Best practices for addressing missing data through multiple imputation

A common challenge in developmental research is the amount of incomplete and missing data that occurs from respondents failing to complete tasks or questionnaires, as well as from disengaging from the study (i.e., attrition). This missingness can lead to biases in parameter estimates and, hence, in the interpretation of findings. These biases can be addressed through statistical techniques that adjust for missing data, such as multiple imputation. Although multiple imputation is highly effective, it has not been widely adopted by developmental scientists given barriers such as lack of training or misconceptions about imputation methods. Utilizing default methods within statistical software programs like listwise deletion is common but may introduce additional bias. This manuscript is intended to provide practical guidelines for developmental researchers to follow when examining their data for missingness, making decisions about how to handle that missingness and reporting the extent of missing data biases and specific multiple imputation procedures in publications

Teaching open and reproducible scholarship: a critical review of the evidence base for current pedagogical methods and their outcomes.

YesIn recent years, the scientific community has called for improvements in the credibility, robustness and reproducibility of research, characterized by increased interest and promotion of open and transparent research practices. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Specifically, a critical overview of the literature which investigates how integrating open and reproducible science may influence student outcomes is needed. In this paper, we provide the first critical review of literature surrounding the integration of open and reproducible scholarship into teaching and learning and its associated outcomes in students. Our review highlighted how embedding open and reproducible scholarship appears to be associated with (i) students' scientific literacies (i.e. students' understanding of open research, consumption of science and the development of transferable skills); (ii) student engagement (i.e. motivation and engagement with learning, collaboration and engagement in open research) and (iii) students' attitudes towards science (i.e. trust in science and confidence in research findings). However, our review also identified a need for more robust and rigorous methods within pedagogical research, including more interventional and experimental evaluations of teaching practice. We discuss implications for teaching and learning scholarship.UKRI/ESRC rapid call grant, ealth Research Board Applying Researchinto Policy and Practice Fellowship, John Templeton Foundation (grant ID: 62631), Northern Ireland Department for the Economy Research Studentshi

An integrative framework for planning and conducting Non-Intervention, Reproducible, and Open Systematic Reviews (NIRO-SR)

Most of the commonly used and endorsed guidelines for systematic review protocols and reporting standards have been developed for intervention research. These excellent guidelines have been adopted as the gold-standard for systematic reviews as an evidence synthesis method. In the current paper, we highlight some issues that may arise from adopting these guidelines beyond intervention designs, including in basic behavioural, cognitive, experimental, and exploratory research. We have adapted and built upon the existing guidelines to establish a complementary, comprehensive, and accessible tool for designing, conducting, and reporting Non-Intervention, Reproducible, and Open Systematic Reviews (NIRO-SR). NIRO-SR is a checklist composed of two parts that provide itemised guidance on the preparation of a systematic review protocol for pre-registration (Part A) and reporting the review (Part B) in a reproducible and transparent manner. This paper, the tool, and an open repository (https://osf.io/f3brw) provide a comprehensive resource for those who aim to conduct a high quality, reproducible, and transparent systematic review of non-intervention studies

Gaeumannomyces graminis, the take-all fungus and its relatives

Take-all, caused by the fungus Gaeumannomyces graminis var. tritici, is the most important root disease of wheat worldwide. Many years of intensive research, reflected by the large volume of literature on take-all, has led to a considerable degree of understanding of many aspects of the disease. However, effective and economic control of the disease remains difficult. The application of molecular techniques to study G. graminis and related fungi has resulted in some significant advances, particularly in the development of improved methods for identification and in elucidating the role of the enzyme avenacinase as a pathogenicity determinant in the closely related oat take-all fungus (G. graminis var. avenae). Some progress in identifying other factors that may be involved in determining host range and pathogenicity has been made, despite the difficulties of performing genetic analyses and the lack of a reliable transformation system.Peer reviewe

Examining the generalizability of research findings from archival data.

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability-for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples