14 SES 05.5 PS, General Poster Session - NW 14
General Poster Session
As the global community continues to advance in science, technology, engineering, and mathematics (STEM) fields, increasing the abilities of secondary students in STEM fields has become a key policy focus for many national governments around the world. Previous research has suggested that students who go on to later success in STEM fields do so because of interest, as well as achievement, in STEM (Maltese & Tai, 2011; Sass, 2015). This points to the idea that increasing interest and achievement in STEM fields may be a promising policy level to boost eventual STEM career pursuit. It is also understood that parents can play a key role in focusing student interest and performance, and may therefore be a strong factor in promoting STEM participation (Jacobs & Bleeker, 2004). Considering this parent-student relationship, we look to answer the following questions:
- How does parent employment in a STEM field predict student interest in STEM fields?
- How does parent employment in a STEM field predict student STEM achievement?
- Are any observable differences in these relationships based on gender or racial/ethnic background?
In this study, we explore whether particular characteristics of parent occupation in STEM fields leads high achievement and interest in STEM fields for their children. Previous literature on cross-generational transmission has focused primarily on occupation and social class, our study on achievement and interest is relevant because of children’s educational preparation as a key mediating mechanism. Previous literature examining cross-generational transmission of occupations has distinguished between big classes, gradational classes, and micro-classes by characteristics of occupations (Jonsson, Di Carlo, Brinton, Grusky, & Pollak, 2009; Treiman, 1976; Weeden & Grusky, 2012). Big class perspectives argue that aggregate occupational categories such as professional, manager, service worker, and laborer delineate key inequalities between social classes. Gradational approaches rely on an indicator of socioeconomic status (SES) prestige, which is associated with occupation, to distinguish between classes. Microclass research argues that distinctions between classes occur at a lower level of aggregation—discrete institutionalized occupations (e.g., doctor, professor, lab researcher). Each off these perspectives provide rationale for how and why individuals with parents employed in STEM occupations may exhibit interest and high achievement in STEM outcomes including advanced STEM coursework participation and STEM achievement test performance.
First, the big class perspective suggests that children tend to end up in the same occupational class as their parents, pursuing appropriate educational outcomes along the way (Wright, 1985). Second, the gradational perspective suggests that children inherit their parents’ SES and prestige, also via economic resources, social networks, and cultural resources like socialization (Hauser & Warren, 1997; Langdon, McKittrick, Beede, Khan, & Doms, 2011). Third, the microclass perspective argues that children adopt class-specific tastes, skills, and networks, but they define class much more narrowly, on the basis of specific occupations (Chakraverty & Tai, 2013). Overall, past research on the transmission of social class and occupations indicates that children of parents employed in STEM occupations may have greater achievement in and orientation to STEM education.
These theorized connections between parent STEM occupation and student STEM interest and achievement have been observed in empirical studies. Across international comparisons using PISA data, research points to parent STEM involvement linking to student STEM career expectations, and interest and aspirations in STEM fields (Kjaernsli & Lie, 2011; OECD, 2007; Sikora & Pokropek, 2012). In the United States, studies have also highlighted the link with STEM major (Harwell, 2012; Moakler Jr. & Kim, 2014). Interestingly, little previous work has examined the direct connection to student math and science achievement.
Methodology Using recent longitudinal student data, we look to understand how parental STEM occupation may predict student STEM outcomes. Our dataset includes multiple observations beginning with baseline data from students’ first year in secondary school. Additional waves of survey collection occurred during students’ third year in secondary school, and collection of student transcript data upon completion of secondary school. STEM Outcomes Our first outcome of interest is student participation in advanced math (beyond algebra II) or science (beyond physical and life sciences) coursework (Gottfried, 2015). We also examine math and science achievement as measured by student scores achievement tests administered during the third year of secondary school. Main Predictor Variable The key predictive variable identifies whether either of an individual’s parents were employed in a STEM field as defined specifically by employment in life and physical sciences, engineering, mathematics, or information technology. Control Variables We identify key control variables related to STEM interest and achievement falling into the following categories: student/family demographics, academic history and attitudes, and school characteristics. Within the demographic category, we include variables on gender, race/ethnicity, household composition, parental education, parental involvement in school, and family income. Academic history includes ninth grade GPA, and base year math and science test scores. Academic attitudes include the following: postsecondary expectations, importance of education, math and science self-efficacy, and extra-curricular participation. Analysis Plan We begin our analysis using a logistic regression to identify the association between parent STEM occupation with advanced STEM coursework. We then turn to an ordinary least squares regression to explore the relationship between parent STEM and student achievement on math and science tests. To account for potential biases, we employ a propensity score technique to match students based on their propensity for having a parent employed in a STEM occupation. Through use of a propensity score analysis, we are able to approximate, as closely as possible, a random experiment when such an experiment is not implemented or possible (Murnane & Willett, 2011). We also perform these analyses as they pertain to identified subpopulations of students which are traditionally underrepresented in STEM: female students and minority students. Specifically, we include an indicator identifying either female or minority status and interact this term with the parent STEM occupation indicator. The coefficients associated with these interaction terms indicates any additional benefit that female or minority students receive from having a parent in a STEM occupation.
We anticipate finding significant relationships between parent STEM occupation and advanced STEM coursework, and achievement as measured by math and science test scores. We expect these relationships to exist in a positive direction. We would expect these results to hold both using our baseline models, as well as under our more strenuous propensity score matching analysis. In regards to our interaction models, we anticipate once again finding positive significant results for female students. However, we would expect male students to receive more of a boost from father employment in a STEM occupation, while female students would receive significant boosts from both father and mother employment in STEM occupation. Additionally, would also anticipate significant additional benefit for minority students who have parents in STEM occupations. Our study builds on literature predicting STEM participation and achievement in formal initiatives – curriculum, standards, and teaching – and explores the role of social contexts in promoting desired STEM outcomes. In this case, we consider the role of parents in promoting STEM outcomes. The results of our study will provide evidence regarding the informal aspects of STEM learning in the form of parental occupation. Researchers, policymakers, and practitioners can identify potential means in promoting STEM persistence. Specifically, students in K-12 education from STEM families are provided an orientation into mathematics and science throughout their academic development, which leads to a familiarity with these subjects as conduits for success in STEM. These informal aspects in which students develop and sustain their abilities in STEM fields represent a growing area of research. Our results would point to the potential for a cascading effect for underrepresented groups. Furthermore, in identifying students who don’t have these informal influences to serve as role models, practitioners could look to provide additional support in the classroom to encourage STEM success.
Chakraverty, D., & Tai, R. H. (2013). Parental Occupation Inspiring Science Interest: Perspectives From Physical Scientists. Bulletin of Science, Technology & Society, 33(1–2), 44–52. http://doi.org/10.1177/0270467613509367 Gottfried, M. A. (2015). The influence of applied STEM coursetaking on advanced mathematics and science coursetaking. The Journal of Educational Research, 108(5), 382–399. Harwell, E. (2012). An analysis of parent occupation and student choice in STEM major. Champaign, IL. Hauser, R. M., & Warren, J. R. (1997). Socioeconomic Indexes for Occupations : A Review, Update, and Critique. American Sociological Association, 27, 177–298. http://doi.org/10.1111/1467-9531.271028 Jacobs, J. E., & Bleeker, M. M. (2004). Girls’ and boys’ developing interests in math and science: Do parents matter? New Directions for Child and Adolescent Development, 2004(106), 5–21. http://doi.org/10.1002/cd.113 Jonsson, J. O., Di Carlo, M., Brinton, M. C., Grusky, D. B., & Pollak, R. (2009). Microclass mobility: social reproduction in four countries. American Journal of Sociology, 114(4), 977–1036. http://doi.org/10.1086/596566 Kjaernsli, M., & Lie, S. (2011). Students’ preference for science careers: Internatioanal comparisons based on PISA 2006. International Journal of Science Education, 33(1), 121–144. http://doi.org/10.1080/09500693.2010.518642 Langdon, D., McKittrick, G., Beede, D., Khan, B., & Doms, M. (2011). STEM: Good Jobs Now and for the Future (No. ESA Issue Brief 03-11). Washington, DC. Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877–907. http://doi.org/10.1002/sce.20441 Moakler Jr., M. W., & Kim, M. M. (2014). College major choice in STEM: Revisiting confidence and demographic factors. The Career Development Quarterly, 62, 128–142. http://doi.org/10.1002/j.2161-0045.2014.00075.x Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. New York, NY: Oxford University Press. OECD. (2007). PISA 2006 science competencies for tomorrow’s world: Volume 1 - Analysis. Paris. Sass, T. R. (2015). Understanding the STEM pipeline (No. 125). Washington, D.C. Sikora, J., & Pokropek, A. (2012). Intergenerational transfers of preferences for science careers in comparative perspective. International Journal of Science Education, 34(16), 2501–2527. http://doi.org/10.1080/09500693.2012.698028 Treiman, D. J. (1976). A Standard Occupational Prestige Scale for Use with Historical Data. The Journal of Interdisciplinary History, 7(2), 283–304. Weeden, K. A., & Grusky, D. B. (2012). The Three Worlds of Inequality. American Journal of Sociology, 117(6), 1723–1785. Wright, E. O. (1985). Classes. London: Verso.
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