From parametric to non-parametric statistics in education and agricultural education research

Abstract

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 non-parametric 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. GRAPHICAL ABSTRACT