Rachel Brown

National Science Foundation Graduate Research Fellow

Program in Human Development and Family Science, Department of Human Sciences, The Ohio State University

NLS user since 2013

  • Brown, R. R, & Kamp Dush, C. M. (2015) Anticipating the “Ball and Chain”? Reciprocal Associations between Marital Expectations and Delinquency. Manuscript currently under peer-review.
  • Brown, R. R, & Kamp Dush, C. M. (2015). “Best-Laid Plans”: Barriers to Met and Unmet Marital Timing Desires in the NLSY79. . Manuscript currently under peer-review.
  • Brown, R. R, & Kamp Dush, C. M. (In preparation). The Intergenerational Transmission of Marital Timing Desires and Behaviors: Evidence from the NLSY79 and NSLY79 Young Adults.
What I learned from NLS data

I began using the NLSY79 data the semester I entered graduate school, and have learned much from working with these amazing data and the other NLSY sets. My projects have focused on desires that youth hold about when they would like to get married. I have learned that background and current context play into both the formulation of desires for marriage and the realization of these desires over the life course. Working with the NLS datasets have also taught me how to conduct empirical research using big data, like how to handle datasets of this magnitude and how to incorporate measures from multiple aspects of a respondents’ life to paint a coherent picture of the attitude or behavior of interest.

Why I chose NLS data

I originally chose to use the NLSY79 because of its immense scope and the attention that had been placed on expectations the youth held for their futures while they were still teenagers and young adults. Being able to track these individuals over decades of their lives is an amazing opportunity and allows me to answer questions that I would not be able to address many other ways. Having the linked Child and Young Adult surveys adds an even greater depth to understanding the intergenerational nature of some of these questions, and comparing these individuals to those in a younger cohort in the NLSY97 is just another reason to appreciate the NLS datasets as a family.