Statistical practices of educational researchers: An analysis of their ANOVA, MANOVA, and ANCOVA analyses

Articles published in several prominent educational journals were examined to investigate the use of data-analysis tools by researchers in four research paradigms: between-subjects univariate designs, between-subjects multivariate designs, repeated measures designs, and covariance designs. In addition to examining specific details pertaining to the research design (e.g., sample size, group size equality/inequality) and methods employed for data analysis, we also catalogued whether: (a) validity assumptions were examined, (b) effect size indices were reported, (c) sample sizes were selected based on power considerations, and (d) appropriate textbooks and/or articles were cited to communicate the nature of the analyses that were performed. Our analyses imply that researchers rarely verify that validity assumptions are satisfied and accordingly typically use analyses that are nonrobust to assumption violations. In addition, researchers rarely report effect size statistics, nor do they routinely perform power analyses to determine sample size requirements. We offer many recommendations to rectify these shortcomings.Social Sciences and Humanities Research Counci

Maximization of power in randomized clinical trials using the minimization treatment allocation technique

Generally the primary goal of randomized clinical trials (RCT) is to make comparisons among two or more treatments hence clinical investigators require the most appropriate treatment allocation procedure to yield reliable results regardless of whether the ultimate data suggest a clinically important difference between the treatments being studied. Although recommended by many researchers, the utilization of minimization has been seldom reported in randomized trials mainly because of the controversy surrounding the statistical efficiency in detecting treatment effect and its complexity in implementation. Methods: A SAS simulation code was designed for allocating patients into two different treatment groups. Categorical prognostic factors were used together with multi-level response variables and demonstration of how simulation of data can help to determine the power of the minimization technique was carried out using ordinal logistic regression models. Results: Several scenarios were simulated in this study. Within the selected scenarios, increasing the sample size significantly increased the power of detecting the treatment effect. This was contrary to the case when the probability of allocation was decreased. Power did not change when the probability of allocation given that the treatment groups are balanced was increased. The probability of allocation { } k P was seen to be the only one with a significant effect on treatment balance. Conclusion: Maximum power can be achieved with a sample of size 300 although a small sample of size 200 can be adequate to attain at least 80% power. In order to have maximum power, the probability of allocation should be fixed at 0.75 and set to 0.5 if the treatment groups are equally balanced

Soil Health Monitoring and Management in Corn and Soybean Agroecosystems of the Midwestern U.S.

Soil health is a concept and condition of the soil where measurable soil properties represent the capacity of a soil fulfilling its intended use, such as producing crops, without constraint to its agro-ecological quality. Soil health assessments are used to estimate the health of a soil by assessing soil biological, chemical, and physical attributes, called soil health indicators, and scoring them on a scale, usually 0 to 100, to guide soil and crop management. However, there are few large-scale analyses of soil health assessment scores and their relationships with crop yield. Understanding how soil health assessments relate to crop yield can support soil health practitioners and growers in making decisions that can direct efforts to improve soil health monitoring and management. The Soil Health Partnership (SHP) was a sizeable farmer-led network of on-farm trials assessing soil health throughout the Midwestern US. The on-farm data was used to explore the relationship between soil health and crop yield in three ways. First, how variability in soil health affects variability in yield. Second, the strength of the relationships between soil health assessment scores and crop yield. And third, the effects of conservation management on soil health indicators, scores, and yield. These analyses found that soil health indicator variation in time accounted for relatively little variability in corn and soybean yield over a two-to-four-year timespan at the SHP sites. Second, soil health scores of individual indicators or composite scores were not often correlated with crop yield on a site-to-site basis. This might suggest to soil health researchers and growers that other soil health outcomes, such as field runoff water quality, be measured to determine how soil health is improving additional soil ecosystem services. Third, the on-farm soil health trials revealed that few soil health indicators were affected by cover crops within a short one to four years of treatment timespan. Overall, these results suggest to growers that a whole-of-ecosystem approach be taken to monitoring soil health and that soil health measurements be taken before beginning a new conservation management plan, then every two to four years to allow time for soil health improvement