Intro Stats Assumptions All Math All hand calculations Not Reality What I want Data based, Applied Include code Difficulties Differing levels of computer literacy Unfamiliar with code Enter IDAS What is it? Cloud or server based computational environment for statistics/data science Interact with computational environment in a browser Embeds code with text Jupyter Lab / Notebooks Can run different kernels: R, Python, Julia RStudio Server Full R IDE Benefits - No Setup Benefits - Interesting Data Benefits - Getting back to applied stats Short Demo if time Notebook from my class Open materials here Thank you https://brandonlebeau.
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Rationale Data entry is an important component for quantitative studies Often neglected in courses Messy data can make data manipulation much more difficult Substantial time could be lost due to poor data entry procedures Strong data entry procedures are particularly important in evidence synthesis Rationale 2 Data Organization in Spreadsheets - Broman and Woo (2018), The American Statistician, https://doi.org/10.1080/00031305.2017.1375989 Tidy Data - Wickham (2014), Journal of Statistical Software, https://www.
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Rationale Extension of work done by Robert Muenchen (http://r4stats.com/articles/popularity/). Focus is on statistical software Addition of quantitative methods Particularly interested in the interaction. Questions of Interest: Exploring which software is popular in published research. How many empirical analyses cite software? Any patterns in software use with quantitative methods? Methods Research synthesis methods were used Web of Knowledge was used to pull in citations for 12 social science journals 1995 to 2018 EndNote’s “Find Text” feature was used to pull in PDFs of all articles from the journals pdfsearch R package was used to perform keyword searching.
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