DBERlibR: Automated Assessment Data Analysis for Discipline-Based
Discipline-Based Education Research scientists repeatedly analyze assessment data to ensure question items’ reliability and examine the efficacy of a new educational intervention. Analyzing assessment data comprises multiple steps and statistical techniques that consume much of researchers’ time and are error-prone. While education research continues to grow across many disciplines of science, technology, engineering, and mathematics (STEM), the discipline-based education research community lacks tools to streamline education research data analysis. ‘DBERlibR’—an ‘R’ package to streamline and automate assessment data processing and analysis—fills this gap. The package reads user-provided assessment data, cleans them, merges multiple datasets (as necessary), checks assumption(s) for specific statistical techniques (as necessary), applies various statistical tests (e.g., one-way analysis of covariance, one-way repeated-measures analysis of variance), and presents and interprets the results all at once. By providing the most frequently used analytic techniques, this package will contribute to education research by facilitating the creation and widespread use of evidence-based knowledge and practices. The outputs contain a sample interpretation of the results for users’ convenience. User inputs are minimal; they only need to prepare the data files as instructed and type a function in the 'R' console to conduct a specific data analysis.\n For descriptions of the statistical methods employed in package, refer to the following Encyclopedia of Research Design, edited by Salkind, N. (2010) <doi:10.4135/9781412961288>.
||car, dplyr, emmeans, ggplot2, ggpubr, ggrepel, psych, readr, reshape, rstatix, tibble
||knitr, rmarkdown, testthat (≥ 3.0.0)
||Changsoo Song [aut, cre],
Resa Helikar [aut],
Wendy Smith [aut],
Tomas Helikar [aut]
||Changsoo Song <csong7 at unl.edu>
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