A Computational Software Program for Acceleration and Independent Learning of Statistical Analysis in Undergraduate Research Labs.
Start Date
April 2024
Location
MCD 208
Abstract
In many research labs, especially in undergraduate settings, projects often remain unfinished due to a backlog of unanalyzed statistical data from experiments. This backlog often results from students' limited familiarity with required statistical analyses and delayed implementation of these tests until the latter stages of data collection Consequently, numerous projects fail to reach completion, depriving researchers of essential feedback and increasing the potential for errors in ongoing projects and associated data. Therefore, raising necessity for an element that not only accelerates the production of statistical analyses but also enhances project efficiency, supports team members in the laboratory, and facilitates deeper comprehension and utilization of software programming for statistical analysis. The convergence of these factors leads to the creation of a computational software program designed for undergraduate research students called SAGD (Statistical Analysis Guide for Dummies). SAGD is a Java-based program that takes in data from experimentation and automatically computes the necessary statistical tests. SAGD automates the computation of necessary statistical tests and includes a one-page quick sheet with prompts, descriptions, and R-script code to aid in understanding and coding tests. This resource enables students to engage in statistical analysis confidently, even without a background in computer science. In conclusion, the development of SAGD addresses a pressing need in undergraduate research, providing a comprehensive solution to the challenges posed by data backlog and statistical analysis complexities. By offering both automated computation and accessible resources for independent learning, SAGD empowers students to navigate statistical analysis with confidence, facilitating smoother project progression and fostering deeper engagement in research.
A Computational Software Program for Acceleration and Independent Learning of Statistical Analysis in Undergraduate Research Labs.
MCD 208
In many research labs, especially in undergraduate settings, projects often remain unfinished due to a backlog of unanalyzed statistical data from experiments. This backlog often results from students' limited familiarity with required statistical analyses and delayed implementation of these tests until the latter stages of data collection Consequently, numerous projects fail to reach completion, depriving researchers of essential feedback and increasing the potential for errors in ongoing projects and associated data. Therefore, raising necessity for an element that not only accelerates the production of statistical analyses but also enhances project efficiency, supports team members in the laboratory, and facilitates deeper comprehension and utilization of software programming for statistical analysis. The convergence of these factors leads to the creation of a computational software program designed for undergraduate research students called SAGD (Statistical Analysis Guide for Dummies). SAGD is a Java-based program that takes in data from experimentation and automatically computes the necessary statistical tests. SAGD automates the computation of necessary statistical tests and includes a one-page quick sheet with prompts, descriptions, and R-script code to aid in understanding and coding tests. This resource enables students to engage in statistical analysis confidently, even without a background in computer science. In conclusion, the development of SAGD addresses a pressing need in undergraduate research, providing a comprehensive solution to the challenges posed by data backlog and statistical analysis complexities. By offering both automated computation and accessible resources for independent learning, SAGD empowers students to navigate statistical analysis with confidence, facilitating smoother project progression and fostering deeper engagement in research.
