examines the relationship between individual and environmental factors
on academic motivation and grades in 199 Mexican-origin adolescents
residing in Los Angeles. Data were collected from self-report surveys,
census data at the block group level, and teacher reports of grades.
Correlation and multiple regression analyses indicate that adolescents’
self-efficacy, maternal and paternal monitoring, and perceived neighborhood
qualities were related to academic motivation and grades. The correlations
also indicated that adolescents’ perceptions of neighborhood qualities,
parental help with schoolwork, and parental academic encouragement were
related to academic motivation and grades. Parents’ education level,
neighborhood census qualities (i.e., median family income, education
level) were not related to the academic outcomes. Implications are discussed.
and Environmental Factors in Mexican-Origin Adolescents' Academics
Mexican-American youth are at-risk of high drop-out rates and academic
failure (Pew Hispanic Center, 2004). Yet, some Mexican-origin youth
are able to succeed academically despite the numerous obstacles in their
environment. These youth may be considered educationally resilient (Wang,
Hartel, & Walberg, 1994). Protective factors of resiliency include
individual and environmental qualities that allow an individual to overcome
adversity and succeed, while risk factors hinder or prevent an individual
from succeeding. Understanding the risk and protective factors that
contribute to educational resiliency in Mexican-origin youth will help
in the development, as well as the modification, of programs that encourage
One individual characteristic that can have an effect on educational
resiliency is self-efficacy. General self-efficacy determines how much
effort adolescents will expend and how long they will continue to work
on an objective even when faced with obstacles and/or aversive experiences
(Bandura, 1982; Sherer et al., 1982). According to Solberg and Villarreal
(1997), adolescents with high self-efficacy are more likely to succeed
academically and perform behaviors that are conducive to academics.
Hence, it is hypothesized that adolescents with higher self-efficacy
will stay on-task with school work and make better grades.
Family characteristics may have considerable influence on youth academic
outcomes. For example, many Mexican-origin parents receive very little
formal schooling which can lead to unfamiliarity with the education
system, thus leaving parents unable to support or advocate for their
children (Romo & Falbo, 1996). Another parenting factor that can
influence academic success is parental monitoring. Parental monitoring
of adolescents’ activities and friends may help prevent adolescents
from engaging in behaviors that hinder academic success (e.g., delinquent
behaviors). In addition, parental monitoring of adolescents’ schoolwork
and school activities may demonstrate to adolescents the high value
the family places on academics. Similarly, perceived parental encouragement
of academics has been found to help Mexican-origin adolescents develop
higher motivation to succeed academically (Arellano & Padilla, 1996).
Moreover, parental ability to help the adolescents with their school
work can also influence academic outcomes (Plunkett & Bámaca-Gómez,
2002). Hence, it was hypothesized that parents’ education levels, monitoring,
academic help, and educational encouragement would be positively related
to academic motivation and classroom grades.
In addition to individual and parenting variables, neighborhood qualities
should be examined to more fully understand academic resiliency in Mexican-American
youth. Specifically, those adolescents who perceived their neighborhoods
as having more protective factors (e.g., educational role models, wealth,
employment) and fewer risk factors (e.g., violent crimes, unemployment)
will report higher academic outcomes than those adolescents who rated
their neighborhoods more negatively (Suárez-Orozco & Suárez-Orozco,
2001). For example, living in neighborhoods with few professional people
has been associated with school drop-out (Brooks-Gunn, Duncan, Klebanov,
& Sealand, 1993). This may be due to the few potential role models
perceived by the adolescents. Hence, it was hypothesized that adolescents
who perceive their neighborhoods more favorably would have higher academic
motivation and grades.
Examining both individual perceptions of neighborhoods and factual data
(e.g., census data) about the neighborhoods in which adolescents reside
can provide a deeper insight into the antecedents of academic failure
and success (Brooks-Gunn, Duncan, Leventhal, & Aber, 1997). Many
researchers have used data at the census tract, but according to Gephart
(1997), census data at the block group level may be the best unit of
analysis for examining neighborhood effects. Hence, census data at the
block group level were used to measure two indicators of neighborhood
qualities: median family income and educational level of adults. Youth
living in communities with lower income and education levels are more
likely to face adverse conditions and be enrolled in schools with fewer
resources (Suárez-Orozco & Suárez-Orozco, 2001). Conversely, those
communities with higher income and educational levels should have more
resources to encourage academic success. Hence, it was hypothesized
that students who live in neighborhoods with higher income and education
levels will report higher academic outcomes.
This study will add to the growing body of knowledge of educational
resiliency with the following contributions. First, an ecological model
is used in which the adolescent is seen as nested in the family which
is nested in the community (Bronfenbrenner, 1989; Furstenberg &
Hughes, 1997). According to Gephart (1997), research on adolescence
has been hampered by a lack of studies which examine individual, family,
and neighborhood qualities together. Second, multiple sources of data
are used (i.e., adolescent perceptions, teacher ratings, and census
data). Given the increased recognition that mothers and fathers influence
their children differently, separate models are considered for mothers
and fathers. And finally, two different academic outcomes are examined
in this study: academic motivation and grades.
Project researchers solicited permission from an administrator and teachers
in one Los Angeles high school. Once the research process was explained,
teachers who agreed to participate were instructed to have their students
return signed parental and adolescent consent forms. The researchers
returned to the classrooms to administer the self-report questionnaires.
School record data were provided by the school data specialist for each
student who participated. The census data were gathered from http://factfinder.census.gov
by trained research assistants. The data were coded, entered, and verified
by trained undergraduate and graduate research assistants.
The sample consisted of 199 Mexican-origin adolescents (47% male and
53% female). The ages ranged from 13 to 20 years (M = 15). A
majority of the students (i.e., 78%) in the sample were in 9th
grade. About 59% of the adolescents and 5% of the parents were born
in the United States. Approximately 40% of the adolescents and 95% of
the parents were born in Mexico. Most adolescents (72%) reported living
with both of their biological parents.
Parents’ education level. The adolescents were asked how much
education each of their parents’ had received: 0 = No education, 1 =
Some elementary school, 2 = Completed elementary school, 3 = Some middle
or junior high school, 4 = Completed middle or junior high school, 5
= Some high school, 6 = Completed high school or GED, 7 = Completed
high school and also had other training, but not college (e.g., technical
training, business school), 8 = Some college, 9 = Completed a college
degree, 10 = Some graduate work, and 11 = Graduate degree, including
M.D., M.A., PhD., J.D., etc.
Parenting behaviors. For each of the parental involvement in
academics variables, the participants were asked to respond to each
item twice, once for mothers/mother figure and once for fathers/father
figure living in their households: 1 = strongly disagree, 2 = disagree,
3 = agree, 4 = strongly agree. The items in each scale were averaged
to get a subscale score.
Parental monitoring was measured with a 7-item subscale from the Parental
Behavior Measure (Peterson, Rollins, & Thomas, 1985). Items included
adolescents’ perceptions of whether parents know where their children
are, who their children’s friends and their parents are, whether parents
know when their children leave and come home, and whether parents monitored
homework completion. The Cronbach’s alphas were .78 for mothers and
.87 for fathers.
help with school work was measured with three items created for this
study: (a) “This person makes me feel good when I study or get good
grades,” (b) “This person knows how to help me do well in my school
work,” and (c) “This person has been important in helping me make good
grades.” The Cronbach’s alphas were .79 for mothers and .82 for fathers.
educational encouragement was measured with the six-item, mother and
father subscales of The Significant Other Academic Support Scale (Sands
& Plunkett, in press). The scale asks the adolescents if their parents
helped, motivated, encouraged, and/or cared about their education. The
Cronbach’s alphas were .90 for mothers and .95 for fathers.
perceptions of neighborhood qualities. Participants were asked
to estimate the level of education in their neighborhoods with one item,
“In my neighborhood most people have this level of education.” The same
response choices as parents’ education level were used. To obtain a measure
of the neighborhood wealth, participants were asked to “How would you
describe the wealth of most of the families in your neighborhood?” The
possible responses follow: 1 = Very poor, 2 = Poor, 3 = Lower middle-class,
4 = Middle-class, 5 = Upper middle-class, 6 = Upper-class/rich.
addition, a neighborhood qualities scale was used where participants
were asked to rate ten neighborhood items using a 4-point scale where
4 indicates “Strongly agree” and 1 indicates “Strongly disagree.” Items
included: (a) “I feel very safe”; (b) “Many people cannot speak English”;
(c) “There is a high value of education”; (d) “Many families are on
welfare”; (e) “Most adults who want to work, have a job”; (f) “Many
individuals are not legal”; (g) “I have seen many illegal acts”; (h)
“Many adults are unemployed”; (i) “I have seen many violent acts”; (j)
“Many people use drugs or have a drinking problem.” Seven of these items
were reverse coded to reflect resources in their neighborhoods instead
of risks. All of the adolescent neighborhood perception items were combined
to make the neighborhood qualities scale. Cronbach’s alpha was .72.
Census variables. Census data at the block group level were gathered
using the addresses of the adolescents from the 2000 census at the American
Fact Finder web page at http://factfinder.census.gov. Tables P53 and
P19 were used to obtain the median family income, and education level
of adults. The educational levels were coded as follows: 1 = No schooling
completed, 2 = Nursery to 4th grade, 3 = 5th-6th
grade, 4 = 7th-8th grade, 5 = 9th grade,
6 = 10th grade, 7 = 11th grade, 8 = 12th
grade, no diploma, 9 = High school graduate (includes equivalency),
10 = Some college, less than 1 year, 11 = Some college, 1 or more years,
no degree, 12 = Associate degree, 13 = Bachelor degree, 14 = Master
degree, 15 = Professional school degree, 16 = Doctorate degree.
Adolescent academic outcomes. Adolescents’ reports of academic
motivation were measured with five items created for this study: (a)
“I try hard in school,” (b) “Grades are very important to me,” (c) “I
usually finish my homework on time,” (d) “Education is so important
that it’s worth it to put up with things about school that I don’t like,”
and (e) “In general, I like school.” The response choices follow: 1
= strongly disagree, 2 = disagree, 3 = agree, 4 = strongly agree. The
items were averaged to create a scale score. Previously established
reliability with a Mexican American sample revealed a Cronbach’s alpha
of .71 (Plunkett & Bámaca-Gómez, 2003). In this study, the Cronbach’s
alpha was .78.
The school data specialist provided the research team with the grades
for each of the six classes the student had during the quarter in which
he/she completed the survey. These grades were coded on a 4.0 scale
(i.e., A = 4, B = 3, C = 2, D = 1, F = 0), and averaged to obtain the
overall GPA obtained by the youth.
Means and standard deviations for all dependent and independent variables
are presented in Table 1. Parent's education levels are lower than the
perceived education level of the neighborhood and those reported by
the 2000 Census. Mothers were perceived by the adolescents as monitoring
more than fathers, providing more help with school work and more encouraging
than their fathers.
1. Means and Standard Deviations Data on Mothers and Fathers
Mothers’ Data Fathers’ Data
M sd M sd
Adolescents’ academic motivation 3.02 .56 3.02 .56
Adolescents’ grades 2.20 .89 2.20 .89
Adolescent self-efficacy 2.87 .42 2.87 .42
Parents’ education level 4.34 2.30 4.49 2.54
Parents’ monitoring 2.99 .54 2.68 .73
Parents’ help with school work 3.07 .67 2.95 .81
Parents’ educational encouragement 3.47 .63 3.32 .72
Perceived neighborhood qualities 2.80 .49 2.80 .49
Neighborhood perceived wealth 3.91 .61 3.91 .61
Neighborhood perceived education level 7.49 1.47 7.49 1.47
Median family income in block group 30515.55 9248.47 30515.55 9248.47
Education level in block group 7.49 1.47 7.49 1.47
Zero-order correlations (i.e., Pearson correlations) were
conducted to determine the bivariate relationships between the variables.
The correlations indicated that adolescents’ self-efficacy, mothers’
monitoring, and fathers’ monitoring were significantly and positively
correlated to academic motivation and grades (see Table 2). Adolescents’
perceptions of their mothers’ and fathers’ ability to help academically
and academic encouragement were both significantly and positively related
to academic motivation, but not grades. Adolescents’ ratings of neighborhood
qualities were significantly and positively related to academic motivation
and grades in the mothers’ and fathers’ models. Parental education levels,
median family income, and neighborhood education level were not significantly
related to academic motivation or grades.
2. Results from Zero-Order Correlation Analyses with Data on Mothers
Mothers’ Data Fathers’ Data
Motivation Grades Motivation Grades
Adolescent self-efficacy .43*** .16* .43*** .16*
Parents’ education level .04 .06 .00 .03
Parents’ monitoring .43*** .22*** .36*** .15*
Parents’ help with school work .39*** .05 .32*** .06
Parents’ educational encouragement .32*** .09 .32*** .10
Perceived neighborhood qualities .22*** .14* .22*** .14*
Neighborhood perceived wealth 05 -.04 .05 -.04
Neighborhood perceived education level-.11 -.05 -.11 -.05
Median family income in block group -.11 .06 -.11 .06
Education level in block group -.11 -.05 -.11 -.05
< .05, **p < .01, ***p < .001
Multiple regression analyses were used to determine whether
the sets of predictor variables explained a significant amount of variance
in the criterion variables. Only the variables that were significantly
related in the correlations were entered into the multiple regression
analyses. The predictor variables accounted for a significant amount
of variance in academic motivation in both models (see Table 3). When
examining the beta coefficients, most of the previously significant
variables in the correlations are no longer significant. Specifically,
in both the mothers’ and fathers’ models, only adolescent self-efficacy
and parental monitoring were significantly related to adolescents’ academic
motivation once entered into the multiple regression equation.
3. Results from Multiple Regression Analyses with Data on Mothers and
Fathers on Academic Motivation
Mothers’ Model Fathers’ Model
ß t ß t
Adolescent self-efficacy .31 4.71*** .37 5.65***
Parental monitoring .19 2.47* .24 2.99**
Parental help with school work .16 1.89 .02 .17
Parental educational encouragement .05 .59 .07 .83
Perceived neighborhood qualities .07 1.10 .07 1.10
R2 = .31 R2 = .29
F = 16.74*** F = 14.59***
< .05, **p < .01, ***p < .001
In the multiple regression models for grades, the predictor variables
accounted for a small but significant amount of variance (i.e., 6%)
in grades in both models (see Table 4). In the mothers’ model, the beta
coefficient for maternal monitoring was significant. In the fathers’
model, adolescent self-efficacy was significantly related.
4. Results from Multiple Regression Analyses with Data on Mothers and
Fathers on Grades
Mothers’ Model Fathers’ Model
ß t ß t
Adolescent self-efficacy .09 1.19 .15 2.05*
Parental monitoring .17 2.22* .11 1.43
Perceived neighborhood qualities .07 .99 .09 1.20
R2 = .06 R2 = .06
F = 4.27** F = 3.65*
< .05, **p < .01, ***p < .001
The purpose of this study was to examine individual, family, and community
qualities in relation to academic achievement in Mexican-origin youth
using adolescent self-report data, teacher reports of grades, and census
data at the block group level. In general, the correlations and multiple
regressions indicated that self-efficacy, maternal and paternal monitoring,
and neighborhood qualities were related to the academic outcomes. In
addition, the correlations indicated that maternal and paternal help
with school work and educational encouragement were related to adolescent
reports of motivation. However, parental education levels, median family
income, and community education levels were not related to the academic
outcomes. In addition, the predictor variables accounted for a significant
amount of the variance in academic motivation and grades.
As noted, self-efficacy was related to both academic outcomes. This
is not surprising since adolescents who strive to succeed, even in the
face of adversity, are also more likely to seek resources to ensure
academic success. Given that self-efficacy is related to academic outcomes
in Mexican-origin youth, the challenge for educators is to find ways
to enhance self-efficacy in youth. Building self-efficacy, however,
should not only be limited to the school setting, parents should also
be involved in this process. Educators and researchers should also develop
programs that allow parents to become involved in building the self-efficacy
of their children inside the home.
This study was consistent with previous literature that indicates that
parental involvement in their children’s education is related to academic
success. Although some of the parenting variables were not significant
in the regressions, it is very likely that the parental involvement
variables are closely related. In other words, if a mother or father
monitor their child’s homework completion, the parents are probably
more likely to help their youth with schoolwork and to encourage their
youth’s academic achievement. Hence, multicollinearity might be an issue
in interpreting the multiple regression results. Educators should find
ways to promote and teach parents to become involved in their children’s
education. One way to do this is through parent classes offered in both
English and Spanish. Another way is to hire bilingual staff which would
allow Mexican-origin parents to feel comfortable approaching the school
and asking questions.
In the correlations, the perception of the neighborhood qualities were
significantly related to academic motivation in the mothers’ model and
to both academic outcomes in the fathers’ model. However, family income
and education level (from the census block group) were not related to
either academic outcome. It appears that the adolescent perceptions
have a stronger relationship to the academic outcomes than the actual
neighborhood qualities. It is possible that census data at the block
group may (a) be too large to be a relevant neighborhood for the adolescent
sample and/or (2) not be consistent with the adolescents’ perceptions
of their actual neighborhoods. Regardless, more research is necessary
to explore the difference between adolescent perceptions and others’
views of the neighborhood qualities. One suggestion for future research
would be to include more qualities of the neighborhoods from the census
data at the block group level (e.g., percentage below the poverty level,
percentage of unemployed adults).
scholars have indicated that perceived neighborhood effects seem to
be less predictive than individual or family qualities (Lehman &
Smeeding, 1997). This may be because the closest environment to youth
is their home. Hence, neighborhoods may play a secondary role in predicting
academic outcomes. More specifically, neighborhoods may impact the family
and/or school environments, which then impact the adolescents’ academic
One pattern that emerged in the regression equations is that the predictor
variables were stronger predictors of adolescent academic motivation
than teacher reports of grades. One explanation is that the protective
factors measured in this study may be more likely to influence motivation
as compared to future grades. Also, shared method variance could account
for the stronger relationship between the predictor variables and adolescents’
reports of their academic motivation (in comparison to the teachers’
reports of grades). Given the different results for each outcome variable,
future research may want to continue examining individual, family, and
community factors in relation to various academic outcomes.
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