No Strong Evidence of Stereotype Threat in Females: A Reassessment of the Meta-Analysis

Recently, Picho-Kiroga (2021) published a meta-analysis on the effect of stereotype threat on females. Their conclusion was that the average effect size for stereotype threat studies was d = .28, but that effects are overstated because the majority of studies on stereotype threat in females include methodological characteristics that inflate the apparent effect size. In this response, I show that Picho-Kiroga et al. (2021) committed fundamental errors in their meta-analysis that undermine confidence in the article and warrant major corrections. But even if the data were not flawed, the conclusion that Picho-Kiroga et al. (2021) should have reached is that their results are most consistent with a population effect size of zero. There is no compelling evidence that stereotype threat is a real phenomenon in females.

The case for default point-H1-hypotheses: a theory-construction perspective

The development of an empirically adequate theoretical construct for a given phenomenon of interest requires an estimate of the population effect size, aka the true effect. Arriving at this estimate in evidence-based ways presupposes access to robust experimental or observational findings, defined as statistically significant test-results with high statistical power. In the behavioral sciences, however, even the best journals typically publish statistically significant test-results with insufficient statistical power, entailing that such findings have insufficient replication probability. Whereas a robust finding formally requires that an empirical study engage with point-specific H0- and H1-hypotheses, behavioral scientists today typically point-specify only the H0, and instead engage a composite (directional) H1. This mismatch renders the prospects for theory-construction poor, because the population effect size—the very parameter that is to be modelled—regularly remains unknown. This can only keep from developing empirically adequate theoretical constructs. Based on the research program strategy (RPS), a sophisticated integration of Frequentist and Bayesian statistical inference elements, here we claim that theoretical progress requires engaging with point-H1-hypotheses by default.

Driving Factors of Carbon Emissions in China's Municipalities: A LMDI Approach

China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities directly under the Central Government, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that promote the growth of carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits the growth of carbon emissions, followed by energy structure effect. The paper thereby puts forward policy implications for China's economic policies.

Earwitness meta-analysis

The present meta-analysis investigates study and sample characteristics of mock earwitness performance. Based on primary studies we disentangled several a-priori moderators that modulate earwitness performance. Despite heterogeneous results in articles, we found experimental studies investigating effects of stimulus modality, stimulus length, retention interval, familiarity of language, and own-group or gender effects on earwitness performance. Including 33 articles with k = 49 experimental studies we performed a bare-bones and an artefact-corrected meta-analysis across all included primary studies and for five a-priori moderators. The results show a substantial ratio of the population effect size and the standard deviation of the population effect size exclusively for bimodal stimuli and concrete stimuli of the moderator stimulus modality. The fail-safe number was calculated to demonstrate which population effect sizes might be changed to zero depending on the number of unpublished primary studies. We highlight study and sample characteristics that facilitate earwitness performance. In power analyses, we show that the experimental design and individual differences should be taken into account to calculate sample sizes for future earwitness studies. We recommend best-practice strategies to investigate earwitness performance in future experimental studies and in individual earwitness assessments.

Inference of population effect and progeny selection via a multi-trait index in soybean breeding

The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.