Analay Perez is a research methodologist who research focuses on advancing mixed methods research methodology. Her research interests include advancing methods for assessing the validity (or legitimation) of mixed methods research studies, sampling, recruitment, and retention of specialized populations in health studies and clinical trials, and community-engaged research approaches. Dr. Perez is also interested in developing training programs to build research capacity for community health workers/promotoras (CHW/Ps) and has collaborated on a NIH-funded grant evaluating a research best practice training for CHW/Ps. She is interested in the use of AI for analyzing large qualitative and mixed methods datasets in healthcare.
How did you end up where you are today? (Your research journey)
My research journey can be described using one word: serendipitous. During my undergraduate education, I was determined to obtain a PhD at the intersection of psychology and law. My research interests primarily centered on investigative interviewing tactics and the use of interpreters during an interrogation by examining the strengths and challenges between the interpreter and interviewee dyad and their effects on a true and false confession. However, before embarking on a journey exploring the intersection of psychology and law, I was eager to learn more about research methodology and its applications across disciplines to ensure the robustness of research studies. Hence, I applied to a master’s program at the University of Nebraska-Lincoln that focused on quantitative, qualitative, and psychometrics methods.
At the beginning of the second year of my master’s program, I started working on my master’s thesis. My master’s thesis project focused on expanding a current validity framework used in mixed methods research to assess and ensure the validity/quality of mixed methods research studies and the generated meta-inferences (i.e., integrated conclusions). After successfully defending my thesis, I submitted the article for publication and was ecstatic to learn it had been accepted to the premier journal for mixed methods research methodology, the Journal of Mixed Methods Research.
As such, my drive to advance research methodology cultivated from my master’s program. I quickly realized I could continue to engage in research at the intersection of psychology and law, while also exploring and conducting research in other disciplines including the health sciences and education by applying robust methods and research approaches. Sound research methods are foundational to all studies and being able to ensure robustness along the research continuum from conceptualization to the dissemination phase are imperative.
I currently conduct research across disciplines in health sciences, education, psychology and law, and several other areas. As a research methodologist, I challenge my thinking across different projects, while also learning about the topic from a substantive perspective. This journey has been stimulating, and I am ecstatic and thrilled to continue advancing research methodology across disciplines through its applications.
What makes you excited about your data science and AI research?
Data science and AI have gained momentum especially in the past decade. This has primarily been seen within the area of quantitative research methodology, and more recently in qualitative research methodology, yet still limited research has been conducted on the use of AI in mixed methods research methodology. However, given that mixed methods research methodology is the intentional integration of quantitative and qualitative research approaches, research is needed to better understand how AI can facilitate research processes in a sound and robust manner across mixed methods research studies. Therefore, I am thrilled to explore how AI can facilitate the data analysis process in qualitative and mixed methods research methodology, particularly when using large datasets. Qualitative research methodology, especially, can be a lengthy process, requiring training, and increased efforts, personnel, and costs, that could potentially be reduced when leveraging with AI. However, the question still stands on how to use AI in a thoughtful and ethical manner, while also ensuring the trustworthiness or validity of findings generated from these AI models. Therefore, the use of AI across methodological approaches fascinates me and leaves many questions left be answered by researchers and scholars.