Joint Quantitative Brownbag


Dr. Dakota Cintron

Dr. Dakota Cintron
Center for Integrative Developmental Science
Cornell University

Dr. Dakota Cintron a postdoctoral associate at the Center for Integrative Developmental Science at Cornell University. Previously, he was a postdoctoral research fellow at Evidence for Action (E4A) Methods Laboratory at University of California, San Francisco. He received my PhD in educational psychology with a concentration in research methods, measurement, and evaluation from the Neag School of Education at the University of Connecticut. He earned an EdM in measurement and evaluation and MS in applied statistics from Teachers College at Columbia. In the past, he has held professional positions at the Institute for Health, Health Care Policy and Aging Research, the National Institute for Early Education Research, and New Visions for Public Schools.

Dr. Cintron’s research focuses on the application, development, and assessment of quantitative methods in the social and behavioral sciences. His areas of research interest include topics such as item response theory, latent variable and structural equation modeling, longitudinal data analysis, hierarchical linear modeling, and causal inference. With the E4A Methods Laboratory, Dr. Cintron’s work is motivated by promoting and developing of quantitative methods that support the creation of rigorous evidence that improves population health and reduces health inequities and disparities.


Advancing Fairness and Equity in Measurement: An Intersectional Approach to Measurement Invariance Testing


Measurement Invariance (MI) across groups is one indicator of the appropriateness of a measure for individuals from different populations. MI indicates that scores are invariant across groups, implying that the same construct is being measured across diverse groups. However, the evaluation of MI is typically limited to comparing independent groups defined by a single demographic variable (e.g., men vs. women or no bachelor’s degree vs. with bachelor’s degree). This approach treats social categories as independent and mutually exclusive. However, intersectionality theory dictates that we consider the intersection of social categories (e.g., females with a bachelor’s degree vs. females without a bachelor’s degree). Using intersectionality as a guiding theoretical framework prompts investigations to excavate how a person’s multiple identities and social positions are embedded within systems of inequality. Building on the recommendations in Han et al. (2019), we consider the evaluation of intersectional MI using the alignment method (AM), which was designed to evaluate MI across many groups (Asparouhov & Muthén, 2014). This research demonstrates an approach for using MI testing for evaluating the intersectional construct validity of an instrument, thereby facilitating instrument development on intersectionally defined population subgroups.