When ANOVA Isn't Ideal: Analyzing Ordinal Data from Practical Work in Biology

Autor/inn/en	Calver, Michael; Fletcher, Douglas
Titel	When ANOVA Isn't Ideal: Analyzing Ordinal Data from Practical Work in Biology
Quelle	In: American Biology Teacher, 82 (2020) 5, S.289-294 (6 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0002-7685
Schlagwörter	Statistical Analysis; Data Collection; Biology; Science Laboratories; Nonparametric Statistics; Data Interpretation; Computer Software; Plants (Botany) + Suchen Sie Ihr Suchwort? Statistische Analyse; Data capture; Datensammlung; Biologie; Data evaluation; Datenauswertung; Pflanze
Abstract	Data collected in many biology laboratory classes are on ratio or interval scales where the size interval between adjacent units on the scale is constant, which is a critical requirement for analysis with parametric statistics such as t-tests or analysis of variance. In other cases, such as ratings of disease or behavior, data are collected on ordinal scales in which observations are placed in a sequence but the intervals between adjacent observations are not necessarily equal. These data can only be interpreted in terms of their order, not in terms of the differences between adjacent points. They are unsuitable for parametric statistical analyses and require a rank-based approach using nonparametric statistics. We describe an application of one such approach, the Kruskal-Wallis test, to biological data using online freeware suitable for classroom settings. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01