Heather Habecker

Headshot of Heather Habecker

Research in Progress

My current research is focused on the evolutionary function of sibling relationships, trade-offs in the maximization of inclusive fitness versus direct reproductive success, and the impact of these factors on the timing of reproductive life history events on health outcomes, particularly reproductive cancers. I am very interested in combining cross-cultural data from ethnographies and big data from national repositories to help answer these questions.

I have also researched the oxytocin system and hypothalamic pituitary adrenal (HPA) axis, especially as they relate to the evolution and function of social relationships. My focus has often centered on variation in oxytocin release and binding in the mesocorticolimbic reward circuitry and the relationship between oxytocin receptor density and distribution in the brain and social behavior across and within species. I have explored the relationship between oxytocin receptor gene expression, cultural norms, and early childhood experience as mechanisms underlying variation in empathy, generosity, trust, altruism, and parochial altruism. Previously, I have researched inter- and intra-species variation in transcription factor binding sites and methylation in the third intron of the oxytocin receptor gene, in order to understand more about between species variation in social behavior as well as within species variation.

I also specialize in data science and analytics and seek to combine primary and secondary datasets to fully develop emic and etic perspectives on social relationships, reproductive life history traits, and health. In the past, I have also used secondary datasets to explore the following topics:

1) National Health and Nutrition Examination Survey (NHANES) analysis of the timing of life history events (menarche and menopause) and number of children on the likelihood of reproductive cancers in women.

2) State-level analysis of implicit and explicit racial bias and racial and ethnic disparities in COVID-19 outcomes in the US.

3) County-level analysis of major socioeconomic predictors of COVID-19 outcomes, drawn from multiple datasets.

4) Analysis of ballistocardiograph sensor data and electronic health records to predict cardiovascular health in residents of assisted living centers and to detect differences in health outcomes before and after the COVID-19 lockdown.


Ph.D. in Behavioral Neuroscience, Baylor University, expected 2022

M.S. in Data Science & Analytics, University of Missouri, 2021

M.A. in Cultural Anthropology, University of Missouri, 2018

B.S. in Anthropology, College of Charleston, 2013


I think that everyone has the potential to become a lifelong learner with burning questions of their own in which to seek out the answers. Through my own passion and engagement with the material in which I specialize as well as the focused mentorship, collaboration, and support I have learned from my own teachers along the way, I hope to awaken the spirit of inquiry in students, while simultaneously providing them the tools that give them the agency to understand these interests and answer their own questions.

I believe that no student is lost. Nor do I believe that there are some subjects that cannot become engaging. I believe that anyone can become immersed in a topic, regardless of their backgrounds, as long as it is presented by someone excited and passionate about it and by a teacher who can find a way to connect the material to the individual interests of the students. The key is to find that connection. My specialization in the social sciences is fortuitous in that it is an inherently fascinating field. I relish the opportunity to change the perspective of students to realize that many of the things we take for granted are actually fascinating and unique puzzles that we still don’t completely understand. The study of human beings, from understanding how the mind works to understanding how we are similar to and different from other people in other parts of the world, is a subject that is truly difficult to avoid. We are all curious about other people and ourselves, particularly different world-views, experiences, traditions, and ways of thinking and living.

That being said, I also specialize in data science and analytics, a field that is considered by many to be dull, difficult, and intimidating. However, the key to engaging students in this field also lies in awakening a curiosity and prompting the students to generate their own questions which they may almost obsessively seek to answer. The answers to many questions are out there, waiting to be found. I can show these students where to find the data they need or, when that data doesn’t exist yet, how to design a research project to collect that data. Moreover, through fostering a deeper understanding of data science, I can give them the tools that they need to analyze the data they have found. Each student with a question to answer and the data to answer it is desperate for the tools to do so. In the course I have developed on data science and bioinformatics for social scientists, I provide interactive tutorials for R that demonstrate the basics of data cleaning, transformation, and manipulation using packages that significantly flatten the learning curve of those new to programming languages. I provide sources for secondary data of interest to social scientists along with sections devoted to processing and analyzing different types of data, from health data to genetic data.

In the past I have been an assistant teacher on the following courses: Monkeys, Apes, and Humans, Evolution of Human Sexuality, Introduction to Anthropology, Human Origins Lecture & Laboratory, Introduction to Biological Anthropology, Introduction to Biological Systems Laboratory, General Biology Laboratory, Osteology and Forensics Lecture and Laboratory R Statistical Programming, Statistical and Mathematical Foundation for Data Analytics, Python Bootcamp, Spatial Analysis in Geography, Advanced Remote Sensing, Geographic Information Systems, Introduction to Data Analytics, Database and Analytics, Big Data Visualization, Data Analytics from Applied Machine Learning, and Advanced Visualization.

I have designed courses on the following topics: Data Analysis & Bioinformatics for Social Scientists, Evolution and Human Behavior, Families Across Cultures, The Evolution of Social Behavior, The Oxytocin System, and Statistics in R.

Recent Publications

Habecker, H. & Flinn, M.V. (2021). Neuroendocrinological mechanisms for human emotions. In L. Al-Shawaf & T. Shackelford (Eds.), Oxford Handbook of Evolutionary Psychology and Emotions. Oxford UK, OUP.

Habecker, H. & Flinn, M.V. (2019). Evolution of hormonal mechanisms for human family relationships. In T. Henley, M. Rossano, & E. Kardas (Eds.), Handbook of Cognitive Archaeology: Psychology in Pre-History (Chapt. 4). Milton Park UK: Routledge Press.