Evolution of Statistical Software and Quantitative Methods Brandon LeBeau & Ariel M. Aloe University of Iowa
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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Simulation and Power Analysis of Generalized Linear Mixed Models Brandon LeBeau University of Iowa
Overview Power simglm package Shiny Demo Power Power is the ability to statistically detect a true effect (i.e. non-zero population effect). For simple models (e.g. t-tests, regression) there are closed form equations for generating power. R has routines for these: power.t.test, power.anova.test Gpower3 Power Example n <- seq(20, 1000, 5) power <- sapply(seq_along(n), function(i) power.

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Make Power Fun (Again?) Brandon LeBeau University of Iowa
Overview (G)LMMs Power simglm package Shiny Demo - Broken! Linear Mixed Model (LMM) Power Power is the ability to statistically detect a true effect (i.e. non-zero population effect). For simple models (e.g. t-tests, regression) there are closed form equations for generating power. R has routines for these: power.t.test, power.anova.test Gpower3 Power Example n <- seq(4, 1000, 2) power <- sapply(seq_along(n), function(i) power.

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