Recruiting participants is a necessary step in many studies. With the advent of online research techniques, scientists are looking for new places where participants can be recruited online, in order to overcome the limitations of current sources and avoid the issues associated with sample overuse. The social media website “Reddit” is a potential source for recruitment, as it allows for free and rapid data collection from large samples, while enabling researchers to target specific populations when needed. The ability to recruit for free is especially important because it enables students and early career researchers, for whom even low recruitment costs can be prohibitive, to benefit from the opportunity of conducting research that they otherwise would not be able to. The current article therefore aims to bring this prospective, untapped resource to the attention of the research community. The article discusses current online recruitment sources and their limitations, provides an overview of Reddit, validates its use for research purposes, examines participation data from previous studies which recruited through Reddit, highlights its advantages and limitations as a participant pool, and suggests guidelines that can improve recruitment and retention rates for scientists looking to use Reddit for their research.

There are few mechanisms that bring the academic and business worlds together in a way that would maximize the success of health technology (health tech) start-ups by increasing researchers’ knowledge about how to operate in the business world. Existing solutions (eg, technology transfer offices and dual degree MD/MBA programs) are often unavailable to researchers from outside the institution or to those who have already completed their primary education, such as practicing physicians. This paper explores current solutions and offers a partial solution: include venture capital (VC) panels in medical conferences. These VC panels educate academics on 2 important and interconnected issues: how to “pitch” their ideas in the business world and what to consider when creating a company. In these sessions, academia-based start-up companies present their ideas before a VC panel composed of professional investors and receive feedback on their idea, business plan, and presentation techniques. Recent panel recommendations from Medicine 2.0 conferences fell into 7 categories: (1) the product, service, or idea you are developing into a company, (2) determine market forces and identify the target audience, (3) describe your competitive advantage, (4) the business plan, (5) current and future resources and capabilities, (6) legal aspects, and (7) general advice on the art of pitching. The academic and business literature validates many of these recommendations suggesting that VC panels may be a viable and cost-effective introduction to business and entrepreneurial education for physicians and other health care professionals. Panels benefit not only the presenting companies, but also the physicians, psychologists, and other health care professionals attending the session. Incorporating VC panels into academic conferences might also illuminate the need for incorporating relevant business training within academia.

No abstract

Statistical methods generally have assumptions (e.g., normality in linear regression models). Violations of these assumptions can cause various issues, like statistical errors and biased estimates, whose impact can range from inconsequential to critical. Accordingly, it is important to check these assumptions, but this is often done in a flawed way. Here, I first present a prevalent but problematic approach to diagnostics—testing assumptions using null hypothesis significance tests (e.g., the Shapiro–Wilk test of normality). Then, I consolidate and illustrate the issues with this approach, primarily using simulations. These issues include statistical errors (i.e., false positives, especially with large samples, and false negatives, especially with small samples), false binarity, limited descriptiveness, misinterpretation (e.g., of p-value as an effect size), and potential testing failure due to unmet test assumptions. Finally, I synthesize the implications of these issues for statistical diagnostics, and provide practical recommendations for improving such diagnostics. Key recommendations include maintaining awareness of the issues with assumption tests (while recognizing they can be useful), using appropriate combinations of diagnostic methods (including visualization and effect sizes) while recognizing their limitations, and distinguishing between testing and checking assumptions. Additional recommendations include judging assumption violations as a complex spectrum (rather than a simplistic binary), using programmatic tools that increase replicability and decrease researcher degrees of freedom, and sharing the material and rationale involved in the diagnostics.

Capitalisation is a salient orthographic feature, which plays an important role in linguistic processing during reading, and in writing assessment. Learners’ second language (L2) capitalisation skills are influenced by their native language (L1), but earlier studies of L1 influence did not focus on learners’ capitalisation, and examined primarily ‘narrow’ samples. This study examines capitalisation error patterns in a large-scale corpus of over 133,000 texts, composed by nearly 38,000 EFL learners, who represent seven different L1s and a wide range of English proficiency levels. The findings show that speakers of all L1s made a large number of capitalisation errors, in terms of errors per word and error proportion (out of all errors), especially at lower L2 proficiency levels. Under-capitalisation was more common than over-capitalisation, though this gap narrowed over time. Interestingly, L1s which share English's Latin script had higher error rates, suggesting that (assumed) perceived similarity between the L1 and the L2 increases interference, though this interference could not be explained only through direct negative transfer. There was also an interaction between L1 influence and L2 proficiency, so that differences between speakers of different L1s became smaller as their L2 proficiency improved.