This highly applied hands-on virtual course is ideal for qualitative and mixed methods researchers. It will feature four methodological frameworks along with their respective free and no-code software tools designed to:

1. Classify long texts like interview or observation data (LACOID published paper access: https://cutt.ly/SwiFCSj4) based on machine learning,
2. Classify short texts like social media posts or open-ended responses (MDCOR published paper access: https://cutt.ly/Xwhk55sP) while also relying on machine learning,
3. Identify the distribution of sentiments and emotions in raw or classified texts (SENA paper in print access: https://cutt.ly/aeXMaBxS) using natural language processing and linguistic analysis, and
4. Capture the geospatial contexts where our participants’ stories took place across time (GeoStoryTelling published paper access: https://cutt.ly/qrgauhna) via interactive visualization, geographical information systems, and multimedia integrations.

The common thread across all these published or accepted methodologies and software tools is their alignment with Truly Equal Status Design (TESD), wherein the balanced interweaving of qualitative and quantitative outputs is vital for researchers to be able to construct deep understandings of the structure and meaning of our qualitative sources of evidence. This interdependence helps researchers maintain clarity and transparency in the rationale driving the interpretation of the data and findings. In other words, following TESD, quantitative outputs are to be rendered useless without going back to our original qualitative inputs. It is only when we integrate and intermingle quantitative outputs with our unaltered textual data that deeply powerful and nuanced understandings may be gained and conveyed. In short, quantitative outputs are to be used as a map that may help us gain better, clearer, and more nuanced understandings of our participants’ experiences, as expressed in their unmodified narratives.

A value added to this course is that all processes may be achieved with software that runs locally, that is, without requiring us to upload our data to any server. Moreover, these software tools conduct a combination of machine learning text classification, natural language processing, and high level interactive visualizations without any statistical or computer programming or coding requirements. Our reliance on no-code software is an attempt to democratize access to data science among qualitative and mixed methods researchers.

Cost: $40 AERA Members/$55 Non-AERA Members

Register online to attend.

About the AERA Virtual Research Learning Series

The AERA Professional Development and Training Program announces the 2025 AERA Virtual Research Learning Series. In these courses, expert instructors share knowledge across multiple topics in qualitative, quantitative, and mixed data analysis, including qualitative thematic analysis, statistical techniques to analyze missing data, and research methods used in machine learning text classification. Graduate students, postdoctoral scholars, and researchers, as well as others who are interested in enhancing their research skills, may find these courses helpful.