Developed by Humad Bari
Flowchart Basics
This step-by-step flowchart describes how you can get started in conducting your own linguistics research. It is intended to support students at various stages of the research process, from identifying a topic of interest to understanding different quantitative and qualitative approaches. It first branches into two major areas, sociolinguistics and bilingualism, each containing a structured set of research topics you can explore in detail. Every topic includes a block introducing commonly used (primarily qualitative) methods, along with hyperlinks to reliable, academically vetted resources for learning those tools and skills. To help contextualize these methods in real-world practice, the flowchart also incorporates examples of prior research. These examples of student research papers are included for each topic, alongside a simplified and concise breakdown of each paper. This way, you may not need to initially read the entirety of a paper to understand if the research topic is something of your interest. The quick-skim approach is especially useful for researchers in their early-stages who may still need time to refine their research question, getting a more digestible summary.
Taking a Quantitative Approach in Linguistic Research
It is common for bilingualism and sociolinguistics to utilize qualitative research methods such as user interviews and discourse analysis. Although qualitative methods can offer depth and nuance, they can sometimes be limited in terms of scalability across larger datasets. Quantitative methods can help mitigate this issue by allowing researchers to analyze a wider range of datasets, identify statistical significance, and support their qualitative claims with empirical evidence. Because of this, the flowchart includes blocks that detail how each method can be approached quantitatively. These blocks are designed to guide students through the process of translating traditionally qualitative research questions into quantitative frameworks. Additionally, the end of each paper breakdown includes guiding questions regarding the possible expansion of quantitative measures in that specific project.
It is also important to learn ways in which we can utilize computational quantitative tools and methods to improve Natural Language Processing (NLP), Machine Learning (ML), and Computational Linguistics, since these fields have a strong linguistic foundation. By outlining relevant tools, sources, and methods, the flowchart aims to aid students in developing an interest in a qualitative topic and guide them into thinking about how learning and using quantitative skills can elevate a project that carries over facets of technological fields. One of the most important and common quantitative methods outlined, which has a low barrier to entry, is statistical analysis. Statistical analysis can be leveraged in a multitude of ways to compare and contrast important linguistic work, which can only help support research efforts. This is largely due to the sheer number of statistical tools publicly available, as advanced programming experience is not required, although it can be helpful in more advanced settings. Even basic statistical tests can reveal meaningful patterns in linguistic data, making them a practical starting point for researchers new to quantitative methods, many of which are outlined in the flowchart.
Additional Resources and Tools at UCLA to Improve Quantitative Skills
UCLA and the internet in general have 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 nine different, but strong and quantitatively relevant committees within ACM at UCLA. All nine can be applied in some sense toward 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. It’s 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 it’s vital to learn the necessary skills to further succeed and add to your arsenal of research tools.