HIGH SCHOOL APPLIED STEM COURSETAKING
The Influence of Applied STEM Coursetaking on Advanced Math and Science Coursetaking
Michael A. Gottfried
University of California Santa Barbara
Note: Since the time of original submission of this proposal to AEFP, this work has been
accepted for publication in Journal of Educational Research.
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HIGH SCHOOL APPLIED STEM COURSETAKING
Abstract
Advanced math and science coursetaking is critical in building the foundation for
students to advance through the STEM pathway – from high school to college to career. To
invigorate students’ persistence in STEM fields, high schools have been introducing applied
STEM courses into the curriculum as a way to reinforce concepts learned in traditional math and
science classes and to motivate students’ interests in a long-term pursuit of these areas. This
study examines the role of taking applied STEM courses early in high school on taking advanced
math and science courses later in high school. The results suggest a positive link between early
applied STEM coursetaking and later advanced math and science coursetaking – one that is
delineated by specific type of applied STEM course and by individual-level demographic
characteristics. The findings of this study thus support policymakers and practitioners’ efforts to
expand the STEM curriculum beyond traditional subjects. Continuing to do so may be one way
to expand the number of students persisting in STEM.
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HIGH SCHOOL APPLIED STEM COURSETAKING
The Influence of Applied STEM Coursetaking on Advanced Math and Science Coursetaking
It is projected that by the end of this decade, the largest growth in jobs in the U.S.
economy will be in science, technology, engineering, and mathematics (STEM) (U.S.
Department of Education, 2010; Wang, 2013). In fact, many argue that current projections may
be underestimating true human capital needs (Tyson et al., 2007), And yet, the number of U.S.
students prepared or motivated to pursue careers in these fields is critically low, especially when
compared to students in other countries (National Science Board, 2010). Moreover, recent
research finds higher salaries for workers with STEM degrees and in STEM fields (Beede et al.,
2011; Melguizo & Wolniak, 2012; Olitsky, 2013). Thus, in an era of a declining quantity and
quality of U.S. students poised to advance through the STEM pathway, educators, policymakers,
and business leaders have been reexamining the steps required to boost students’ pursuit of and
persistence in these fields over an individual’s lifetime. For example, many recent federal
initiatives have been enacted specifically to promote interest and prepare students for the rigor of
advancing through the STEM pathway in college and subsequently in career (e.g., Honda, 2011;
Schultz et al., 2011).
In more detail, this ‘STEM pathway’, as defined by Tyson et al. (2007), is put forth as
follows: high school coursetaking, transition into and out of higher education, and workforce
participation. It is no surprise then that significant research has focused on the starting point of
this pathway: rigorous math and science courses in high school provide the necessary foundation
for students to advance through this pathway (Tyson et al., 2007; Wang, 2013). High school
advanced math and science coursetaking has been linked to improved STEM achievement in
high school (Adelman, 2006; Brody & Benbow, 1990; Burkam & Lee, 2003; Csikszentmihalyi
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HIGH SCHOOL APPLIED STEM COURSETAKING
& Schneider, 2000; McClure, 1998; Lee & Frank, 1990; Long et al., 2012; Riegle-Crumb, 2006).
High school advanced coursetaking has also been linked to greater success in STEM college
courses (Long et al., 2012; Wimberly & Noeth, 2005), to an increased probability of selecting a
STEM college major (Federman, 2007; Schneider, Swanson, & Riegle-Crumb, 1998; Trusty,
2002), and to college graduation (Schneider, Swanson, & Riegle-Crumb, 1998). Ultimately,
advanced high school coursetaking prepares students for STEM careers (Tyson et al., 2007).
Given that research has well established that advanced math and science coursetaking
plays such a key role, it is concerning that students are still not pursuing these course sequences.
Part of the reason may be that classrooms and schools lack the appropriate materials and
resources, such as laboratory equipment and textbooks and other resources (Hutchison, 2012;
National Resource Council, 2011). But even so, it might be that the traditional STEM curriculum
tends to be fractured and disconnected (Stone & Lewis, 2012). That is, STEM material is
delivered in silos, where concepts taught in traditional academic math courses are never fully
linked to concepts taught in other STEM courses. Hence, traditional math and science courses
may be perceived as lacking real-life or societal applications, and thus students alternatively
pursue non-STEM pathways as engagement in these fields declines (Hampden-Thompson &
Bennett, 2013; Weinberger, 2004; Wilson, 2003). This may be particularly the case for women
(Baker & Leary, 1995; Sax, 1994, 2001; Thompson & Windschitl, 2005).
Research suggests that when high school students learn the inter-connectedness of STEM
concepts, they are in a better position to develop skills required further down the STEM pathway
(Stone et al., 2008; Stone & Lewis, 2012). Hence, recent educational policy has focused on
enriching the academic STEM curriculum in high schools with applied STEM courses (e.g., The
Carl D. Perkins Career and Technical Education Improvement Act of 2006, Next Generation of
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HIGH SCHOOL APPLIED STEM COURSETAKING
Science Standards, and Common Core State Standards). In doing so, it becomes possible to
provide students with integrated high school STEM coursework that links academic STEM
content with applied STEM content.
In more detail, academic STEM courses include traditional math and science courses.
They are typically taught from a theoretical approach that stresses procedures, observation,
identification, documentation, and computation. To integrate and contextualize the material
taught in the traditional academic curriculum, high schools have begun to offer applied STEM
courses (Author et al., in press). These emphasize the application of academic math and science
concepts to practical job experiences while incorporating quantitative reasoning, logic, and
problem solving skills. Applied courses do range in rigor, much like in traditional academic math
and science courses. The distinction, then, is in the fact that they impart skills and knowledge
that have direct relevance to the daily challenges and problems students will face should they
pursue a STEM career.
The U.S. Department of Education classifies applied STEM courses in high school into
two strands: ‘Scientific Research & Engineering Courses’ (SRE) courses and ‘Information
Technology’ (IT) courses. Going forward, the phrase “applied STEM courses” will refer
collectively to both SRE and IT courses. SRE courses integrate basic concepts in math and
science to instruct students on the steps of the engineering process (i.e. identify the problemdesign-build-test-evaluate). These courses teach students how to solve problems within the
context of planning, managing, and providing scientific research and professional and technical
services, including laboratory and testing services, and research and development services.
Examples of SRE courses include surveying, electrical engineering, structural engineering, and
computer-assisted design/drafting. IT courses, on the other hand, teach basic programming and
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HIGH SCHOOL APPLIED STEM COURSETAKING
systems functionality, with a focus on practical problem solving. They involve the design,
development, support, and management of hardware, software, multimedia, and systems
integration services. Examples of IT courses include introduction to computer science, C++
programming, visual basic programming, and data processing.
It is imperative to further explore what factors might influence students’ advanced math
and science coursetaking. Doing so will help educators and policymakers make curricular
adjustments to ensure proper exposure to this critical STEM material that has been previously
established as improving the chances of students pursuing and persisting in STEM both in school
and in career (e.g., Long et al., 2012). And yet, given the national-level issues STEM issues
facing the U.S., surprisingly little empirical research using national-level data has been
conducted in identifying those factors that influence students’ enrollment in advanced math and
science courses (as an exception see e.g., Pearson, Crissey, & Riegle-Crumb, 2009). None had
considered the role of applied STEM courses in influencing students’ advanced math and science
coursetaking.
That said, it does seem highly likely that applied STEM courses taken early in high
school might influence advanced math and science coursetaking (which is typically taken during
the later years of high school). Though this current study is the first to address this issue, the
potential direction of this relationship is informed by and adapted from the work of Author et al.
(in press), Federman (2007), and Tyson et al. (2007). Unifying the concepts in these prior
studies, there are three potential ways that students’ applied STEM coursetaking early in high
school might influence advanced math and science coursetaking. First is augmentation. The
guiding purpose of applied STEM courses is to reinforce the concepts learned in academic math
and science courses; hence, applied STEM courses provide students with the opportunity to re-
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HIGH SCHOOL APPLIED STEM COURSETAKING
apply their math and science skills in new ways. By embarking on early opportunities to
reinforce skills used in academic math and science courses, the material in advanced courses
may become more accessible and digestible later in high school.
Second is relevance. Because applied STEM courses translate theoretical and abstract
material into relevant and job-specific applications, students might not only grasp traditional
math and science concepts more effectively but might ultimately find traditional math and
science concepts to be more relevant and engaging (Stone & Lewis, 2012). Hence, with the
connection being made between STEM concepts and college and career applications in applied
STEM courses, early exposure to applied and integrated STEM material may influence students’
decisions to continue to pursue more advanced math and science courses in order to attain these
longer-term educational and professional goals. Finally, applied courses might altogether
promote the development of new skill sets: as mentioned, applied courses stress the development
of reasoning, logic, and problem solving. Hence, by having an early advantage of developing
these skills in conjunction with fostering skills imparted in academic math and science courses,
students may find advanced courses to be easier and more accessible (Federman, 2007).
In light of these three mechanisms, this study explores the link between high school
applied STEM coursetaking and advanced math and science coursetaking. This study will be the
first to examine this issue through the following three research questions:
1. Does early applied STEM coursetaking in high school improve the odds of taking
advanced math and science courses?
2. Do these odds differ by type of applied STEM course taken?
3. Do these odds differ by student demographics?
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HIGH SCHOOL APPLIED STEM COURSETAKING
To address these questions, this study relies on a large-scale dataset of U.S. high school
students, developed by the U.S. Department of Education. It is a noteworthy source of data in
that it incluces full transcripts and contextual information for each student. The focus on the
influence of applied STEM courses on advanced math and science courses is key, as the former
are intended to build upon and to contextualize the material delivered in academic courses and
the latter are highly correlated with the pursuit of and persistence in STEM across the pipeline.
Further delineating these patterns by precise applied STEM course taken is critical, as some
these courses (e.g., IT courses) are becoming increasingly pervasive in states’ required high
school curricula. Finally, examining these issues by student demographics is also critical: if
differential effects exist, the new findings from this study can inform educators’ and policy
makers’ efforts at reducing common STEM gaps.
Method
Education Longitudinal Study of 2002
To determine how applied STEM coursetaking early in high school predicts advanced
academic STEM coursetaking later in high school, it is necessary to utilize a longitudinal dataset
that documents a student’s entire STEM coursetaking history in high school. A dataset that
contains this information is the Education Longitudinal Study of 2002 (ELS:2002), which was
created by the National Center for Education Statistics (NCES) at the U.S. Department of
Education. ELS:2002 followed a cohort of 10th grade students in the U.S. over time. The spring
of 2002 was the first year of data collection by NCES, during which survey questionnaires were
administered to students, parents, teachers, and school administrators. Students in the dataset
were in 10th grade in this base year.
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HIGH SCHOOL APPLIED STEM COURSETAKING
Students were then surveyed two years later in the spring of 2004 when the majority of
students in the sample were 12th grade (i.e., the first follow-up of ELS:2002). This article utilizes
data from the base year and first follow up year. Note that parent and teacher questionnaires were
administered only in the base year (2002), while the student and school administrator
questionnaires were administrated in the base year and first follow-up (2004).
In 2005, NCES then collected official high school transcripts for students in the sample
and then merged them with the 10th and 12th grade survey data. The reason for delaying and
collecting the transcript data in 2005 (rather than in 2002 and 2004) was most students in the
sample had finished high school by then and high school degree verification processes was
complete.
In more detail, these student transcripts contain complete coursetaking histories for each
student in the sample over four years of high school – including course names, grades earned,
and credits earned. Coursetaking files are available for approximately 91% of the original
ELS:2002 base-year sample. All course record files were calibrated to indicate Carnegie units as
a standardized measure of credits earned. A Carnegie unit is equal to a course taken every day,
one period per day, for a full school year.
To clean the transcript data file for the analysis in this specific study, a number of editing
and consistency checks were performed. First, any discrepancies between credits earned and the
course grade were resolved to ensure that course credit was awarded only when the student
received a passing grade. Second, any duplicate course records were removed. Third, the number
of credits assigned was inspected to ensure compatibility with the school’s calendar system (e.g.,
semester, trimester, etc.). After these data cleaning steps, the data file indicated the number of
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HIGH SCHOOL APPLIED STEM COURSETAKING
credits that a student earned for each course and in which year of high school that course was
taken.
For the purposes of this study, the final analytic data file included a comprehensive
picture about each student in the ELS:2002 sample, with contextual data collected from the 10th
and 12th grade survey interviews, and coursetaking information spanning all four years of high
school coming from the transcript files. The present study is based on a sample of approximately
11,000 students for whom valid transcript information was collected and who had non-missing
measures on background measures in the base-year (10th grade, 2002). Additionally, as described
below in the analytic approach, this sample is limited to students who were in the same high
school in both base year and first follow up interviews. Note that the ELS:2002 data includes a
probability weight so that estimates based on this subsample are representative of all students in
the U.S.
Outcomes: Advanced Math and Science Coursetaking
Table 1 presents the means and standard deviations of all variables in the proceeding
analyses. The outcome measures in this study are two binary variables: one indicating whether a
student had taken advanced math courses in 11th or 12th grades and one indicating if a student
had taken advanced science courses in 11th or 12th grades. To determine if a student had taken an
advanced math or science course, this study relies on an adapted taxomony of basic, average, and
advanced coursetaking designations originally developed by Burkam and Lee (2003).
For math, the pipeline to determine advanced coursetaking follows the adapted taxonomy
adopted by Author et al (in press): (1) Below average math, which includes basic math up
through pre-Algebra; (2) Average math, which includes Algebra and Geometry; (3) Above
average math, which includes Trigonometry, Statistics and pre-Calculus; and (4) Highest math,
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HIGH SCHOOL APPLIED STEM COURSETAKING
which includes Calculus. The pipeline measure is exhaustive of all courses classified as
academic math. In the dataset, nearly all students take academic math classes in high school,
with less than one percent finishing 12th grade with no math credits earned. For science, an
analogous pipeline exists: 1) Below average science, which includes earth science and physical
science; (2) Average science, which includes biology; (3) Above average science, which includes
chemistry and/or physics; and (4) Highest science, which more advanced chemistry and physics
courses. Like with math, this pipeline measure is exhaustive of all courses classified as academic
science. Also as consistent with math, almost 100% of all students graduate from high school
having taken at least one science course.
Typically, math and science courses that extend beyond pipeline 2 are considered
electives beyond state minimums (Pearson, Crissey, Riegle-Crumb, 2009; U.S. Department of
Education, 2002, 2003). Hence, in conjunction with Author et al., (in press), Burkam & Lee
(2003), and Pearson, Crissey, and Riegle-Crumb (2009), this study defines advanced
coursetaking in the following ways. Math courses taken beyond Algebra II (i.e, beyond pipeline
2) will be considered advanced math coursetaking. Thus, if students had enrolled in pipeline 3 or
4 math courses in 11th and 12th grade, the indicator variable assigned a student a value of 1, and 0
otherwise. Almost 50% of the sample had taken an advanced math course in 11th or 12th grades.
Similarly, advanced science coursetaking occurs when a student is beyond physical and
life sciences (i.e, pipline 2) – i.e, when a student has taken courses at pipeline 3 or pipeline 4 in
11th or 12th grade. Thus, a student who had taken any pipeline 3 or 4 science courses in 11th and
12th grades would be indicated as such. Approximately 35% of the sample had taken an
advanced science course in 11th or 12th grades. Note that only a negligible percentage of students
in the sample had taken advanced math or science courses prior to the start of 11th grade.
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HIGH SCHOOL APPLIED STEM COURSETAKING
---------------------------------Insert Table 1 about here
----------------------------------Key Predictor: Applied STEM Coursetaking
Applied STEM courses are classified as such based on the Secondary School Taxonomy
published by NCES (Bradby & Hudson, 2007). This taxonomy organizes all high school courses
recorded on students’ transcripts into four distinct curricula: academic, career and technical
education (CTE), enrichment/other, and special education. The math and science courses
described above are considered part of the academic curriculum. Applied courses (STEM and
non-STEM) are part of the CTE curriculum. This taxonomy is mutually exclusive such that
courses classified as academic cannot also be classified as CTE. It should be noted that the
academic versus CTE distinction is one that NCES had made based on content and focus of the
course material. It is not necessarily linked to college-bound versus non-college postsecondary
goals: courses in both academic and applied categories can be equally as rigorous.
Within the CTE curriculum, there are 16 categories of applied courses. Two of these
categories contain applied STEM coursework (Author et al., in press): Scientific Research and
Engineering (SRE) and Information Technology (IT). Within these clusters, course titles are
identified and assigned unique course classification codes to fit within the Secondary School
Taxonomy. A student was categorized as participating in applied STEM coursework if the
student received credit for the course and the course classification code fell within the SRE or IT
designation. As noted in Table 1, approximately 20% of the sample had taken applied STEM
courses by the end of 10th grade.
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HIGH SCHOOL APPLIED STEM COURSETAKING
Note that most students take only one applied STEM course during high school – either
an IT course or an SRE course (Author et al., in press). A small percentage of the sample has
taken both an IT course and an SRE course (i.e, approximately 3%, as noted by the sum of IT
course enrollment and SRE course enrollment). Also note that applied STEM coursetaking is
distributed fairly evenly across all four years of high school (Author et al., in press). Hence, early
applied STEM coursetaking is not systematically related to the timing of when these courses
were taken.
Additional Covariates
Table 1 also presents the control variables that are utilized in the analyses. This is the first
large-scale study of the influence of applied STEM coursetaking on advanced math and science
coursetaking. That said, the control measures selected for this present analysis are grounded in
prior research empirical research on traditional math and science coursetaking (e.g., Adelman,
1999; Brody & Benbow, 1990; Lee & Frank, 1990; Long et al, 2012; McClure, 1998; Pearson,
Crissey, Riegle-Crumb, 2009; Riegle-Crumb, 2006; Tyson et al., 2007; Wimberly & Noeth,
2005). Based on this large body of literature, this study utilized control variables that could be
classified as belonging into one of three main categories: socio-demographic characteristics,
family characteristics, and measures pertaining to investments in schooling. Consistent with
Pearson, Crissey, and Riegle-Crumb (2009), the control measures are derived from the base year
survey, such that the dependent variable would not be correlated with these measures had they
been selected from the first follow-up survey. In this way, all independent variables are derived
from the base year survey (i.e., end of 10th grade) and all outcomes are derived from the
transcript files.
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HIGH SCHOOL APPLIED STEM COURSETAKING
Socio-demographic background variables and family characteristics are taken from the
10th grade student and parent surveys. There are six unique socio-demographic control variables,
and include indictors for gender, race/ethnicity, and an indicator for English as a second
language. These measures were derived from the 10th grade student survey. Family variables
include household composition, mother’s and father’s education, and family income and are
from the 10th grade parent survey. Variables measuring a student’s investments in schooling are
taken from the 10th student surveys and official school records. The variables include a baseline
indicator of math ability (i.e., 10th grade math achievement score, note: science was not tested in
the dataset), an indicator for the importance that students place on education, an indicator for
whether or not the student had expectations for attending college, a four-point scale rating selfefficacy in math, a three-point scale measuring a student’s interpretation of parental involvement
in schooling, a series of indicators for participation extracurricular activities, and an indicator for
whether or not students held a job for pay during school.
To determine if systematic patterns exist in the wide span of independent variables
utilized in this study, Table 2 presents partial correlation coefficients and their significance levels
between the indicator that a student completed applied STEM coursework by 10th grade and the
control variables. Generally-speaking, the correlation coefficients in the table approximate to
zero. Examining the first column, students who have taken applied STEM coursework do not
appear to be different in some way that might bias the estimation of applied STEM coursetaking
on advanced math and science coursetaking. While the correlation coefficients of some sociodemographic characteristics may be larger than others (i.e., gender), the practical significance is
minimal given the actual sizes of these coefficient values.
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HIGH SCHOOL APPLIED STEM COURSETAKING
---------------------------------Insert Table 2 about here
----------------------------------Rather than strictly relying on the aggregated applied STEM measure, the latter two
columns of Table 2 break the correlation values out by whether students had enrolled in IT
courses or SRE courses by the end of 10th grade. The correlation coefficient values continue to
present weak relationships between the control measures and these key independent variables –
either aggregately (applied STEM) or within course type (SRE or IT). Again, this suggests that
there is nothing systematic in the relationships between having taken any type of applied STEM
coursework and the characteristics of students and their families.
Analytic Approach
To assess the influence of applied STEM coursetaking early in high school on advanced
math and science coursetaking in the later years of high school, this analysis begins with a
baseline logistic regression model:
(1)
In the model,
is a binary indicator as to whether or not student who attended high school
in survey waves and
(i.e., throughout high school) had taken advanced math and science
courses by end the of high school (i.e, survey wave ). Note that this model will be run separately
for math and for science, as presented in the results section below. Given that
represents a
binary indicator, an ordinary least squares regression is not appropriate because it violates the
model’s basic assumptions about the error structure (Cameron & Trivedi, 2010). As a result, this
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model is conducted as a logistic regression model. All models in this study are conducted as
logistic regression models, as every outcome is binary.
The independent covariates in the model as described as follows.
represents an
indicator variable, designating if a student had taken applied STEM coursework in 9th or 10th
grades – i.e., base year;
represents each of the student’s socio-demographic characteristics
discussed above, measured at base year;
characteristics, measured at base year; and
represents the set of a student’s family
represents the previously-defined set of
measures for investments in schooling, derived as well from the base year surveys. Note that all
control variables are entered as individual items into the regression, as described in Table 1. That
is, these latter terms S, F, and I represents sets of variables rather than constructs of variables.
The error term ε includes all unobserved determinants of advanced math and science
coursetaking. Empirically, this component is estimated with standard errors adjusted for high
school clustering. It is in this error term that the multilevel structure of the data is taken into
account. Because students are nested within high schools and hence are likely to have shared
common but unobservable characteristics and experiences, clustering student data at the high
school level provides for a corrected estimate of the variance of the error term given this nonindependence of student experiences.
Note that as consistent with prior research that has examined which characteristics predict
advanced math and science coursetaking in high school (e.g., Pearson, Crissey, & Riegle-Crumb,
2009), this study examines how the independent covariates mediate the relationship between
applied STEM coursetaking and academic math and science coursetaking. In the tables to follow,
model 1 examines solely the prediction of applied STEM coursetaking on academic math or
science without any additional measures. Model 2 examines whether including socio16
HIGH SCHOOL APPLIED STEM COURSETAKING
demographic and family measures mediate this relationship. Model 3 then includes measures
pertaining to investments in schooling.
Addressing school heterogeneity. In the baseline approach, it is tested whether applied
STEM coursetaking early in high school can predict later advanced math and science
coursetaking. Observable characteristics are included and examined for whether or not they
mediate the relationship between applied and academic coursetaking. That said, however, there
may be unobserved school-level influences that might be biasing the estimates. Hence, a further
examination as to how to control for these unobserved school effects is necessary.
As a very tangible example of this potential issue, it might be the case that a student
attends a school in which STEM is a theme focus (e.g., a STEM charter school). In this case, a
student in this school may have more opportunities to take applied STEM courses early in high
school. That is, in a STEM school, a greater proportion of electives may be STEM focused
(compared to a non-STEM school) such that a student in this school would be simultaneously
enrolled in math and science course requirements and an applied STEM elective in 9th and 10th
grades. While school theme might be observed, at the same time the school may be making
unobserved investments to ensure that these same students are enrolled in advanced math and
science courses once they enter 11th or 12th grades. Hence, for a student in this school, the
coefficient of applied STEM coursetaking would be overestimated due to not having accounted
for the unobserved school environment that a student experiences over the course of high school.
There may be additional nuances of the schooling environment that would also influence both
the independent and dependent variables.
A fixed effects strategy is one method of addressing this potential omitted variable bias.
Using a logistic regression model, this strategy is put forth as follows:
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HIGH SCHOOL APPLIED STEM COURSETAKING
prob[
] = prob [
=
+ β1
+
+
+
+ εist > 0 ].
(2)
In equation 2,
represents school fixed effects for every high school attended by students in the
sample. This is accomplished by including k -1 binary variables that indicate if a student had
attended a particular high school, where one high school is omitted as the reference group. As
such, this school fixed effects method averages the terms in equation 2 over all of the
observations for a given school and subtracts the average within a school for all of the students
who attended that high school. Consequently, including school fixed effects control for the
unobserved influences of schools by capturing unobserved differences between schools, such as
a STEM-themed curriculum. By holding constant those omitted but nuanced school-specific
factors (and hence the variation between schools), the effect of applied STEM coursework can be
better identified using this logistic regression model. The importance of relying on a sample of
students who attended the same high school for all four years also becomes evident in the school
fixed effects model. Examining students who were continuously exposed to only one curriculum
and school environment provides a more precise estimation of the relationship when controlling
for potential unobserved school effects.
Results
Advanced Math Coursetaking
Table 3 presents odds ratios from baseline and school fixed effects logistic regressions of
advanced math coursetaking on applied STEM coursetaking. Odds ratios and corresponding
significance indicators are presented as is common with logistic regression analyses, and robust
errors clustered at the school level are found below in parentheses.
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HIGH SCHOOL APPLIED STEM COURSETAKING
Each analytical section presents three analogous logistic regression models, where a more
complex model builds upon the model that came before it. Model 1 includes only the key
parameter – having taken an applied STEM course in 9th or 10th grade. Model 2 builds directly
on model 1 by including socio-demographic and family variables in order to examine if these
variables mediate the effect of applied STEM coursetaking. Finally, model 3 is analogous to
model 2, as it tests for mediating effects by including the set of variables pertaining to
investments in schooling. This set-up is equivalent in both baseline and school fixed effects
sections, and hence models 1a and 1b are analogous, as are 2a and 2b and 3a and 3b.
---------------------------------Insert Table 3 about here
----------------------------------Beginning with the baseline set of specifications across models 1a, 2a, and 3a, the results
indicate that having taken an applied STEM course early in high school (i.e., 9th or 10th grade)
predicts a greater odds of enrolling in advanced math courses later in high school (i.e., 11th or
12th grade). This is evidenced by the statistically significant odds ratios across the first row of
results, regardless of which model is examined.
Model 2a then tests for the mediating effects of including the span of unique sociodemographic and family variables described previously. Model 3a includes the span of control
variables pertaining to investments in schooling. As an initial finding, the failure to include these
covariates in model 1a led to an underestimation of the effect of applied STEM coursetaking, as
exemplified by the increase in size of the applied STEM odds ratio across the columns.
Overall in the three baseline specifications, the prediction of applied STEM coursetaking
remains robust to the inclusion of this wide span of covariates. That is, even after controlling for
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HIGH SCHOOL APPLIED STEM COURSETAKING
a range of socio-demographics, family measures, and schooling characteristics, the final baseline
model 3a shows a 37% higher odds of taking an advanced math course in 11th or 12th grade for
those students who took an applied STEM course in 9th or 10th grade. Hence, the set of three
baseline models provides substantial, formative evidence of a positive influence of early applied
STEM coursetaking.
Once including school fixed effects into the logistic regression model, as depicted in the
second portion of Table 3, the prediction of earlier applied STEM coursetaking on later advanced
math coursetaking remains equally as prevalent. The construction of models 1b, 2b, and 3b are
analogous to the baseline specifications – with the sole difference here being the inclusion of
school fixed effects. Within these models, the patterns found are similar to those found in the
baseline models. As before, there appears to have been an underestimation in the effects of
earlier applied STEM coursework on later advanced math coursetaking by not having included
these statistically-significant control variables.
Between the baseline and school fixed effects models, Likelihood Ratio tests statistically
prefer all school fixed effects models over baseline models (i.e., model 1b is preferred to model
1a, model 2b is preferred to model 2a, and model 3b is preferred to model 3a). Within the fixed
effects models themselves, the Likelihood Ratio test also favors each more complex model over
the less complex model. That is, model 3b is the most statistically preferred model in the table.
Therefore, turning to this final model (3b) in the table, there is an approximate 24%
higher odds of taking advanced math coursework in 11th or 12th grade for those students who
took earlier applied STEM coursework. Given that the sizes of the odds ratios are fairly
consistent across the table, these more complex models do little to alter this study’s premise that
20
HIGH SCHOOL APPLIED STEM COURSETAKING
students who enroll in applied STEM courses early in high school are more likely to enroll in
advance math courses later in high school.
Also, given that odds ratios can serve as measures of effect sizes (Author, 2011), the
effects of early applied STEM coursetaking are fairly large relative to the effects of other
covariates in the models. To put it into perspective, the effect of having taken an applied STEM
course is as large as the Black-White gap and almost as large as the gender gap. Indeed, turning
to the lower portion of the table, the effect of applied STEM coursekaing is larger than the effect
of baseline math ability and almost equivalent to the size of math self-efficacy.
Advanced Science Coursetaking
Table 4 presents logistic regression results predicting the effects of early applied STEM
coursetaking on later advanced science coursetaking. As with predicting math coursetaking, the
models in the table show a strong predictive relationship between having taken applied STEM
courses and advanced science coursetaking in high school. This finding is prevalent regardless of
which model is examined in the table. Moreover, the magnitudes of the estimated odds ratios on
applied STEM coursetaking are consistent with those from the models predicting advanced math
coursetaking. This suggests a robustness in the empirical specification selected in this study, as
the findings are consistent across multiple, related outcomes.
---------------------------------Insert Table 4 about here
----------------------------------In more detail, even after including socio-demographic, family, and investments in
schooling control variables in models 2a and 3a, the applied STEM indicator remains statistically
21
HIGH SCHOOL APPLIED STEM COURSETAKING
significant. This is noteworthy because these control measures include key measures such as
baseline math ability, efficacy, and college expectations.
Like in Table 3, the Likelihood Ratio test also statistically prefers the school fixed effects
models to the baseline models in Table 4. Also as consistent with Table 3, in Table 4 model 3b is
the statistically-preferred school fixed effects logistic regression model. Turning to this final
column in the table, the results continue to suggest a positive influence of applied STEM
coursetaking on advanced science coursetaking: there is a 21% greater chance of taking an
advanced science course in 11th or 12th grade for those students who took an applied STEM
course earlier in high school.
To put these effects into perspective by interpreting odds ratios as effect sizes, the effects
of applied STEM coursetaking are larger than both the Black-White gap as well as the gender
gap, as consistent with the models predicting math advanced coursetaking. Also similar to math,
the effect of applied STEM courses is larger than the effects of base year math ability and math
self-efficacy and are in line with the remainder of the investments in schooling variables.
Applied STEM Breakout
In Table 5, the applied STEM indicator has been replaced by indicators for whether a
student took an IT or SRE course. Because the school fixed effects logistic regression models
were statistically preferred in Tables 3 and 4, only those are presented in Table 5. The results in
the first section of columns pertain to predicting advanced math coursetaking in 11th or 12th
grade, and those in the second section of columns predict advanced science coursetaking in 11th
or 12th grade. Odds ratios are presented along with corresponding standard errors.
22
HIGH SCHOOL APPLIED STEM COURSETAKING
---------------------------------Insert Table 5 about here
----------------------------------When applied STEM courses are subsequently separated into more specific categories –
IT and SRE – the results in Table 5 reveal a unique finding. Here, the results suggest that taking
IT courses early in high school have predictive power on both advanced math coursetaking and
advanced science coursetaking later in high school. Indeed, across all models in the table, the
findings suggest that students who take IT courses in 9th or 10th grade have an approximate 30%
greater odds of enrolling in advanced math or science courses in 11th or 12th grade. On the other
hand, none of the models suggest that SRE courses hold predictive power in the math and
science models.
Note that the statistical patterns are similar to those from prior tables: the observable
characteristics and use of school fixed effects do not reduce the predictive power of the IT
coursetaking odds ratios. Also as consistent with the prior tables, the effects of IT coursetaking
approximate the sizes of the Black-White and gender gaps. The IT effects are also larger than the
effect of base year math ability and the measure of math self-efficacy.
Heterogeneity in Results
The final set of analyses breaks out the results by gender, race, and English language
learner status. The intention is to examine for heterogeneity of results based upon common gaps
in STEM (e.g., Eitle, 2005; Melguizo & Wolniak, 2012; Olitsky, 2013; Pearson, Crissey, RiegleCrumb, 2009; Xie & Shauman, 2003). Examining the results by individual-level characteristic, it
is possible to determine if different demographic groups are differentially influenced by having
taken applied STEM courses.
23
HIGH SCHOOL APPLIED STEM COURSETAKING
Table 6 presents odds ratios from a series of school fixed effects logistic regression
models, including all independent covariates. In the first column, each odds ratio represents the
coefficient on a separate model where applied STEM coursetaking was the key predictor, similar
to models 3b in Tables 3 and 4. And similar to models 3a and 3b in Table 5, the second set of
models are broken out by IT and SRE indicators.
---------------------------------Insert Table 6 about here
----------------------------------For both advanced math coursetaking and advanced science coursetaking, the effects of
applied STEM coursetaking appear to differ by gender. This is evidenced by the differential sizes
of the odds ratios between male and female logistic regressions. Beginning with the first column
of results, females who take applied STEM courses in 9th or 10th grade tend to have greater odds
of taking advanced math and science courses in 11th or 12th grade compared to males. This is
particularly evident in math, where the odds are approximately one-third larger for females than
for males. When the results are broken out by IT versus SRE coursetaking, females have higher
odds of taking advanced math courses later in high school when having taken IT courses earlier
in high school. However, the results do not appear to be differentiated by gender across IT versus
SRE when it comes to the influence on advanced science coursetaking. Generally-speaking,
however, Table 6 does indicate that applied STEM coursetaking does boost the odds for all
students, but especially so for females.
For advanced math coursetaking, the results suggest that White students tend to have
greater odds of taking advanced math courses when having taken applied STEM courses, though
the results are not as clearly differentiated once breaking applied STEM into IT and SRE
24
HIGH SCHOOL APPLIED STEM COURSETAKING
categories: except for Black students, all other racial groups experience similarly-sized odds
ratios. For advanced science coursetaking, White and Black students tend to have larger odds
based on having taken applied STEM courses compared to students in other racial groups. The
patterns are reflected when applied STEM is broken out into IT and SRE categories as well.
Finally, the results are differentiated by students whose primary language is and is not
English. For math, the results indicate no difference between the odds for students based on
primary language spoken. On the other hand, the odds are differential for advanced science
coursetaking. Here, students whose primary language is English have greater odds of taking
advanced science courses after having taken applied STEM courses. Given these differential
findings, delineating the results by demographics, by applied course taken, as well as by outcome
proves to be critical.
Discussion
Given the established importance of advanced math and science coursetaking in high
school, this study examined new factors that might improve students’ pursuit of and persistence
in these fields. Prior to this study, little work has been conducted in evaluating the effectiveness
of high school applied STEM coursetaking; none had considered how taking these courses early
in high school might boost the chances of enrolling in advanced math and science courses later in
high school. Recent educational policy has been expanding the high school STEM curriculum to
include applied STEM courses as a way to expand interest and skills in STEM. As such, this
study is timely by providing unique insight into the whether these curricular changes are in fact
strengthening the STEM pathway.
This study relied on a longitudinal sample of high school students in the U.S. For the
purposes of this study, two waves of survey data were assessed: the base wave when students
25
HIGH SCHOOL APPLIED STEM COURSETAKING
were in 10th grade, and first follow-up wave when students were in 12th grade. In addition to
compiling data on student and family characteristics, this study utilized official transcript data in
order to assess precise applied STEM and math and science coursetaking patterns. The advanced
math and science coursetaking rubric utilized has been established by prior research (e.g.,
Burkham & Lee, 2003; Pearson, Crissey, & Riegle-Crumb, 2009; Tyson et al., 2007), thereby
enabling the methods in this study to be comparable to others in this area.
As for identifying applied STEM coursetaking, this study focused on key strands of
applied STEM courses in high school designated as such by the U.S. Department of Education:
IT and SRE courses. These applied STEM courses were specifically designed to reinforce
instructional material in the traditional math and science curricula, to foster interest in STEM
areas, and to support the development of skills that are central to the long-term persistence in
STEM fields. Hence, by examining the influence of these previously-unexplored coursetaking
patterns on well-established measures of advanced math and science coursetaking, this research
has important policy implications for how a changing STEM curriculum can support STEM
pathways.
This study relied on two main approaches. The first was a baseline logistic regression
model, where an indicator for students having taken advanced math or science courses in grades
11 or 12 was modeled based on having taken an applied STEM course in grades 9 or 10 and on
base year observable characteristics. The second approach extended the baseline model by
incorporating school fixed effects into a logistic regression model in order to account for
unobservable school-level factors that may be influencing having taken applied STEM
coursework as well as enrolling in advanced math or science coursework. Empirical models with
fixed effects are supported in empirical educational research on large-scale datasets as
26
HIGH SCHOOL APPLIED STEM COURSETAKING
appropriate and have been employed in previous studies on STEM coursetaking (e.g., Author et
al., in press).
The findings across both empirical approaches were consistent: students who had taken
an applied STEM course early in high school had higher odds of taking advanced math or
science courses later in high school. After testing for mediating effects, there were two key
conclusions. First, including student socio-demographic data and family covariates did not
reduce the size of this effect – neither in the math models nor in the science models. Second,
incorporating critical measures that represented to investments in schooling (i.e., baseline math
ability, math self-efficacy, college expectations) also did not reduce the magnitude of the effect
of applied STEM coursetaking.
A second research question inquired into whether the odds were differentiated by type of
applied STEM course. The findings indicated that having taken an IT course in 9th or 10th grade
was related to a higher probability of taking advanced math or science courses in 11th or 12th
grade, whereas taking SRE courses was not. Hence, the importance of differentiating types of
applied STEM coursework was critical in order to distinguish the precise pathways by which the
initial results were actualized. Here, it might be hypothesized that IT courses (e.g., computer
science) may not only be intrinsically interesting and motivating but also provide an opportunity
to build skills and to mix STEM aspirations with practical applications. Future work, as
described below, can inquire into the precise content of these courses in order to determine the
links between applied and academic STEM coursetaking. This, however, is not possible in a
current large-scale dataset.
A final research question examined the results for heterogeneity among common
demographic gaps in STEM – i.e., gender and race (e.g., Tyson et al., 2007). For instance, the
27
HIGH SCHOOL APPLIED STEM COURSETAKING
results suggested that female students in applied STEM courses had a higher odds of taking
advanced math and science courses than did male students. This was more apparent with math,
though also prevalent in the results for science as well. It might be hypothesized that applied
coursework may be especially important for women, who have been shown to perceive many
STEM fields as lacking real-life or societal applications (Baker & Leary, 1995; Sax, 1994, 2001).
Given these findings, there is consistent support for the framework put forth in the
introduction of this article. Having taken applied STEM courses early in high school has the
potential to strengthen STEM pathways. Applied STEM courses translate academic math and
science concepts into accessible material, stress the application of academic concepts to more
practical experiences, and incorporate quantitative thinking, logic, and problem solving. Hence,
given these features, applied STEM courses may serve as an alternative way to reinforce and
augment the required skills necessary for math and science coursework. Moreover, applied
coursework may facilitate students’ interests in applied science and math material, thereby hence
motivating them to further pursue math and science.
These findings, based on this study’s three research questions, each have relevance in
educational policy. First, previous research has stressed the importance of math and science
coursetaking. That said, little research has been developed around the identifying those drivers of
advanced math and science coursetaking, though these courses are supported as being the
foundation for postsecondary and career pathways in STEM (Long et al., 2012; Tyson et al.,
2007). In demonstrating the importance of applied STEM courses in boosting the chances of
taking high school math and science courses, the findings of this study support policymakers and
practitioners’ efforts to expand the STEM curriculum beyond traditional subjects. Continuing to
do so may be one way to expand the number of students persisting in STEM.
28
HIGH SCHOOL APPLIED STEM COURSETAKING
Second, having examined IT and SRE courses separately proved to be significant. The
demonstrated importance of IT courses in particular bolster current policy when it comes to
inserting applied STEM coursetaking into high schools’ curricula. For instance, at present, nine
states count computer science as a core graduation requirement (Wilson, Sudol, Stephenson, &
Stehlik, 2010), while zero states count SRE as a core graduation requirement. Moreover, many
states are considering including computer science questions in their standardized tests (Farrell,
2013). The results of this study would support these continued efforts to specifically include IT
courses into graduation requirements. IT courses may not only serve to enhance students’ interest
in STEM, but may also provide key foundational skills that enable students to access traditional
STEM material.
Lastly, in conjunction with prior research in academic STEM coursetaking (Melguizo &
Wolniak, 2012; Olitsky, 2013; Tyson et al., 2007; Wang, 2013), the findings of this study urge
policymakers and practitioners to consider new ways to reduce demographic gaps in STEM.
Given that women and under-represented minority groups continue to lag behind in the pursuit of
and persistence in STEM, applied STEM courses might serve as an alternative way to reduce
educational disparities in these areas. By providing additional opportunities for these groups to
attain school-based STEM skills and experiences, applied STEM courses could reinforce skill
sets that are attained in the academic curriculum with additional applied curricular material. This
may boost the probability that all students receive exposure to advanced math and science
courses, which are critical in laying the foundation for a strong STEM pipeline.
Future Research
In sum, this study was the first to examine the relationship between applied STEM and
advanced math and science coursetaking. The relationship was examined with a large-scale
29
HIGH SCHOOL APPLIED STEM COURSETAKING
NCES dataset of high school students, including full transcript information. Consistent across
this study, the findings indicated positive, predictive relationships between taking applied STEM
courses in early high school years and taking advanced math or science coursework. The results
were robust across multiple methodologies and the inclusion of a wide span of covariates. Hence,
this study has provided new evidence and insight into the role of high school STEM coursework,
which comes as a critical juncture as policymakers and practitioners themselves are concurrently
revamping the STEM curriculum.
There are several research extensions of this study. First, relying on a large-scale NCES
dataset allowed for trends and patterns to be identified through the inclusion of a wide array of
measures and multiple methods. However, one limitation of using survey data is that they are do
not contain precise information on the content of applied STEM courses. Hence, it is not possible
to determine exactly what it is about these courses that links to the math and science curriculum.
Thus, future research could rely on the findings of this study to develop a smaller-scale research
study that explores the specific aspects of course content in applied STEM courses. For instance,
working with a single school or district may allow for additional insights into the mechanisms
discovered in this present study.
A second extension is related to the first. Survey data do not contain information on
students’ perceptions of applied STEM coursetaking and how it relates to STEM pursuit and
persistence. Thus, a future study might pair these findings with qualitative research, where
students provide their insights into how having taken applied STEM courses relates to advanced
math and science coursetaking.
Finally, this study has shown that applied STEM courses may have the ability to boost
the probability of taking advanced math and science courses in high school. This was particularly
30
HIGH SCHOOL APPLIED STEM COURSETAKING
evident for students who took IT courses. While the first two avenues for future research have
suggested smaller-scale studies, there is also room for additional research with national datasets.
For instance, future research can evaluate the longer-term effects of applied STEM courses on
course selection in postsecondary education. For instance, it would be noteworthy to determine
how applied STEM coursetaking in high school is linked not only to college major selection, but
also to college course selection at the start of postsecondary education (before college majors are
selected). Doing so will continue to evaluate whether these applied courses taken early in a
students’ educational trajectory have longer-term implications with respect to ensuring that
students continue through the STEM pathway, in high school, in college, and ultimately, over a
student’s lifetime.
31
HIGH SCHOOL APPLIED STEM COURSETAKING
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Table 1: Descriptive Statistics for 12th Grade Students (N = 10,828)
Mean
Standard
deviation
Dependent Variable
Advanced math courses taken by end of high school
Advanced science courses taken by end of high school
0.47
0.35
0.50
0.48
Key Predictor Variables
Participation in applied courses by end of grade 10
Participation in SRE courses by end of grade 10
Participation in IT courses by end of grade 10
0.20
0.15
0.08
0.40
0.36
0.27
0.53
0.50
0.62
0.10
0.12
0.10
0.01
0.15
0.49
0.30
0.33
0.30
0.09
0.36
0.62
0.21
0.16
0.49
0.41
0.37
0.11
0.27
0.12
0.11
0.11
0.20
0.07
0.02
0.31
0.44
0.33
0.31
0.31
0.40
0.26
0.14
Socio-demographic variables
Male
Race
White, non-Hispanic (reference)
Black
Hispanic
Asian
Other
English as a second language
Family data
Household composition
Two biological parents (reference)
Single parent household
Other arrangement
Mother's education
Did not finish HS (reference)
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
Family data (continued)
Father's education
Did not finish HS (reference)
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
Income
No income (reference)
<= $1,000
1,001-5,000
5,001-10,000
10,001-15,000
15,001-20,000
20,001-25,000
25,001-35,000
35,001-50,000
50,001-75,000
75,001-100,000
100,001-200,000
>= 200,001
Investments in schooling
10th grade math IRT score
Importance of education
College expectations
Math self-efficacy
Parent involvement in student's schooling
Extracurricular activities
0 hours (reference)
>0 to 4 hours
5 to 9 hours
10 to 14 hours
15 or more hours
Employment during school
Mean
Standard
deviation
0.11
0.28
0.10
0.08
0.09
0.19
0.09
0.06
0.32
0.45
0.29
0.27
0.29
0.39
0.29
0.24
0.00
0.01
0.01
0.02
0.04
0.04
0.06
0.11
0.19
0.22
0.14
0.13
0.04
0.06
0.09
0.12
0.13
0.19
0.20
0.23
0.31
0.39
0.41
0.35
0.33
0.19
39.65
0.83
0.77
2.53
2.21
11.64
0.37
0.42
0.84
0.53
0.35
0.25
0.14
0.17
0.10
0.58
0.48
0.43
0.34
0.38
0.29
0.49 37
HIGH SCHOOL APPLIED STEM COURSETAKING
Table 2: Correlations Between Applied STEM Coursetaking and Model Covariates
Applied
STEM
Socio-demographic variables
Male
Race
Black
Hispanic
Asian
Other
English as a second language
Family data
Household composition
Single parent household
Other arrangement
Mother's education
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
Father's education
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
-0.07
-0.01
-0.02
0.02
0.00
0.00
SRE
Course
IT Course
***
*
**
-0.08
-0.01
-0.01
0.03
0.00
0.00
***
**
-0.06
0.00
-0.02
-0.01
0.00
0.01
0.00
0.00
0.01
0.01
0.00
-0.01
0.00
0.00
0.00
-0.01
0.00
0.00
0.01
0.01
0.01
-0.01
-0.01
0.00
0.00
0.00
0.00
-0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-0.02
0.01
-0.02
0.01
0.00
0.01
0.01
0.00
0.01
-0.01
0.00
-0.01
-0.01
0.00
-0.02
0.00
-0.02
*
Applied
STEM
***
**
Family data (continued)
Income
<= $1,000
1,001-5,000
5,001-10,000
10,001-15,000
15,001-20,000
20,001-25,000
25,001-35,000
35,001-50,000
50,001-75,000
75,001-100,000
100,001-200,000
>= 200,001
Investments in schooling
10th grade math IRT score
Importance of education
College expectations
Math self-efficacy
Parent involvement in student's schooling
Extracurricular activities
>0 to 4 hours
5 to 9 hours
10 to 14 hours
15 or more hours
Employment during school
SRE
Course
IT Course
0.01
0.00
0.01
0.01
0.01
0.01
0.00
0.01
0.00
0.00
0.00
0.01
0.03
-0.01
0.01
0.02
-0.01
***
0.03
0.00
-0.01
0.00
-0.01
**
0.00
0.00
0.01
0.01
0.01
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.01
0.00
0.01
0.01
0.01
0.01
0.01
0.00
0.01
0.00
0.00
0.01
0.02
0.00
0.03
0.01
-0.01
0.02
-0.01
-0.01
0.01
0.01
*
0.02
0.00
0.00
0.00
-0.02
**
0.02
0.00
-0.02
0.00
0.01
***
*
*
*
**
38
HIGH SCHOOL APPLIED STEM COURSETAKING
Table 3: Odds Ratios from Logistic Regression Models Predicting Advanced Math Coursetaking
Baseline Models
Model 1a
Participation in applied courses by end of grade 10
Socio-demographic variables
Female
Race
Black
Hispanic
Asian
Other
English as a second language
Family data
Household composition
Single parent household
Other arrangement
Mother's education
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
Father's education
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
1.19
(0.10)
Model 2a
***
School Fixed Effects Models
Model 3a
Model 1b
1.26
(0.10)
***
1.37
(0.13)
***
1.24
(0.06)
***
1.45
(0.09)
0.85
(0.08)
0.69
(0.07)
2.00
(0.22)
0.22
(0.04)
0.95
(0.09)
*
1.62
(0.19)
0.97
(0.11)
1.64
(0.22)
0.30
(0.10)
0.84
(0.09)
0.76
(0.05)
0.63
(0.04)
***
1.30
(0.12)
1.39
(0.14)
1.56
(0.16)
1.82
(0.19)
2.40
(0.25)
2.88
(0.38)
2.05
(0.38)
***
1.05
(0.09)
1.32
(0.13)
1.35
(0.15)
1.50
(0.15)
1.90
(0.18)
2.11
(0.25)
2.65
(0.36)
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
0.90
(0.07)
0.72
(0.05)
1.08
(0.11)
1.10
(0.13)
1.09
(0.13)
1.32
(0.16)
1.58
(0.18)
1.77
(0.26)
1.48
(0.32)
0.90
(0.09)
0.93
(0.11)
0.99
(0.13)
1.09
(0.13)
1.26
(0.14)
1.16
(0.16)
1.44
(0.24)
Model 2b
1.25
(0.11)
***
1.24
(0.13)
**
***
1.29
(0.07)
***
1.65
(0.12)
***
***
0.57
(0.07)
0.65
(0.08)
2.37
(0.32)
0.33
(0.14)
0.97
(0.11)
***
1.24
(0.19)
0.96
(0.14)
2.29
(0.38)
0.49
(0.25)
0.85
(0.12)
0.68
(0.05)
0.64
(0.05)
***
***
***
*
***
**
***
***
*
**
**
1.15
(0.09)
**
Model 3b
1.18
(0.13)
1.22
(0.15)
1.55
(0.20)
1.60
(0.21)
2.17
(0.28)
2.70
(0.43)
1.65
(0.37)
1.08
(0.11)
1.52
(0.18)
1.43
(0.19)
1.44
(0.18)
1.96
(0.23)
1.97
(0.28)
2.65
(0.44)
***
***
**
***
***
***
***
***
**
***
***
***
***
***
***
0.83
(0.08)
0.72
(0.07)
0.93
(0.13)
0.88
(0.13)
1.01
(0.16)
1.11
(0.18)
1.36
(0.21)
1.55
(0.29)
1.21
(0.35)
0.90
(0.12)
0.93
(0.11)
1.00
(0.15)
1.09
(0.18)
0.98
(0.15)
1.28
(0.18)
1.46
(0.30)
***
**
***
**
**
*
*
39
HIGH SCHOOL APPLIED STEM COURSETAKING
Income
<= $1,000
0.69
(0.33)
0.52
(0.22)
0.42
(0.18)
0.62
(0.24)
0.79
(0.31)
0.73
(0.28)
0.89
(0.33)
0.90
(0.34)
1.12
(0.42)
1.16
(0.44)
1.55
(0.59)
2.25
(0.89)
1,001-5,000
5,001-10,000
10,001-15,000
15,001-20,000
20,001-25,000
25,001-35,000
35,001-50,000
50,001-75,000
75,001-100,000
100,001-200,000
>= 200,001
Investments in schooling
10th grade math IRT score
Importance of education
College expectations
Math self-efficacy
Parent involvement in student's schooling
Extracurricular activities
>0 to 4 hours
5 to 9 hours
10 to 14 hours
15 or more hours
Employment during school
n
adjusted R2
10,036
0.00
10,036
0.11
**
**
0.83
(0.41)
0.64
(0.27)
0.42
(0.19)
0.58
(0.23)
0.77
(0.31)
0.61
(0.24)
0.70
(0.26)
0.69
(0.26)
0.82
(0.31)
0.76
(0.29)
0.98
(0.38)
1.32
(0.54)
**
1.10
(0.00)
1.54
(0.12)
1.81
(0.13)
1.33
(0.05)
1.17
(0.06)
***
1.50
(0.11)
1.47
(0.12)
1.80
(0.15)
1.76
(0.17)
0.80
(0.05)
***
10,036
0.30
1.18
(0.68)
0.94
(0.48)
0.61
(0.32)
0.95
(0.46)
1.34
(0.66)
1.25
(0.60)
1.48
(0.70)
1.51
(0.71)
1.81
(0.87)
1.63
(0.78)
1.86
(0.90)
2.05
(1.04)
***
***
***
***
***
***
***
***
9,419
0.17
9,419
0.23
1.21
(0.83)
1.17
(0.65)
0.67
(0.39)
0.73
(0.38)
1.33
(0.69)
0.99
(0.51)
1.11
(0.55)
1.13
(0.56)
1.32
(0.66)
1.01
(0.51)
1.16
(0.59)
1.25
(0.68)
1.15
(0.01)
1.71
(0.17)
1.86
(0.18)
1.38
(0.06)
1.26
(0.09)
***
1.64
(0.14)
1.63
(0.16)
2.03
(0.20)
2.08
(0.26)
0.72
(0.05)
***
***
***
***
***
***
***
***
***
9,419
0.43
Note: *** p < 0.01, ** p < 0.05, * p < 0.10.
All models also includue indicators for parental occupation.
40
HIGH SCHOOL APPLIED STEM COURSETAKING
Table 4: Odds Ratios from Logistic Regression Models Predicting Advanced Science Coursetaking
Baseline Models
Model 1a
Participation in applied courses by end of grade 10
Socio-demographic variables
Female
Race
Black
Hispanic
Asian
Other
English as a second language
Family data
Household composition
Single parent household
Other arrangement
Mother's education
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
Father's education
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
1.19
(0.10)
Model 2a
**
1.22
(0.10)
**
1.04
(0.05)
0.65
(0.07)
0.71
(0.07)
2.04
(0.22)
0.43
(0.15)
0.86
(0.08)
***
0.76
(0.05)
0.61
(0.04)
***
1.13
(0.11)
1.29
(0.14)
1.48
(0.16)
1.67
(0.18)
1.85
(0.20)
2.31
(0.28)
2.05
(0.36)
1.25
(0.12)
1.55
(0.17)
1.76
(0.19)
1.68
(0.18)
2.03
(0.21)
2.23
(0.27)
2.66
(0.34)
School Fixed Effects Models
Model 3a
***
***
**
***
**
***
***
***
***
***
**
***
***
***
***
***
***
Model 1b
1.27
(0.12)
***
1.14
(0.06)
**
1.04
(0.13)
0.92
(0.10)
1.72
(0.19)
0.61
(0.20)
0.78
(0.08)
0.86
(0.06)
0.69
(0.05)
0.95
(0.10)
1.05
(0.13)
1.12
(0.13)
1.26
(0.16)
1.27
(0.15)
1.49
(0.20)
1.53
(0.29)
1.16
(0.12)
1.27
(0.15)
1.50
(0.18)
1.37
(0.16)
1.50
(0.17)
1.45
(0.19)
1.68
(0.24)
***
**
**
***
*
**
***
**
*
***
***
***
***
***
1.24
(0.10)
Model 2b
***
1.23
(0.11)
Model 3b
**
1.00
(0.06)
0.58
(0.07)
0.79
(0.09)
2.35
(0.30)
0.52
(0.24)
0.81
(0.09)
***
0.68
(0.05)
0.65
(0.05)
***
1.08
(0.13)
1.21
(0.16)
1.48
(0.19)
1.62
(0.21)
1.80
(0.23)
2.29
(0.34)
1.78
(0.38)
1.30
(0.15)
1.82
(0.25)
1.87
(0.25)
1.84
(0.24)
2.19
(0.27)
2.43
(0.35)
2.69
(0.42)
**
***
*
***
***
***
***
***
***
**
***
***
***
***
***
***
1.21
(0.12)
**
1.13
(0.08)
*
1.06
(0.16)
1.06
(0.14)
2.01
(0.28)
0.80
(0.44)
0.69
(0.09)
0.77
(0.07)
0.76
(0.07)
0.90
(0.12)
0.92
(0.14)
1.09
(0.17)
1.17
(0.18)
1.21
(0.17)
1.42
(0.24)
1.31
(0.31)
1.22
(0.16)
1.46
(0.24)
1.68
(0.26)
1.58
(0.24)
1.67
(0.24)
1.66
(0.27)
1.80
(0.31)
***
***
***
***
**
**
***
***
***
***
***
41
HIGH SCHOOL APPLIED STEM COURSETAKING
Income
<= $1,000
0.54
(0.25)
0.51
(0.21)
0.58
(0.23)
0.54
(0.21)
0.49
(0.18)
0.68
(0.26)
0.77
(0.27)
0.76
(0.28)
0.91
(0.33)
0.83
(0.30)
1.02
(0.37)
1.25
(0.48)
1,001-5,000
5,001-10,000
10,001-15,000
15,001-20,000
20,001-25,000
25,001-35,000
35,001-50,000
50,001-75,000
75,001-100,000
100,001-200,000
>= 200,001
Investments in schooling
10th grade math IRT score
Importance of education
College expectations
Math self-efficacy
Parent involvement in student's schooling
Extracurricular activities
>0 to 4 hours
5 to 9 hours
10 to 14 hours
15 or more hours
Employment during school
n
adjusted R2
10,036
0.00
10,036
0.09
*
*
0.66
(0.33)
0.63
(0.28)
0.64
(0.29)
0.53
(0.23)
0.46
(0.20)
0.61
(0.27)
0.65
(0.27)
0.63
(0.26)
0.71
(0.30)
0.59
(0.25)
0.69
(0.29)
0.78
(0.35)
*
1.07
(0.00)
1.41
(0.11)
1.38
(0.10)
1.20
(0.04)
1.02
(0.05)
***
1.33
(0.09)
1.62
(0.13)
1.57
(0.12)
1.42
(0.13)
0.88
(0.05)
***
10,036
0.20
0.58
(0.30)
0.66
(0.32)
0.70
(0.34)
0.71
(0.33)
0.58
(0.26)
0.96
(0.44)
0.96
(0.41)
1.02
(0.44)
1.15
(0.50)
1.01
(0.44)
1.15
(0.50)
1.31
(0.61)
***
***
***
***
***
***
*
9,290
0.17
9,290
0.22
0.45
(0.31)
0.62
(0.39)
0.74
(0.49)
0.55
(0.34)
0.44
(0.26)
0.74
(0.46)
0.63
(0.37)
0.68
(0.40)
0.73
(0.43)
0.58
(0.35)
0.65
(0.39)
0.75
(0.47)
1.10
(0.01)
1.50
(0.14)
1.52
(0.14)
1.29
(0.06)
1.10
(0.07)
***
1.48
(0.12)
1.79
(0.18)
1.83
(0.17)
1.65
(0.19)
0.77
(0.05)
***
***
***
***
***
***
***
***
9,290
0.35
Note: *** p < 0.01, ** p < 0.05, * p < 0.10.
All models also includue indicators for parental occupation.
42
HIGH SCHOOL APPLIED STEM COURSETAKING
Table 5: Odds Ratios from Logistic Regression Models Predicting Advanced Math or Science Coursetaking
Advanced Math Coursetaking
Model 1
Participation in applied IT courses by end of grade 10
Participation in applied SRE courses by end of grade 10
Socio-demographic variables
Female
Race
Black
Hispanic
Asian
Other
English as a second language
Family data
Household composition
Single parent household
Other arrangement
Mother's education
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
Father's education
High school diploma or GED
Attended 2-year college, no degree
Graduated from 2-year college
Attended 4-year college, no degree
Graduated from 4-year college
Completed Master's or equivalent
Completed PhD/MD/advanced degree
1.28
(0.12)
0.85
(0.10)
Model 2
***
Advanced Science Coursetaking
Model 3
Model 1
1.33
(0.14)
0.95
(0.11)
***
1.29
(0.16)
0.90
(0.13)
**
1.27
(0.07)
***
1.61
(0.11)
***
0.57
(0.07)
0.65
(0.08)
2.36
(0.32)
0.33
(0.14)
0.97
(0.11)
***
1.24
(0.19)
0.96
(0.14)
2.28
(0.38)
0.48
(0.25)
0.85
(0.11)
0.68
(0.05)
0.64
(0.05)
***
1.17
(0.13)
1.21
(0.15)
1.55
(0.20)
1.60
(0.21)
2.16
(0.28)
2.70
(0.43)
1.66
(0.37)
1.08
(0.11)
1.51
(0.18)
1.43
(0.19)
1.43
(0.18)
1.95
(0.23)
1.98
(0.28)
2.63
(0.44)
***
***
***
***
***
***
***
***
**
***
***
***
***
***
***
0.83
(0.08)
0.72
(0.07)
0.93
(0.13)
0.87
(0.13)
1.01
(0.16)
1.11
(0.18)
1.36
(0.21)
1.54
(0.29)
1.21
(0.35)
0.90
(0.12)
1.00
(0.15)
1.09
(0.18)
0.98
(0.15)
1.27
(0.18)
1.09
(0.19)
1.45
(0.29)
***
**
***
**
**
*
*
1.28
(0.13)
0.95
(0.11)
Model 2
**
1.29
(0.13)
0.96
(0.12)
Model 3
**
0.99
(0.06)
***
0.68
(0.05)
0.64
(0.05)
***
1.30
(0.15)
1.81
(0.25)
1.87
(0.25)
1.84
(0.24)
2.18
(0.27)
2.43
(0.35)
2.68
(0.42)
**
1.11
(0.07)
0.58
(0.07)
0.79
(0.09)
2.34
(0.30)
0.52
(0.24)
0.81
(0.09)
1.08
(0.13)
1.21
(0.16)
1.48
(0.19)
1.62
(0.21)
1.79
(0.23)
2.29
(0.34)
1.79
(0.38)
1.26
(0.14)
0.92
(0.13)
**
***
*
***
***
***
***
***
***
**
***
***
***
***
***
***
1.07
(0.16)
1.06
(0.14)
2.00
(0.28)
0.80
(0.45)
0.69
(0.09)
0.77
(0.07)
0.75
(0.07)
0.90
(0.12)
0.92
(0.14)
1.09
(0.17)
1.17
(0.18)
1.20
(0.17)
1.41
(0.24)
1.31
(0.31)
1.22
(0.16)
1.45
(0.24)
1.68
(0.26)
1.58
(0.24)
1.66
(0.24)
1.66
(0.27)
1.79
(0.31)
***
***
***
***
**
**
***
***
***
***
***
43
HIGH SCHOOL APPLIED STEM COURSETAKING
Income
<= $1,000
1.15
(0.67)
0.92
(0.47)
0.59
(0.31)
0.93
(0.45)
1.32
(0.64)
1.23
(0.59)
1.45
(0.68)
1.48
(0.69)
1.77
(0.84)
1.60
(0.76)
1.82
(0.87)
2.01
(1.02)
1,001-5,000
5,001-10,000
10,001-15,000
15,001-20,000
20,001-25,000
25,001-35,000
35,001-50,000
50,001-75,000
75,001-100,000
100,001-200,000
>= 200,001
Investments in schooling
10th grade math IRT score
Importance of education
College expectations
Math self-efficacy
Parent involvement in student's schooling
Extracurricular activities
>0 to 4 hours
5 to 9 hours
10 to 14 hours
15 or more hours
Employment during school
n
adjusted R2
9,419
0.17
9,419
0.23
1.18
(0.81)
1.15
(0.64)
0.66
(0.38)
0.72
(0.38)
1.30
(0.68)
0.97
(0.49)
1.09
(0.54)
1.10
(0.55)
1.28
(0.64)
0.98
(0.50)
1.13
(0.58)
1.22
(0.66)
0.57
(0.29)
0.65
(0.32)
0.69
(0.33)
0.70
(0.32)
0.57
(0.25)
0.96
(0.43)
0.95
(0.40)
1.00
(0.43)
1.14
(0.49)
1.00
(0.43)
1.14
(0.49)
1.30
(0.59)
1.15
(0.01)
1.71
(0.17)
1.84
(0.17)
1.38
(0.06)
1.25
(0.09)
***
1.63
(0.14)
1.62
(0.16)
2.03
(0.20)
2.07
(0.25)
0.72
(0.05)
***
9,419
0.43
***
***
***
***
***
***
***
***
9,290
0.17
9,290
0.22
0.45
(0.31)
0.61
(0.39)
0.73
(0.48)
0.54
(0.33)
0.43
(0.25)
0.73
(0.45)
0.63
(0.37)
0.67
(0.39)
0.72
(0.42)
0.57
(0.34)
0.64
(0.38)
0.74
(0.46)
1.10
(0.01)
1.50
(0.14)
1.52
(0.14)
1.29
(0.06)
1.10
(0.07)
***
1.48
(0.12)
1.78
(0.18)
1.82
(0.17)
1.64
(0.19)
0.78
(0.05)
***
***
***
***
***
***
***
***
9,290
0.35
Note: *** p < 0.01, ** p < 0.05, * p < 0.10.
All models also includue indicators for parental occupation.
44
HIGH SCHOOL APPLIED STEM COURSETAKING
Table 6: Odds Ratios from Logistic Regression Models Predicting Advanced Math or Science Coursetaking
Applied STEM
Cousetaking
IT and SRE Coursetaking
IT
Outcome: advanced math coursetaking
Male
Female
White
Black
Hispanic
Asian
English is second language
English is primary language
Outcome: advanced science coursetaking
Male
Female
White
Black
Hispanic
Asian
English is second language
English is primary language
1.22
(0.13)
1.61
(0.22)
1.37
(0.17)
1.32
(0.32)
1.34
(0.29)
1.38
(0.30)
1.44
(0.28)
1.38
(0.14)
*
1.23
(0.12)
1.30
(0.17)
1.25
(0.14)
2.03
(0.53)
1.15
(0.24)
1.07
(0.22)
1.22
(0.22)
1.30
(0.13)
**
***
***
*
***
**
1.33
(0.17)
1.60
(0.25)
1.44
(0.21)
1.16
(0.32)
1.61
(0.40)
1.43
(0.31)
1.46
(0.34)
1.48
(0.18)
***
**
***
**
*
*
*
***
1.22
(0.14)
1.18
(0.17)
**
***
SRE
*
(0.16)
1.63
(0.45)
0.93
(0.21)
1.10
(0.23)
1.39
(0.30)
1.22
(0.13)
*
*
0.92
(0.13)
1.02
(0.26)
1.16
(0.32)
1.43
(0.50)
0.55
(0.19)
0.82
(0.27)
0.85
(0.25)
0.96
(0.15)
1.13
(0.15)
1.24
(0.33)
1.11
(0.17)
1.93
(0.86)
1.75
(0.52)
0.87
(0.31)
0.83
(0.24)
1.21
(0.18)
*
Note: *** p < 0.01, ** p < 0.05, * p < 0.10.
All models also includue indicators for parental occupation.
45