Alan Stanley
Wed, 11/03/2021 - 06:47
Edited Text
THE EFFECTS OF RISK AND PROMOTIVE FACTORS ON
ACADEMIC ACHIEVEMENT AMONG ADOLESCENTS

by
Rachel M. Cline, David K. Fleming, and Isabel M. Zarate

A thesis submitted in partial fulfillment of the requirements
for graduation with Honors in Psychology.

Whitman College
2014

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EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
Abstract
We utilized a systems approach to a risk and resilience model to examine individual and
environmental factors of academic achievement. We investigated the effects of potential
protective factors – self-regulated learning, growth mindset, and social support – on the
relationship between risk and academic achievement. We predicted that social support,
self-regulated learning, and growth mindset would each have a protective effect on the
relationship between risk and achievement. We also investigated whether these three key
factors would have an additive protective-stabilizing effect on the relationship between
risk and academic achievement. Participants completed an online survey. The sample
included 73 high school freshmen and sophomores, ages 14-16. Although we found no
moderating effects on the relationship between risk and GPA, we identified teacher social
support and self-regulated learning as significant promotive factors. The findings are
consistent with a compensatory model of resilience. Results are discussed in light of
achievement disparities between high and low SES students.
Keywords: risk, resilience, social support, self-regulated learning, growth
mindset, academic achievement, adolescence

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The Effects of Risk and Promotive Factors on
Academic Achievement among Adolescents
Pervasive ethnic and racial disparities in education follow a pattern in which
African Americans and Latinos tend to underperform academically, relative to Whites
and Asian Americans (Winerman, 2011). These educational inequalities mirror ethnic
and racial disparities in socioeconomic status (SES). Group differences are consistently
reflected in achievement test scores, such as reading and mathematics (Winerman,
2011). We utilize a systems approach to consider how both individual factors, such as
growth mindset and self-regulated learning, and environmental factors, such as social
support, are related to students’ academic achievement in the face of risk. The current
study will address the issue of educational disparities and contribute to risk and resilience
literature by investigating how these individual and environmental factors are related to
academic achievement.
Risk
Risk is defined as psychosocial adversity, or exposure to negative life events that
pose a threat to the development of competent behavioral and mental health outcomes
(Arrington & Wilson, 2000). Specific examples of risk factors for children and
adolescents include single parent households, low engagement in school and community,
and stressful home environments (Swanson, Valiente, & Lemery-Chalfant, 2012; Taylor,
2010). Stress encountered from negative life events can be used to estimate risk, as stress
can cause maladaptive development (Arrington & Wilson, 2000). Youths who report
more stressful life events also report more negative outcomes (Arrington & Wilson,
2000; Dornbusch, Ritter, & Steinberg, 1991). Risk factors are related to such negative

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outcomes as behavioral problems, parent-adolescent conflict, psychological distress, and
academic problems (Gutman, McLoyd, & Tokoyama, 2005; Kim & Brody, 2005;
Loukas, Prelow, Suzzo, & Allua, 2008; Mistry et al., 2002). Socioeconomic status
(SES), which includes education level and income, is a broad gauge for risk level
(Arrington & Wilson, 2000). Adverse economic conditions (e.g., low family income)
may negatively affect family environments and cause the psychological distress
associated with risk (Taylor, 2010). Adolescents living in poverty are also at risk due to
normative stressors associated with living in low-income and single parent households
(Arrington & Wilson, 2000). Some normative stressors that may affect low SES
adolescents on a daily basis could include school pressures, experiences of
discrimination, and risky sexual relationships (Arrington & Wilson, 2000).
Risk is negatively related to academic achievement and, similarly, SES is
positively related to academic achievement (Gutman, Sameroff, & Eccles, 2002;
Shumow, Vandell, & Posner, 1999; Stull, 2013; Swanson et al., 2012; Taylor, 2010).
Shumow et al. (1999) found that academic performance was negatively associated with
neighborhood risk even when controlling for familial risk (e.g., parental involvement,
emotional support) among fifth graders. Having low resources within one’s
neighborhood could influence a child’s access to learning opportunities and academic
growth, whereas family structure could also influence one’s success in school. Risk
factors threaten the development of competent academic behaviors and mindsets, leading
to lower academic achievement (Arrington & Wilson, 2000).

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Resilience
Despite exposure to risk associated with low SES and exposure to stress, some
individuals avoid or overcome negative outcomes through the process of resilience
(Fergus & Zimmerman, 2005). Resilience is both a process and an outcome of avoiding
or overcoming problems of vulnerability through assets or resources when risk factors are
present (Fergus & Zimmerman, 2005). The presence of risk is necessary for resilience,
as the resilience process works in opposition to risk factors to affect outcomes.
Resilience is not a static individual trait, and there are not overarching protective factors
for all types of risk and negative outcomes. Researchers suggest that resilience may be
content- and context-specific and that different risks may require different types of
resilience (Fergus & Zimmerman, 2005; Knowles, 1977). For example, in terms of drug
and alcohol abuse as a risk outcome, family structure serves as either a risk factor or a
resilience factor. Adolescents living with only a father tend to have less drug use
compared to those living with only a mother, especially if the adolescent-mother
relationship is distressed (Farrell & White, 1998). Having a father may serve as a
resilience factor within several variations of family structures, as it moderates the
relationship between peer pressure and drug use. While self-efficacy and self-esteem are
commonly considered in resilience research, more research is needed to investigate
resilience in an academic context (Arrington & Wilson, 2000).
A variety of approaches to resilience models exist, because there are differences
in which an asset or resource may affect outcomes across risk levels in a population.
Three different models to examine relationships between risk and risk outcomes are
compensatory, challenging, and protective approaches. In a compensatory approach, the

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promotive factor has a direct effect on an outcome, independent from risk (Fergus &
Zimmerman, 2005). For example, risk may influence an adolescent’s violent behavior,
while adult monitoring could compensate for risk factors. However, in this approach, the
compensatory factor may not moderate the relationship between the risk factor and the
risk outcome. In the challenging approach, the relationship between risk and the outcome
is curvilinear. In this case, both risk and promotive factors studied are the same
variables, and it is expected that moderate to low levels of exposure to the variable (risk
factor) may lead to the least negative outcomes by preparing the individual with coping
skills (Fergus & Zimmerman, 2005). The challenging approach differs from the
protective and compensatory approaches, which both assume a linear relationship
between the variables. In the current study, we use a protective approach to assess risk
associated with low SES by examining individual characteristics and experiences.
Furthermore, we utilized a protective-stabilizing model of resilience in which the
presence of individual and environmental protective factors is hypothesized to minimize
or additively neutralize the relationship between SES and academic achievement. We
examined three factors: social support, self-regulated learning, and growth mindset. Each
of these factors has been shown to be positively correlated with academic achievement,
yet more research is needed on how these factors might moderate the effects of risk on
academic outcomes. We investigated a comprehensive analysis of resilience by including
individual assets, growth mindset and self-regulated learning, as well as the
environmental resource of social support.

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Social Support
Social support can be defined as “an individual’s perceptions of general support
or specific supportive behaviors (available or enacted upon) from people in their social
network which enhances functioning and/or may buffer them from adverse outcomes”
(Demaray & Malecki, 2002, p. 306). Researchers have analyzed children’s and
adolescent’s social support network, and identify parents, teachers, and peers (close
friends or classmates) as the members that make up their social support system (Demaray
& Malecki, 2002; Wenztel, 1998).
Previous scholarship has identified social support as an important factor of
academic achievement (Malecki, & Demaray, 2006; Wentzel & Asher, 1995). In
general, students who perceive higher social support among their parents, teachers, and
peers fare better academically than do their less supported counterparts (Levitt et al.,
1994; Malecki & Demaray, 2006; Wentzel et al., 2010). The strength of the relationship
between perceived social support and academic achievement varies depending on the
type of relationship as well as the age of the child receiving the support (Rueger et al.,
2010). However, regardless of age or type of relationship, students who perceive higher
levels of support fare better academically than do their less supported counterparts (Levitt
et al., 1994; Wentzel et al., 2010).
Researchers have identified social support as a protective factor in the relationship
between risk and academic outcomes (Gutman et al., 2002; Gutman & Midgley, 2000).
Supportive relationships with people in one’s social support network may be especially
important for students of low SES backgrounds (Clark, 1983; Comer, 1980; Rutter,
1979). In a study of protective factors supporting the academic achievement of low SES

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African American students, Gutman and Midgley (2000) found that instead of focusing
on either parental involvement or the school environment, the combination of both family
and school factors may be most effective in supporting at risk youth. Additionally,
Taylor (2010) showed that parental support was a crucial protective factor for at risk
youth, and may alleviate some of the challenges facing low-income students. However,
he found that these students are often those with the least access to parental social
support. In a comparison study that considered characteristics of friendship networks and
levels of high school engagement, at-risk students had more friends drop out, more
working friends, and fewer school friends compared to students who were not at risk
(Ellenbogen & Chamberland, 1997).
In the context of risk and resilience, social support serves as a protective factor.
Yet, some researchers suggest that social support serves as a promotive factor. For
example, Levitt et al. (1994) found that having supportive teachers is positively
associated with standardized test scores and GPA, independent of the student’s racial and
socioeconomic background. This means that regardless of a student’s race or income,
supportive teachers may benefit all students equally. Although some researchers identify
social support as a moderator on the effects of risk on academic achievement, more
research is needed to address the inconsistencies in the literature as to whether social
support serves as a protective or a promotive factor.
In the current study we address two gaps in the social support literature. First, we
investigate social support in the context of risk and academic outcomes. Second, we
address gaps by combining social support with individual factors to determine how
together these factors might moderate the effects of risk on academic outcomes.

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Specifically, we examine self-regulated learning and growth mindset, which are
individual factors that have been well documented in the literature to be associated with
academic outcomes (Dweck, 2007; Pintrich, 1999). In researching social support in the
context of risk, along with self-regulated learning and growth mindset, we hope to
contribute to risk and resilience literature in an academic context.
Self-regulated Learning
Self-regulated learning is defined as the systematic use of metacognitive
processes, learning strategies, and motivational beliefs (Pintrich, 1999; Zimmerman,
1990; Zimmerman & Schunk, 2001). An example of using metacognitive processes is
the implementation of providing feedback for oneself after completing a task in order to
help adjust goals and strategies (Schunk and Zimmerman, 1998). Self-regulatory
strategies are an important aspect of metacognitive processes (Pintrich, 1999;
Zimmerman, 1990). Self-monitoring involves both self-regulation and metacognition
and enables a student to gauge whether or not he or she understands a lecture or the
material in a text comprehension task. Metacognitive processes and self-regulatory
strategies are important in improving acquisition of material, enhancing comprehension
of a subject, and nurturing the further use of the cognitive strategies (Elliot-Faust &
Pressley, 1986; Pintrich, 1999). Learning strategies consist of cognitive learning
strategies and task management strategies. Examples of cognitive learning strategies are
rehearsal, elaboration, and organizational strategies (Pintrich, 1999). Task management
strategies include strategies that promote effective use of time, picking a study
environment that will not be distracting, and seeking help on classwork (Corno, 1986;
Pintrich, 1999; Ryan & Pintrich, 1998; Zimmerman & Martinez-Pons, 1986; Zimmerman

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& Martinez-Pons, 1988). Another essential learning strategy entails planning activities
(e.g., goal setting and self-instruction), which involve using cognitive processes to
enhance attention, problem solving, response guidance, and motivation (Pintrich, 1999;
Zimmerman, 1990).
Lastly, motivational beliefs entail self-efficacy, task value beliefs, and the role of
goal orientation (Pintrich, 1999). Self-efficacy is the belief in one’s abilities to complete
a task and reach goals (Bandura, 1986; Schunk, 1985). Researchers found that selfefficacy is positively related to self-regulated learning (Pintrich, 1989; Pintrich & De
Groot, 1990; Pintrich & Garcia, 1991; Schunk & Ertmer, 2000). Furthermore, mastery
goal orientation (e.g. setting goals to improve) is positively related to use of cognitive
strategies, self-regulatory strategies, and academic performance (Barzegar, 2012;
Pintrich, 1999). When a student finds the course work interesting, important, or useful,
the student is more likely to use self-regulated learning due to belief in the value of the
task (Pintrich et al., 1993). The use of metacognitive processes, self-regulatory
strategies, learning strategies, and motivational beliefs are the foundational components
of self-regulated learning, which support academic functioning.
Self-regulated learning is associated with academic achievement (Barzegar, 2012;
Pintritch, 1999; Zimmerman & Martinez-Pons, 1988). When metacognitive processes,
learning strategies, and motivational beliefs combine to form self-regulated learning,
students show the highest levels of academic achievement (Zimmerman & MartinezPons, 1988). Students with high academic functioning (i.e., 99th percentile on mental
ability tests) tend to use cognitive, metacognitive, and self-regulatory strategies
significantly more often and more effectively compared to other students (Zimmerman &

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Martinez-Pons, 1986). However, researchers suggest these learning strategies alone are
not enough to improve academic achievement (Zimmerman, 1990). Motivation is also a
crucial element because students with higher levels of motivation tend to use learning
strategies, such as rehearsal and setting goals, more often than students with lower levels
of motivation (Bandura, 1989; Zimmerman & Martinez-Pons, 1986). Without
motivational beliefs to implement the learning strategies, students may seldom use
learning strategies in the first place while working on a task (Zimmerman, 2008). In a
path analysis conducted by Barzegar (2012), participants in multiple studies were
assessed on achievement goal orientations and motivational strategies for learning.
Metacognitive strategies and resource management skills, two elements of self-regulated
learning, were positively correlated with academic achievement (Barzegar 2012). While
self-regulated learning is associated with academic achievement, it has not been
considered as a protective factor in the context of risk. We believe self-regulated
learning may serve as a protective factor because it could be especially important for
students from lower SES backgrounds to self-monitor their learning progress, effectively
implement learning strategies, and cultivate motivation to learn. This research will
contribute to the literature by examining the potential moderating effect of self-regulated
learning on the relationship between SES and academic achievement.
Growth Mindset
Mindsets are beliefs about the most basic qualities of the self and an individual’s
control over them; they have been shown to be important in academic success (Dweck,
2007). Dweck (2007) proposed that students tend to have one of two implicit theories of
intelligence, or two different mindsets: an incremental view of intelligence (growth

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mindset) or an entity view of intelligence (fixed mindset). In an academic setting,
students with a fixed mindset believe that their intelligence cannot change or be
developed. In contrast, growth mindset is when students believe that their intelligence
can improve.
Growth mindset is positively correlated with academic achievement (Dweck,
2007). In a study of students undergoing a junior high school transition, Henderson and
Dweck (1990) found that students who endorsed a growth mindset earned significantly
higher grades in the first year of junior high school. People with growth mindset have
core beliefs that can set up beneficial patterns of response to challenges and setbacks
(Dweck, 1999; Dweck & Leggett, 1988; Dweck & Sorich, 1999; Henderson & Dweck,
1990). Given this, we expect that with these adaptive responses to challenges and
setbacks, growth mindset could serve as a protective factor against the negative effects of
risk. This research may contribute to the literature by examining the potential moderating
effect of growth mindset on the relationship between SES and academic achievement.
Current Study
The first hypothesis is that risk will be associated with academic achievement;
socioeconomic status will be positively correlated with academic achievement and
negative life events will be negatively correlated with academic achievement. Second,
social support will moderate the relationship between risk and academic achievement.
Third, self-regulated learning will moderate the relationship between risk and academic
achievement. Fourth, growth mindset will moderate the relationship between risk and
academic achievement. The final hypothesis is consistent with the protective approach;
we hypothesize that social support, self-regulated learning, and growth mindset will have

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an additive protective-stabilizing effect on the relationship between risk and academic
achievement.
Methods
Participants
The participants in our study were 73 high school students. There were 38 ninth
graders and 30 tenth graders; one person did not disclose grade level, although their age
was reported. The average age of the participants was 15.14 (SD = 0.71). Most of our
participants attended Walla Walla High School in Walla Walla (n = 62), a Washington
State public high school with a racially and ethnically diverse student body. Participants
were also from Eisenhower High School (n = 1), A.C. Davis (n = 2), and East Valley
High School in Yakima, Washington (n= 1). We recruited participants from LickWilmerding High School (n = 1) and Del Oro High School (n = 1) in California, as well
as Seattle Preparatory High School (n = 1). We had 36 Hispanic participants, 24 White
participants, five bi-racial participants, one Asian participant, one Native American
participant, and two participants who chose not to identify their race. Participants
included 28 boys, 39 girls, and two students that chose not to identify their sex. Based on
participants’ responses, most students were from low SES backgrounds, with 27
participants who reported their father having some or no high school education.
Additionally, seven had GEDs, 15 had High School Diplomas, 12 had Associate Degrees,
five had Bachelor’s Degrees, and three had Graduate Degrees.
Procedure
After contacting the school and teachers about their students taking our survey,
we provided potential participants and parents with a cover letter and informed consent

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form. Parents received the letter about the study as well as the informed consent form, in
hard copy or online, and were given the opportunity to grant consent for their child to
participate.
We collected participants’ informed consent forms (signed on paper or
electronically by their parents) in person or through Qualtrics, a survey database. All
participants then signed an informed assent form on Qualtrics, which informed
participants that there were no right or wrong answers, all answers were confidential, and
they had the option to stop participating in the study at any point.
After collecting both the consent and assent forms, participants completed the
survey online. Participants completed the survey in approximately 7-15 minutes,
according to Qualtrics output. Participants were given a Debriefing Statement and
researchers were available to answer any questions through email. Participants’
confidentiality was assured by identifying each participant by number codes rather than
by name. Only the researchers had access to the data through a secure log-in on
Qualtrics.
Measures
Academic achievement. To assess academic achievement, participants reported
their unweighted cumulative GPA. We also asked participants to report their most
common letter grade using the responses (1) A’s, (2) A’s and B’s, (3) B’s, (4) B’s and
C’s, (5) C’s, (6) C’s and D’s, and (7) D’s and below. Three students did not report their
unweighted cumulative GPA’s, so we estimated their GPA using the information about
their most common letter grade (e.g., if a student answered (1) A’s, their estimated GPA
was a 4.00, if a student answered (2) A’s and B’s, their estimated GPA was a 3.50, etc.).

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All other students’ self-reported GPA was used as a primary measure for academic
achievement.
Negative life events. To assess risk associated with exposure to stress, we used a
modified version of the Life Events for Adolescents (LEQ-A) measure (Masten et al.,
1988). Participants answered “Yes” or “No” for each of the 10 questions. One example
is: “My brother or sister became seriously ill or was injured during this past year.” We
computed the score as a total out of 10.
Socioeconomic status. We used father’s education level as a proxy for
socioeconomic status. Response options included (1) Some or no high school, (2) GED,
(3) High School Diploma, (4) Associate Degree (Community College), (5) Bachelor’s
Degree, (6) Graduate Degree, (7) PhD or Doctorate Level Degree.
Social support. To assess social support, we used a modified version of the
Child and Adolescent Social Support Scale (CASSS; Malecki & Demaray, 2000). The
adapted scale included three subscales (parents, friends, and teachers) with eight
questions each. Participants rated these 24 questions regarding how often the statement
occurs with their parents, friends, or teacher from Never to Always on a 6-point Likert
scale. An example item is: “My teachers care about me.” The composite social support
scale was reliable (α =.928), the parent support subscale was reliable (α = .914), the
friend support subscale was reliable (α = .916), and the teacher support subscale was
reliable (α = .907).
Self-regulated learning. In order to evaluate self-regulated learning levels of the
participants we used a modified version of the Motivated Strategies for Learning
Questionnaire (Pintrich, Garcia, & McKeachie, 1991). Participants rated their answer

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from Strongly Disagree to Strongly Agree on a 5-point Likert scale for each of the 24
questions. An example item is: “When studying, I read my class notes and the course
readings over and over again.” The scale was reliable (α = .840).
Growth mindset. To assess growth mindset, we used Implicit Theories of
Intelligence Scale by Dweck (1999). Participant answered six questions about their
beliefs about the malleability of intelligence using a 5-point Likert scale that ranged from
Strongly Disagree to Strongly Agree. An example item is: “You can always greatly
change how intelligent you are.” The scale was reliable (α = .882).
Results
The data analysis was a four step process. In the first step, we used a bivariate
correlational analysis to specifically examine the relations between risk, academic
achievement, social support, self-regulated learning, and growth mindset. Second, we
used linear regression analyses to examine the relations between academic achievement
and the key factors individually, while controlling for SES. The third step consisted of
testing for moderation effects by including an interaction variable in the linear regression
equations, to test our second, third, and fourth hypotheses. Lastly, we used a linear
regression analysis to test whether or not there was an additive protective-stabilizing
effect of high levels of the three key factors on the relationship between risk and
academic achievement.
Risk
Negative life events. Participants reported a moderately low number of negative
life events (M = 1.64, SD = 1.79) on a scale from 0 to 10. To test the relationship
between negative life events and academic achievement, we used a correlational test.

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There was no correlation between negative life events and unweighted GPA (see Table
1).
Socioeconomic status. We used a correlational test to evaluate the relationship
between father’s education level and GPA. As predicted, there was a significant positive
correlation between father’s highest education level and GPA (see Table 1).
Social Support
Students reported moderately high levels of social support (M = 4.10, SD = 0.83)
on a scale of 1 to 6. To test the hypothesis that social support is positively correlated
with academic achievement, we used a correlational test. Contrary to predictions, there
was not a significant correlation between overall social support and GPA (see Table 1).
Parental support. Students reported having moderately high levels of parental
social support (M = 4.02, SD = 1.09). In order to see whether there was a positive
correlation between parental support and academic achievement, we used a correlational
test. There was not a significant correlation between parent support and GPA (see Table
1).
Friend support. Students reported having moderate levels of friend social
support (M = 3.98, SD = 1.09). In order to see whether there was a positive correlation
between friend support and academic achievement, we used a correlational test. There
was not a significant correlation between friend support and GPA (see Table 1).
Teacher support. Students reported having moderately high levels of teacher
social support (M = 4.30, SD = 1.09). In order to see whether there was a positive
correlation between teacher support and academic achievement, we used a correlational

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test. Consistent with our expectations, there was a significant positive correlation
between teacher support and GPA (see Table 1).
Self-regulated Learning
Students reported having moderate self-regulated learning scores (M = 3.45, SD =
0.45) on a scale of 1 to 5. We used a correlational test to examine the relationship
between self-regulated learning and academic achievement. Consistent with past
research, there was a positive correlation between self-regulated learning and GPA (see
Table 1). In an additional correlation analysis we found that there was a significant
positive correlation between self-regulated learning and social support (see Table 1).
Growth Mindset
Students reported having moderately high growth mindset scores (M = 3.97, SD =
0.74) on a scale from 1 to 5. We used a correlational test to test our hypothesis that
growth mindset is positively correlated with academic achievement. Inconsistent with
past research, there was no significant correlation between growth mindset and GPA (see
Table 1).
Tests of Potential Moderators
Because there was a significant positive correlation between father’s education
level and GPA, the remaining analyses focusing on risk use SES as the measure of risk.
To test our second, third, and fourth hypotheses regarding the moderating effects of
social support, self-regulated learning, and growth mindset on the relationship between
SES and academic achievement, we ran hierarchical linear regression analyses. All
analyses use mean centered predictor variables.

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Social support. To test our second hypothesis regarding the potential moderating
effect of social support, we used a hierarchical linear regression analysis. In the first step,
we entered SES. In the second step, we entered SES and social support. Social support
was not significantly related to GPA while controlling for SES (see Table 2). SES
remained a significant predictor of GPA. In the third step, we added the SESxSocial
support interaction term. Contrary to our second hypothesis, there was no moderating
effect of social support on the relationship between SES and GPA; the interaction term
was not a significant predictor (see Table 2). To further investigate social support as a
moderator, we used hierarchical linear regression analyses for each of the social support
subscales.
Parent social support. In the first step, we entered SES. In the second step, we
entered SES and parental social support. Parent social support was not significantly
related to GPA while controlling for SES (see Table 3). SES remained a significant
predictor of GPA. In the third step, we added the SESxParent social support interaction
term. There was no moderating effect of parent social support on the relationship
between SES and GPA; the interaction term was not a significant predictor (see Table 3).
Friend social support. In the first step, we entered SES. In the second step, we
entered SES and friend social support. Friend social support was not significantly related
to GPA while controlling for SES (see Table 3). SES remained a significant predictor of
GPA. In the third step, we added the SESxFriend social support interaction term. There
was no moderating effect of friend social support on the relationship between SES and
GPA; the interaction term was not a significant predictor (see Table 3).

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Teacher social support. In the first step, we entered SES. In the second step, we
entered SES and teacher social support. SES remained a significant predictor of GPA.
Additionally, teacher social support was significantly related to GPA while controlling
for SES (see Table 3). In the third step, we added the SESxTeacher social support
interaction term. There was no moderating effect of teacher social support on the
relationship between SES and GPA; the interaction term was not a significant predictor
(see Table 3).
Self-regulated learning. To test our third hypothesis regarding the potential
moderating effect of self-regulated learning, we used a hierarchical linear regression
analysis. In the first step, we entered SES. In the second step, we entered SES and selfregulated learning. Self-regulated learning was significantly positively related to GPA,
while controlling for SES (See Table 2). SES also remained a significant predictor of
GPA. Taken together, self-regulated learning and SES explained a significant proportion
of variance in GPA, R2 = .14, F(1, 66) = 5.34, p = .02. In the third step, we added the
SESxSelf-regulated learning interaction term. Contrary to our third hypothesis, there was
no moderating effect of self-regulated learning on the relationship between SES and
GPA; the interaction term was not a significant predictor (See Table 2).
Growth mindset. To test our fourth hypothesis regarding the potential
moderating effect of growth mindset, we used a hierarchical linear regression analysis.
In the first step, we entered SES. In the second step, we entered SES and growth
mindset. Growth mindset was not significantly related to GPA while controlling for SES
(See Table 2). SES remained a significant predictor of GPA. In the third step, we added
the SESxGrowth mindset interaction term. Contrary to our second hypothesis, there was

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no moderating effect of growth mindset on the relationship between SES and GPA; the
interaction term was not a significant predictor (see Table 2).
Additive effects. To test our hypothesis that social support, self-regulated
learning, and growth mindset would have an additive protective moderating effect on the
relationship between SES and academic achievement, we first selected the top 34% of
scores within each key factor (teacher social support, self-regulated learning, and growth
mindset) and assigned each participant in that group the number 1 to identify them as
highly rated on these key factors. We then assigned participants with a 0 for any key
factor on which they were not highly rated. After tallying the number of factors in which
each participants was in the top 34%, we created a combined variable that described
which participants were highly rated on each social support, self-regulated learning, and
growth mindset. These students were assigned a score of “1” on the dummy coded
variable, High Protective Rating. We then used a hierarchical linear regression analysis
with the dummy coded variable, High Protective Rating, and SES. The combined factor
was not significantly related to GPA while controlling for SES (see Table 4). Lastly, we
added the SESxHigh Protective Rating interaction term. Contrary to expectations, there
was no significant interaction of the three key factors on the relationship between SES
and GPA (see Table 4).
Discussion
The results support an association between the economic components of risk and
academic achievement. We found a significant positive correlation between father’s
education level and GPA. This finding is consistent with past research, as researchers
have shown that socioeconomic status is positively related to academic outcomes

22
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
(Swanson et al., 2012). We add to the literature by demonstrating that being in a low
SES family, as indicated by father’s education level, may put adolescents at risk for lower
academic achievement compared to adolescents from high SES families. This correlation
may indicate an underlying issue of the systemic injustices which perpetuate school
systems and adversely affect low income youth, as shown by disparities in standardized
test scores and several other academic outcomes.
We did not find an association between negative life events and GPA. This may
be due to floor effects, as most individuals had a score of 0 or 1 negative life events out
of 10. The results would also be affected if students with relatively high negative life
event scores have overcome the risks associated with their experiences. Some students
may have certain resilience factors to protect against the stress of negative life events,
which were not measured in the current study. Given the findings, we propose that the
lack of relationship between these variables may be due to a resilience process already
underway for those students who have experienced negative life events but not for
students with the risks associated with low SES.
Within a protective model, we predicted that social support would moderate the
relationship between SES and GPA. Though overall levels of social support were not
significantly correlated with GPA, teacher social support was significantly positively
correlated with GPA. This means that students whose teachers provided emotional
support, like caring, and instrumental support, like helping with work, fared better
academically. Additionally, teacher social support was associated with GPA, even when
controlling for SES. Regardless of SES, students with teacher social support tended to
have higher GPAs. While teacher support seems to be important to student success, it did

23
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
not serve as a protective factor for individuals with risk associated with SES (see Table
3). Neither overall social support levels nor the social support subscales had a
moderating effect on the relationship between SES and academic achievement. This is
inconsistent with past research, which has shown that social support can serve as a
protective factor for academic outcomes (Gutman et al., 2002). A larger sample size or
more representative sample may be needed to show such an effect.
Self-regulated learning was positively correlated with academic achievement,
such that individuals who used metacognitive strategies and learning strategies, and had
motivational beliefs had higher achievement. Additionally, self-regulated learning was
associated with GPA, even when controlling for SES. This means that, regardless of
SES, students reporting higher levels of self-regulated learning tended to have higher
GPAs. Contrary to our third hypothesis, self-regulated learning did not moderate the
relationship between SES and GPA. Self-regulated learning did not particularly benefit
low SES students in their academic achievement. This may be because self-regulated
learning strategies are highly beneficial to all students, regardless of economic resources,
as they encompass several of the crucial behaviors that promote effective learning.
Inconsistent with our fourth hypothesis, we did not find a correlation between
growth mindset and GPA, and there was no association between growth mindset and
GPA while controlling for SES. However, in additional analysis we found that students
enrolled in honors classes had a significantly higher growth mindset than students not
enrolled in honors classes, t(67) = 2.57, p = .01. As we recognize that GPA is one
indicator of academic achievement, we consider enrollment in honors classes to be
another gauge for achievement, given that honors levels classes typically include more

24
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
challenging course content than non-honors classes. Given our results, growth mindset
may serve as a promotive factor for the academic outcome of taking challenging classes,
but not for grade outcomes. Our findings may indicate that having incremental beliefs of
intelligence may vary in effect depending on the achievement context, measure or
outcome.
We suggest that having growth mindset could lead students to challenge
themselves in their coursework, but improving mindsets may not be enough to boost
scores in the classroom as indicated by the lack of correlation between growth mindset
and GPA. One could speculate that having growth mindset, self-regulated learning, and
teacher social support is important in academic achievement, as both grade outcomes and
taking challenging coursework are important for advancing to college.
Both teacher social support and self-regulated learning had significant positive
correlations with GPA, though neither was found to have a moderating effect on the
relationship between SES and GPA. Given these findings, we suggest that a
compensatory approach would be appropriate for examining the relationship between our
key factors, as they are strong predictors of academic outcomes. Teacher social support
and self-regulated learning are significant promotive, rather than protective, factors for
academic achievement. Our research informs resilience literature, as we suggest that
both social support and self-regulated learning are promotive factors for academic
achievement.
The finding that teacher social support is positively correlated with self-regulated
learning is consistent with past literature. Ryan and Patrick (2001) researched classroom
social environment and changes in engagement and motivation during middle school and

25
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
found that increased self-regulated learning was positively associated with teacher
support. Perceived social support from teachers may facilitate and promote the use of
self-regulated learning strategies (Ryan & Patrick, 2001). The overlap between these two
factors may be due to the role of teachers in promoting the motivational beliefs that are
crucial aspect of self-regulated learning. Additionally, these findings are highly
applicable as teacher support and self-regulated learning can be implemented in a school
environment. Teachers and self-regulated learning strategies are resources that are
readily available within schools, as teachers already play a vital role in classrooms, and
additional reflection on learning and processing of material can be incorporated into the
curriculum to encourage self-regulated learning.
We did not find a significant moderating effect of the three key factors on the
relationship between SES and GPA. Yet, all three factors have been shown to serve as
promotive factors for academic outcomes. These findings would further suggest that a
compensatory model of resilience may be more relevant for teacher social support and
self-regulated learning, in terms of GPA, and growth mindset, in terms of enrollment in
challenging coursework.
Strengths and Limitations
The external validity of the study was limited by convenience sampling; the
majority of our students was from one Washington State public high school and enrolled
in AVID (Advancement via Individual Determination). This is a class designated for
students from low SES backgrounds to enhance their academic success. However, we
also recruited students from five other high schools and we gathered data from a large
sample. Another limitation is that a majority of the students had the same teacher, which

26
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
may have accounted for the high levels of social support and self-regulated learning. We
are unable to make conclusions about the causality of the relationships between social
support, self-regulated learning, and GPA as our study is not longitudinal, which is an
area future researchers could address. The scales for our key factors each have high
reliability. Additionally, we believe that our social support, self-regulated learning, and
growth mindset scales have strong validity and measure the constructs effectively.
Future Directions
The current study has broad implications for the importance of teacher support
and self-regulated learning in promoting academic success. Motivation is an influential
aspect of human behavior and teachers play a crucial role in the motivation of their
students. Teacher support can have a strong influence on one’s motivation and
engagement in school. Motivational beliefs are essential to self-regulated learning, which
has been shown to be strongly related to academic achievement. Research should further
investigate the relationship between teacher social support and self-regulated learning.
Academic achievement gaps persist despite programs like AVID, which promote
the use of self-regulated learning within a supportive class environment. Future research
should address how support classes like AVID may yield adaptive use of learning
strategies and access to social support that boost academic achievement. Additionally,
more research should investigate potential protective factors that may moderate the
relationship between SES and academic outcomes.
It is not enough to provide students from low SES families with the promotive
factors crucial for academic success to compensate for the risk associated with living in
economic hardship. Protective factors should be identified so that schools can equip

27
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
students with the assets and resources that provide resilience from the negative effects of
stress and SES on access to resources and academic outcomes.

28
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
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EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
Table 1
Relations Among Key Variables
Variable
1
1. Negative Life
Events
2. Father’s
Education Level
3. Social Support

2

3

5

1.00 -.14 -.39*** -.46*** -.26*
1.00 .13
1.00

4. Parent Social
Support
5. Friend Social
Support
6. Teacher Social
Support
7. Self-Regulated
Learning
8. Growth Mindset
9. Current G.P.A.
*

4

p < .05. ** p < .01. *** p ≤ .001.

.06

.22

6

7

8

-.21

-.35*** .14

.01

.06

9
.01

-.10 .26*

.85*** .80*** .76*** .52*** -.04 .16
1.00

.52*** .52*** .48*** -.09 .06
1.00

.37** .27*
1.00

-.07 -.05

. 51*** .07

.44***

1.00

.28*

.05

1.00 .14
1.00

36
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
Table 2
Hierarchical Linear Regression Models Testing Moderators of Relations between
Socioeconomic Status (SES) and Academic Achievement
Step Independent Variables
b
(se) b
β
Test of Social Support as a Moderator of SES on Grades
ep
1
Socioeconomic status
.105*
.047
.262
2

3

Socioeconomic status

.098*

.047

.245

Social support

.098

.088

.133

Socioeconomic status

097*

.048

.243

Social support

.098

.088

.132

Interaction

.011

.056

.023

R2
.068
.086

.086

Test of Self-regulated Learning as a Moderator of SES on Grades
1

Socioeconomic status

.105*

.047

.262

.068

2

Socioeconomic status

.098*

.046

.245

.138

Self-regulated learning

.364*

.158

.265

Socioeconomic status

105*

.048

.263

Self-regulated learning

.362*

.158

.263

Interaction

-.062

.110

-.067

3

.142

Test of Growth Mindset as a Moderator of SES on Grades
1

Socioeconomic status

.105*

.047

.262

.068

2

Socioeconomic status

.111*

.047

.279

.098

Growth mindset

.141

.097

.172

Socioeconomic status

104*

.048

.260

Growth mindset

.161

.100

.195

-.015

.020

-.093

3

Interaction

Note. * p < .05. ** p < .01. *** p ≤ .001.

.105

37
EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
Table 3
Hierarchical Linear Regression Models Testing Social Support (Parent, Friend, and
Teacher) Moderators of Relations between Socioeconomic Status (SES) and Academic
Achievement
Step Independent Variables
b
(se) b
β
Test of Parent Social Support as a Moderator of SES on Grades
ep
1
Socioeconomic status
.105*
.047
.262
2

3

Socioeconomic status

.104*

.048

.259

Parent social support

.022

.067

.040

Socioeconomic status

.101*

.048

.254

Parent social support

.016

.068

.028

Interaction

.036

.045

.097

R2
.068
.070

.079

Test of Friend Social Support as a Moderator of SES on Grades
1

Socioeconomic status

.105*

.047

.262

.068

2

Socioeconomic status

.115*

.048

.288

.081

Friend social support

-.066

.068

-.117

Socioeconomic status

.112*

.050

.279

Friend social support

-.057

.075

-.100

.016

.019

.042

3

Interaction

.083

Test of Teacher Social Support as a Moderator of SES on Grades
1

Socioeconomic status

.105*

.047

.262

.068

2

Socioeconomic status

.103*

.042

.258

.260

Teacher social support

.290***

.070

.438

Socioeconomic status

.388

.200

.972

Teacher social support

.467***

.140

.705

-.066

.045

-.779

3

Interaction

Note. * p < .05. ** p < .01. *** p ≤ .001.

.283

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EFFECTS OF RISK AND PROMOTIVE FACTORS ON ACHIEVEMENT
Table 4
Hierarchical Linear Regression Models Testing the Additive Effect of Protective Factors
– Social Support, Self-regulated Learning, and Growth Mindset – on the Relationship
between Socioeconomic status and Academic Achievement
Step

Independent Variables

b

(se) b

β

R2

ep
1

Socioeconomic status

.105*

.047

.262

.068

2

Socioeconomic status

.104*

.047

.261

.069

High protective rating

.023

.257

.011

Socioeconomic status

.104*

.049

.261

High protective rating

.023

.259

.011

Interaction

.003

.205

.002

3

Note. * p < .05. ** p < .01. *** p ≤ .001.

.069