Intraclass correlation (ICC) is one of the most commonly misused indicators of interrater reliability, but a simple step-by-step process will get it right. In this article, I provide a brief review of reliability theory and interrater reliability, followed by a set of practical guidelines for the calculation of ICC in SPSS.
Big science is on the rise. Recent endeavors, such as the Large Hadron Collider and the Human Genome Project, illustrate the rise in large-scale scientific inquiries. To assess whether big science is part of a general trend towards increased authorship, we queried the publicly available database Pubmed and measured the trend in number of authors per paper over the last century. Here we show that authorship has increased five-fold since 1913 and predict that by 2034, publications will boast an average of 8 authors.
We argue that a theory of the evolution of Empathizing (E) and Systemizing (S) needs first to clarify that these are personality traits, as distinct from cognitive abilities. The theory should explain both the observed reciprocity of, and the sexual difference between, E and S in a context of the historical emergence of these traits and their balance in relation to local selection pressures. We suggest that the baseline state is that (since humans are social animals) ancestral human hunter gatherers are assumed to be relatively High Empathizers, lower in Systemizing: thus more interested in people than in things. Changes related to the development of agriculture and technology meant that it became economically useful for some men to become more interested in ‘things’ than in people, as a motivation for them to learn and practice skills that were vital to personal and (secondarily) social survival, reproduction and expansion. This selection pressure applied most strongly to men since in the sexual division of labour it was typically men’s role to perform such tasks. We further hypothesize that High Systemizing men were rewarded for their socially vital work by increased resources and high status. Because marriages were arranged in traditional societies mainly by parental choice (and the role of parental choice was probably increased by agriculture), it is presumed that the most valued women, that is young and healthy women thereby having high reproductive potential, were differentially allocated to be wives of economically successful High Systemizers. Such unions of economically successful High Systemizing men with the most reproductively valuable women would be expected to lead to greater-than-average reproductive success, thereby amplifying the population representation of genes that cause high systematizing in the population. This hypothesis makes several testable predictions.
Hiya. I’m David McCandless, a London-based author, writer, designer and founder of Information is Beautiful (Facebook / Twitter). I’m interested in how visualized information & data can help us understand the world, and reveal the hidden connections, patterns & stories beneath the surface. Edit (12:00 ET): I’m back, chomping through these great questions. Keep asking. Edit (12:21 ET): Nice (inevitable) discussion on pie charts already: https://www.reddit.com/r/dataisbeautiful/comments/3ol03x/hi_im_david_mccandless_founder_of_information_is/cvy3emu Edit (12:37 ET): Getting stuck into Excel now too… https://www.reddit.com/r/dataisbeautiful/comments/3ol03x/hi_im_david_mccandless_founder_of_information_is/cvy3eq3 **edit (13:50 ET): Taking a break - back in 10 or so. Back and on it. edit (15:12 ET) I’m done. My brain is cooked! What amazing and insightful questions. Thank you all very much for a great experience. I’ll try to pop back later and answer some more questions. I’ve been a big lurker on Reddit for years but maybe now I will come out a bit more. At least to polish off some of the fights below… My main passion is visualizing data & information about anything I don’t fully understand, such as Snake Oil? Evidence for Nutritional Supplements, A Million Lines of Code, or How Many Gigatons of CO2 Will it Take to End the World?. The more stupified or confused by a subject I am, the better the resulting viz, I’ve found. I particularly love applying a visualization / design lens to unusual subject matter. Like The Left vs Right Political Spectrum, Psychological Defenses, Rhetological Fallacies or The Best Data Dog. Before design, I freelanced for outlets like The Guardian and Wired. Before that, I was a video games reviewer and Doom champion (I have eerie gaming skills). And yes, it’s true. I made The Helicopter Game. These days, I’ve been playing with software, developing a platform called VizSweet to generate static & interactive data-visualisations. Examples: World’s Biggest Data Breaches, The Internet of Things or every key relationship in the Middle East. I’ve recently started teaching too so happy to answer questions on What Makes a Good Visualization?. I see visualization as a new language, culture and form of expression. I’m very excited about its future. I’m a longterm Reddit lurker - so very honoured to be here. Here’s proof that it’s me. I’ll be back at noon ET to answer all your questions. In the meantime, Ask Me Anything.
Are dreidels fair? In other words, does the average dreidel have an equal chance of turning up any one of its four sides? To explore this hypothesis, three different dreidels were each spun hundreds of times with the number of occurrences of each side recorded. It was found that all three dreidels tested -- a cheap plastic dreidel, an old wooden dreidel, and a dreidel that came embossed with a picture of Santa Claus -- were not fair. Statistically, for each dreidel, some sides came up significantly more often than others. Although an unfair dreidel does not necessarily make the game itself unfair, it is conjectured that hundreds of pounds of chocolate have been distributed during Chanukah under false pretenses.
This article is about whether the factors which drive online sharing of non-scholarly content also apply to academic journal titles. It uses Altmetric scores as a measure of online attention to articles from Frontiers in Psychology published in 2013 and 2014. Article titles with result-oriented positive framing and more interesting phrasing receive higher Altmetric scores, i.e., get more online attention. Article titles with wordplay and longer article titles receive lower Altmetric scores. This suggests that the same factors that affect how widely non-scholarly content is shared extend to academia, which has implications for how academics can make their work more likely to have more impact.
Blog and podcast use is rising among learners in the health professions. The lack of a standardized method to assess the quality of these resources prompted a research agenda aimed at solving this problem. Through a rigorous research process, a list of 151 quality indicators for blogs and podcasts was formed and subsequently refined to elicit the most important quality indicators. These indicators are presented as Quality Checklists to assist with quality appraisal of medical blogs and podcasts.
Evolutionary artificial life systems have demonstrated many exciting behaviors. However, there is a general consensus that these systems are missing some element of the consistent evolutionary innovation that we see in nature. Many have sought to create more “open-ended” evolutionary systems in which no stagnation occurs, but have been stymied by the difficulty of quantifying progress towards such a nebulous concept. Here, we propose an alternate framework for thinking about these problems. By measuring obstacles to continued innovation, we can move towards a mechanistic understanding of what drives various evolutionary dynamics. We propose that this framework will allow for more rigorous hypothesis testing and clearer applications of these concepts to evolutionary computation.
Broadly, I’m interested in the process of data analysis/science and how to make it easier, faster, and more fun. That’s what has lead to the development of my most popular packages like ggplot2, dplyr, tidyr, stringr. This year, I’ve been particularly interested in making it as easy as possible to get data into R. That’s lead to my work on the DBI, haven, readr, readxl, and httr packages. Please feel free to ask me anything about the craft of data science. I’m also broadly interested in the craft of programming, and the design of programming languages. I’m interested in helping people see the beauty at the heart of R and learn to master it as easily as possible. As well as a number of packages like devtools, testthat, and roxygen2, I’ve written two books along those lines: Advanced R, which teaches R as a programming language, mostly divorced from its usual application as a data analysis tool. R packages, which teaches software development best practices for R: documentation, unit testing, etc. Please ask me anything about R programming! Other things you might want to ask me about: I work at RStudio. I’m the chair of the infrastructure steering committee of the R Consortium. I’m a member of the R Foundation. I’m a fellow in the American Statistical Association. I’m an Adjunct Professor of Statistics at Rice University: that means they don’t pay me and I don’t do any work for them, but I still get to use the library. I was a full time Assistant Professor for four years before joining RStudio. These days I do a lot of programming in C++ via Rcpp. Many questions about my background, and how I got into R, are answered in my interview at priceonomics. A lot of people ask me how I can get so much done: there are some good answers at quora. In either case, feel free to ask for more details! Outside of work, I enjoy baking, cocktails, and bbq: you can see my efforts at all three on my instagram. I’m unlikely to be able to answer any terribly specific questions (I’m an amateur at all three), but I can point you to my favourite recipes and things that have helped me learn. I’ll be back at 3 PM ET to answer your questions. ASK ME ANYTHING! Update: proof that it’s me Update: taking a break. Will check back in later and answer any remaining popular/interesting questions
Preprints have become a popular topic of conversation among publishers, researchers, funders, librarians, technology builders, and service providers. Their attention is spurring explorations into building technology that will accommodate the uptake of preprints by the researcher community. I propose that the attention that preprints are currently receiving provides us with a rare opportunity to build technology that will facilitate a new era of research communication.