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Topic Modeling in Management Research – Dr. Timothy Hannigan

Research Methods Education Program (RMEP)
2024-2025 Webcast Lecture Series
Topic Modeling in Management Research – Dr. Timothy Hannigan

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Question 1 of 6

1. Question

1. What is the primary purpose of a topic model in management research?

a. Predict future trends in financial markets
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b. Generate coding categories from textual data with minimal human intervention
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c. Translate texts into numerical datasets for regression
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d. Evaluate employee satisfaction levels
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Question 2 of 6

2. Question

2. Which topic modeling algorithm is noted for incorporating metadata such as time or author characteristics?

a. LDA
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b. BTM
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c. STM
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d. PCA
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Question 3 of 6

3. Question

3. What is the correct order of the rendering process as described by Hannigan et al. (2019)?

a. Fit model → Trim data → Interpret topics
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b. Collect metadata → Build theory → Apply model
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c. Select and trim data → Apply algorithm → Build theoretical artifacts
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d. Interpret data → Validate topics → Select corpus
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Question 4 of 6

4. Question

4. What are two key metrics used to assess topic model fir in STM?

a. Validity and reliability
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b. Accuracy and recall
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c. Perplexity and exclusivity
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d. Semantic coherence and exclusivity
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Question 5 of 6

5. Question

5. Which algorithm is most suitable for modeling short texts such as tweets?

a. LDA
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b. STM
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c. BTM
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d. PCA
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Question 6 of 6

6. Question

6. What role do covariates play in Structural Topic Modeling (STM)?

a. They are ignored in STM
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b. They help determine topic-word probabilities based on document metadata
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c. They serve only as labels for output topics
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d. They are only used for text preprocessing
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By Liliana Gomez|2025-08-26T11:50:42-04:00May 2nd, 2025|Comments Off on Topic Modeling in Management Research – Dr. Timothy Hannigan

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