Due to the COVID-19 events, we are canceling the LPRC methods workshop. We are working to reschedule the speaker for Fall 2020 or Spring 2021. Thank you for your continued support. –Brian French
2020 Methods Workshop
Presented by the Learning Performance Research Center
Additional sponsorship by Educational Psychology and Alaska Airlines
May 14-15, 2020 – 9:00 a.m. to 5:00 p.m.
About the Workshop
Network Analysis of Qualitative Data: Relying on Freeware, Rigor, and Transparency for the Public Good
This two-day workshop provides participants with an innovative tool to ease the sense-making process in qualitative analysis. Relying on Network Analysis and freeware, this course teaches participants how to generate roadmaps that detect structure emerging from qualitative content. This sense-making process can be applied to static and dynamic qualitative data. In the static version, the information provided by participants happens in one-to-one settings (e.g., individual interviews). With dynamic data, participants’ responses are recorded in settings where they influence the responses or behaviors of a group and are influenced by the group as well (e.g., video analysis, focus groups). In both cases, the product is a map (or sociogram) that detects the most influential content (captured by codes) and the most influential actors (captured by participants’ contributions). This map, in addition to easing the sense-making process, provides transparency to the findings and conclusions reached. Anyone who sees the map will be able to detect the most influential actors and codes. However, the resulting structure captured by the maps is just a tool; it does not replace the expertise and knowledge of qualitative researchers.
Presenter: Dr. Manuel González Canché
Manuel González Canché is an Associate Professor at University of Pennsylvania in Higher Education, and an affiliated faculty with the Human Development and Quantitative Methods division and the International Educational Development Program. He brings an innovative set of tools — including econometric, geospatial, and network analysis methods — to study the structural factors that influence minority and at-risk students’ likelihood of success, including less access to financial, academic, and social resources. He aims to identify plans of action capable of closing these social and economic gaps. His work has already challenged traditional ideas about access, persistence, and success in higher education, and has led to a better understanding of the effect of location, influence, and competition. As a low-income and first-generation college student, Dr. González Canché has a special interest in understanding structural factors that influence minority and at-risk students’ likelihood of educational and occupational success. He aims to identify plans of action capable of closing social and economic gaps resulting from students’ reduced access to financial, academic, and social resources. Dr. González Canché is the 2016 recipient of the Association for the Study of Higher Education’s Promising Scholar/Early Career Award. He has secured funding for research from the Spencer Foundation, the American Education Research Association/National Science Foundation, the Association for Institutional Research, and the Institute of Education Sciences.
Remote (videoconference): $40
2019 – Matthew McCrudden, Pennsylvania State University – Mixed Methods
2018 – Greg Hancock, University of Maryland – Latent Growth Curve Modeling
2017 – Roy Levy, Arizona State University – Foundations of Bayesian Statistical Modeling
2016 – Todd D. Little, Texas Tech University – Measurement, design, and analysis issues in longitudinal modeling with a particular focus on the longitudinal CFA model as the basis for both panel and latent growth curve modeling.
2015 – Greg Hancock, University of Maryland – Second Course in Structural Equation Modeling
2014 – Bethany Bray, Pennsylvania State University – An Introduction to Latent Class and Latent Profile Analysis
2013 – Greg Hancock, University of Maryland – A First Course in Structural Equation Modeling