Data Analysis: Strengthening Inferences in Quantitative Education Studies Conducted by Novice Researchers

Autor/inn/en	Abulela, Mohammed A. A.; Harwell, Michael M.
Titel	Data Analysis: Strengthening Inferences in Quantitative Education Studies Conducted by Novice Researchers
Quelle	In: Educational Sciences: Theory and Practice, 20 (2020) 1, S.59-78 (20 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2630-5984
Schlagwörter	Educational Research; Educational Researchers; Novices; Data Analysis; Statistical Inference; Research Problems; Validity; Achievement Tests; Foreign Countries; International Assessment; Secondary School Students; Reliability; Evidence; Models; Measurement; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Bildungsforschung; Pädagogische Forschung; Erziehungswissenschaftler; Erziehungswissenschaftlerin; Auswertung; Inferential statistics; Schließende Statistik; Forschungskritik; Gültigkeit; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Sekundarschüler; Reliabilität; Evidenz; Analogiemodell; Messverfahren
Abstract	Data analysis is a significant methodological component when conducting quantitative education studies. Guidelines for conducting data analyses in quantitative education studies are common but often underemphasize four important methodological components impacting the validity of inferences: quality of constructed measures, proper handling of missing data, proper level of measurement of a dependent variable, and model checking. This paper highlights these components for novice researchers to help ensure statistical inferences are valid. We used empirical examples involving contingency tables, group comparisons, regression analysis, and multilevel modelling to illustrate these components using the Program for International Student Assessment (PISA) data. For every example, we stated a research question and provided evidence related to the quality of constructed measures since measures with weak reliability and validity evidence can bias estimates and distort inferences. The adequate strategies for handling missing data were also illustrated. The level of measurement for the dependent variable was assessed and the proper statistical technique was utilized accordingly. Model residuals were checked for normality and homogeneity of variance. Recommendations for obtaining stronger inferences and reporting related evidence were also illustrated. This work provides an important methodological resource for novice researchers conducting data analyses by promoting improved practice and stronger inferences. (As Provided).
Anmerkungen	Educational Consultancy, Ltd (EDAM). Kisikli Mh. Alemdag Cd. Yan Yol Sk., SBK Is Merkezi No:5 Kat:1, Uskudar-Istanbul, 34692 Turkey. Tel: +90-216-481-30-23; Fax: +90-216-481-31-36; e-mail: editor@edam.com.tr; Web site: http://www.estp.com.tr/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01