Overview
Description
This course equips graduate students with essential analytical techniques to model complex relationships using Partial Least Squares Structural Equation Modeling (PLS-SEM) and to extract meaningful insights from unstructured text data using modern text analytics methods. Given the prevalence of diverse data sources in business today, this course emphasizes the application of these methods to behavioral research in Information Systems using real-world data such as user reviews, survey responses, and online platform interactions.
Requirements
Units
Lecture3
Catalog Details
Offering
Offered: Every Fall
Terms
fall
Attributes
Standard
Learning Outcomes
- design and specify SEM models using PLS techniques, including measurement and structural models.
- evaluate construct validity and model fit using appropriate metrics such as composite reliability, AVE, SRMR, and HTMT.
- interpret mediation, moderation, and second-order constructs in structural models.
- preprocess and represent text data using tokenization, stemming, TF-IDF, and document-term matrices.
- apply sentiment analysis and topic modeling to extract patterns from user-generated text.
- use software tools such as SmartPLS, R, or Python to conduct empirical analyses related to structural equation modeling and text analytics.
- critically evaluate ethical considerations and limitations in using PLS-SEM and text analytics in IS research.