Estimating and Comparing Growth Using Longitudinal Interim Achievement Data with Seasonal Trends

Autor/inn/en	Soland, James; Thum, Yeow Meng
Titel	Estimating and Comparing Growth Using Longitudinal Interim Achievement Data with Seasonal Trends
Quelle	In: Journal of Research on Educational Effectiveness, 15 (2022) 3, S.635-654 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Soland, James)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2021.2018744
Schlagwörter	Academic Achievement; Longitudinal Studies; Data Use; Computation; Comparative Analysis; Statistical Analysis; Growth Models; Achievement Gains; Achievement Tests; Effect Size; Grade 3; Grade 4; Grade 5; Grade 6; Grade 7; Grade 8; Measures of Academic Progress + Suchen Sie Ihr Suchwort? Schulleistung; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Statistische Analyse; Achievement gain; Leistungssteigerung; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; School year 03; 3. Schuljahr; Schuljahr 03; School year 04; 4. Schuljahr; Schuljahr 04; School year 05; 5. Schuljahr; Schuljahr 05; School year 06; 6. Schuljahr; Schuljahr 06; School year 07; 7. Schuljahr; Schuljahr 07; School year 08; 8. Schuljahr; Schuljahr 08
Abstract	Sources of longitudinal achievement data are increasing thanks partially to the expansion of available interim assessments. These tests are often used to monitor the progress of students, classrooms, and schools within and across school years. Yet, few statistical models equipped to approximate the distinctly seasonal patterns in the data exist, nor is there much guidance on how to choose among models. In this study, we present a general statistical model motivated by the seasonal character of interim achievement data and conduct analyses aimed at reducing barriers to the generation of empirical benchmarks for repeated measures achievement data. The model is designed to combine features from traditional polynomial models that estimate year-to-year growth but ignore within-year gains and losses with those from piecewise models, which directly estimate within-year gains/losses but do not include terms for year-to-year growth. Implications for research and policy are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01