Courses including lab- or experiential-based learning may shy away from an online lab format. Using an empirically driven approach, an online research methods in psychology lab section was developed and compared to a face-to-face lab section. Results indicated that there were no differences in student knowledge regarding the American Psychological Association style and in the quality of the term papers (as evaluated by independent coders). Although students did not know whether they would be in a face-to-face or online lab when they enrolled, at the end of the semester, students expressed a preference for the style of lab in which they were enrolled. Recommendations for presenting labs or experiential learning content in an online format are discussed.

The global pandemic of 2020 caused by the novel coronavirus of 2019 (COVID-19) has uprooted the education system of the United States. As American colleges and universities try to resume regular instruction for the 2020-2021 academic year, outbreaks have begun to emerge and university towns across the country are now virus hotspots. The current paper provides two studies. First, the current work investigates how the growth of COVID-19 compares in areas with large universities against those without. Results showed markedly increase case growth in counties with large universities at the start of the fall 2020 semester. Secondly, this work provides a highly accessible and modifiable epidemiological tool known as a susceptible-infected-removed model for educational administrators that will allow users to see the impact of COVID-19 historically and predictively. The results of an exemplar model using a large public research university, Texas Tech University, are discussed.

Universities play a central role in a rural or small town’s economy. They are often the main forms of enrichment to the lives of the longtime residents, the students, and the employees. Unfortunately, during a global pandemic, the migration and movement of young people in these communities can likely cause a rapid infection spike and drive spread easily, especially relative to larger urban areas. The current study investigates the relationship between COVID-19 case growth, university-county rurality, and time at the beginning of the Fall 2020 academic semester. Findings showed that small metro and non-metro counties with universities had a dramatic infection spike near the beginning of the semester and infection growth remained significantly higher than their large and medium metro counterparts for the duration of the study. Suggestions to slow the spread in rural communities are discussed.

Objectives: Various individual factors have been shown to influence Covid-19 mortalities, but these factors do not exist in isolation. Unique to this study is a multivariate approach that has yet to be fully explored by previous research. Using an interconnected multifactor model, this work investigated social determinant, geographic, prior health, and political behavioral factors likely to influence Covid-19 per capita fatalities in Texas. Methods: County-level income, rurality, insurance, health status, 2020 presidential vote percentage, and fatality rate data were collected and analyzed in a path analysis model with Covid-19 per capita fatalities as the key variable of interest. Results: The analysis found strong support for the proposed model structure (R2 = 37.6%). The strongest overall effects on the Covid-19 per capita fatality rate came from income levels and voting behaviors. Conclusion: The model explained a substantial amount of variability in mortalities attributed to Covid-19. Socioeconomic and political factors provided the strongest contribution to the per-capita Covid-19 death rate, controlling for the other variables studied. The Covid-19 pandemic was highly politicized by various leaders and media outlets. The current analysis showed that political trends were one of the key overall factors related to Covid-19 mortality. The strongest overall factor was median income. Income is used to enhance one's current health or acquire adequate treatment which may safeguard people from the most severe effects of Covid-19. Counties with lower income levels had higher rates of Covid-19 per capita fatalities.