Flowchart Basics
This step-by-step flowchart describes how you can get started in conducting your own linguistics research. Two major blocks are initially met, breaking the guide down to sociolinguistics and bilingualism-related topics. Within these two categories is a structured guide of different research areas that you can get started with. Each topic has a block dedicated towards explaining the common methods that are traditionally qualitative, with hyperlinks towards those resources. These resources are academically vetted and have great reliability when learning about new research methods, tools, and skills. Following the “Common Methods” blocks is an example of a research paper in accordance to the topic, as well a simplified and quick breakdown of the paper. This way, you may not need to initially read the entirety of a paper to understand if the research area is something of your interest. Instead, you can scan the breakdown of the research paper to get a more digestible summary.
Taking a Quantitative Approach in Linguistic Research
At the end of the research paper breakdown, as well as the next two blocks, is a detailed response in gearing this specific topic into a quantitative sense. It’s incredibly common for bilingualism and sociolinguistics to utilize qualitative research methods such as user interviews and discourse analysis. But as we grow into a more technologically advanced state where linguistics plays an increasingly crucial role, its important to learn and understand the ways in which we can utilize computational quantitative tools and methods to improve upon Natural Language Processing (NLP), Machine Learning (ML), and Computational Linguistics as a whole. The linked resources in the research breakdown paper as well as the last two blocks, explain in detail of how this particularly selected research project can be geared towards a quantitative lens, the tools and pre-requisites to this aspect, the linked quantitative methods that you can utilize, and an example of a posed topic that can be funneled in a quantitative focus. One of the most important and common quantitative areas that have a lower entry to barrier is utilizing statistical analysis. Statistical analysis can be leveraged in a multitude of ways to compare and contrast important linguistic work that can only help support your research efforts.
Additional Resources and Tools at UCLA to Improve Quantitative Skills
UCLA and the internet in general has incredible resources to self-learn quantitative tools that can expand your options of research greatly. The greatest opportunity to dive into is ACM at UCLA. ACM at UCLA is the largest Computer Science student community in all of Southern California. There are 9 different, but strong and quantitatively relevant committees within ACM at UCLA. All 9 can be applied in some sense towards linguistic-related research. However, the most important and applicable committees pertain to acm.design and acm.ai. Acm.design encompasses UI/UX, UX Research, and HTML/CSS. They host consistent workshops where you don’t need any prior experience. This opportunity is free and simply one click away from learning how you can establish a connection between linguistic research and UI/UX design. Many of the skills taught within this workshop such as user interviews and wireframing are directly applicable to a variety of linguistic and linguistic-related research topics. Acm.ai is the next committee that has the most profound impact in terms of skills and usage in linguistic research today. They host workshops that are categorized in three different tracks by your skill level, so no prior experience is needed. Its a strong way to grasp machine learning concepts that you can further apply in your research, with the methods described in the flowchart ranging from programming to statistics. A huge connection and correlation can be within NLP, a subsection of AI that lays the foundation for language models. NLP is incredibly hot and demanding in terms of job opportunities and research development. If computational linguistics, NLP, or AI Research is an interest of yours, then its vital to learn the necessary skills to further succeed and add to your arsenal of research tools.