This course coversa range of approachesthat aim to enhance the transparency and reproducibility of social science research. It is appropriate for Ph.D. students in social science disciplines and related fields.
One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required …
“… there's this desert prison, see, with an old prisoner, resigned to his life, and a young one just arrived. The young one talks constantly of escape, and, after a few months, he makes a break. He's gone a week, and then he's brought back by the …
We estimated the statistical power of the first and last statistical test presented in 697 papers from 10 behavioral journals. First tests had significantly greater statistical power and reported more significant results (smaller p values) than did …
Statistical power is an inherent part of empirical studies that employ significance testing and is essential for the planning of studies, for the interpretation of study results, and for the validity of study conclusions. This paper reports a …
We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value …
Monte-Carlo simulation was used to model the biasing of effect sizes in published studies. The findings from the simulation indicate that, when a predominant bias to publish studies with statistically significant results is coupled with inadequate …
We review and evaluate selection methods, a prominent class of techniques first proposed by Hedges (1984) that assess and adjust for publication bias in meta-analysis, via an extensive simulation study. Our simulation covers both restrictive settings …