihr Suchwort? Normwert; Psychometry; Psychometrie; Datenbank; Messfehler; Child; Kind; Kinder; 'Children''s language'; Kindersprache; Speech disorder; Speech disorders; Speech disabilities; Speech disability; Speech handicap; Speech handicaps; Speech impairment; Speech impairments; Language handicaps; Sprachbehinderung
Abstract	Purpose: This exploratory study sought to establish the psychometric stability of a dynamic norming system using the Systematic Analysis of Language Transcripts (SALT) databases. Dynamic norming is the process by which clinicians select a subset of the normative database sample matched to their individual client's demographic characteristics. Method: The English Conversation and Student-Selected Story (SSS) Narrative databases from SALT were used to conduct the analyses in two phases. Phase 1 was an exploratory examination of the standard error of measure (SEM) of six clinically relevant transcript metrics at predetermined sampling intervals to determine (a) whether the dynamic norming process resulted in samples with adequate stability and (b) the minimum sample size required for stable results. Phase 2 was confirmatory, as random samples were taken from the SALT databases to simulate clinical comparison samples. These samples were examined (a) for stability of SEM estimations and (b) to confirm the sample size findings from Phase 1. Results: Results of Phase 1 indicated that the SEMs for the six transcript metrics across both databases were low relative to each metric's scale. Samples as small as 40-50 children in the Conversation database and 20-30 children in the SSS Narrative database resulted in stable SEM estimations. Phase 2 confirmed these findings, indicating that age bands as small as ±4 months from a given center-point resulted in stable estimations provided there were approximately 35 children or more in the comparison sample. Conclusion: Psychometrically stable comparison samples can be achieved using SALT's dynamic norming system that are much smaller than the standard sample size recommended in most tests of children's language. (As Provided).
Anmerkungen	American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01