USpaceThis link opens in a new windowUSpace is the University of Utah's open access institutional repository. We currently offer data consultation for datasets of all sizes and archiving services for datasets and related documentation for files up to 40 MB. If USpace is not the right repository for you, our data librarians can help you find an appropriate repository for your data.
This database provides access to a collection of sociological and demographic data. The majority of ICPSR data holdings are public-use files with no restrictions on their access. ICPSR provides access to restricted use versions that retain confidential data by imposing stringent requirements for accessing them. The datasets which are available to the University of Utah are included in this collection. Topics covered by this database include criminal justice, health and aging, substance abuse and mental health, child care, and health and medical care.
Tsilidis KK, Panagiotou OA, Sena ES, Aretouli E, Evangelou E, Howells DW, et al. (2013) Evaluation of Excess Significance Bias in Animal Studies of Neurological Diseases. PLoS Biol 11(7): e1001609. doi:10.1371/journal.pbio.1001609
Reproducible Research with R and RStudio by Christopher GandrudWith straightforward examples, the book takes you through a reproducible research workflow, showing you how to use: R for dynamic data gathering and automated results presentation; knitr for combining statistical analysis and results into one document; LaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web.
Call Number: Marriott Q180.55.S7 G36 2014
The Cult of Statistical Significance by Deirdre Nansen McCloskey; Steve ZiliakThe Cult of Statistical Significance shows, field by field, how "statistical significance," a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ "testing" that doesn't test and "estimating" that doesn't estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots.
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