By Allison Theobold and Stacey Hancock, Montana State University
Computational ability is increasingly required to apply statistics to modern scientific problems, but students in the sciences typically lack these integral skills. Consequently, many scientific graduate degree programs expect students to acquire these skills in an applied statistics course. However, it is readily apparent that these students are not being prepared with the data manipulation, data visualization, and data analysis computing skills required for the implementation of statistics in scientific research. I interviewed environmental science faculty members about the statistical computing expectations they have of graduate students from their program, and the statistical computing skills these faculty believe are necessary to conduct research in their field. I then conducted in-depth interviews with environmental science graduate students regarding their computational experiences in implementing statistics in their research. Qualitative analysis of the interviews provides evidence of a substantial gap between students’ coursework preparation and the statistical computing requirements of their research. These graduate students described themes of support structures, rather than curriculum, that helped them acquire the statistical computing skills necessary for their research. Based on these findings, we have created an ongoing series of four 3-hour statistical computing workshops to help alleviate the computational burdens experienced by these students when implementing statistics in their research. These workshops cover the key statistical computing skills outlined by environmental science faculty as well as the key statistical computing skills found to be necessary for graduate students to carry out their entire research process. Unlike other resources, these workshops have no barriers to entry and each workshop builds off the skills acquired in the previous workshop(s), starting with an introduction to R and progressing through intermediate concepts of data manipulation and visualization. These workshops are offered at a state land grant university through a partnership with the institution’s library, allowing for free access, and the use of an interactive learning environment for up to 35 faculty, staff, and graduate students. We present how our research on faculty member’s statistical computing expectations and the experiences of graduate students informed the structure and content of this series of statistical computing workshops. We also provide an outline of these workshops, their content, structure, and results from participants’ pre- and post-workshop surveys. This poster is intended for instructors of statistics, who will learn the importance of statistical computing preparation of students, key statistical computing skills that students are using when implementing statistics in their research, and tools with which to teach these skills, both inside and outside of the statistics curriculum.