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1. Using administrative records to support the linkage of census data: protocol for building a longitudinal infrastructure of U.S. census records

   https://doi.org/10.23889/ijpds.v7i4.1764  

   Alexander, Joseph; Genadek, Katie (September 2022, International Journal of Population Data Science)

   This article describes the linkage methods that will be used in the Decennial Census Digitization and Linkage project (DCDL), which is completing the final four decades of a longitudinal census infrastructure covering the past 170 years of United States history. DCDL is digitizing and creating linkages between nearly a billion records across the 1960 through 1990 U.S. censuses, as well as to already-linked records from the censuses of 1940, 2000, 2010, and 2020. Our main goals in this article are to (1) describe the development of the DCDL and the protocol we will follow to build the linkages between the census files, (2) outline the techniques we will use to evaluate the quality of the links, and (3) show how the assignment and evaluation of these linkages leverages the joint use of routinely collected administrative data and non-routine survey data. 

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2. Analysis of Household Pulse Survey Public-Use Microdata via Unit-Level Models for Informative Sampling

   https://doi.org/10.3390/stats5010010  

   Sun, Alexander; Parker, Paul A.; Holan, Scott H. (March 2022, Stats)

   The Household Pulse Survey, recently released by the U.S. Census Bureau, gathers information about the respondents’ experiences regarding employment status, food security, housing, physical and mental health, access to health care, and education disruption. Design-based estimates are produced for all 50 states and the District of Columbia (DC), as well as 15 Metropolitan Statistical Areas (MSAs). Using public-use microdata, this paper explores the effectiveness of using unit-level model-based estimators that incorporate spatial dependence for the Household Pulse Survey. In particular, we consider Bayesian hierarchical model-based spatial estimates for both a binomial and a multinomial response under informative sampling. Importantly, we demonstrate that these models can be easily estimated using Hamiltonian Monte Carlo through the Stan software package. In doing so, these models can readily be implemented in a production environment. For both the binomial and multinomial responses, an empirical simulation study is conducted, which compares spatial and non-spatial models. Finally, using public-use Household Pulse Survey micro-data, we provide an analysis that compares both design-based and model-based estimators and demonstrates a reduction in standard errors for the model-based approaches. 

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3. Correlates of Native American engineering students’ career interests and efficacy: A test of SCCT

   Turner, Sherri L.; Mason-Chagil, Gale; Jacobs, Sue C.; Colston, Nicole; Johnson, Sarah; Patzkowsky, K. (August 2019, American Psychological Association 127th Annual Convention)

   There is an urgent need for young people to prepare for and pursue engineering careers. Engineering occupations comprise 20% of the science, technology, engineering, and math (STEM) jobs in the U.S. (Bureau of Labor Statistics, 2017). The average wage for STEM occupations is nearly double that of non-STEM occupations, with engineers commanding some of the highest salaries in STEM (Bureau of Labor Statistics, 2017). Moreover, engineering occupations are expected to be some of the fastest growing occupations in the U.S. over the next 10 years (Occupational Outlook Handbook, 2018); yet, there are current and projected shortages of workers in the engineering workforce so that many engineering jobs will go unfilled (Bureau of Labor Statistics, 2015) Native Americans are highly underrepresented in engineering (NSF, 2017). They comprise approximately 2% of the U.S. population (U.S. Census Bureau, 2013), but only 0.3% of engineers (Sandia National Laboratories, 2016). Thus, they are not positioned to attain a high-demand, high-growth, highly rewarding engineering job, nor to provide engineering expertise to meet the needs of their own communities or society at large. The purpose of this study was to examine factors that encourage or discourage Native American college students’ entry into engineering. Using Social Cognitive Career Theory (SCCT; Lent, Brown, & Hackett, 1994; 2000), we examined the correlates of these students’ interests and efficacy in engineering to accomplish this goal. Participants were N = 30 Native American engineering college students from the Midwest; 65% men, 30% women, and 4% other. The mean age was 25.87 (SD = 6.98). Data were collected over the period of one year on college campuses and at professional development conferences via an online survey hosted on Qualtrics. Three scales were used in the study: Mapping Vocational Challenges – Engineering (Lapan & Turner, 2000, 2016), the Perceptions of Barriers Scale (POB; McWhirter, 1998), and the Structured Career Development Inventory (Lapan & Turner, 2004). An a priori Power Analysis (f2 = .50; α = .05, 1 – β = .90) indicated our sample size was adequate. For all scales, full-scale Cronbach’s α reliabilities ranged from .82 to .86. Results of correlation analyses indicated that engineering efficacy was negatively related to lack of academic preparation (r = -.50, p = .016), and perceived lack of ability (r = -.53, p = .009), and positively related to academic achievement (r = .43, p = .043), career exploration (r = .47, p = .022), and approaching engineering studies proactively (r = .53, p = .009). Engineering interests were negatively related to perceived lack of ability (r = -.55, p = .007), and positively to proactivity (r = .42, p = .044), and academic achievement (r = .45, p = .033). Engineering interests were also related to support from parents, teachers, and friends to study engineering and pursue an engineering career. There was no significant relationship between engineering interests and engineering efficacy among these students. The relevance of these results will be discussed in light of SCCT, and recommendations for practice will be included. 

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4. Impact‐Based Thresholds for Investigation of High‐Tide Flooding in the United States

   https://doi.org/10.1029/2024EF005850  

   Piecuch, Christopher G; Das, Sarah B; Gorrell, Levi; Dangendorf, Sönke; Hamlington, Benjamin D; Thompson, Philip R; Wahl, Thomas (April 2025, Earth's Future)

   Abstract High‐tide flooding—minor, disruptive coastal inundation—is expected to become more frequent as sea levels rise. However, quantifying just how quickly high‐tide flooding rates are changing, and whether some places experience more high‐tide flooding than others, is challenging. To quantify trends in high‐tide flooding from tide‐gauge observations, flood thresholds—elevations above which flooding begins—must be specified. Past studies of high‐tide flooding in the United States have used different data sets and approaches for specifying flood thresholds, only some of which directly relate to coastal impacts, which has lead to sometimes conflicting and ambiguous results. Here we present a novel method for quantifying, with uncertainty, high‐tide flooding thresholds along the United States coast based on sparsely available impact‐based flood thresholds. We use those newly modeled thresholds to make an updated assessment of changes in high‐tide flooding across the United States over the past few decades. From 1990–2000 to 2010–2020, high‐tide flooding rates almost certainly (probability ) increased along the United States East Coast, Gulf Coast, California, and Pacific Islands, while they very likely decreased along Alaska during that time; significant changes in high‐tide flooding rates between the two decades were not detected in Oregon, Washington, and the Caribbean. Averaging spatially, we find that high‐tide flooding rates probably more than doubled nationally between 1990–2000 and 2010–2020. Our approach lays a foundation for future studies to more accurately model high‐tide flood thresholds and trends along the global coastline. 

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5. Building toward critical data literacy with investigations of income inequality

   Louie, J. (October 2022, Educational technology society)

   To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality in the United States. Designed as a multi-week set of applied data investigations, the module supports student analyses of income inequality using U.S. Census Bureau microdata and the online data analysis tool the Common Online Data Analysis Platform (CODAP). Pre- and post-module data show that use of this module was associated with statistically significant growth in students’ understanding of fundamental data concepts and individual interests in statistics and data analysis, with small to moderate effect sizes. Student survey responses and interview data from students and teachers suggest that the topic of income inequality, features within CODAP, the use of person-level data, and opportunities to engage in multivariable thinking helped to support critical data literacy and its foundations among participating students. We describe our definitions of data literacy and critical data literacy and discuss curriculum strategies to develop them. 

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