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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Community Psychol. Author manuscript; available in PMC 2010 September 1.
Published in final edited form as:
PMCID: PMC2715446
NIHMSID: NIHMS110149

Family and Neighborhood Fit or Misfit and the Adaptation of Mexican Americans

Abstract

In this study, a person-environment fit model was used to understand the independent and combined roles of family and neighborhood characteristics on the adjustment of adults and children in a sample of 750 Mexican American families. Latent class analysis was used to identify six qualitatively distinct family types and three quantitatively distinct neighborhood types using socioeconomic and cultural indicators at each level. The results showed that members of single-parent Mexican American families may be particularly at-risk, members of the lowest-income immigrant families reported fewer adaptation problems if they lived in low-income neighborhoods dominated by immigrants, members of economically successful immigrant families may be more at-risk in integrated middle class neighborhoods than in low-income neighborhoods dominated by immigrants, and members of two-parent immigrant families appear to be rather resilient in most settings despite their low socioeconomic status.

Keywords: family, goodness of fit, mental health, Mexican American, neighborhood

Numerous studies have shown that neighborhood characteristics are related to adult and child physical, social, and psychological problems (e.g., Duprere & Perkins, 2007; Kupersmidt, Giesler, DeRosier, Patterson, & Davis, 1995; Roosa et al., 2005). Most studies have characterized neighborhoods as sources of stress with which residents contend. Despite the utility of stress process models for understanding variations in adjustment due to neighborhood characteristics, these usually are applied mechanistically such that neighborhood characteristics influence individuals who are characterized as relatively passive participants in the process (Roosa, Jones, Tein, & Cree, 2003). In contrast, human development and adaptation clearly unfold because of the constant interaction between individuals and the contexts in which they are embedded (Bronfenbrenner & Ceci, 1994; Lerner, 1983; 1985; Rutter et al., 1997). Thus, adaptation depends upon characteristics of the context, characteristics of the individual, and relationships between characteristics of the context and those of the individual. Missing from most studies of the relations between neighborhood characteristics and individual well-being is the simultaneous consideration of the characteristics of individuals and their neighborhoods.

Neighborhoods are rich, multidimensional ecological niches, that provide potential resources and supports as well as risks and stressors. Furthermore, what may be a resource for some may be a stressor for others (Kupersmidt et al., 1995). Thus, a neighborhood can be well matched to some individuals’ characteristics or needs, leaving these individuals relatively unstrained and well adjusted. The same neighborhood, however, can be a poor match for others, creating strains and increasing the likelihood of maladjustment. A person-environment fit perspective offers a framework for operationalizing the combined roles of individuals and their neighborhoods in individuals’ adjustment. The greater the match or fit of the characteristics of contexts and of individuals, the more likely that the individual will have favorable adjustment (e,g, Braucht, 1979; Caplan, 1987; Lerner, 1983, 1985). Similarly, the greater the mismatch or misfit, the greater the likelihood that poor adjustment will result. Importantly, determination of fit and misfit is most effective when person and context characteristics are assessed along similar dimensions (Caplan, 1997). Thus, the person-environment fit perspective would use information about commensurate dimensions of neighborhoods and individuals to explain individual differences in adjustment to the same neighborhoods. Despite the potential value of this perspective, this theory rarely has been applied to neighborhood research.

Person-environment fit theory may be particularly useful in research with Mexican Americans, although little research on neighborhood effects has included significant numbers of Mexican Americans (operationalized as anyone of Mexican heritage living in the U.S.). Mexican Americans are primarily urban residents (U.S. Census Bureau, 2001) and are at high risk for experiencing a number of adjustment problems (e.g., mental health problems, substance abuse, school failure) that have been linked to neighborhood qualities in other populations (e.g., Centers for Disease Control, 2006; Roberts & Chen, 1995; U.S. Department of Education, 2000). In addition, Mexican Americans are an extremely diverse population. They range from the very poor, primarily or exclusively Spanish-speaking immigrants to middle and upper class, primarily or exclusively English-speaking U.S. citizens (U.S. Census Bureau, 2001;U.S. Census Bureau, 2004). Some Mexican Americans live in ethnic enclaves often dominated by low-income immigrants and served by businesses that cater to Spanish speakers while others live in more integrated middle class communities where Spanish is rarely spoken. Immigrants and others who choose to live in ethnic enclaves are likely to adhere to more traditional lifestyles than those living in more integrated neighborhoods (Zivkovic, 1994).

This diversity, in the context of the person-environment fit model, may help in explaining some of the diversity in adjustment among Mexican Americans. For instance, low-income immigrants may feel more comfortable and experience less stress if they reside in neighborhoods with high percentages of low-income immigrants. In such neighborhoods they can adjust to their new cultural environment with little pressure to learn English or to make radical adjustments in values or lifestyles (Chiswick & Miller, 1992; Rumbaut, 1994). Thus, despite living in low-income neighborhoods, immigrants in ethnic enclaves dominated by immigrants may experience relatively little acculturative stress (i.e., adjustments to a new culture). In addition, their neighborhoods may be relatively high in social cohesion due to similarities in values and expectations among residents and resulting strong social ties (Sampson, 1999; Suárez-Orozco & Suárez-Orozco, 2001). It is difficult, however, to predict whether the social advantages for immigrants in ethnic enclaves can overcome problems usually associated with low-income neighborhoods (e.g., increased crime). In contrast, low-income immigrants in more affluent and ethnically integrated neighborhoods may feel out-of-place because they have little in common with their neighbors and experience communication problems and other stressors as they adapt to their new environment. Because of higher acculturative stress and a lower likelihood of social support from neighbors, immigrants in more affluent and integrated neighborhoods may have a greater likelihood of adaptation problems than those in ethnic enclaves (Georgiades, Boyle, & Duku, 2007). However, having affluent neighbors may have positive effects for low-income youth (Brooks-Gunn, Duncan, Klebanov, & Sealand, 1993). Finally, living in neighborhoods of mostly low-income, Spanish-speaking immigrants may be stressful to higher income and English-speaking Mexican American families or those with lifestyles (e.g., single parents) that do not conform to traditional Mexican norms. Thus, a person-environment fit model may be useful in understanding some of the diversity in adjustment among Mexican Americans.

Although few have applied a person-environment fit framework to neighborhood studies, those studies have enhanced our understanding of suicidal behavior (Braucht 1979), child-peer relationships (Kupersmidt, et al., 1995), and child emotional adjustment (Georgiades et al., 2007; Gordon, at al., 2003). Braucht (1979) demonstrated that neighborhood type, individual characteristics, and the degree of fit/misfit on multiple dimensions were associated with suicidal behaviors. Kupersmidt et al. (1995) found that family type by neighborhood interactions helped explain childhood aggression, peer rejection, and peer popularity. For instance, low-income White children in single-parent homes experienced more peer rejection in middle-income neighborhoods than those living in low-income neighborhoods. However, this result did not hold for Black children. Gordon et al. (2003) found that symptoms of attention-deficit/hyperactivity disorder were higher when family income did not match neighborhood income regardless of whether family income was higher or lower than neighborhood income. Finally, Georgiades et al. (2007) found that children in immigrant families reported fewer emotional-behavioral problems if they lived in neighborhoods with high concentrations of immigrant families regardless of neighborhood socioeconomic status. Children of non-immigrants, however, reported more emotional-behavioral problems if they lived in neighborhoods with high concentrations of immigrants than if they lived in neighborhoods with fewer immigrants.

In the current study, the person-environment fit model was tested using data from a study of 750 Mexican American families with children in 5th grade. Rather than using a variable-centered approach to exploring relationships between neighborhood characteristics, family characteristics, and adjustment, this study used a combination of a family-centered (i.e., using multiple family characteristics simultaneously; Weaver & Kim, 2007) and a neighborhood-centered (using multiple characteristics of neighborhoods simultaneously) approach to better understand the complex influences on adjustment. Similar dimensions were used to characterize families and neighborhoods. A socioeconomic dimension included family variables of parent education, income, and number of parents and the neighborhood variables of percent of families living below the poverty line and percent of individuals with a high school or higher level of education. A cultural dimension was represented by mothers’ nativity (U.S. versus Mexico) at the family level and, at the neighborhood level, the percent of Hispanics within a neighborhood. In contrast to other studies that tested a person-environment fit model to understand how neighborhood characteristics relate to adjustment, this study used an ethnic homogenous design; in more integrated samples, race/ethnicity and social class often are confounded making it difficult to interpret results (Mertens, 1998). In addition, the sample was very diverse in terms of social class, family structure, cultural orientation, and neighborhood characteristics, whereas most studies of Mexican Americans have focused on low-income, English speaking, inner city residents (Gonzales, Knight, Morgan-Lopez, Saenz, & Sirolli, 2002). We used a variation of the person-centered approach (Bergman & Magnusson, 1997) via latent class cluster analysis to identify clusters of relatively homogenous types of families and of neighborhoods, respectively. Then we integrated the family- and neighborhood-centered methods by using these clusters as variables in hierarchical linear models to determine how well, individually and jointly, they helped explain diversity in adjustment of Mexican American adults and children.

Methods

Sample

Data for this study came from an investigation of the roles of culture and context in the lives of Mexican Americans in a large southwestern metropolitan area (Roosa et al., 2008). Participants were 750 Mexican American students in 5th grade and their families who were selected from schools in ethnically, economically, and linguistically diverse communities. Eligible families met the following criteria: (a) the child and mother agreed to participate; (b) they had a fifth grader attending a sampled school; (c) the participating mother was the child’s biological mother, lived with the child, and self-identified as Mexican or Mexican American; (d) the child’s biological father was of Mexican origin; (e) the child was not severely learning disabled; and (f) no step-father was living with the child. Of the 570 two-parent families (76.6% of the sample) in the study, 460 (80.7%) fathers were interviewed. On average, mothers were about 36 years old, fathers about 38 years old, and children about 10 years old. Most mothers (69.9%) and fathers (76.7%) were interviewed in Spanish while most children (82.5%) were interviewed in English. Table 1 presents demographic characteristics of families included in the current analyses (n=738). Twelve families were excluded due to missing data for at least one of the variables used in the creation of the family and neighborhood profiles.

Table 1
Descriptive Statistics at the Individual and Community Level

To represent the diversity of Mexican Americans on acculturation, social class, and the cultural/ecological niches in which they live, a multi-step sampling procedure was employed that included (1) identifying the range of community contexts inhabited by Mexican Americans in the metropolitan area, (2) using random and purposive sampling to select communities; and (3) selecting and recruiting families from each community. Details of the sample and sampling design are described elsewhere (Roosa et al., 2008). We summarize the following procedures: (a) the school selection and neighborhood identification process (b) the family selection process, and (c) the interview process. All procedures were reviewed and approved by the Institutional Review Board at the first author’s university and conformed with APA ethical standards.

Procedures

School selection and neighborhood identification

We employed a mix of random and purposive sampling strategies to ensure the representation of a wide range of contexts in which Mexican American families live: from barrio-style communities that support traditional Mexican values and lifestyles to more mainstream communities. In total, 47 schools from 18 school districts, the Catholic Diocese, and charter schools were selected. All public and Catholic schools selected agreed to participate, whereas only one of three selected alternative schools participated.

We operationalized neighborhood at the level of the census block group, assigning each family to a census block group based on home address. Census block groups contain approximately 1,500 residents; they are delineated with the assistance of local participants to enhance their relevance as an identifiable geographic space (U.S. Census Bureau, 2000). Using this operationalization, a total of 257 neighborhoods were identified within the 47 school catchment areas selected.

Family selection and interviews

Upon obtaining family contact information, families whose ethnicity was indicated as Hispanic or families with Hispanic/Latino surnames were randomly selected for screening. In total, 73.2% (n=750) of eligible families completed interviews. Recruiters scheduled in-home Computer Assisted Personal Interviews lasting about 2½ hours with each participating family member. Family members were paid $45 each.

Neighborhood Level Measures

For each neighborhood, block group-level 2000 U.S. Census data of (a) the percent of families below the poverty level, (b) the percent of the population that was Hispanic, and (c) the percent of the population 25 years and over who had graduated from high school/high school equivalent, were used as indicators of the neighborhood context.

Individual Level Measures

Mothers reported on their country of birth (1 = Mexico and 0 = U.S). Both fathers and mothers provided data regarding their level of education (What is the highest level of education you have completed?) and income (Estimate your total family income for the past year.). For parent income, responses choices ranged from 1 (less than or equal to $5,000) and 20 (≥$95,001). In two-parent families, the mean of mother and father reports was used. In mother-only families, mothers’ reports were used. Mothers reported on their marital and cohabitation statuses (1 = two parent family and 0 = single parent family).

Parental depression

The Center for Epidemiologic Studies Depression Scale (CES-D, Radloff, 1977) is a 20-item, self-report scale designed to measure depressive symptomatology in the general population (e.g., “You were bothered by things that usually don’t bother you”). Prior work has shown reliability and validity of this scale in research with Spanish- and English-speaking Latinos (Mosicicki, Locke, Rae, & Boyd, 1989). In the current study, a Cronbach’s alpha of .80 was obtained for both mothers and fathers.

Parental antisocial behavior

The 11-item Parent Deviant Behavior Scale was developed from Huizinga, Esbensen, and Weiher’s (1991) Self-Report of Offense Scale (SRO) and the Offense History Measure (Brame, Fagan, Piquero, Schubert, & Steinberg, 2004). Respondents were asked whether they did each of the described deviant behaviors (e.g., purposely damage or destroy property that did not belong to you) during the past year. Higher scores indicate more deviant behaviors. This is a count score so no reliability is reported.

Child mental health

Both mother and child responded to the computerized version of the Diagnostic Interview Schedule for Children (DISC-IV; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000), a structured diagnostic instrument designed for use by nonclinicians to assess child psychopathological symptoms using DSM-IV criteria. The instrument has been successfully translated into Spanish (Bravo et al., 2001; Bravo, Woodbury-Farina, Canino, & Rubio-Stipec, 1993; Ribera, Canino, Rubio-Stipec, & Bravo, 1996). Total symptom counts for attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), generalized anxiety disorder (GAD) and major depressive disorder (MDD) were derived according to symptoms endorsed by either the parent or child (scoring algorithm version N).

Analytic Procedures

To test the fit/misfit model, we identified family and neighborhood types, separately, using latent class analyses. We then examined whether parent and child psychological problems were related to the fit/misfit of neighborhood and family types, using the subgroups that emerged from the latent class analyses. We applied multilevel models to examine the latter effects.

Latent class analysis was employed to examine latent heterogeneity amongst both family and neighborhood groups with the aim of identifying and enumerating distinct family and neighborhood types based upon the respective family and neighborhood indicators. The latent class model, which falls under the rubric of latent variable mixture modeling or finite mixture analysis, can be viewed as a latent model analog to cluster analysis (Vermunt & Magidson, 2002). In the absence of specific a priori hypotheses regarding the number and form of family and neighborhood types, we explored the number of classes, for which multiple indices and solution interpretability were considered when identifying the best solution. Interpretable and conceptually meaningful solutions with the lowest values on the Akaike information criteria (AIC) based indices Akaike, 1987) and sample-size adjusted Bayesian information criteria (SABIC; Sclove, 1987), which represented a statistically significant improvement in fit over a model with one fewer classes (indicated by a statistically significant LMR LRT test; Lo, Mendell, & Rubin, 2001), were considered more optimal than other solutions. To protect against local solutions, models were estimated with multiple, software-generated random start value sets (Hipp & Bauer, 2006). Using the optimal solutions, families and neighborhoods were each classified into their most likely class based on estimated posterior probabilities. Latent class analyses were conducted using a robust maximum likelihood estimator with the Mplus software package (v. 4.2; Muthén & Muthén, 2006).

To investigate whether family-neighborhood fit or misfit was related to parent and child psychological outcomes, three-level hierarchical linear models were tested. Multilevel modeling was used to account for the possible non-independency of the variables due to the fact that families (level-1) were nested within neighborhoods (level-2), which, in turn, were nested within school catchment areas (level-3). However, the intraclass correlations (ICC) for psychological problems were generally low for this sample (i.e., mean = .03; range = 0 to .08 for neighborhood-level variances; mean = .01; range = 0 to .03 for school-level variances) across the studied variables. In the multilevel model, family type was the level-1 predictor and neighborhood type was the level-2 predictor. The cross-level interaction of family type and neighborhood type signified fit/misfit effects. Specifically, the interaction evaluated whether neighborhood types had similar or different effects on psychological problems across family types and vise versa. SAS PROC MIXED was used to conduct the analyses.

Results

Family and Neighborhood Latent Classes

Parental education, household income, family structure (1- vs. 2-parent household), and maternal nativity (born in Mexico vs. born in U.S.) were specified as indicators in the family latent class models. Household income and parental education were allowed to be correlated within each class. Models consisting of one- through seven-classes were estimated. As shown in Table 2, the six-class solution was supported by both information criterion indices (AIC and SABIC) as being the best fitting model among the admissible solutions, although this solution was not a statistically better fit to the data than the five-class solution (see LMR LRT tests). The primary distinction between the five- and six-class solutions was the emergence of a class comprised of families characterized by the highest parental education and household income levels, a subpopulation rarely examined in research with Mexican Americans. Overall, we viewed the six-class solution as being the most interpretable and conceptually meaningful.

Table 2
Fit indices and entropies for latent class cluster models of family and neighborhood variables.

Estimated within-class means and probabilities for these six family types, including descriptive labels, are shown in Table 3. Economically Distressed families reported the lowest household income (approximately $10,000-$15,000/yr) of all family types. Their low-income may be because most (78%) of these families were single-parent households. Classic Immigrant families comprised the largest class, representing nearly 50% of the families. Characterized predominantly by two-parent households with nearly all mothers born in Mexico, these families had the least parental education (less than 9th grade) and a relatively low household income (approximately $20,000–$25,000/yr). Despite reporting a similar parental education level and similarly high percentage of immigrant mothers, Most mothers in Struggling Later Generation families were born in the U.S. Despite having higher parental education (high school graduate) than the previous groups, these families reported household income (approximately $25,000–$30,000/yr) only marginally greater than the Classic Immigrant families. Again, this low income may have been attributable to the high percentage of single-parent households (69%) in this class. In contrast, Successful Adapters were families with comparatively less parental education (some high school) than Struggling – Later Generation families but with substantially higher household incomes (approximately $45,000–$50,000/yr) than the previously described types. These families primarily had two parents with four-fifths of the mothers born in Mexico. The remaining two family types, Middle Class – Later Generation and Upper Middle Class families, were distinguished from the others by socio-economic advantage, with the highest parental education (some college and college graduate, respectively) and household incomes (about $65,000 and $75,000, respectively). These families were predominantly two-parent households with most mothers being U.S.-born. In summary, the latent class analysis revealed six qualitatively distinct and meaningful family types.

Table 3
Summary of the 6-class solution for the latent class cluster model of families.

U.S. Census data reflecting the percentage of (a) Hispanics, (b) individuals with a high school or greater education, and (c) families living below the federal poverty level within each neighborhood (census block-group level) were specified as indicators of the neighborhood latent cluster models. These indicators were allowed to be correlated within each class. Models consisting of one- through four-class solutions were estimated (Table 2). Both information criterion indices and the LMR LRT were consistent in supporting the three-class solution over the one- and two-class solutions. Overall, this typology reflects quantitatively distinct neighborhood types distinguished by the degree of socioeconomic advantage as reflected by percentage of residents who possessed a high school education (i.e., 36%, 61%, or 88%) and lived below the poverty level (i.e., 36%, 16%, or 14%). Hispanic residents, on average, comprised over 80% of the residents in the lower socioeconomic status (SES) neighborhoods, but comprised, on average, less than 20% of the residents in the upper SES neighborhoods.

Families and neighborhood block groups were each classified into their most likely class based on estimated posterior probabilities. The cross-classification of family type by neighborhood type is provided in Table 4. A majority of families resided in Middle SES neighborhoods. The family type to which families were classified and the neighborhoods in which they resided were not statistically independent [χ2(10) = 83.28, p < .001; ϕ coefficient = .36]. In general, poorer families were more likely to reside in the lower and middle SES neighborhoods, whereas the Middle Class – Later Generations and Upper Middle Class families were more likely to reside in upper SES neighborhoods. Due to the small frequency of Upper Middle Class families, resulting small cell sizes, and the similarity of these families to the Middle Class- Later Generation families in income, education, and nativity, we combined them with Middle Class - Later Generation families for all subsequent analyses.

Table 4
Cross-classification of family classes by neighborhood classes.

A few combinations had very small sample sizes. For example, only eight Economically Distressed families lived in Upper SES neighborhoods and only six Successful Adapters families and fourteen Middle Class – Later Generation families lived in Lower SES neighborhoods. Therefore, findings associated with these three conditions should be interpreted with caution.

Testing the Relations of Family and Neighborhood Fit/Misfit to Psychological Adjustment

Using the family type (with five levels after combining the two higher income groups) and the neighborhood type (with three levels) variables that resulted from the latent class analyses as the level-1 and level- 2 predictors, respectively, and the interaction of the two types as the cross-level predictor, multilevel analyses were conducted on each of the psychological outcomes. Table 5 summarizes the Type III F statistics for the overall fixed effects. Family type appeared to have stronger effects on parent and child psychological problems than neighborhood type. There were six significant main effects for family type but only one significant main effect for neighborhood type. There was one significant cross-level interaction. For significant main effects not conditioned by an interaction, we conducted post-hoc pairwise comparisons of the least-square means using Tukey-Kramer tests (West, Welch, & Galecki, 2006). For the cross-level interaction, with three levels of neighborhood type and five levels of family type, post-hoc comparisons could be done in many different ways. We focused on the simple effects of family type differences for each neighborhood type. Scheffe tests, which are more appropriate for post-hoc comparisons with interaction effects, were conducted to test for significant differences of the simple effects.

Table 5
Multilevel Models. Type III F-statistics of the overall fixed effects.

Neighborhood type had a significant main effect on mothers’ depressive symptoms. The least square means were 1.76 (SE = .06), 1.86 (SE = .03), and 1.67 (SE = .05) for the Lower SES, Middle SES, and Upper SES neighborhoods, respectively. Post-hoc comparisons showed that mothers in the Middle SES neighborhoods had a significantly higher level of depression than mothers in Upper SES neighborhoods (t = 3.30, p < .01).

Family type had significant main effects on mother antisocial behaviors, mother depression, father depression, and child oppositional defiant disorder and major depression symptom counts. Table 6 shows the least-square mean, standard error, and sample size for each family type on these outcomes. Except for mother’s depression, Struggling-Later Generation families had the highest score on each of these adjustment problems. Mothers from Economically Distressed families and Struggling-Later Generation families had comparable levels of antisocial behaviors, which were significantly higher than mothers from the other three groups. Mothers from Economically Distressed families had the highest level of depressive symptoms, whereas mothers from Middle Class – Later Generation families had the lowest level. Excluding fathers from Economically Distressed families (n=6 making comparisons unreliable), fathers from Middle Class Later Generation and Successful Adapters families had significantly lower depression score than fathers from Classic Immigrant families. Children from StrugglingLater Generation families had significantly higher symptom counts for oppositional defiant disorder than children from all other family types. Children from StrugglingLater Generations families also had significantly higher symptoms counts for major depression than children from Classic Immigrant, Economically Distressed families, and Middle Class-Later Generation families.

Table 6
Least square means and standard errors (in parentheses) for each of the family types.

The effects of family type on the psychological outcomes above were statistically constant across neighborhood types and the effect of neighborhood type on mother’s depressive symptoms was consistent across family types. However, the effects of the family type on child ADHD symptom counts depended on the neighborhood type, or vice versa, as shown by the significant interaction effect and illustrated in Figure 1. As mentioned above, there were very few Economically Distressed families in Upper SES neighborhoods and very few Successful Adapters or Middle Class – Later Generation families living in Lower SES neighborhoods thus findings for these family type-by-neighborhood-type combinations should be considered as tentative.

Figure 1
The cross-level interaction of neighborhood structure and family structure on ADHD.

Children in the Lower SES Neighborhoods

Because very few Successful Adapters and Middle Class – Later Generation families lived in this kind of neighborhood, we excluded these two types of families from our reporting. Children from Struggling - Later Generation families had significant higher levels of ADHD than the children from Economically Distressed and Classical Immigrant families (t = 2.30, p < .05 and t = 3.03, p < .01, respectively).

Children in the Middle SES Neighborhoods

Children from the Classic Immigrant families had significantly lower levels of ADHD than those from Economically Distressed, Struggling - Later Generation, and Middle Class - Later Generation families (t = −2.11, p < .05, t = −3.19, p < .01, and t = −3.43, p < .01, respectively). In addition, children from Successful Adapters families had significant lower ADHD symptom counts than those from the Struggling -Later Generation, and Middle Class - Later Generation families (t = −2.01, p < .05 and t = −2.47, p < .05, respectively).

Children in the Upper SES Neighborhoods

There were very few Economically Distressed families in Upper SES neighborhoods, so we excluded these families from the comparisons. Children in these neighborhoods did not significantly differ from each other in their ADHD levels regardless of which family type they were from.

Discussion

This study applied a person-environment fit model to examine how family and neighborhood characteristics, separately and jointly, were related to variations in adjustment in a sample of Mexican Americans. Latent class analysis was used to identify six distinct family types based on combinations of parent education, income, family structure, and mother’s nativity. Similarly, three distinct neighborhood types were identified based on distributions of families living in poverty, individuals with a high school or higher education, and Hispanics. Multilevel modeling was used to determine the main and interactive effects of family and neighborhood types on parent and child adjustment. Not surprisingly, family type was a better direct predictor of adjustment. The value of including neighborhood quality in studies of adjustment is the possibility of adding to the variance explained by models using information about more proximal environments (Leventhal & Brooks-Gunn, 2000). Neighborhoods are distal influences while the family is both more proximal to the individual and the primary developmental context for children (Roosa et al., 2003). Moreover, for ADHD, neighborhood effects were dependent on the characteristics of resident families, or vice versa.

Neighborhood type had a direct effect only on mother’s depression. Interestingly, Mexican American mothers in Middle SES neighborhoods reported greater depression than those in Upper SES neighborhoods; mothers from Lower SES neighborhoods had scores between these groups. Mothers in Upper SES neighborhoods likely had resources that made meeting family needs easy while experiencing few contextual stressors. Lower SES neighborhood mothers likely experienced many contextual stressors but may have received sufficient support from neighbors coping with similar stressors (Suárez-Orozco & Suárez-Orozco, 2001). Social bonds with neighbors similar to themselves may have provided some protection for these mothers. On the other hand, mothers in Middle SES neighborhoods may have struggled to meet the social and economic demands of a middle class lifestyle while being socially isolated because of the norm for independence in such neighborhoods. Furthermore, Middle SES neighborhoods were the most ethnically integrated, perhaps adding to social isolation or reduced social support among neighbors. These are hypotheses to be tested in future studies.

Although social class, family structure, and nativity individually have been associated with adult and child adjustment, the results for family type main effects supported the value of more holistic analyses that consider these characteristics simultaneously, a family-centered analysis. Comparing the results in Table 6 with characteristics of family types in Table 3 provides insight into which components of these family types were most strongly associated with mental health problems. For instance, the fact that Economically Distressed and Struggling Later Generation families had the highest levels of maternal antisocial behavior suggests that single-parent status was the critical factor. Both groups had high percentages of single-parent families, nothing else distinguished them from all other groups, and the remaining groups had very low scores on antisocial behavior. Similarly, parental depression seemed primarily related to family income except that both mothers and fathers in Classic Immigrant families showed lower depression than one would expect from income alone. These parents may be more resilient than the other low-income groups because most had a marital partner to provide support and they also may have benefited from adherence to traditional values and lifestyles of Mexico (i.e., they had the highest percentage of Mexico-born mothers) that evolved to help families cope in circumstances of limited resources (Roosa, Morgan-Lopez, Cree, & Specter, 2002). Immigrants would be more likely to adhere to such values and lifestyles than non-immigrants.

However, child adjustment seemed more complexly related to family characteristics. Children in the Struggling Later Generations families had the highest levels of oppositional defiant disorder and major depression. These households had lower than expected incomes given the level of parental education, there were few two parent households, and few mothers were born in Mexico. Apparently, it was the combination of these characteristics that was important; there are no patterns to suggest that low-income status, single-parent status, parent education, or nativity alone were related to children’s mental health across groups. An interesting departure from these patterns is that Middle Class – Later Generation children reported the second highest level of oppositional defiant disorder. It is possible that children in many of these families had adapted to the U.S. norm of relatively high levels of autonomy among early adolescents, whereas many of their parents continued to adhere somewhat to traditional Mexican values and parenting practices thereby contributing to a clash of value systems and resulting conflicts (e.g., Szapocznik, Kurtines, & Fernandez, 1980).

In contrast, despite low family income and education, children in Classic Immigrant families showed little risk for mental health problems and their mothers had low antisocial behavior scores. Only parental depression was elevated in these families. These results suggest that lower social class alone may not significantly raise the risk for mental health problems among Mexican American children. Children in Classic Immigrant families may have been somewhat protected from mental health problems due to a combination of having two parents and the protective effects of traditional Mexican values (Gonzales et al., 2002). Perhaps parental struggles to cope with financial challenges, adapt to a new culture, and protect children from threats from the neighborhood and new culture contributed to parents’ higher depression levels.

Results of multilevel analyses demonstrated the value of using a family-neighborhood fit model to understand variations in ADHD in Mexican Americans; children’s levels of ADHD symptoms depended on the combination of family and neighborhood types for several groups. For instance, Middle Class – Later Generation children were at greater risk for ADHD problems when their families lived in Middle SES neighborhoods than when they lived in Upper SES neighborhoods (there were too few of these families in Lower SES neighborhoods to reliable comparisons). The privileged status of these children (i.e., high parents’ income and education) may have made it difficult for them to fit in with children in neighborhoods where relatively few parents had a high school level education. In addition, because of their later-generation status, few of these children spoke Spanish making it difficult for them to communicate with many of the adults and at least some of their peers in Middle SES neighborhoods.

On the other hand, children in Economically Distressed families reported fewer adaptation problems if they lived in Lower SES neighborhoods. Despite the high number of single parents in this group, Lower SES neighborhoods probably provided these families in which most mothers were born in Mexico with more opportunities for social interaction and access to social support than they would find in other neighborhood types. Relatively high levels of similarity with their neighbors may have made Lower SES neighborhoods relatively comfortable for these families (cf.: Georgiades et al., 2007). Further, the experiences of most mothers in this group in Mexico may have prepared them for survival in Lower SES contexts (Roosa et al., 2002). Although one might expect a protective effect from living in a less risky neighborhood, these families had very low incomes and may have struggled economically and socially in Middle SES neighborhoods. In addition, many of the mothers in these families were Spanish speakers, which would make them more isolated in Middle SES neighborhoods.

As with child opposition defiant disorder and major depression, children in Struggling – Later Generation families had the highest ADHD symptom counts of all family types. However, context still mattered: children in these families were most clearly at risk, and distinct from all other groups, in Lower SES neighborhoods. These families may have been ill equipped to handle the numerous stressors common in lower income neighborhoods. In addition, because they were more acculturated to U.S. values and lifestyles, and probably less committed to traditional Mexican values and lifestyles, these families may be less capable of dealing with limited personal and neighborhood resources than immigrant families (Roosa et al., 2002). In addition, these English-speaking families may have been trapped in the barrio because of income and therefore surrounded by Spanish speaking immigrants making it difficult for them to communicate and associate with, or obtain support from neighbors (cf.: Georgiades et al., 2007). Furthermore, most families in this group were single-parent families and, therefore, a small minority in neighborhoods with high percentages of Mexican Americans and immigrants. Single-parent status and their inability to speak Spanish may have increased the social distance between them and their neighbors in low-income neighborhoods. In this case, cultural dissimilarities may have been more important than social class similarities.

One apparent exception to the family-neighborhood fit model came from the well-adapted children from Classic Immigrant families; these children showed low levels of ADHD symptoms regardless of neighborhood type. Perhaps living in stable two-parent households that adhered to traditional Mexican values provided these children with an extended social support network and other forms of protection from the stressors of poverty (family and neighborhood) and the linguistic, cultural, and social isolation they would have experienced in various neighborhood types (Gonzales et al., 2002; Roosa et al., 2002). Only continued research into this question will provide an answer.

Overall, the use of family and neighborhood typologies and a family-neighborhood fit model provided useful insights into the diversity in mental health among Mexican Americans. The family-environment fit model may be particularly helpful in studying adjustment among populations with large portions of immigrants because of the challenges they face adapting to what are often dramatically different living circumstances than they experienced in their home countries. Obviously, more study is needed to help explain the specifics of fit/misfit found in this study. In addition, the family-environment fit model should be tested longitudinally to help determine causality. Still, the results of this study provide a beginning point for developing interventions for different Mexican American subgroups based on both family and neighborhood characteristics.

Acknowledgments

Work on this paper was supported, in part, by grant MH 68920 (Culture, context, and Mexican American mental health), grant T-32-MH18387 to support training in prevention research, and the Cowden Fellowship program of the School of Social and Family Dynamics at Arizona State University. The authors are thankful for the support of Marisela Torres, Jaimee Virgo, our Community Advisory Board and interviewers, and the families who participated in the study. All authors were affiliated with Arizona State University’s Prevention Research Center. Roosa is in the School of Social and Family Dynamics; Weaver, Tein, Knight, Gonzales, and Saenz are in the Psychology Departments of their respective universities; and White is in the School of Health Management and Policy.

Footnotes

This article may not exactly replicate the final version published in the journal. It is not the copy of record.

Contributor Information

Mark W. Roosa, Arizona State University.

Scott R. Weaver, Georgia State University.

Rebecca M. B. White, Arizona State University.

Jenn-Yun Tein, Arizona State University.

George P Knight, Arizona State University.

Nancy Gonzales, Arizona State University.

Delia Saenz, Arizona State University.

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