To build out my data sample, the first step was to determine the time frame that I would be analyzing. News sources mainly published between 2012, when Acts 20 and 22 were passed, and present day, 2023 were included in this study. To help distinguish between and fully encompass the different political parties, I used the media bias chart on allsides.org which clearly outlines the political bias of each news source. To find the news sources, I used non probability sampling, specifically purposive sampling. This is to help keep my data sample more focused and surrounding the topic of gentrification in Puerto Rico. In the research process, words such as displacement, tax haven, and power outage were plugged into news.google.com as well as the news source website itself making this a purposive sample.
When analyzing the news articles, both quantitative and qualitative analyses were conducted. Quantitatively, the frequency of specific language use such as relocation, wealth, and infrastructure is worth noting. Not only that but the use of images as well as their captions can be analyzed by examining the number of people, buildings, and Puerto Rican representation (Puerto Rican flag vs. American flag being shown). Furthermore, qualitative analysis explores the meanings and feelings behind these words and images. Looking at the tone and the message behind these news sources will be useful in understanding quantitative analysis as well as clues to understand the article as a whole. When qualitatively analyzing the images, looking at where the image is placed, the emotions that people in the image are emitting, and any prevalent bias that it may be displayed is worth noting. The combination of quantitative analysis, focusing on language frequency and visual elements, alongside qualitative analysis, delving into the deeper meanings and biases within the articles and images, provides a comprehensive approach to understanding the complex dynamics of gentrification discourse in Puerto Rico portrayed by various political parties.