No Evidence Against the Greater Male Variability Hypothesis: A Commentary on Harrison et al.’s Meta-Analysis of Animal Personality

Harrison et al. (2021) set out to test the greater male variability hypothesis with respect to personality in non-human animals. Based on the non-significant results of their meta-analysis, they concluded that there is no evidence to support the hypothesis, and that biological explanations for greater male variability in human psychological traits should be called into question. Here, we show that these conclusions are unwarranted. Specifically: (a) in mammals, birds, and reptiles/amphibians, the magnitude of the sex differences in variability found in the meta-analysis is entirely in line with previous findings from both humans and non-human animals; (b) the generalized lack of statistical significance does not imply that effect sizes were too small to be considered meaningful, as the study was severely underpowered to detect effect sizes in the plausible range; (c) the results of the meta-analysis can be expected to underestimate the true magnitude of sex differences in the variability of personality, because the behavioral measures employed in most of the original studies contain large amounts of measurement error; and (d) variability effect sizes based on personality scores, latencies, and proportions suffer from lac of statistical validity, adding even more noise to the meta-analysis. In total, Harrison et al.’s study does nothing to disprove the greater male variability hypothesis in mammals, let alone in humans. To the extent that they are valid, the data remain compatible with a wide range of plausible scenarios.

Farmers’ choice of varieties and demand for improved cassava stems in Nigeria

In this study, we used a household level survey to assess choices of varieties and demand for improved cassava varieties. A multivariate probit model was used to examine the determinants of choice decisions of the farmers for different varieties preventing potential endogeneity and measurement error. A Linearized Almost Ideal Demand System (LA/AIDS) model was used to analyze the demand system for improved cassava varieties. The results of the (LA/AIDS) model indicated farmers were very price sensitive to changes in improved stem prices and incomes. We suggest that intervention program that will improve the purchasing power of the farmers should be targeted towards the smallholder cassava farmers to accelerate adoption of improved cassava varieties. Int. J. Agril. Res. Innov. Tech. 11(2): 42-51, Dec 2021

A new gamma degradation process with random effect and state-dependent measurement error

In this paper, a new gamma-based degradation process with random effect is proposed that allows to account for the presence of measurement error that depends in stochastic sense on the measured degradation level. This new model extends a perturbed gamma model recently suggested in the literature, by allowing for the presence of a unit to unit variability. As the original one, the extended model is not mathematically tractable. The main features of the proposed model are illustrated. Maximum likelihood estimation of its parameters from perturbed degradation measurements is addressed. The likelihood function is formulated. Hence, a new maximization procedure that combines a particle filter and an expectation-maximization algorithm is suggested that allows to overcome the numerical issues posed by its direct maximization. Moreover, a simple algorithm based on the same particle filter method is also described that allows to compute the cumulative distribution function of the remaining useful life and the conditional probability density function of the hidden degradation level, given the past noisy measurements. Finally, two numerical applications are developed where the model parameters are estimated from two sets of perturbed degradation measurements of carbon-film resistors and fuel cell membranes. In the first example the presence of random effect is statistically significant while in the second example it is not significant. In the applications, the presence of random effect is checked via appropriate statistical procedures. In both the examples, the influence of accounting for the presence of random effect on the estimates of the cumulative distribution function of the remaining useful life of the considered units is also discussed. Obtained results demonstrate the affordability of the proposed approach and the usefulness of the proposed model.

Bias for Treatment Effect by Measurement Error in Pretest in ANCOVA Analysis

This paper investigated consequences of measurement error in the pretest on the estimate of the treatment effect in a pretest–posttest design with the analysis of covariance (ANCOVA) model, focusing on both the direction and magnitude of its bias. Some prior studies have examined the magnitude of the bias due to measurement error and suggested ways to correct it. However, none of them clarified how the direction of bias is affected by measurement error. This study analytically derived a formula for the asymptotic bias for the treatment effect. The derived formula is a function of the reliability of the pretest, the standardized population group mean difference for the pretest, and the correlation between pretest and posttest true scores. It revealed a concerning consequence of ignoring measurement errors in pretest scores: treatment effects could be overestimated or underestimated, and positive treatment effects can be estimated as negative effects in certain conditions. A simulation study was also conducted to verify the derived bias formula.