From Science Student to Scientist: Predictors and Outcomes of Heterogeneous Science Identity Trajectories in College

Abstract

This five-year longitudinal study investigates the development of science identity throughout college from an expectancy-value perspective. Specifically, heterogeneous developmental patterns of science identity across four years of college were examined using growth mixture modeling. Gender, race/ethnicity, and competence beliefs (efficacy for science tasks, perceived competence in science) were modeled as antecedents, and participation in a science career after graduation was modeled as a distal outcome of these identity development trajectories. Three latent classes (High with Transitory Incline, Moderate-High and Stable, and Moderate-Low with Early Decline) were identified. Gender, race/ethnicity, and competence beliefs in the first year of college significantly predicted latent class membership. In addition, students in the two highest classes were significantly more likely to report being involved in science careers or science fields after college graduation than students in the Moderate-Low with Early Decline class.

Full text

Running Head: FROM SCIENCE STUDENT TO SCIENTIST 1
From Science Student to Scientist: Predictors and Outcomes of Heterogeneous Science Identity
Trajectories in College
Kristy A. Robinsona,*, Tony Perezb, Amy K. Nuttalla, Cary J. Rosetha, & Lisa Linnenbrink-Garciaa
aMichigan State University, East Lansing, MI, United States
bOld Dominion University, Norfolk, VA, United States
Manuscript accepted for publication in Developmental Psychology
AUTHOR’S COPY – MAY DIFFER FROM PUBLISHED MANUSCRIPT
Author Note
* The authors gratefully acknowledge You-kyung Lee for her helpful feedback on drafts of this
manuscript. Correspondence concerning this article should be addressed to Kristy A. Robinson,
Department of Counseling, Educational Psychology & Special Education, Erickson Hall, Michigan State
University, 620 Farm Lane, East Lansing, MI 48824. Phone: 385-321-0373 Email: robi1004@msu.edu

FROM SCIENCE STUDENT TO SCIENTIST 2
Abstract
This five-year longitudinal study investigates the development of science identity throughout college from
an expectancy-value perspective. Specifically, heterogeneous developmental patterns of science identity
across four years of college were examined using growth mixture modeling. Gender, race/ethnicity, and
competence beliefs (efficacy for science tasks, perceived competence in science) were modeled as
antecedents, and participation in a science career after graduation was modeled as a distal outcome of
these identity development trajectories. Three latent classes (High with Transitory Incline, Moderate-
High and Stable, and Moderate-Low with Early Decline) were identified. Gender, race/ethnicity, and
competence beliefs in the first year of college significantly predicted latent class membership. In addition,
students in the two highest classes were significantly more likely to report being involved in science
careers or science fields after college graduation than students in the Moderate-Low with Early Decline
class.
Keywords: science identity development; growth mixture modeling; expectancy-value; STEM
persistence

FROM SCIENCE STUDENT TO SCIENTIST 3
From Science Student to Scientist: Predictors and Outcomes of Heterogeneous Science Identity
Trajectories in College
While college is a time of potentially volatile change in students’ academic and professional
identities (Côté, 2006; Eccles, 2009; Marcia, 1993; Roisman, Masten, Coatsworth, & Tellegen, 2004;
Waterman, 1993), few prior studies have examined developmental trajectories during these years. This
gap in knowledge is especially problematic in science, technology, engineering, and math (STEM)
domains where there is an acute need to expand and diversify the workforce (National Science and
Technology Council, 2013). After all, many students start college with the intention of majoring in a
STEM discipline and pursuing a STEM career, but a large proportion “leak” out of the STEM pipeline,
particularly students who are traditionally underrepresented in science fields (National Science Board,
2016; Koenig, 2009; Myers & Pavel, 2011; Penner, 2015). Different patterns of identity development
may help to explain why this occurs.
Extant work on identity development in STEM (e.g., Hernandez, Schultz, Estrada, Woodcock, &
Chance, 2013) generally considers changes in identity on the average, without considering whether
developmental patterns vary among individuals. However, there is likely to be a great deal of
heterogeneity in identity trajectories, given the variety of barriers that some students may face during
college such as stereotype threat and the often competitive climate of introductory STEM courses
(Murphy, Steele, & Gross, 2007; Seymour & Hewitt, 1997). Thus, a person-oriented developmental
approach may be needed to account for heterogeneous patterns of science identity development.
Moreover, theory and prior research suggest that students’ competence beliefs are important in identity
development (Eccles 2009; Chemers, Zurbriggen, Syed, Goza, & Bearman, 2011; Robnett, Chemers, &
Zurbriggen, 2015), and that identity development processes have important implications for career
outcomes (Eccles, 2009; Estrada, Woodcock, Hernandez, & Schultz, 2011; Hernandez et al., 2013;
Woodcock, Hernandez, Estrada, & Schultz, 2012). However, there is little empirical research
investigating these claims, especially longitudinal research that follows traditional college students (i.e.,
student who enter college directly from high school) from the start of college until after graduation.

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In the current study, we use an expectancy-value theory perspective to investigate science identity
development among a diverse population of traditional college students who begin college with a focus in
science. Specifically, we aim to (1) identify and describe varying science identity trajectories across four
years of college, (2) consider predictors of these science identity trajectories, and (3) examine how
science identity trajectories predict post-graduation participation in science careers.
Conceptualization of Identity
Students who begin college intending to pursue science often engage in academic pursuits and
career preparation activities that have the potential to solidify or destabilize academic and career identities
(Eccles, 2009; Waterman, 1993). Contemporary Expectancy-Value Theory (Eccles, 2009) posits that an
individual’s expectancies for success and appraisals of value for a task or domain (i.e., task value) are the
most proximal predictors of academic and occupational choices (Eccles et al., 1983). Task value is
conceptualized as multifaceted, with individuals valuing tasks or domains for multiple reasons including
the personal importance of a task or domain because of its relevance to their personal and collective (or
social) identities (i.e., attainment value; Eccles, 2009). Attainment value in particular is conceptualized as
a central, defining component of an individual’s personal and collective identities.
Expectancy-value theory’s conceptualization of identity is similar to the self-theories or schemas
as conceptualized by Markus and Nurius (1986). It is also has much in common with identity
commitments that define Marcia’s (1993) foreclosed (i.e., committed to an identity that is valued by
important others) and achieved (i.e., committed to an identity after personal exploration) identity statuses,
although Eccles does not differentiate between commitments made through differing exploration
processes as Marcia does in the ego-identity status model. An expectancy-value view of identity is also
similar to the concept of ‘identification with commitment’ (e.g., embracing and integrating commitments
into one’s sense of self) in Luyckx and colleagues’ dual-cycle model of identity formation (Luyckx,
Goossens, Soenens, & Beyers, 2006; Schwartz, Zamboanga, Luyckx, Meca, & Ritchie, 2013). However,
the focus in expectancy-value theory is on salient identity contents (e.g., importance of a particular
identity), with academic and occupational choices viewed as potential enactments of such identity

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contents. Thus, if majoring in science and pursuing a science career is an enactment of one’s science
identity, then one should have high expectancies for success in science, high attainment value for science,
and be more likely to have a career in science after graduation.
Development of Science Identity During College
According to expectancy-value theory, the importance, salience, and contents of one’s identities
represent dynamic processes that change over time in response to information gleaned from the
environment, from introspection, and through experience (Eccles, 2009). This means that students who
enter college highly valuing a science identity may continue to explore the importance of this and other
identities over time. It also means that students may encounter periods of identity instability as a result of
destabilizing events, such as receiving a poor grade in a course or experiencing stereotype threat (cf.
Luyckx et al., 2006). However, much of the identity literature has focused on identity processes broadly,
either examining identity processes in general (e.g. exploration and commitment; Luyckx, Teppers,
Klimstra, & Rassart, 2014) or in relation to broad domains such as education and friendship (e.g. Klimstra
et al., 2010). Thus, the present student contributes to this literature by focusing specifically on identity in
science and examining different patterns of change over time.
Despite the potential importance of college for stabilizing or destabilizing science identity, there
exists little longitudinal research examining the development of science identity during this time. In one
relevant study, Hernandez and colleagues (2013) found that science identity
1
, examined over three years
in a sample of college students from underrepresented ethnic and racial groups, was relatively stable,
suggesting that there are very few changes in science identity towards the latter half of college. This
finding aligns with other research reporting slight, but statistically significant change in vocational
identity across three time points during high school (Negru-Subtirica, Pop, & Crocetti, 2015). Taking a
1
Science identity was conceptualized somewhat differently in terms of role orientation (e.g., a composite of identity
beliefs related to belonging, interest, and self-categorization as a scientist; see Estrada et al., 2011 for a more
detailed discussion) rather than identity-related attainment value.

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person-oriented approach, Musu-Gillette, Wigfield, Harring, and Eccles (2015) also examined whether
there were multiple, distinct trajectories of value
2
for math during adolescence and early adulthood.
Results indicated that mathematics value declined rapidly for some students (fast decline), slowly for
others (slow decline), and remained relatively stable, though lower overall (low steady) for a third class of
students. However, given their broader developmental focus, Musu-Gillette and colleagues only included
one measurement point in college, which makes it difficult to make claims about developmental
trajectories within college.
One important implication of Musu-Gillette et al.’s (2015) findings is that solely examining one
average trajectory may mask sub-group differences in identity development. Thus, using a person-
oriented approach to complement variable-oriented research on science identity development may provide
insight into the nature of the construct and advance theoretical understanding. For practice, this approach
may also indicate whether interventions to support science identity should be administered to all students
or only to subgroups of students.
Predictors of Science Identity Trajectories
In addition to describing heterogeneity in developmental trajectories, further research is also
needed to illuminate the processes through which science identity can be supported or destabilized.
According to expectancy-value theory, competence beliefs, derived from appraisals of success or failure
in the domain, are important predictors of the value one places on a domain (Wigfield & Cambria, 2010),
including identity-related attainment value. Specifically, a student’s perceived failure on domain-specific
tasks can lead that student to devalue his or her identity and seek other options, while a student’s apparent
success may lead the student to seek out future opportunities to re-engage. Citing a large body of research
linking task-specific competence beliefs to behavior, Eccles (2009) further posits that identity formation
processes may act as a mediator between competence beliefs and behavioral choices. Thus, students who
2
Value was conceptualized in terms of both utility and attainment value, and attainment value was assessed in terms
of broad personal importance. Thus, the measure of value used does not fully align with Eccles’ (2009)
conceptualization of identity-related importance.

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pursue science when beginning college may be at risk for lower identity appraisals over time if they are
unsure about their science abilities.
While there are a variety of relevant competence beliefs, we focus on two common types:
perceived competence, or students’ beliefs in their ability to learn content in a given domain, and self-
efficacy, which is students’ confidence in their ability to successfully complete specific tasks (Schunk &
Pajares, 2005). We conceptualize perceived competence in terms of students’ general beliefs about their
ability to learn and do academic work in science courses and self-efficacy as students’ beliefs about their
ability to successfully complete specific tasks related to the scientific process (e.g., generate a hypothesis,
analyze/interpret data). Both ability beliefs may influence students’ science identity beliefs: self-efficacy
via experiences that inform students’ beliefs about their ability to perform scientific tasks, and perceived
competence via students’ beliefs that they can successfully navigate the academic challenges associated
with pursuing a scientific career. But self-efficacy may be a more relevant predictor of science identity
because it pertains to “authentic” scientific skills used by scientists (at least in terms of how we
conceptualize self-efficacy here), as opposed to academic skills that may or may not be useful in a science
career. Students who lack confidence in their ability to pass a test in a science course may or may not
view this as relevant to their identities as scientists, whereas lack of confidence in one’s ability to form
and test a scientific hypothesis may directly inform appraisals of science identity. Supporting this view,
prior research indicates that self-efficacy mediates the relation between research experiences and science
identity, both concurrently (Chemers et al., 2011) and longitudinally (Robnett et al., 2015). However, no
prior research investigates perceived competence as a predictor of science identity trajectories, nor does it
assess both self-efficacy and perceived competence as predictors. Thus, the present study extends prior
work by testing whether both of these competence beliefs predict membership in different science identity
trajectories.
In addition to competence beliefs, gender as well as race and ethnicity may predict differences in
college students’ developing science identities. Women and students from underrepresented ethnic/racial
groups (e.g., African American, Hispanic/Latino) may face additional barriers to identifying with science,

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as evidenced by low representation in STEM fields and a lower likelihood of completing degrees in
STEM in the United States (National Science Board, 2016; Koenig, 2009; Myers & Pavel, 2011).
However, empirical support for this claim is mixed. Within the math domain, for example, gender
predicted different trajectories of value-related identity change among adolescents in some studies (Nagy
et al., 2008; Watt, 2004), but not others (Musu-Gillette et al., 2015). Importantly, this prior work did not
consider science specifically nor did it follow students throughout college. And, there is little, if any,
research that examines whether racial/ethnic minority students experience declines in science-related
identity throughout college in comparison to majority groups. Thus, while there is research suggesting
possible mechanisms for disidentification in science by both women and racial and ethnic minorities (e.g.,
stereotype threat, Cokley, 2002; Osborne, 1995; 1997), we know very little about whether
underrepresented groups in the sciences actually experience distinct patterns of science identity
development longitudinally throughout college relative to male and Caucasian or Asian students. As such,
it is critical to first understand whether women and racial/ethnic minorities are more likely to experience
steeper declines in identity throughout college, before attempting to further investigate the particular
contextual and psychological elements that may be associated with varying patterns of identity
development during college among gender and racial/ethnic groups traditionally underrepresented in
STEM fields.
Science Identity Trajectories as Predictors of Science Careers
As a defining component of identity, attainment value provides a framework for organizing
overall self-perceptions, worldviews, and behavior (Eccles, 2009; Kaplan & Flum, 2012; Oyserman,
2015; Rosenberg, 1979). Thus, the more a student values science as a part of his or her identity, the more
he or she also self-evaluates through the lens of a scientist, thinks like a scientist, and makes choices that
are congruent with being a scientist. Developmentally, it follows that students reporting high science
identity should also be more likely to choose and achieve science careers after graduation. However, these
links between identity and behavior have not yet been fully investigated, as most studies integrating
identity and motivation constructs as important predictors of college science students’ retention have been

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limited by cross-sectional designs (e.g., Andersen & Ward, 2013; Chemers et al., 2011; Hazari, Sonnert,
Sadler, & Shanahan, 2010). One exception to this pattern is the study by Estrada and colleagues (2011),
who found that science identity in a sample of underrepresented minority students was a powerful
predictor of science career intentions and behavioral involvement in science (e.g., independent research
and graduate school attendance) assessed one year later. Likewise, in two subsequent studies using a
similar sample, Woodcock et al. (2012) found that science identity predicted underrepresented minority
college students’ intentions to pursue a scientific research career (one year later), and Hernandez and
colleagues (2013) found that science identity trajectories positively correlated with stability in mastery
goals, which was in turn a significant positive predictor of undergraduate GPA.
Extant research focused specifically on value is also relevant to the relation between identity
beliefs and STEM persistence. For instance, Musu-Gillette and colleagues (2015) found that adolescents
whose value for mathematics declined more rapidly were less likely to choose a math-intensive major in
college. Focusing specifically on identity development in science, other research has found that students
who engaged in identity exploration reported higher value for science and lower perceived costs
associated with pursuing a science major than students who chose a major based on others’ expectations
(Perez, Cromley, & Kaplan, 2014). Importantly, commitment to a career identity after exploration was
related to persistence intentions via students’ value for science. That is, students who reported higher
value (including attainment value conceptualized as personal importance) and lower costs associated with
pursuing STEM fields reported lower intentions to leave science.
Present Study
In summary, extant literature provides some information about the development of science
identity, as well as its predictors and implications for involvement in science careers, but also leaves
important questions unexamined. For example, prior studies used varied conceptualizations of science
identity, often combining numerous identity facets into one measure (e.g., Chemers et al., 2011; Estrada
et al., 2011). Additionally, while two prior studies considered how identity trajectories predict subsequent
persistence-related outcomes (Hernandez et al., 2013; Musu-Gillete et al., 2015), only one has followed

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students after graduation to assess whether or not they were involved in science fields (Estrada et al.,
2011), which is perhaps the most important outcome of interest to science educators and policy makers.
Accordingly, the current study extends theory and prior research by examining heterogeneous change
trajectories in traditional college students’ science identities over the entirety of the undergraduate leaky
STEM pipeline, from the beginning of college through one year after graduation. Three research
questions guided the work:
(1) Are there multiple patterns (or latent classes) of science identity development during college?
(2) How do gender, race/ethnicity, perceived competence, and self-efficacy predict science identity
class membership?
(3) Do differential science identity trajectories (class membership) predict science career outcomes
after college graduation?
Based on prior research identifying heterogeneous developmental patterns of value for academic
subjects (e.g., Musu-Gillette et al., 2015), we expected to find multiple classes of development in science
identity. Given that these were students enrolled in courses for science majors, we expected that the
science identity intercept for at least some classes would be relatively high. We also expected that some
students would exhibit declines in science identity over time (Marcia, 1993) and others would report
stable or increasing patterns of science identity over time, presumably as a result of different affirming or
destabilizing experiences. We also tested for non-linear change based on the assumption that identity
development is dynamic and on-going and therefore not necessarily linear (Eccles, 2009). We did not
have specific hypotheses about the number of classes, particularly as testing quadratic growth trajectories
in addition to linear growth expanded the possible growth patterns.
As posited by expectancy-value theory (Eccles, 2009) and prior research showing strong relations
between self-efficacy and science identity (Chemers et al., 2011; Estrada et al., 2011), we hypothesized
that both competence beliefs (self-efficacy for science tasks, academic perceived competence in science)
would predict class membership, with higher competence beliefs in the first year predicting membership
in classes with higher initial science identity and either growth or stability in science identity during

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college. Given its relevance to future science careers, we expected that science self-efficacy would
explain unique variance when controlling for perceived competence. We also expected that perceived
competence would explain significant, unique variance in class membership because of the importance of
academic success for pursuing science careers.
We also examined whether women and underrepresented minority students were more likely to
belong to identity trajectories characterized by declines in science identity. The assumption was that these
groups are underrepresented in STEM and would therefore encounter different opportunities and barriers
for developing their identities as scientists. Attrition from science is disproportionately higher for women
and racial/ethnic minority students (National Science Foundation, 2015), and prior research provides
evidence that domain-specific values and competence beliefs vary by gender and race/ethnicity (Cokley,
2002; Gaspard et al., 2015; Jacobs, Lanza, Osgood, Eccles, & Wigfield, 2002; Nagy et al., 2008).
Therefore, we hypothesized that female students and students from underrepresented racial/ethnic groups
would be more likely to exhibit declines in science identity over time.
Finally, in alignment with theory (Eccles, 2009) and prior research (e.g., Chemers et al., 2011;
Estrada et al., 2011; Musu-Gillette et al., 2015; Woodcock et al., 2012), we expected that science identity
class membership would differentially predict participants’ involvement in science careers or fields after
college. We hypothesized that students with relatively high and stable or increasing science identity
would be more likely to be involved in science careers than students for whom science identity decreased
over time and/or whose levels of science identity were initially low.
Method
Participants and Procedure
Data for the current study were collected as part of an ongoing intervention study at an elite
university in the United States. The study, titled “Self-Generated Research Experiences to Support
Biomedical/Behavioral Research Careers,” was approved by the Institutional Review Board at Lisa
Linnenbrink-Garcia’s former and current institutions (IRB Nos. A0166 and x16-881e). Students who
participated in the intervention (n = 197) were excluded from our analyses, because the intervention may

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have impacted the constructs examined in the current study. Using a longitudinal design that
prospectively assessed students across five years following a three-year original enrollment period, data
collection began during fall of participants’ freshman year (2010, 2011, or 2012) and continued annually
through the year after graduation. Therefore, our data collection period spanned seven years. Participants
were recruited from freshman chemistry courses required for science majors. With the permission of
course instructors, we visited each chemistry course to describe the study and invite participation.
Students aged 18 and over who were in their first year of college were eligible to participate. Those who
were not yet 18 (n = 56) at the time of recruitment were invited to participate after turning 18. Of the
2,581 students enrolled in the recruitment courses, 75% (n = 1,934) agreed to participate in the study.
Students provided informed consent and completed paper surveys in class, receiving $10 for participation.
From this larger sample of students who completed the first-year baseline survey, a longitudinal
comparison group was randomly selected from those who did not participate in the intervention, with
stratification to oversample women and students from underrepresented ethnic and racial groups.
3
The
selected comparison group, consisting of 1,023 participants, was invited annually to take follow-up
surveys via email during spring semester of their sophomore, junior, and senior years as well as eight
months post-graduation, resulting in 5 waves of data. Of those invited to take follow-up surveys, 49% (n
= 506) responded to the sophomore (T2) survey, 45% (n = 456) responded to the junior year (T3) survey,
46% (n = 475) responded to the senior year (T4) survey, and 47% (n = 483) responded to the post-
graduation survey (T5). Students who did not complete one or more of the follow-up surveys were still
invited to participate in subsequent follow-up surveys. College dropout was quite rare in this sample
(4%), and participants were invited to complete follow-up surveys whether or not they dropped out of
college.
3
The intervention group had a larger proportion of women and underrepresented ethnic and racial minority groups
than the student population; thus, we also oversampled women and racial/ethnic minority groups in the comparison
group in an attempt to match the intervention sample.

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The final sample for the present study included the entire comparison group sample of 1,023
undergraduate students (58% female; 25% White, 43% Asian, 13% African American, 11%
Hispanic/Latino, 8% multi-racial/other). Results of missing data analyses are provided in the results
section.
Measures
Science identity. Science identity was assessed annually each of the four years of college using a
4-item self-report scale adapted from two scales: a science identity scale developed by Pugh,
Linnenbrink-Garcia, Koskey, Stewart, and Manzey (2009) and an attainment value scale developed by
Conley (2012). The four items measure individual appraisals of the personal importance or value of
science to one’s identity (α = .83-.90), for example: “Being involved in science is a key part of who I
am.” Students rated items on a Likert-type scale ranging from 1 to 5, with 1 = strongly disagree and 5 =
strongly agree.
Competence beliefs. Self-efficacy for science tasks was measured in the first year using a six-
item scale (α =.84), adapted by Estrada et al. (2011) from Chemers et al. (2011) assessing students’
confidence in their ability to complete scientific tasks. An example item read, “I am confident that I can
generate a research question to answer.” Perceived competence for science coursework, or students’
confidence in their ability to succeed at academic work in science, was measured in the first year using a
five-item scale (Midgley et al., 2000; α =.87). A sample item for this scale is, “Even if the work in science
is hard, I can learn it.” Items for both scales were rated on a 5-point Likert-type scale (1 = strongly
disagree, 5 = strongly agree).
Science involvement. To assess students’ participation in science careers or graduate programs
after college graduation, participants responded to a self-report item collected in the spring following
graduation. The question asked, “Do you consider yourself to be involved in a science-related career or
field?” Following the question, science-related careers or fields were defined for the participant: “A
science-related career is one that is based on scientific knowledge or principles, uses scientific
methodology and techniques, and/or engages in scientific research.” Participants could choose 1 = “Yes,

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I’m involved in a traditional science career/field (e.g., work in a lab, science research analyst/consultant,
continuing my education in a science career (e.g., medical school, PhD program in science, etc.))”, 2 =
“Somewhat, my work is related to science but is not in a traditional field (e.g., science writer, investment
advisor in biotech, drug regulatory affairs)”, or 3 = “Definitely not”. Categories 1 and 2 were collapsed
for the current study to create a dichotomous measure, with 1 indicating involvement in a science-related
career or field and 0 indicating no involvement in science.
Data Analytic Strategy
All analyses were conducted using Mplus Version 8 (Muthén & Muthén, 1998-2017) and missing
data were handled using full information maximum likelihood (FIML) estimation. Prior to fitting growth
models, preliminary analyses included descriptive statistics, correlations, and examining individual
trajectory plots to inform the selection of plausible models for examination (Ram & Grimm, 2009).
Latent growth model. First, latent growth modeling, which represents a one-class growth
mixture model, was used to select a baseline model for GMM (Masyn, 2013; Ram & Grimm, 2009).
Intercept-only (no growth), linear, and quadratic models were fit to the full sample to find the best-fitting
representation of change for the overall sample.
Growth mixture models. Growth mixture modeling (GMM) was used to identify classes of
participants based on observed heterogeneity in patterns of change (Nylund, Asparouhov, & Muthén,
2007). Rather than relying on a single growth curve model, which assumes all participants belong to a
single population, or on multi-group models defined by observed group membership, GMM was used to
classify heterogeneity in patterns of change. Bayesian Information Criterion (BIC) was used to select the
class solution, with smaller values of BIC indicating better fit, because simulation studies demonstrate the
utility of the BIC in GMM (Nylund et al., 2007). The theoretical interpretability of class solutions was
also considered in the selection of the final classes (Grimm & Ram, 2009b; Nylund et al., 2007).
The introduction of predictors (termed covariates in the GMM literature) and distal outcomes of
class membership can be approached in a number of ways. One approach, the one-step method, involves
adding covariates or distal outcomes directly to the GMM; however, adding these variables to a GMM

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provides additional information that influences estimation, often changing the class solution. This can
make interpretation difficult, particularly when researchers aim to understand covariates as predictors of
class membership (Vermunt, 2010). Another approach involves a 3-step procedure: after the GMM is
specified (Step 1), each case is assigned to the most likely class based on probabilities of latent class
membership (Step 2). Class membership is then used as a categorical variable which can be modeled as a
predictor or an outcome (Step 3). However, because this method treats latent class membership as known,
standard errors and estimates of model parameters are biased (Vermunt, 2010).
In the current study, we used an updated 3-step approach (Asparouhov & Muthén, 2014)
implemented in the Mplus software (Muthén & Muthén, 1998-2017) to introduce covariates (gender,
race/ethnicity, self-efficacy, and perceived competence) and a distal outcome (post-graduation science
involvement) to the GMM. After estimating the GMM, this approach is similar to the earlier 3-step
approach in that it assigns each case to the most likely class in Step 2, then regresses the most likely class
on predictor variables (or distal outcomes on class). However, the updated approach improves upon the
one-step approach by taking into account the uncertainty of classification in the second step using logit
probabilities that have been shown to result in less biased estimates than the original 3-step procedure
while maintaining a stable class solution for the GMM and interpretable coefficients for predictors and
outcomes of class membership (Asparouhov & Muthén, 2014). The command R3STEP was used for
covariates, and the DCATEGORICAL command was used for the distal outcome, as recommended by
Muthén & Muthén (1998-2017; see also Asparouhov & Muthén, 2012; Vermunt, 2010).
Results
Preliminary Analyses
Correlations and descriptive statistics. Table 1 displays correlations and descriptive statistics
for each study variable. Science identity at all time points was significantly positively correlated with self-
efficacy and perceived competence at Time 1, and self-efficacy and perceived competence were also
positively correlated. As expected, repeated measures of science identity were also positively correlated

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over time and means of science identity were relatively stable over time. Overall, 77% of participants
reported being involved in a science-related career or field after graduation.
Missing data. To address the assumptions for full information maximum likelihood estimation
(FIML), we examined patterns of missing data in relation to study variables. Study recruitment (e.g.,
invitations to complete follow-up surveys) was randomly selected with follow-up invitations regardless of
prior participation, and the amount of missing data at each time point was within the expected range (52-
57%). In addition, participants with any missing data were compared to subjects with complete data on
demographic variables, initial competence beliefs, and initial science identity. Students with missing data
did not significantly differ from students with complete data on first-generation college student status [χ2
(2) = .314, p = 0.86], but the two groups did differ significantly with respect to membership in an
underrepresented ethnic/racial minority group [χ2 (1) = 10.55, p < .01] and gender [χ2 (1) = 15.83, p <
.001]. Those with complete data were more likely to be female and White or Asian/Asian American. The
MANOVA comparing year 1 perceived competence, self-efficacy, and science identity was not
significant, Wilks’ λ (3, 1014) = 0.99, p = .06, η2 = 0.01.
Confirmatory factor analyses. Confirmatory factor analyses (CFAs) for Time 1 perceived
competence and self-efficacy indicated that the two-factor model fit the data well, χ2 (43) = 191.966,
RMSEA = .06, CFI = .97, TLI = .96, providing evidence that participants differentiated self-efficacy for
scientific tasks from perceived competence for science coursework in their responses. Below, we describe
the measurement model for science identity.
Second-Order Growth Model
Measurement invariance. In order to make inferences about change over time, it is necessary to
first establish evidence that the same construct is being measured over time. Observed change over time
can then be attributed to true change rather than change in the meaning of the construct over time
(Widaman & Reise, 1997). Measurement invariance over four time points for the first-order common
factor model for science identity was evaluated by successively fitting configural, weak, strong, and strict
invariance models. The configural model constrained the factor structure to be the same across time.

FROM SCIENCE STUDENT TO SCIENTIST 17
Weak invariance was specified by additionally constraining factor loadings to be equal across time, and
strong invariance additionally assumed equal observed intercepts over time. Lastly, the strict invariance
model constrained residual variances for observed factor indicators over time. Model comparisons
resulted in less than .01 change in CFI between models (see Table 2; Cheung & Rensvold, 2002).
Therefore, the strict invariance model of science identity, with invariant factor loadings, intercepts, and
unique factor variances across time (Widaman, Ferrer, & Conger, 2010), fit the data well and provided
evidence that the same construct was measured over time. This model was used as the first-order
measurement model for science identity in subsequent analyses.
We also conducted tests of group invariance by gender and race/ethnicity assuming time
invariance within groups (i.e., factor loadings, intercepts, and residual variances were constrained to be
equal over time). Similar to the test of measurement invariance over time, we fit configural, weak, strong,
and strict invariance models including all four time points of science identity with parameters
successively constrained to be equal across groups. As displayed in Table 2, results showed evidence of
strict measurement invariance in science identity across gender and race/ethnicity groups.
Second-order latent growth curve model. With strict invariance constraints imposed on the
first-order measurement model, science identity trajectories were modeled as second-order latent factors
first with an intercept-only (no growth) model, a linear growth model, and finally a quadratic model.
Model fit was acceptable for the intercept-only model, χ2 (139) = 638.12, RMSEA = .059, CFI = .914, TLI
= .926. The linear model, χ2 (136) = 532.49, RMSEA = .053, CFI = .932, TLI = .940, appeared to fit the
data better than the intercept-only model, with a difference in CFI of > .01. The quadratic model, χ2 (132)
= 491.28, RMSEA = .052, CFI = .939, TLI = .944, appeared to fit the data similarly to the linear model,
but the quadratic factor was non-significant (M = -.005, SE = .01, p = .69). Importantly, for the intercept-
only, linear, and quadratic models, the addition of gender and race/ethnicity as auxiliary variables in
FIML estimation resulted in no changes to fit indices or model parameters.
For the entire sample, these findings suggest that the linear model offered the most parsimonious
fit to the data, describing initial mean science identity at 3.74 (SE = 0.04, p < .001), a slight, non-

FROM SCIENCE STUDENT TO SCIENTIST 18
significant negative linear slope of -.03 (SE = .01, p = .06), and significant estimated variation in the
intercept (σ2 = 0.39, SE = 0.04, p < .001) and slope (σ2 = 0.04, SE = .01, p < .001). However, for the
GMM analyses, we tested both linear and quadratic models because the two unconditional models fit
similarly and quadratic patterns could be identified for subgroups in the sample, even if the overall pattern
is linear.
Growth mixture model selection. The next step in our analyses was to examine heterogeneity in
developmental trajectories of science identity among the students in our sample using GMM. A series of
2-, 3-, and 4-class linear and quadratic models were specified and compared to a 1-class baseline model,
successively freeing between-class equality constraints in order to examine increasingly complex models
(Grimm & Ram, 2009a). In order to test heterogeneity in the initial level and slope of change in science
identity across classes, we successively freed the means, variances, and covariances of the intercept and
slope factors to be class-specific (e.g., vary across classes; see Table 3, 4, and 5 for the 2-, 3-, and 4-class
models, respectively). In addition, we also successively freed residual variances, factor intercepts, and
factor loadings of the first-order science identity factors to be class-specific in order to assess
measurement invariance across classes, avoid over-extraction of classes, and minimize bias in parameter
estimates (Enders & Tofighi, 2008). We selected a three-class solution with class-specific means of the
intercept, linear slope, and quadratic slope factors, and class-specific residual variances of observed items;
all other parameters were class-invariant (see Table 4, Model 10). This model had the lowest BIC value,
was interpretable, and aligned with theoretical expectations. Classification quality was acceptable, with
average latent class probabilities for most likely latent class memberships greater than .80. Therefore, we
identified three classes that differed in terms of mean level, rate of change, and unexplained variance.
Table 6 displays parameter estimates for each class, and Figure 1 displays model-implied
trajectories of science identity for each class. Sample sizes and proportions are based on most likely class
memberships for each case. In one class (High with Transitory Incline; n = 404; 40% of sample), science
identity was initially high, with a positive linear slope and a negative quadratic slope. In other words,
these students reported high science identity at the beginning and end of college, which then increased

FROM SCIENCE STUDENT TO SCIENTIST 19
and decreased slightly across college. The second class (Moderate-High and Stable; n = 513; 50% of
sample) was characterized by relatively lower initial science identity and non-significant mean linear and
quadratic slopes. These students reported moderately high science identity in their first year and relative
stability in their beliefs throughout college. The third class (Moderate-Low with Early Decline; n = 106;
10% of sample) reported relatively low levels of initial science identity, with a sharply decreasing linear
slope and a positive quadratic slope. This class was characterized by lower science identity in the first
year, which then decreased sharply from the first to second years then leveled off between the third and
fourth years. Non-overlapping confidence intervals confirmed that the intercepts differed significantly
across the three classes. The intercept in the High with Transitory Incline class, M = 4.12, 95% CI [3.97,
4.27], was significantly higher than the intercept of the Moderate-High and Stable class, M = 3.61, 95%
CI [3.38, 3.84], and both were significantly higher than the intercept of the Moderate-Low with Early
Decline class M = 2.93, 95% CI [2.62, 3.25].
Predictors of Class Membership
Following the selection of the 3-class GMM, auxiliary variables were added to the model as
predictors of class membership using the updated 3-step method in Mplus (Asparouhov & Muthén, 2014).
Composite scores for perceived competence and self-efficacy were calculated by averaging the items due
to specification limitations with regards to the inclusion of latent variables in the 3-step approach as
implemented in Mplus. Gender (male = 0, female = 1), membership in an underrepresented racial/ethnic
group (African American, Hispanic/Latino, or Native American = 1, White or Asian/Asian American =
0), self-efficacy, and perceived competence were modeled as predictors of class membership.
Multinomial logistic regression coefficients and odds ratios for each pairwise comparison are presented
below and each coefficient can be interpreted as the difference in log odds of being in a class (vs. the
reference class) associated with a 1-unit difference in the predictor variable, controlling for the other
predictors.
The coefficients for gender indicate that women were nearly two times more likely than men to
be in the Moderate-High and Stable class compared to the High with Transitory Incline class (b = 0.64, p

FROM SCIENCE STUDENT TO SCIENTIST 20
< .01, odds ratio = 1.90). There was no evidence of gender differences in the likelihood of being in the
Moderate-Low with Early Decline class compared to the High with Transitory Incline class (b = 0.52, p =
.09, odds ratio = 1.68) or in the likelihood of being in the Moderate-High and Stable vs. the Moderate-
Low with Early Decline class (b = -0.12, p = .72, odds ratio = .89).
Coefficients for membership in an underrepresented racial/ethnic group (URM) indicate that
African American, Native American, and Hispanic students were more than two times as likely as
racial/ethnic majority students to be in the Moderate-Low with Early Decline class compared to the High
with Transitory Incline class (b = 0.75, p <.05, odds ratio = 2.12). There was no evidence of racial/ethnic
group differences in the likelihood of being in the Moderate-Low with Early Decline compared to the
Moderate-High and Stable class (b = 0.48, p =.15, odds ratio = 1.62), nor was there any evidence of
differences in the likelihood of being in the Moderate-High and Stable compared to the High with
Transitory Incline class (b = 0.28, p =.23, odds ratio = 1.32).
Higher self-efficacy predicted a greater likelihood of being in the High with Transitory Incline
class (b = 0.92, p < .001, odds ratio = 2.51) or the Moderate-High and Stable class (b = 0.92, p < .001,
odds ratio = 2.51) compared to the Moderate-Low with Early Decline class. Self-efficacy was not a
significant predictor of membership in the High with Transitory Incline class vs. the Moderate-High and
Stable class (b = 0.001, p = .59, odds ratio = 1.00). These coefficients indicate that self-efficacy was an
important predictor of whether students were most likely to be in either of the two relatively high and
stable classes of science identity versus the low and declining class.
Higher perceived competence predicted a greater likelihood of being in the High with Transitory
Incline class vs. the Moderate-Low with Early Decline class (b = 0.56, p < .05, odds ratio = 1.75), but was
not a significant predictor of being in the High with Transitory Incline class vs. the Moderate-High and
Stable class (b = 0.29, p = .08, odds ratio = 1.34) or of membership in the Moderate-High and Stable vs.
Moderate Low with Early Decline classes (b = 0.28, p = .24, odds ratio = 1.32). These coefficients
indicate that variation in perceived competence was an important discriminator between low and high

FROM SCIENCE STUDENT TO SCIENTIST 21
science identity trajectories, but was not predictive of less extreme differences (i.e., Moderate-High vs.
High class or Moderate-High vs. Low).
Class Membership and Science Career Outcomes
Finally, we tested whether science identity class membership predicted participants’ involvement
in science careers after graduation. This model did not include predictors of class membership, as the 3-
step method does not allow for both predictor and outcome auxiliary variables to be modeled
simultaneously. Class membership was significantly associated with involvement in a science career or
field after graduation, χ2 (2) = 60.50, p < .001. Specifically, 88.7% of students in the High with Transitory
Incline class and 83.3% of participants in the Moderate-High and Stable class reported being in a science-
related career or field after graduation, compared to 27.2% of those in the Moderate-Low with Early
Decline class. Follow-up chi-square comparisons indicated that there was no significant difference in the
likelihood of High with Transitory Incline and Moderate-High and Stable students being in a science
career after graduation, χ2 (1) = 1.19, p = .28, whereas those in the Moderate-Low with Early Decline
class were less likely to be in a science career compared to both the High with Transitory Incline, χ2 (1) =
58.66, p < .001, and the Moderate-High and Stable classes, χ2 (1) = 49.23, p < .001.
Discussion
Using expectancy-value theory as a framework, the current study examined the development of
science identity during college, a key developmental period for career-related identities. Results showed
evidence of three latent developmental trajectories that were differentially associated with gender,
race/ethnicity, academic perceived competence, and science self-efficacy. The trajectories also
differentially predicted participation in a science-related career after graduation. By examining changes in
science identity across four years of college, a key time period of the “leaky pipeline” in science career
pursuit, and tracking students beyond graduation to examine post-college career outcomes, our results
provide critical information about science identity development during college and its consequences for
science career persistence after graduation.

FROM SCIENCE STUDENT TO SCIENTIST 22
We identified three latent classes that exhibited qualitative differences in the development of
science identity across four years. We labeled one High with Transitory Incline because it was
characterized by high science identity in the first year with a slight increase followed by a slight decrease
in science identity during the four years of college, but overall reflected very high levels throughout
college. Another class was labeled Moderate-High and Stable because it was characterized by moderately
high science identity in the first year with little to no evidence of change over four years. The third class
was labeled Moderate-Low with Early Decline because it reflected relatively low science identity in the
first year with a sharp decrease, followed by a less dramatic decrease in the final two years. These
findings suggest that students belonging to the High with Transitory Incline and Moderate-High and
Stable groups remained strongly identified with the field throughout their undergraduate career, while the
Moderate-Low with Early Decline group disidentified with science early in college. In fact, the magnitude
of decrease in science identity corresponded to an average change from “neutral” to “disagree” responses
to science identity items in later years. The identification of multiple patterns of science identity
development extends prior research showing multiple developmental trajectories for literacy value
(Archambault, Eccles, & Vida, 2010) and math value (Musu-Gillette et al., 2015) among adolescents and
early college students by focusing on science identity across all four years of college.
These findings lend support to the idea that identities are relatively stable once commitments are
made (Eccles, 2009), with value remaining high and relatively stable for two of the three latent classes.
Our results also suggest that consistently high science identity was the most commonly reported
experience among the students in our sample, with 50% of participants classified in the Moderate-High
and Stable class and 40% in the High with Transitory Incline class. The high occurrence of these patterns
is likely a result of our initial sampling procedure and focus on the leaky pipeline among prospective
science majors; students who were less strongly identified with science might have delayed enrollment in
the required introductory chemistry course until a later semester. Given the heavy emphasis on pre-
professional goals (e.g., pre-med, pre-law) at this elite university, it is also possible that our sample

FROM SCIENCE STUDENT TO SCIENTIST 23
included students who were more strongly committed (cf. Luyckx et al., 2006) to a particular career path
than might be observed in a broader population of college students.
At the same time, however, our finding of a class experiencing initially lower science identity
followed by a sharp decline is also consistent with the idea that college is a time of potentially volatile
changes in identity as students confront new experiences and challenges (Eccles, 2009; Waterman, 1993).
Following Marcia (1993), the Moderate-Low with Early Decline class may also represent a class of
students in moratorium, with students beginning their college years unsure about their commitment to
science and experiencing subsequent instability in their science identity. While this experience represents
only 10% of the sample, these findings suggest that identities can be re-evaluated and change rather
dramatically for some students, perhaps as a result of changing circumstances (e.g., beginning college; see
also Luyckx, Schwartz, Goossens, Beyers, & Missotten, 2011), hostile environments, or gender/race-
based discrimination in STEM fields.
Looking at predictors of class membership, the findings regarding race/ethnicity also indicate that
students from underrepresented racial/ethnic groups were more likely to report moderate to low levels of
science identity that decreased throughout college. Additionally, both women and URMs were least likely
to be in the class with the highest levels of science identity. This aligns with our hypotheses and with
prior research indicating that Black, Hispanic/Latino, and female students encounter unique difficulties
while navigating White-, Asian-, and male-dominated science fields (Seymour & Hewitt, 1997).
However, the results did not indicate that women were more likely to be in the Moderate-Low with Early
Decline class vs. the other two classes, which was unexpected. According to the expectancy-value model,
values for science are shaped by sociocultural factors, and these factors lead to differences in both value
and expectancies for success across gender and racial/ethnic groups. As such, it is likely that observed
gender and racial/ethnic differences may be a result of discrimination and inequality (Wong, Eccles, &
Sameroff, 2003) as well as differences in critical psychological processes related to discrimination and
stereotypes about science as the domain of men and individuals of Caucasian or Asian descent. Although
not measured in this study, it seems likely that key psychological processes such as stereotype threat

FROM SCIENCE STUDENT TO SCIENTIST 24
(Murphy et al., 2007), belonging threat (Walton & Cohen, 2007), or perceptions that science is
incongruent with gender or racial/ethnic roles (Cheryan, Plaut, Davies, & Steele, 2009; Diekman, Brown,
Johnston, & Clark, 2010; Settles, Jellison, & Pratt-Hyatt, 2009) may have accounted for these gender and
racial/ethnic differences in class membership. Indeed, the sciences are still dominated by men, White
students, and Asian students at this university. For example, in the first-year chemistry courses where
participants were recruited for the current study, only 33% of faculty were women.
Our research also provides support for theorized relations among competence beliefs and identity
processes (Eccles, 2009). As we expected and in alignment with prior research exhibiting relations
between competence beliefs and science identity (Eccles, 2009; Chemers et al., 2011; Robnett et al.,
2015), both self-efficacy and perceived competence were significant predictors of class membership.
Students who felt more confident in their ability to succeed at academic tasks, form and test hypotheses,
use scientific equipment, and form conclusions using the scientific method were more likely to report
higher, more stable science identity throughout college. In addition, the two competence beliefs exhibited
differential patterns of relations to science identity trajectories: while self-efficacy appeared to be an
important factor for differentiating the highest two trajectories from the Moderate-Low science identity
trajectory, perceived competence was only associated with membership in the highest pattern of science
identity relative to the lowest pattern. This indicates that while both competence beliefs may be important
factors supporting science identity among those who are unsure of their commitment, variation in self-
efficacy better discriminates the Moderate-Low with Early Decline class from the Moderate-High and
Stable class. This is particularly significant considering that it may be easier to shift students from
moderately low to moderately high levels of science identity than to very high levels, with important
implications for career attainment. This suggests a need for expectancy-value theorists to differentially
examine competence beliefs for academic and career-related tasks, as they appear to be differentially
related to identity development.
Consistent with our hypotheses and with prior research showing strong relations between science
identity and career outcomes (Chemers et al., 2011; Estrada et al., 2011; Hernandez et al., 2013), we also

FROM SCIENCE STUDENT TO SCIENTIST 25
found that class membership was a significant predictor of participants’ reported involvement in science
fields after graduation. Students in the two classes with higher identity were more likely to be involved in
science careers after graduation than students in the Moderate-Low with Early Decline class. This is in
accord with expectancy-value conceptualizations of identity and suggests that for students who enter
college aiming for science careers, attrition from science fields may be explained in part by lower initial
levels of and declines in science identity. Even among well-prepared students at an elite university with
numerous supports for retention, lower initial levels and declines in science identity significantly
decreased the likelihood of post-graduation involvement in science. Similarly, lower competence beliefs
at the beginning of college may put students at risk for lower initial identities and greater instability in
their science identities. Among a sample of first-year undergraduates enrolled in chemistry courses at an
elite university, it is not necessarily surprising that most students had higher initial science identity and
remained stable over time. However, the 10% of students in the Moderate-Low with Early Decline class
may be important to target for intervention, as they show early interest in science but are lost along the
way. An important question is whether contextual barriers explain this pattern and whether it is possible
to develop or use existing interventions to mitigate the loss of these students from science. We discuss
this further in the Implications section below.
Finally, our focus on identity-related conceptions of attainment value also supports and refines
expectancy-value theory. While earlier expectancy-value conceptualizations of attainment value (e.g.,
Wigfield & Eccles, 2000; Wigfield et al., 1997) characterized attainment value as personal importance
broadly (e.g., “For me, being good at math is important”), more recent conceptualizations (Conley, 2012;
Eccles, 2009) emphasize the importance of a task or domain to the individual’s identity. Recent research
by Gaspard and colleagues (2015) examined two types of attainment value for math: personal importance
and broad importance of high achievement, the former relating more closely to identity. They found that
this differentiation accurately reflected distinct types of value for academic subjects and revealed
differential relations to other constructs, such as gender and other types of value. Indeed, tasks may be
important to individuals for a variety of reasons, just as students can perceive tasks to be costly along

FROM SCIENCE STUDENT TO SCIENTIST 26
several dimensions (Eccles, 1983). Greater precision in conceptualizing and measuring value constructs
can refine our understanding of the phenomena and its relations to important correlates. The current study
helps to clarify this issue by focusing on identity-related attainment value specifically and over time.
Implications for Supporting Persistence in Science
In addition to implications for theory and research, the current study has numerous practical
implications for supporting persistence in science. First, significant relations between class membership
and post-graduation involvement in science suggest that identity development is an important predictor of
science careers and is worth investigating as a possible point of intervention for increasing persistence.
Thus, efforts at broadening and diversifying the workforce in science fields should aim to support not
only necessary motivation, skills, and knowledge, but also students’ science identity development, and
more particularly sustained high value for their identities as scientists.
Importantly, our identification of multiple classes suggests a need for differential supports for
identity development, as only 10% of students reported declines in science identity during college. In
addition, while students from racial/ethnic minority groups were more than twice as likely as their White
and Asian peers to be in the Moderate-Low class vs. the High class, and women were almost twice as
likely as men to be in the Moderate-High class vs. the High class, our data suggests that what matters for
science career outcomes is not whether students have high or moderately high science identity, but rather
whether they start college with lower science identity and experience declines. Lastly, it is important to
note that students may have begun college with different levels of science identity and it was those with
lower initial levels who experienced declines over time. This suggests a multi-pronged approach may be
necessary for supporting science identity, and ultimately, persistence in science careers by supporting
both initial levels and stability throughout college.
Given our findings that both perceived competence and self-efficacy predicted whether students
were most likely to be classified into the Moderate-Low with Early Decline class, one potential
mechanism for supporting scientific identity development is through pre-college interventions that target
students’ confidence in their ability to complete scientific and academic tasks. This is encouraging,

FROM SCIENCE STUDENT TO SCIENTIST 27
particularly as self-efficacy is fairly malleable over short periods of time (Bong & Skaalvik, 2003), and
can be supported via mastery experiences, vicarious experiences, social persuasion, and affective states
(Bandura, 1986; Usher, 2009). Thus, we see the need for future research aimed at helping high school
students begin college with greater confidence in their science abilities.
It may also be possible to target science identity more directly either before students enter college
or as they first begin to take challenging science coursework. Relevance interventions (Hulleman &
Harackiewicz, 2009), which directly target task value, could be most appropriately used in high school or
the first year of college to help students connect science to their lives and so increase initial science
identity. However, even when students begin college with high value and competence in science, low
perceived belonging, discrimination, or other experiences relevant to their personal identities may prompt
students to devalue the importance of science to their identities (Steele, 1997). Designing more equitable
environments, particularly to minimize discriminatory practices and subtle cues that may activate
belonging threat or stereotype threat (e.g., Murphy et al., 2007), should be a primary concern of
educational institutions. In addition, values affirmation interventions (Cohen, Garcia, Apfel, & Master,
2006) and belonging interventions (Walton & Cohen, 2007) could be used after students begin college to
prevent students from construing academic setbacks as reflections of their abilities or belonging within
science, potentially preventing sharp declines in science identity.
More broadly, involvement in STEM enrichment programs may be an additional way to support
science identity directly, as those who strongly identify with both a personal identity and a scientific
identity appear to maintain stronger commitments to science than those who perceive their personal and
scientific identities to be incompatible (Settles, 2004). There is some evidence that mentoring or other
targeted science programs can minimize stereotype threat and influence feelings of belonging in science
(Carlone & Johnson, 2007; Merolla, Serpe, Stryker, & Schultz, 2012; Merolla & Serpe, 2013). These,
along with the relevance, belonging, and values affirmation interventions mentioned above, may directly
support science identity development among those most at risk for declines and help to reduce the
racial/ethnic gap in pursuing science career attainment (National Science Foundation, 2015).

FROM SCIENCE STUDENT TO SCIENTIST 28
Limitations and Future Directions
A few limitations should be considered when interpreting the results. First, our sample consisted
of students at an elite, private university who may be more highly qualified for science training and
careers. Our sampling procedure also targeted only those students who were enrolled in chemistry courses
required for natural science majors in their first semester, which may have excluded students who delayed
taking a required chemistry course in in their first semester because they were unsure about whether they
wanted to pursue a science degree. Thus, replication is needed in other university settings and among
other groups of students. As a balance to this limitation, however, it is important to note that our findings
signal that scientific identity is a key factor for retention in science even among students who are well
qualified and receive high-quality training. Indeed, the 10% of these highly-qualified students in the
Moderate-Low with Early Decline class suggest that interventions could prove useful even in this sample.
Furthermore, our sample was comprised of “traditional” college students, and these findings may not
generalize to non-traditional college students. For example, an older undergraduate student with a family
may start college more committed to a science identity but also may face more barriers due to their family
responsibilities.
Second, replication of these results in additional samples is also an important direction for future
work for statistical reasons. As with any longitudinal study, our study had some missing data and it is
possible that results may be biased due to the overrepresentation of female, White, and Asian students
among those with data at all five time points. Replication is therefore necessary to understand
heterogeneous developmental trajectories of science identity groups with higher representation of males
and those from racial/ethnic groups that are underrepresented in science fields. In addition, models
estimating class-specific variance parameters of initial levels and change factors did not converge.
Instead, our model assumed that variances were the same across classes. This is a limitation of the data,
and replication with a new data set may provide sufficient resolution to allow for estimating class-specific
variances of these parameters. As is true with any application of growth mixture modeling, replication is
important considering the data-driven nature of GMM.

FROM SCIENCE STUDENT TO SCIENTIST 29
A third limitation is that we only measured science identity once per year. While this
measurement interval aligns with the theorized slow pace of change in identity constructs, research with
shorter intervals of measurement is needed to test this assumption. This may be particularly relevant for
identity salience, which has been manipulated in lab settings (LeBoeuf, Shafir, & Bayuk, 2010) and may
be a more appropriate construct on which to intervene in support of identity development given its
potentially greater malleability.
Fourth, our study was also potentially limited by the self-report nature of the science career
outcome measure. While participants’ reports of whether they are involved in science careers are likely to
be accurate (e.g., Estrada et al., 2011), particularly considering that we clearly defined what we meant by
science careers when posing the question to participants, it is also possible that participants still had
varied perceptions of what constitutes a science field. For instance, some who are involved in social
science fields (e.g., psychology) may consider themselves to be involved in science while others may not.
Future research should combine self-report and objective measures of science career involvement, when
possible, in order to assess and limit error in examining relations between science identity trajectories and
career outcomes.
Lastly, a key future direction for increasing understanding of how science identity develops
should involve directly examining experiences that facilitate or act as barriers to identity development in
science (e.g., Estrada, Hernandez, & Schultz, 2018). Our focus on psychological and demographic
predictors does not provide empirical evidence of the specific experiences leading to between-student
differences. Thus, it is important to more closely examine the mechanisms by which contextual influences
lead to differences in science identity. Such research could provide evidence both about the barriers
students experience as well as ways to overcome these barriers through direct support for students’
psychological development and more broad-based institutional changes to the design of programs and
courses.

FROM SCIENCE STUDENT TO SCIENTIST 30
Conclusion
A strong STEM workforce is vital to the health of our society. For students who enter college
aiming for science careers, attrition from science may be partially explained by both lower initial levels
and greater instability in science identity over time. Our study aimed to describe heterogeneity in
developmental trajectories of science identity from an expectancy-value perspective across four years of
college, and to examine relations among these trajectories with first-year competence beliefs, gender,
race/ethnicity, and post-graduation science careers. As hypothesized, we found latent classes of students
with differential trajectories in science identity, and these trajectories were related to gender,
race/ethnicity, and first-year competence beliefs as well as to career outcomes after college.
The findings from this five-year longitudinal study underscore the importance of understanding
and supporting high initial levels and stability in science identity over time for traditional college students
and also considering identity development before college. Furthermore, though our results indicate that a
disproportionate number of underrepresented minority students report lower initial levels and declines in
science identity throughout college, these declines could possibly be buffered by institutional supports for
self-efficacy to perform scientific tasks and academic perceived competence prior to entry into college or
during the first year. These findings contribute essential understanding of how science identity develops
and also provide insight into the future design of interventions aimed at broadening participation in
science fields. In short, becoming a scientist appears to involve more than cultivating skills and
knowledge; students who also come to think of themselves as scientists may be best equipped to achieve
success in science.

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FROM SCIENCE STUDENT TO SCIENTIST 40
Table 1
Descriptive Statistics of Study Variables
1
2
3
4
5
6
7
1. Sci Identity T1
--
2. Sci Identity T2
.60**
--
3. Sci Identity T3
.53**
.73**
--
4. Sci Identity T4
.54**
.69**
.77**
--
5. Self-Eff. T1
.38**
.27**
.21**
.23**
--
6. Per. Comp. T1
.41**
.24**
.18**
.17**
.43**
--
7. Sci Career
.22**
.32**
.31**
.43**
.11*
.06
--
Mean
3.82
3.81
3.83
3.73
3.74
4.05
0.77
SD
0.73
0.82
0.83
0.88
0.67
0.65
0.42
Minimum
1.25
1.00
1.25
1.00
1.83
1.20
0.00
Maximum
5.00
5.00
5.00
5.00
5.00
5.00
1.00
Note: All observed correlations, means, and SDs were calculated in SPSS. Self-Eff T1 = Self-
efficacy at Time 1, Per. Comp. T1 = Perceived competence at Time 1, Sci Career = post-graduation
involvement in science careers.
** p < .001, * p < .05

FROM SCIENCE STUDENT TO SCIENTIST 41
Table 2
Fit Statistics for Time, Gender, and URM Invariance Models
Model
χ2 (df)
RMSEA
CFI
TLI
Time
Configural
387.704 (98)
0.054
0.950
0.939
Weak
401.937 (107)
0.052
0.950
0.943
Strong
423.053 (116)
0.051
0.947
0.946
Strict
480.229 (128)
0.052
0.940
0.943
Gender
Configural
676.568 (256)
0.057
0.928
0.933
Weak
686.565 (259)
0.057
0.927
0.932
Strong
700.402 (262)
0.057
0.925
0.932
Strict
717.486 (266)
0.058
0.923
0.931
URM
Configural
654.101 (256)
0.055
0.932
0.936
Weak
662.921 (259)
0.055
0.931
0.936
Strong
677.112 (262)
0.056
0.929
0.935
Strict
680.358 (266)
0.055
0.929
0.936
Note: URM = underrepresented racial/ethnic minority.

Running Head: FROM SCIENCE STUDENT TO SCIENTIST 42
Table 3
Fit Statistics for the Second-Order Linear Growth Model and 2-class Second-Order Growth Mixture
Models
Second-Order Component
of Model
First-Order Component
of Model
Class Proportions
Model
Class-
Specific
Parameters
Class-
Invariant
Parameters
Class-
Specific
Parameters
Class-
Invariant
Parameters
N1
N2
N3
N4
# Param.
Est.
BIC
M0
-
-
-
-
1023 (100%)
16
20984.44
M1
M
V & CV
-
R, I, L
119 (12%)
904 (88%)
19
20895.75
M2
M
V & CV
R
I, L
592 (58%)
431 (42%)
23
20274.64
M3
M
V & CV
R, I
L
601 (59%)
422 (41%)
26
20293.09
M4
M
V & CV
R, I, L
-
613 (60%)
410 (40%)
29
20297.71
M5*
M, V, & CV
-
-
R, I, L
740 (72%)
283 (27%)
20
20880.22
M6
M, V, & CV
-
R
I, L
421 (41%)
602 (59%)
26
20250.71
M7
M, V, & CV
-
R, I
L
607 (59%)
416 (41%)
29
20268.98
M8
M, V, & CV
-
R, I, L
-
615 (60%)
408 (40%)
32
20279.03
M9
M
V & CV
-
R, I, L
909 (89%)
114 (11%)
24
20887.44
M10
M
V & CV
R
I, L
432 (42%)
591 (58%)
28
20264.83
M11
M
V & CV
R, I
L
596 (58%)
427 (42%)
31
20283.57
M12
M
V & CV
R, I, L
-
597 (58%)
426 (42%)
34
20289.38
M13*
M, V, & CV
-
-
R, I, L
328 (32%)
695 (68%)
27
20869.08
M14
M, V, & CV
-
R
I, L
599 (59%)
424 (41%)
34
20252.73
M15
M, V, & CV
-
R, I
L
604 (59%)
419 (41%)
37
20270.96
M16
M, V, & CV
-
R, I, L
-
Non-interpretable solution.
40
Note: Models 0-8 were linear models and Models 9-16 were quadratic models. M = latent means of intercept and slope factors, V = variances of
latent intercept and slope factors, CV = covariances of latent intercept and slope factors, R = residual variances of observed items, I = intercepts of
observed items, L = loadings of observed items. *Variance and covariance in 1 class was fixed to resolve estimation issues with variances.

FROM SCIENCE STUDENT TO SCIENTIST 43
Table 4
Fit Statistics for the 3-class Second-Order Growth Mixture Models
Second-Order Component
of Model
First-Order Component
of Model
Class Proportions
Model
Class-
Specific
Parameters
Class-
Invariant
Parameters
Class-
Specific
Parameters
Class-
Invariant
Parameters
N1
N2
N3
N4
# Param.
Est.
BIC
M1
M
V & CV
-
R, I, L
49 (5%)
910 (89%)
64 (6%)
22
20902.72
M2
M
V & CV
R
I, L
Non-interpretable solution.
30
M3
M
V & CV
R, I
L
Non-interpretable solution.
36
M4
M
V & CV
R, I, L
-
Non-interpretable solution.
42
M5
M, V, & CV
-
-
R, I, L
Non-interpretable solution.
28
M6
M, V, & CV
-
R
I, L
Non-interpretable solution.
36
M7
M, V, & CV
-
R, I
L
Non-interpretable solution.
42
M8
M, V, & CV
-
R, I, L
-
Non-interpretable solution.
48
M9
M
V & CV
-
R, I, L
106 (10%)
909 (89%)
8 (1%)
28
20895.57
M10
M
V & CV
R
I, L
404 (40%)
513 (50%)
106 (10%)
36
20209.27
M11
M
V & CV
R, I
L
Non-interpretable solution.
44
M12
M
V & CV
R, I, L
-
Non-interpretable solution.
50
M13
M, V, & CV
-
-
R, I, L
Non-interpretable solution.
40
M14
M, V, & CV
-
R
I, L
Non-interpretable solution.
44
M15
M, V, & CV
-
R, I
L
Non-interpretable solution.
50
M16
M, V, & CV
-
R, I, L
-
Non-interpretable solution.
56
Note: Models 1-8 were linear models and Models 9-16 were quadratic models. M = latent means of intercept and slope factors, V = variances of
latent intercept and slope factors, CV = covariances of latent intercept and slope factors, R = residual variances of observed items, I = intercepts of
observed items, L = loadings of observed items.

FROM SCIENCE STUDENT TO SCIENTIST 44
Table 5
Fit Statistics for the 4-class Second-Order Growth Mixture Models
Second-Order Component
of Model
First-Order Component
of Model
Class Proportions
Model
Class-
Specific
Parameters
Class-
Invariant
Parameters
Class-
Specific
Parameters
Class-
Invariant
Parameters
N1
N2
N3
N4
# Param.
Est.
BIC
M1
M
V & CV
-
R, I, L
911 (89%)
59 (6%)
52 (5%)
1 (0%)
25
20911.61
M2
M
V & CV
R
I, L
Non-interpretable solution.
37
M3
M
V & CV
R, I
L
Non-interpretable solution.
46
M4
M
V & CV
R, I, L
-
Non-interpretable solution.
55
M5
M, V, & CV
-
-
R, I, L
Non-interpretable solution.
34
M6
M, V, & CV
-
R
I, L
Non-interpretable solution.
46
M7
M, V, & CV
-
R, I
L
Non-interpretable solution.
55
M8
M, V, & CV
-
R, I, L
-
Non-interpretable solution.
64
M9
M
V & CV
-
R, I, L
8 (1%)
69 (7%)
40 (4%)
906 (89%)
32
20905.92
M10
M
V & CV
R
I, L
Non-interpretable solution.
44
M11
M
V & CV
R, I
L
Non-interpretable solution.
53
M12
M
V & CV
R, I, L
-
Non-interpretable solution.
62
M13
M, V, & CV
-
-
R, I, L
Non-interpretable solution.
50
M14
M, V, & CV
-
R
I, L
Non-interpretable solution.
58
M15
M, V, & CV
-
R, I
L
Non-interpretable solution.
67
M16
M, V, & CV
-
R, I, L
-
Non-interpretable solution.
76
Note: Models 1-8 were linear models and Models 9-16 were quadratic models. M = latent means of intercept and slope factors, V = variances of
latent intercept and slope factors, CV = covariances of latent intercept and slope factors, R = residual variances of observed items, I = intercepts of
observed items, L = loadings of observed items.

Running Head: FROM SCIENCE STUDENT TO SCIENTIST 45
Table 6.
Parameter Estimates for Selected 3-class Growth Mixture Model
Parameter
Class 1
Class 2
Class 3
(n)
404
513
106
Means
Intercept
4.12***
3.61***
2.93***
Linear Slope
0.16**
0.04
-0.79***
Quadratic Slope
-0.05**
-0.004
0.15*
Variances
Intercept
0.29**
0.29**
0.29**
Linear Slope
0.19**
0.19**
0.19**
Quadratic Slope
0.01*
0.01*
0.01*
Covariances
Intercept, Linear Slope
-0.11***
-0.11***
-0.11***
Intercept, Quadratic
0.02*
0.02*
0.02*
Linear Slope, Quadratic Slope
0.05*
0.05*
0.05*
Note: *p < .05, **p < .01, ***p < .001, indicating parameter estimates that are significantly different from
zero.

FROM SCIENCE STUDENT TO SCIENTIST 46
Figure 1. Model-implied trajectories of science identity for three-class solution over four years.
1
1.5
2
2.5
3
3.5
4
4.5
5
1 2 3 4
Science Identity
Year
High with Transitory
Incline, n = 404
Moderate-High and
Stable, n = 513
Moderate-Low with
Early Decline, n = 106

FROM SCIENCE STUDENT TO SCIENTIST 47
Appendix A: Full List of Scale Items
Science Identity
1. I consider myself a science person.
2. Being involved in science is a key part of who I am.
3. Being someone who is good at science is important to me.
4. Being good in science is an important part of who I am.
Academic Perceived Competence
1. I'm certain I can master the skills taught in science classes.
2. I'm certain I can figure out how to do the most difficult class work in science.
3. I can do almost all the work in science classes if I don't give up.
4. Even if the work in science is hard, I can learn it.
5. I can do even the hardest work in science if I try.
Science Self-Efficacy
I am confident that I can…
1. Use technical science skills (use of tools, instruments, and/or techniques).
2. Generate a research question to answer.
3. Figure out what data/observations to collect and how to collect them.
4. Create explanations for the results of the study.
5. Use scientific literature and/or reports to guide research.
6. Develop theories (integrate and coordinate results from multiple studies).