From Parametric to Non-Parametric Statistics in Education and Agricultural Education Research

Autor/inn/en	Silva-Lugo, Jose L.; Warner, Laura A.; Galindo, Sebastian
Titel	From Parametric to Non-Parametric Statistics in Education and Agricultural Education Research
Quelle	In: Journal of Agricultural Education and Extension, 28 (2022) 4, S.393-413 (21 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Silva-Lugo, Jose L.) ORCID (Warner, Laura A.) ORCID (Galindo, Sebastian)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1389-224X
DOI	10.1080/1389224X.2021.1936089
Schlagwörter	Agricultural Education; Nonparametric Statistics; Educational Research; Models; Research Methodology; Statistical Analysis; Behavior; Conservation (Environment); Water; Predictor Variables; Intention; Attitudes; Behavior Theories + Suchen Sie Ihr Suchwort? Agriculture; Education; Landwirtschaftliche Ausbildung; Landwirtschaft; Ausbildung; Bildungsforschung; Pädagogische Forschung; Analogiemodell; Research method; Forschungsmethode; Statistische Analyse; Conservation; Environment; Konservierung; Bewahung; Umwelt; Wasser; Prädiktor; Attitude; Einstellung; Verhalten
Abstract	Purpose: A literature research conducted in education and agricultural education journals published during a period of 10 years revealed that 98% of the studies used parametric analyses. In general, model assumptions were not tested, and statistical criteria were not followed to apply the parametric approach. The objective of this paper is to persuade researchers to use the most appropriate statistical analysis for their data. Design/Methodology/approach: We present a case study in agricultural education where a parametric multiple linear regression (MLR) could be applied. A survey was designed to find out how Theory of Planned Behavior and Importance-Performance variables were associated to Behavioral Intent concerning landscape water conservation practices. Although model assumptions were not met, we initially carried out a MLR analysis based on the premise that the results could be reported descriptively if they were double cross-validated successfully. Findings: The double cross-validation of the MLR was not successful, and model assumptions were not held even though the sample size was large. A quantile regression (QR) model fitted the data well. Theory of Planned Behavior and Importance-Performance variables were good predictors of Behavioral Intent, excepting Attitude. Practical implications: Researchers must rely on statistical criteria to support decisions regarding the use of parametric or nonparametric procedures. Theoretical implications: The adherence to best practices in the utilization of statistical procedures must be discussed as an ethical matter in research across all fields of science. Originality/value: We demonstrate that imposing the Central Limit Theorem to use the MLR model is not the correct criterion to apply a parametric approach. We should use double cross-validation. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01