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1. 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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2. Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2000-2010)

   https://doi.org/10.5281/zenodo.7406675  

   Porter, Nicholas ; Silver, Adrienne ; Gangopadhyay, Avijit ( January 2022 , Zenodo)

   This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings from 2000-2010. This work builds upon Silver et al. (2022a) ( https://doi.org/10.5281/zenodo.6436380) which contained Warm Core Ring trajectory information from 2011 to 2020. Combining the two datasets a total of 21 years of weekly Warm Core Ring trajectories can be obtained. An example of how to use such a dataset can be found in Silver et al. (2022b).

   The format of the dataset is similar to that of  Silver et al. (2022a), and the following description is adapted from their dataset. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 374 WCRs present between January 1, 2000 and December 31, 2010. Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting.  For example, the first ring in 2002 having a trailing alphabet of 'F' indicates that five rings were carried over from 2001 which were still observed on January 1, 2002. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km², and “Date” in days - representing the days since Jan 01, 0000. 

   The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2000-2010. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986). 

    

   Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022a). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380

   Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. Journal of Geophysical Research: Oceans, 127(8), e2022JC018737. https://doi.org/10.1029/2022JC018737 

   Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.

   Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020.  A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.

   QGIS Development Team. QGIS Geographic Information System (2016).

   Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).

    

   Funded by two NSF US grants OCE-1851242, OCE-212328 {"references": ["Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380", "Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea.\u00a0Journal of Geophysical Research: Oceans,\u00a0127(8), e2022JC018737.\u00a0https://doi.org/10.1029/2022JC018737", "Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.", "Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.", "QGIS Development Team. QGIS Geographic Information System (2016).", "Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986)."]} 

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3. The importance of classification schema choice in internal mobility: An examination in contemporary Mexico

   https://doi.org/10.1002/psp.2733  

   Riosmena, Fernando ; Balk, Deborah ( November 2023 , Population, Space and Place)

   A large number of Censuses and surveys around the globe only measure ‘migrations’ crossing particular politico‐administrative boundaries, most commonly ‘major’ areas like states. These moves, in turn, are often assumed to be representative of all long‐distance or, in some settings, urban–urban moves. While important because such boundaries signal relevant policy environments, little research has tested these assumptions and, more broadly, the implications of examining mobility using an inter/intrastate classification schema versus other substantively‐relevant approaches. Because these examinations have been particularly absent in developing nations, we compare the dynamics and correlates of mobility across inter/intrastate, distance‐ and rural/urban‐based classification schemata in Mexico, a nation with heterogeneous mobility similar to other large middle‐income countries and overall good data availability. We use 2000, 2010 and 2020 Census long‐form data to examine the changing dynamics of mobility patterns and correlates across the classification schemata. While we find that interstate mobility does cover a large majority of long‐distance and many urban–urban moves, we find that the correlates of interstate movement vary considerably from urban–urban movement in particular. We also find that excluding intrametropolitan from other types of moves may be a sensible strategy to better characterize some processes, an issue of increasing relevance in a more urban world and where city‐regions span across major administrative areas. Given these findings, for a better understanding socioeconomic patterns and trends, we recommend that studies of internal migrationavoidintra/interstate schema, consider separating intrametropolitan moves, and combine distance‐based and rural‐urban‐metropolitan approaches.

    

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4. 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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5. Neighborhood Socioeconomic and demographic changes in Baltimore's (BES) Neighborhoods: 1930 to 2010

   https://doi.org/10.6073/pasta/5eb49006749681aaa3bb33e4afb7f072  

   Locke, Dexter H ( January 2022 , Environmental Data Initiative)

   This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015. 

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