Module 4: Strategic Reading
Organize your Readings with a Literature Review Matrix
The next step after reading and evaluating your sources is to organize them in a way that will help you start the writing process.
Review Matrix
One way to organize your literature is with a review matrix. The review matrix is a chart that sorts and categorizes the different arguments presented per topic or issue. Using a matrix enables you to quickly compare and contrast your sources in order to determine the scope of research across time. This will allow you to spot similarities and differences between sources. It is particularly useful in the synthesis and analysis stages of a review (See Module 1 Conducting a Literature Review with the SALSA Framework).
Example of a Review Matrix
My research question:
How can we use machine learning to analyze social media data related to HIV?
| Sources | Methods | Concept 1 | Concept 2 | Concept 3 | Gaps, Problems, Questions, Notes |
|---|---|---|---|---|---|
| Source 1:
Signorini, A., Segre, A. M., & Polgreen, P. M. (2011). The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PloS one, 6(5), e19467. |
Collected and stored a large sample of public tweets that matched a set of pre-specified search terms and geocoded. Estimated rate of disease and public sentiment toward swine flu | Able to make predictions about swine flu using social media data. This data is vital given that “an influenza surveillance program does not exist” (p. 3) | “When and where tweets are less frequent (or where only a subset of tweets contain geographic information), the performance of our model may suffer.” | ||
| Source 2:
Chiu, C. J., Menacho, L., Fisher, C., & Young, S. D. (2015). Ethics issues in social media–based HIV prevention in low-and middle-income countries. Cambridge Quarterly of Healthcare Ethics, 24(3), 303-310. |
Quantitative survey assessing participants’ perspectives on educational intervention | Increasing social media use in low- and middle-income countries. Participant took part in an HIV education program on Facebook | Most participants felt like they benefited positively from the program and learned about HIV prevention. All participants were MSM | Note: Helpful article for including diverse perspectives | |
| Source 3:
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8 |
Collected public tweets and analyzed mood | Gathered data from Twitter posts that explicitly states moods (e.g. “I’m feeling…”). Found that positive/negative sentiment on Twitter is 87.6% accurate for predicting stock market average | Used a “Self-Organizing Fuzzy Neural Network” to predict Dow Jones Industrial Average (p. 1) |
Writing a Literature Review Modified from The WI+RE Team, UCLA. Creative Commons CC-BY-NA-SA
Create a Review Matrix
Start with a charting tool you are most familiar with (for example MS Word, MS Exc