Defensible Progress Monitoring Data for Medium- and High-Stakes Decisions

Autor/inn/en	Parker, Richard I.; Vannest, Kimberly J.; Davis, John L.; Clemens, Nathan H.
Titel	Defensible Progress Monitoring Data for Medium- and High-Stakes Decisions
Quelle	In: Journal of Special Education, 46 (2012) 3, S.141-151 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-4669
DOI	10.1177/0022466910376837
Schlagwörter	Measurement; Computation; Response to Intervention; Regression (Statistics); Test Theory; Error of Measurement; Teachers; Special Education; Special Education Teachers; Curriculum Based Assessment; Intervals; Decision Making; Scores; Reliability; High Stakes Tests; Reading Fluency; Oral Reading; Disabilities + Suchen Sie Ihr Suchwort? Messverfahren; Regression; Regressionsanalyse; Testtheorie; Messfehler; Lehrer; Lehrerin; Lehrende; Special needs education; Sonderpädagogik; Sonderschulwesen; Special education; Teacher; Teachers; Sonderpädagoge; Decision-making; Entscheidungsfindung; Reliabilität; Oral work; Reading; Mündliche Übung; Leseprozess; Lesen; Handicap; Behinderung
Abstract	Within a response to intervention model, educators increasingly use progress monitoring (PM) to support medium- to high-stakes decisions for individual students. For PM to serve these more demanding decisions requires more careful consideration of measurement error. That error should be calculated within a fixed linear regression model rather than a classical test theory model, which has been more common. Seven practical skills are described for improving the use of PM data for medium- to high-stakes decisions: (a) estimating a static performance level in PM, (b) fitting a level of confidence to an educational decision, (c) expressing an estimated score ("Yhat") with its measurement error, (d) judging reliable improvement from one time to a later time, (e) properly using slope versus trendedness, (f) expressing "rate of improvement" (slope) with error, and (g) controlling autocorrelation. An example data set and PM graphs are used to illustrate each. (Contains 1 table and 6 figures.) (As Provided).
Anmerkungen	SAGE Publications and Hammill Institute on Disabilities. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10