Students in the Department of Psychology spent the month of January learning RStudio , an open-source statistical programming language used in the field of data analysis, while simultaneously carrying out independent data analysis and writing scientific reports.
A relatively new program offered in psychology, the course is both an introduction to the R programming language and an opportunity to become more proficient with scientific data analysis, says instructor Pete Naegele.
“If a student is not familiar with programming or programming languages, but is experienced with scientific research, winter term affords them the extra time to devote to the programming aspects while simultaneously carrying out data analysis,” Naegele says. “Likewise, if a student has little scientific research background but is competent with programming languages, this Winter Term project gives them the same advantage.”
Since its development in the 1990s, R programming has become one of the most widely used and powerful tools in the field of data analysis worldwide. From higher education to health care, financial services, insurance, and government administration, R has become the most important language of data analysis.
“It applies to psychologists specifically because we are scientists in the practice of data collection and analysis to better understand the world and its inhabitants,” Naegele says. “R is a tool that not only allows us to carry out data analysis, but to also communicate those results in a common language with other scientists around the world.”
In fall 2019, the psychology department implemented Passion Driven Statistics , a National Science Foundation-funded curriculum in which students conduct their own research asking questions in the areas of health, geography and earth science, government, business, education, genetics, and more. Students carry out their own data analysis and then communicate their findings in the form of scientific reports, posters, and presentations.
“What makes Passion Driven Statistics important is that it is an inclusive pedagogy that engages students from diverse educational, social, and economic backgrounds, and empowers them to bring themselves into the experience and process of scientific research and data analysis in a way that inspires the pursuit of knowledge at advanced levels,” Naegele says.
“The goal of this learning is to increase the size and the diversity of the population in the field of data science.”
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