URC
Individual and Environmental Factors in Mexican-Origin Adolescents' Academics

 

Sandra Abarca
Scott W. Plunkett*
Tovah Sands*

California State University Northridge


Abstract

This study 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.

Individual 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 academic success.

            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.

Methodology

            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.   

Measurement

            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.

Parental 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.

Parental 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.

Adolescent 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.

In 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.

Results

Descriptive Statistics

            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.

Table 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

            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.

Table 2. Results from Zero-Order Correlation Analyses with Data on Mothers and Fathers

         
                                        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 
        

*p < .05, **p < .01, ***p < .001

Multiple Regression Analysis

            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.

Table 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***
        *p 
        < .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. 

Table 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*
        *p 
        < .05, **p < .01, ***p < .001

      

Discussion & Implications

            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).

Previous 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 outcomes.

            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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