Comparing Hierarchical Data Structures and Hierarchical Data Analysis

Real world data is inherently noisy and data analysis can be especially complex when noise is compounded in hierarchical and multilevel data structures. Since such data structures can be described using multiple approaches, the way data is collapsed and grouped within these structures can influence its resulting interpretation and analyses. To avoid discrepancies in data collapsing and grouping, multiple statistical approaches have been developed specifically to analyze multilevel data structures. Examples of multilevel statistical models are the two-factor ANOVA and the general linear model with repeated-measures (GLM-RR) which is typically used in the context of looking at change over time. Unlike simple summary-statistics such as t-tests, multilevel models allow for precision in the effect of each level on the observed data. In this study, analyses will be done using both simple statistical models and multilevel models with a dataset from a behavioral decision-making assay that aims to see whether phototactic preference changes over 24 hours in larval zebrafish. The simple and multilevel analyses will be compared through the descriptive analyses and hypothesis testing. The descriptive analyses will provide insight into the practicality of collapsing levels of data in hierarchical data structures and the hypothesis testing will provide comparative insight into the use of both simple and multilevel statistical models

The Role of Community in Distance Learning Across Disciplines During COVID-19

Distance learning is a method of education that utilizes online platforms to make a course accessible to those geographically separated. Though it has never been the most prominent teaching approach, it became common beginning March 2020 when most universities that were traditionally taught in person transitioned to an online platform due to the COVID-19 pandemic. Because distance learning during COVID-19 is mandatory and not an option, prior studies that looked into distance learning before COVID-19 may not be applicable in today’s context. In addition, stressors relating to the current pandemic, social isolation, social movements, and political disputes may have a larger effect on distance learning. Therefore, it is important to understand the impact of this mode of education, including the impact of discipline on distance learning experiences since the online limitations across disciplines may vary. In this study, the Community of Inquiry (COI) framework will be used to analyze student engagement, a predictive factor of course performance, in terms of the social, cognitive, and teaching presence. In addition, data on demographics, potential sources of stressors, and distance learning experiences during COVID-19 will be collected, all through a survey given to undergraduate students at Loyola Marymount University. After data collection, analysis will be performed to look at general correlations found between student engagement and demographics, stressors, and distance learning experience as well as potential interdisciplinary correlations. Results aim to highlight different factors that impact students in different disciplines so colleges can respond to the needs of students appropriately