IS 705 - Data Analysis in Information Systems II

(3 Units)

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.

Maximum units a student may earn: 3

Prerequisite(s): IS 704 or BUS 706

Grading Basis: Graded
Units of Lecture: 3
Offered: Every Fall

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. design and specify SEM models using PLS techniques, including measurement and structural models.
2. evaluate construct validity and model fit using appropriate metrics such as composite reliability, AVE, SRMR, and HTMT.
3. interpret mediation, moderation, and second-order constructs in structural models.
4. preprocess and represent text data using tokenization, stemming, TF-IDF, and document-term matrices.
5. apply sentiment analysis and topic modeling to extract patterns from user-generated text.
6. use software tools such as SmartPLS, R, or Python to conduct empirical analyses related to structural equation modeling and text analytics.
7. critically evaluate ethical considerations and limitations in using PLS-SEM and text analytics in IS research.

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