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Agreement the Influence of Race/Ethnicity, Gender, and Class on Inequalities in Academic and Not-Academic Outcomes among Eighth-Course Students: Findings from an Intersectionality Approach

• Naomi Priest

Agreement the Influence of Race/Ethnicity, Gender, and Class on Inequalities in Academic and Non-Academic Outcomes among 8th-Grade Students: Findings from an Intersectionality Arroyo

• Laia Bécares,
• Naomi Priest

x

• Published: Oct 27, 2015
• https://doi.org/ten.1371/journal.pone.0141363

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Abstract

Socioeconomic, racial/ethnic, and gender inequalities in academic accomplishment have been widely reported in the United states of america, but how these three axes of inequality intersect to determine academic and not-academic outcomes among school-anile children is not well understood. Using data from the Us Early Childhood Longitudinal Study—Kindergarten (ECLS-K; N = 10,115), nosotros apply an intersectionality approach to examine inequalities beyond eighth-course outcomes at the intersection of vi racial/ethnic and gender groups (Latino girls and boys, Blackness girls and boys, and White girls and boys) and 4 classes of socioeconomic advantage/disadvantage. Results of mixture models evidence large inequalities in socioemotional outcomes (internalizing behavior, locus of command, and self-concept) across classes of reward/disadvantage. Within classes of advantage/disadvantage, racial/indigenous and gender inequalities are predominantly found in the most advantaged class, where Black boys and girls, and Latina girls, underperform White boys in academic assessments, but not in socioemotional outcomes. In these latter outcomes, Blackness boys and girls perform better than White boys. Latino boys prove small differences as compared to White boys, mainly in science assessments. The contrasting outcomes betwixt racial/ethnic and gender minorities in self-assessment and socioemotional outcomes, as compared to standardized assessments, highlight the detrimental effect that intersecting racial/ethnic and gender discrimination accept in patterning academic outcomes that predict success in adult life. Interventions to eliminate achievement gaps cannot fully succeed equally long as social stratification caused by gender and racial bigotry is not addressed.

Citation: Bécares L, Priest North (2015) Understanding the Influence of Race/Ethnicity, Gender, and Form on Inequalities in Academic and Non-Bookish Outcomes among Eighth-Form Students: Findings from an Intersectionality Approach. PLoS ONE 10(ten): e0141363. https://doi.org/10.1371/periodical.pone.0141363

Editor: Emmanuel Manalo, Kyoto University, JAPAN

Received: June 10, 2015; Accepted: October 6, 2015; Published: October 27, 2015

Copyright: © 2015 Bécares, Priest. This is an open admission commodity distributed nether the terms of the Creative Eatables Attribution License, which permits unrestricted use, distribution, and reproduction in whatever medium, provided the original author and source are credited

Data Availability: All ECLS-K Kindergarten-Eighth Grade Public-utilise File are bachelor from the National Center for Didactics Statistics website (https://nces.ed.gov/ecls/dataproducts.asp#K-viii).

Funding: This work was funded by an ESRC grant (ES/K001582/1) and a Hallsworth Inquiry Fellowship to LB.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The US racial/ethnic academic achievement gap is a well-documented social inequality [1]. National assessments for science, mathematics, and reading show that White students score college on average than all other racial/indigenous groups, particularly when compared to Black and Hispanic students [2, 3]. Explanations for these gaps tend to focus on the influence of socioeconomic resources, neighborhood and schoolhouse characteristics, and family composition in patterning socioeconomic inequalities, and on the racialized nature of socioeconomic inequalities every bit key drivers of racial/ethnic academic accomplishment gaps [4–10]. Substantial show documents that indicators of socioeconomic status, such as free or reduced-cost schoolhouse lunch, are highly predictive of academic outcomes [two, 3]. However, the relative contribution of family, neighborhood and school level socioeconomic inequalities to racial/ethnic academic inequalities continues to be debated, with evidence suggesting none of these factors fully explain racial/ethnic academic accomplishment gaps, peculiarly every bit students motility through elementary schoolhouse [11]. Attitudinal outcomes have been proposed by some as i explanatory gene for racial/indigenous inequalities in bookish achievement [12], simply differences in educational attitudes and aspirations beyond groups do not fully reflect inequalities in academic assessment. For case, while students of poorer socioeconomic status accept lower educational aspirations than more advantaged students [13], racial/indigenous minority students report higher educational aspirations than White students, particularly afterward accounting for socioeconomic characteristics [14–sixteen]. Similarly, while socio-emotional evolution is considered highly predictive of bookish achievement in school students, some racial/ethnic minority children report better socio-emotional outcomes than their White peers on some indicators, although findings are inconsistent [17–22].

In addition to inequalities in academic achievement, racial/ethnic and socioeconomic inequalities as well exist across measures of socio-emotional development [23–26]. And as with academic achievement, although socioeconomic factors are highly predictive of socio-emotional outcomes, they do not completely explain racial/ethnic inequalities in school-related outcomes not focused on standardized assessments [xi].

Farther complication in agreement how academic and non-academic outcomes are patterned by socioeconomic factors, and how this contributes to racial/ethnic inequalities, is added by the multi-dimensional nature of socioeconomic status. Socioeconomic status is widely recognized as comprising diverse factors that operate across different levels (e.g. private, household, neighborhood), and influence outcomes through different causal pathways [27]. The lack of interchangeability between measures of socioeconomic status within and between levels (e.g. income, education, occupation, wealth, neighborhood socioeconomic characteristics, or past socioeconomic circumstances) is also well established, as is the non-equivalence of measures between racial/indigenous groups [27]. For example, large inequalities accept been reported across racial/indigenous groups within the same educational level, and inequalities in wealth have been shown across racial/ethnic that have similar income. It is therefore imperative that studies consider these multiple dimensions of socioeconomic status so that critical social gradients across the entire socioeconomic spectrum are not missed [27], and racial/ethnic inequalities within levels of socioeconomic status are adequately documented. It is besides of import that differences in school outcomes are considered beyond levels of socioeconomic status within and between racial/ethnic groups, and then that the influence of specific socioeconomic factors on outcomes within specific racial/ethnic groups tin can be studied [28]. However, while these analytic approaches have been identified as inquiry priorities in order to enhance our understanding of the complex ways in which socioeconomic condition and race/ethnicity intersect to influence schoolhouse outcomes, research that operationalizes these recommendations across academic and non-bookish outcomes of school children is scant.

In add-on to the complexity that arises from race/ethnicity, socioeconomic condition, and intersections between them, different patterns in academic and not-academic outcomes by gender have also received longstanding attending. Comparisons across gender testify that, on boilerplate, boys take college scores in mathematics and science, whereas girls have college scores in reading [two, 3, 29]. In contrast to explanations for socioeconomic inequalities, gender differences have been mainly attributed to social conditioning and stereotyping within families, schools, communities, and the wider social club [30–35]. These socialization and stereotyping processes are also highly relevant determining factors in explaining racial/ethnic academic and non-academic inequalities [35, 36], equally are processes of racial discrimination and stigmatization [37, 38]. Gender differences in academic outcomes take been documented every bit differently patterned beyond racial/ethnic groups and across levels of socioeconomic status. For example, gender inequalities in math and science are largest amongst White and Latino students, and smallest amid Asian American and African American students [39–43], while gender gaps in test scores are more than pronounced amidst socioeconomically disadvantaged children [44, 45]. In terms of attitudes towards math and sciences, gender differences in attitudes towards math are largest among Latino students, but gender differences in attitudes towards science are largest among White students [39, xl]. Gender differences in socio-developmental outcomes and in non-cognitive bookish outcomes, across race/ethnicity and socio-economic status, accept received far less attention; studies that consider multiple bookish and not-bookish outcomes among schoolhouse aged children beyond race/ethnicity, socioeconomic status and gender are express in the U.s.a. and internationally.

Understanding how different academic and not-academic outcomes are differently patterned by race/ethnicity, socio-economic status, and gender, including within and between grouping differences, is an important research area that may help in understanding the potential causal pathways and explanations for observed inequalities, and in identifying central population groups and points at which interventions should exist targeted to address inequalities in detail outcomes [28, 46]. Not only is such knowledge critical for population level policy and/or local level activeness within affected communities, but failing to discover potential factors for interventions and potential solutions is argued as reinforcing perceptions of the unmodifiable nature of inequality and injustice [46].

Nonetheless the importance of documenting patterns of inequality in relation to a item social identity (due east.thou. race/ethnicity, gender, course), there is increasing acknowledgement within both theoretical and empirical inquiry of the need to move across analyzing single categories to consider simultaneous interactions between different aspects of social identity, and the touch on of systems and processes of oppression and domination (east.g., racism, classism, sexism) that operate at the micro and macro level [47, 48]. Such intersectional approaches claiming practices that isolate and prioritize a single social position, and emphasize the potential of varied inter-relationships of social identities and interacting social processes in the production of inequities [49–51]. To date, exploration of how social identities interact in an intersectional way to influence outcomes has largely been theoretical and qualitative in nature. Explanations offered for interactions betwixt privileged and marginalized identities, and associated outcomes, include family unit and instructor socialization of gender performance (eastward.g. math and scientific discipline as male domains, exact and emotional skills as female), every bit well as racialized stereotypes and expectations from teachers and wider society regarding racial/indigenous minorities that are also gendered (eastward.g. Blackness males equally violent prone and aggressive, Asian females every bit submissive) [52–57]. That is, social processes that socialize and design opportunities and outcomes are both racialized and gendered, with racism and sexism operating in intersecting ways to influence the development and achievements of children and youth [58–60]. Socioeconomic condition adds a third important dimension to these processes, with individuals of the same race/ethnicity and gender having access to vastly different resource and opportunities across levels of socioeconomic status. Moreover, access to resources besides as socialization experiences and expectations differ considerably by race and gender within the same level of socio-economic status. Thus, neither gender nor race nor socio-economical status alone can fully explain the interacting social processes influencing outcomes for youth [27, 28]. Disentangling such interactions is therefore an important research priority in social club to inform intervention to address inequalities at a population level and within local communities.

In the realm of quantitative approaches to the study of inequality, studies often examine separate social identities independently to assess which of these axes of stratification is most prominent, and for the most role do not consider claims that the varied dimensions of social stratification are frequently juxtaposed [56, 61]. A pressing need remains for quantitative research to consider how multiple forms of social stratification are interrelated, and how they combine interactively, not just additively, to influence outcomes [46]. Doing so enables analyses that consider in greater item the representation of the embodied positions of individuals, peculiarly problems of multiple marginalization as well equally the co-occurrence of some form of privilege with marginalization [46]. It is important to note that the languages of statistical interaction and of intersectionality demand to exist carefully distinguished (e.g. intersectional additivity or additive assumptions, versus additive scale and cross-product interaction terms) to avoid misinterpretation of findings, and to ensure advisable awarding of statistical interaction to enable the description of outcome measures for groups of individuals at each cross-stratified intersection [46]. Ultimately this will provide more than nuanced and realistic understandings of the determinants of inequality in order to inform intervention strategies.

This written report fills these gaps in the literature by examining inequalities across several eighth grade academic and not-academic outcomes at the intersection of race/ethnicity, gender, and socioeconomic status. It aims to do this by: identifying classes of socioeconomic advantage/disadvantage from kindergarten to eighth form; then ascertaining whether membership into classes of socioeconomic reward/disadvantage differ for racial/indigenous and gender groups; and finally, by contrasting academic and non-academic outcomes at the intersection of race/ethnicity, gender and socioeconomic advantage/disadvantage. Intersecting identities of race/ethnicity, gender, and socioeconomic characteristics are compared to the reference group of White boys in the about advantaged socioeconomic category, as these are the iii identities (male person, White, socioeconomically privileged) that feel the least marginalization when compared to racial/indigenous and gender minority groups in disadvantaged socioeconomic positions.

Methods

Data

This written report used data on singleton children from the Early Childhood Longitudinal Written report—Kindergarten (ECLS-M). The ECLS-Thou employed a multistage probability sample blueprint to select a nationally representative sample of children attending kindergarten in 1998–99. In the base year the primary sampling units (PSUs) were geographic areas consisting of counties or groups of counties. The second-stage units were schools within sampled PSUs. The third- and final-phase units were children within schools [62]. Analyses were conducted on information collected from direct child assessments, as well as information provided by parents and school administrators.

Ideals Statement

This article is based on the secondary analysis of anonymized and de-identified Public-Use Data Files available to researchers via the Inter-University Consortium for Political and Social Research (ICPSR). Human participants were not straight involved in the enquiry reported in this article; therefore, no institutional review lath approval was sought.

Measures

Outcome Variables.

Eight result variables, all assessed in eighth class, were selected to examine the study aims: 2 measures relating to non-cognitive academic skills (perceived interest/competence in reading, and in math); three measures capturing socioemotional evolution (internalizing behavior, locus of command, cocky-concept); and three measures of cognitive skills (math, reading and scientific discipline assessment scores).

For the eighth-grade data collection, children completed the 16-detail Cocky Description Questionnaire (SDQ) Two [63], where they provided self-assessments of their academic skills by rating their perceived competence and interest in English and mathematics. The SDQ also asked children to report on problem behaviors with which they might struggle. 3 subscales were produced from the SDQ items: The SDQ Perceived Interest/Competence in Reading, including four items on grades in English and the child's interest in and enjoyment of reading. The SDQ Perceived Interest/Competence in Math, including four items on mathematics grades and the child's involvement in and enjoyment of mathematics. And the SDQ Internalizing Beliefs subscale, which includes eight items on internalizing problem behaviors such as feeling distressing, alone, ashamed of mistakes, frustrated, and worrying almost schoolhouse and friendships [62].

The Cocky-Concept and Locus of Control scales ask children about their self-perceptions and the amount of control they have over their own lives. These scales, adopted from the National Educational activity Longitudinal Study of 1988, asked children to betoken the degree to which they agreed with thirteen statements (seven items in the Self-Concept calibration, and six items in the Locus of Control Calibration) about themselves, including "I feel good about myself," "I don't have enough command over the direction my life is taking," and "At times I remember I am no good at all." Responses ranged from "strongly concur" to "strongly disagree." Some items were reversed coded and then that higher scores signal more positive self-concept and a greater perception of control over one's ain life. The vii items in the Self-Concept calibration, and the half-dozen items in the Locus of Command were standardized separately to a mean of nothing and a standard deviation of 1. The scores of each scale are an boilerplate of the standardized scores [62].

Academic achievement in reading, mathematics and science was measured with the eighth-class direct cognitive assessment battery [62].

Children were given carve up routing assessment forms to decide the level (loftier/low) of their reading, mathematics, and science assessments. The ii-stage cerebral assessment approach was used to maximize the accuracy of measurement and reduce administration time by using the child's responses from a brief first-stage routing form to select the advisable second-stage level grade. First, children read items in a booklet and recorded their responses on an answer grade. These answer forms were and so scored by the test administrator. Based on the score of the respective routing forms, the test administrator so assigned a high or depression 2nd-stage level course of the reading and mathematics assessments. For the second-stage level tests, children read items in the assessment booklet and recorded their responses in the aforementioned assessment booklet. The routing tests and the 2nd-stage tests were timed for 80 minutes [62]. The present analyses apply the standardized scores (T-scores), allowing relative comparisons of children confronting their peers.

Private and Contextual Disadvantage Variables.

Latent Grade Assay, described in greater detail below, was used to classify students into classes of private and contextual reward or disadvantage. Nine constructs, measuring characteristics at the private-, schoolhouse-, and neighborhood-level, were captured using 42 dichotomous variables measured across the unlike waves of the ECLS-K.

Private-level variables captured household composition, cloth disadvantage, and parental expectations of the children's success. Measures included whether the child lived in a single-parent household at kindergarten, outset, 3rd, fifth and eighth grades; whether the household was beneath the poverty threshold level at kindergarten, 5th and 8th grades; food insecurity at kindergarten, outset, second and tertiary grades; and parental expectations of the kid'southward bookish achievement (categorized as upwards to loftier school and more than high school) at kindergarten, first, third, fifth and eighth grades. An indicator of whether parents had moved since the previous interview (measured at kindergarten, beginning, 3rd, fifth and eighth grades) was included to capture stability in the children's life. A household-level composite alphabetize of socioeconomic status, derived by the National Center for Education Statistics, was also included at kindergarten, first, 3rd, fifth and 8th grades. This measure captured the begetter/male guardian's education and occupation, the mother/female person guardian'southward education and occupation, and the household income. College scores reflect higher levels of educational attainment, occupational prestige, and income. In the nowadays analyses, the socioeconomic composite alphabetize was categorized into quintiles and further divided into the lowest first and second quintiles, versus the third, fourth and fifth quintiles.

Two variables measured the schoolhouse-level environment: percentage of students eligible for free school meals, and percentage of students from a racial/ethnic background other than White non-Hispanic. These two variables were dichotomized equally more than or equal to fifty% of students belonging to each category. Both variables were measured in the kindergarten, showtime, third, fifth and eighth course information collections.

To capture the neighborhood environment, a variable was included which measured the level of safety of the neighborhood in kindergarten, first, third, fifth and 8th grades. Parents were asked "How safe is it for children to play exterior during the mean solar day in your neighborhood?" with responses ranging from 1, not at all prophylactic, to 3, very safe. For the present analyses, response categories were recoded into 1 "non at all and somewhat rubber," and 0 "very safety."

Predictor Variables.

The race/ethnicity and gender of the children were assessed during the parent interview. In society to empirically measure the intersection between race/ethnicity and gender in the classes of disadvantage, a set up of six dummy variables were created that combined racial/ethnic and gender categories into White boys, White girls, Blackness boys, Black girls, Latino boys, and Latina girls.

Statistical Analyses

This written report used the transmission 3-step arroyo in mixture modeling with auxiliary variables [64, 65] to independently evaluate the relationship between the predictor auxiliary variables (the combined race/ethnicity and gender groups), the latent class variable of reward/disadvantage, and the issue (non-cerebral skills, socioemotional development, cerebral assessments). This is a data-driven, mixture modelling technique which uses indicator variables (in this case the variables described under Private and Contextual Disadvantage Variables section) to identify a number of latent classes. It also includes auxiliary information in the class of covariates (the race/ethnicity and gender combinations described under Predictor Variables) and distal outcomes (the eight effect variables), to ameliorate explore the relationships between the characteristics that make upwards the latent classes, the predictors of class membership, and the associated consequences of membership into each form.

The get-go step in the iii-step process is to estimate the measurement function of the articulation model (i.e., the latent class model) by creating the latent classes without adding covariates. Latent grade analyses first evaluated the fit of a 2-form model, and systematically increased the number of classes in subsequent models until the addition of latent classes did not further improve model fit. For each model, replication of the best log-likelihood was verified to avoid local maxima. To decide the optimal number of classes, models were compared beyond several model fit criteria. Offset, the sample-size adapted Bayesian Information Criterion (BIC) [66] was evaluated; lower relative BIC values indicate improved model fit. Given that the BIC benchmark tends to favor models with fewer latent classes [67], the Lo, Mendell, and Rubin likelihood ratio test (LMR-LRT) statistic [68] was also considered. The LMR-LRT tin be used in mixture modeling to compare the fit of the specified class solution (grand-course model) to a model with fewer classes (k-1 class model). A non-meaning chi-square value suggests that a model with i fewer course is preferred. Entropy statistics, which measure the separation of the classes based on the posterior class membership probabilities, were also examined; entropy values approaching 1 indicate articulate separation between classes [69].

After determining the latent course model in footstep one, the second footstep of the analyses used the latent course posterior distribution to generate a nominal variable North, which represented the most likely class [64]. During the third step, the measurement fault for Due north was accounted for while the model was estimated with the outcomes and predictor auxiliary variables [64]. The last stride of the analysis examined whether race/ethnic and gender categories predict course membership, and whether class membership predicts the outcomes of involvement.

All analyses were conducted using MPlus v. 7.xi [70], and used longitudinal weights to account for differential probabilities of pick at each sampling stage and to adapt for the effects of not-response. A robust standard error estimator was used in MPlus to business relationship for the clustering of observations in the ECLS-One thousand.

Results

Four distinct classes of advantage/disadvantage were identified in the latent course analysis (see Table ane).

Form characteristics are shown in Tabular array A in S1 File. Trajectories of advantage and disadvantage were stable across ECLS-M waves, so that none of the classes identified changed in private and contextual characteristics across time. The largest proportion of the sample (47%; Class iii: Individually and Contextually Wealthy) lived in individual and contextual privilege, with very low proportions of children in socioeconomic deprived contexts. A grade representing the contrary characteristics (children living in individually- and contextually-deprived circumstances) was besides identified in the analyses (19%; Class one: Individually and Contextually Disadvantaged). Class 1 had the highest proportion of children living in socioeconomic deprivation, attending schools with more than than 50% racial/ethnic minority students, and living in unsafe neighborhoods, but did not accept a high proportion of children with the lowest parental expectations. Course 4 (xix%; Individually Disadvantaged, Contextually Wealthy) had the highest proportion of children with the lowest parental expectations (parents reporting beyond waves that they expected children to reach up to a loftier schoolhouse education). Grade 4 (Individually Disadvantaged, Contextually Wealthy) also had loftier proportions of children living in individual-level socioeconomic deprivation, merely had low proportions of children attending a school with over l% of children eligible for gratuitous school meals. It also had relatively low proportions of children living in unsafe neighborhoods and depression proportions of children attention diverse schools, forming a grade with a mixture of individual-level deprivation, and contextual-level advantage. The last form was composed of children who lived in individually-wealthy environments, but who also lived in unsafe neighborhoods and attended diverse schools where more than 50% of pupils were eligible for gratuitous school meals (xiii%; Class two: Individually Wealthy, Contextually Disadvantaged; see Table A in S1 File).

The combined intersecting racial/indigenous and gender characteristics yielded six groups consisting of White boys (n = 2998), White girls (n = 2899), Black boys (n = 553), Blackness girls (n = 560), Latino boys (n = 961), and Latina girls (north = 949). All pairs containing at least i minority status of either race/ethnicity or gender (e.grand., Black boys, Black girls, Latino boys, Latina girls) were more probable than White boys to exist assigned to the more disadvantaged classes, as compared to beingness assigned to Grade 3, the least disadvantaged (run into Table B in S1 File).

Racial/Ethnic and Gender Differences in Eighth-Grade Academic Outcomes

Table 2 shows broad patterns of intersecting racial/ethnic and gender inequalities in academic outcomes, although interesting differences sally beyond racial/ethnic and gender groups. Whereas Black boys achieved lower scores than White boys across all classes on the math, reading and science assessments, this was not the case for Latino boys, who only underperformed White boys on the scientific discipline assessment within the most privileged class (Course 3: Individually and Contextually Wealthy). Latina girls, in dissimilarity, outperformed White boys on reading scores inside Class 4 (Individually Disadvantaged, Contextually Wealthy), but scored lower than White boys on science and math assessments, although only when in the ii most privileged classes (Class 3 and 4). For Black girls the outcome of class membership was not as pronounced, and they had lower science and math scores than White boys beyond all but i case.

In general, the largest inequalities in bookish outcomes across racial/ethnic and gender groups appeared in the near privileged classes. For example, results testify no differences in math scores across racial/ethnic and gender categories within Class 4, the virtually disadvantaged class, but in all other classes that contain an element of reward, and particularly in Class 3 (Individually and Contextually Wealthy), there are large gaps in math scores across racial/ethnic and gender groups, when compared to White boys. These patterns of heightened inequality in the most advantaged classes are similar for reading and science scores (see Tabular array 2).

Racial/Ethnic and Gender Differences in Eighth-Grade Non-Academic Outcomes

Interestingly, racialized and gendered patterns of inequality observed in academic outcomes were not every bit stark in non-cognitive academic outcomes (meet Table 3).

Racial/ethnic and gender differences were small across socioemotional outcomes, and in fact, White boys were outperformed on several outcomes. Blackness boys scored lower than White boys on internalizing behavior and higher on self-concept within Classes 2 (Individually Wealthy, Contextually Disadvantaged) and 4 (Individually Disadvantaged, Contextually Wealthy), and Blackness girls scored higher than White boys on cocky-concept within Classes 2 and 3 (Individually Wealthy, Contextually Disadvantaged, and Individually and Contextually Wealthy, respectively). White and Latina girls, but non Black girls, scored higher than White boys on internalizing behavior (inside Classes 3 and 4 for White girls, and within Classes one and 3 for Latina girls; run into Table 3).

As with academic outcomes, most racial/ethnic and gender differences as well emerged within the nearly privileged classes, and particularly in Grade 3 (Individually and Contextually Wealthy), although in the case of perceived interest/competence in reading, White and Latina girls performed better than White boys. White girls also reported higher perceived interest/competence in reading than White boys in Class 4: Individually Disadvantaged, Contextually Wealthy.

Discussion

This study set out to examine inequalities across several 8th form academic and non-academic outcomes at the intersection of race/ethnicity, gender, and socioeconomic status. It offset identified four classes of longstanding individual- and contextual-level disadvantage; then adamant membership to these classes depending on racial/ethnic and gender groups; and finally compared non-cerebral skills, academic assessment scores, and socioemotional outcomes across intersecting gender, racial/ethnic and socioeconomic social positions.

Results evidence the clear influence of race/ethnicity in determining membership to the almost disadvantaged classes. Across gender dichotomies, Black students were more likely than White boys to be assigned to all classes of disadvantage as compared to the most advantaged course, and this was particularly strong for the most disadvantaged form, which included elements of both individual- and contextual-level disadvantage. Latino boys and girls were likewise more likely than White boys to be assigned to all the disadvantaged classes, but the forcefulness of the association was much smaller than for Black students. Whereas membership into classes of disadvantage appears to be more than a result of structural inequalities strongly driven by race/ethnicity, the salience of gender is credible in the distribution of academic assessment outcomes within classes of disadvantage. Results show a gendered pattern of math, reading and scientific discipline assessments, particularly in the most privileged course, where girls from all ethnic/racial groups (although mostly from Black and Latino racial/indigenous groups) underperform White boys in math and science, and where Black boys score lower, and White girls higher, than White boys in reading.

With the exception of educational assessments, gender and racial/indigenous inequalities inside classes are either not very pronounced or in the opposite direction (e.g. racial/ethnic and gender minorities outperform White males), but differences in outcomes beyond classes are stark. The forcefulness of the association between race/ethnicity and class membership, and the reduced racial/ethnic and gender inequalities within classes of reward and disadvantage, attest to the importance of socioeconomic status and wealth in explaining racial/ethnic inequalities; should individual and contextual disadvantage be comparable beyond racial/ethnic groups, racial/ethnic inequalities would exist essentially reduced. This being said, about within-class differences were observed in the most privileged classes, showing that benefits brought almost by affluence and advantage are not equal across racial/ethnic and gender groups. The measures of advantage and disadvantage captured in this study relate to characteristics afforded by parental resources, implying an intergenerational manual of disadvantage, regardless of the presence of absolute adversity in babyhood. This pattern of differential returns of affluence has been shown in other studies, which report that White teenagers benefit more from the presence of affluent neighbors than do Black teenagers [71]. Amongst developed populations, studies show that across several health outcomes, highly educated Black adults fare worse than White adults with the everyman education [72]. Intersectional approaches such as the ane applied in this report reveal how power within gendered and racialized institutional settings operates to undermine access to and use of resources that would otherwise be bachelor to individuals of advantaged classes [72]. The present report further contributes to this literature by documenting how, in a key phase of the life course, similar levels of advantage, simply not disadvantage, lead to different academic outcomes across racial/indigenous and gender groups. These findings suggest that, should socioeconomic inequalities be addressed, and levels of advantage were similar across racial/ethnic and gender groups, systems of oppression that pattern the racialization and socialization of children into racial/indigenous and gender roles in social club would nonetheless ensure that inequalities in academic outcomes existed beyond racial/ethnic and gender categories. In other words, racism and sexism have a straight outcome on academic and non-bookish outcomes among 8^th graders, independent of the event of socioeconomic disadvantage on these outcomes. An important limitation of the electric current report is that although it uses a comprehensive measure out of advantage/disadvantage, including elements of deprivation and affluence at the family, school and neighborhood levels through time, it failed to capture these two key causal determinants of racial/ethnic and gender inequality: experiences of racial and gender bigotry.

Despite this limitation, it is of import to note that socioeconomic inequalities in the US are driven by racial and gender bias and discrimination at structural and individual levels, with race and gender discrimination exerting a strong influence on academic and not-bookish inequalities. Racial bigotry, prevalent in the US and in other industrialized nations [38, 73] determines differential life opportunities and resource across racial/ethnic groups, and is a crucial determinant of racial/ethnic inequalities in health and development throughout life and across generations [37, 38]. In the context of this study'south primary outcomes within school settings, racism and racial discrimination experienced past both the parents and the children are likely to contribute towards explaining observed racial/indigenous inequalities in outcomes within classes of disadvantage. Gender bigotry—another system of oppression—is apparent in this report in relation to academic subjects socially considered as typically male or female orientated. For example, results show no departure between Black girls and White boys from the near advantaged class in terms of perceived involvement and competence in math but, in this same class, Black girls score much lower than White boys in the math assessment. This divergence, not explained past intrinsic or socioeconomic differences, tin be contextualized as a consequence of experienced intersecting racial and gender discrimination. The consequences of the intersection between two marginalized identities are found throughout the results of this study when comparing across wide categorizations of race/ethnicity and gender, and in more than detailed conceptualizations of minority status. Growing up Black, Latino or White in the United states of america is not the same for boys and girls, and growing up as a male child or a girl in America does non lead to the aforementioned outcomes and opportunities for Blackness, Latino and White children equally they become adults. With this study'south approach of intersectionality one tin can notice the complication of how gender and race/ethnicity intersect to create unique academic and not-academic outcomes. This includes the contrasting results constitute for Black and Latino boys, when compared to White boys, which bear witness very few examples of poorer outcomes amid Latino boys, but several instances amongst Black boys. Results too bear witness different racialization for Blackness and Latina girls. Latina girls, but not Black girls, study higher internalizing behavior than White boys, whereas Black girls, but non Latina girls, written report higher self-concept than White boys. Black boys also study college self-concept and lower internalizing behavior than White boys, findings that mirror research on self-esteem amidst Blackness adolescents [74, 75]. In cognitive assessments, intersecting racial/ethnic and gender differences emerge across classes of disadvantage. For case, Black girls in all four classes score lower on scientific discipline scores than White boys, simply only Latina girls in the about advantaged class score lower than White boys. Although 1 can notice differences in the racialization of Black and Latino boys and girls across classes of disadvantage, findings about broad differences across Latino children compared to Black and White children should be interpreted with circumspection. The Latino ethnic group is a big, heterogeneous group, representing sixteen.7% of the full US population [76]. The Latino population is composed of a variety of dissimilar sub-groups with various national origins and migration histories [77], which has led to differences in sociodemographic characteristics and lived experiences of ethnicity and minority status amongst the diverse groups. Differences across Latino sub-groups are widely documented, and pooled analyses such as those reported here are masking differences beyond Latino sub-groups, and providing biased comparisons between Latino children, and Black and White children.

Poorer performance of girls and racial/ethnic minority students in science and math assessments (just not in cocky-perceived competence and interest) might result from stereotype threat, whereby negative stereotypes of a group influence their member's operation [78]. Stereotype threat posits that sensation of a social stereotype that reflects negatively on one's social group can negatively touch on the functioning of group members [35]. Reduced performance only occurs in a threatening state of affairs (e.one thousand., a test) where individuals are aware of the stereotype. Studies show that early adolescence is a time when youth become aware of and begin to endorse traditional gender and racial/indigenous stereotypes [79]. Findings amidst youth parallel findings among developed populations, which testify that developed men are generally perceived to be more competent than women, but that these perceptions do not necessarily hold for Black men [80]. These stereotypes accept stiff implications for interpersonal interactions and for the wider structuring of systemic racial/ethnic and gender inequalities. An example of the consequences of negative racial/ethnic and gender stereotypes as children grow upward is the well-documented racial/ethnic and gender pay gap: women earn less than men [81], and racial/ethnic minority women and men earn less than White men [82].

In addition to the focus on intersectionality, a force of this report is its person-centered methodological approach, which incorporates measures of advantage and disadvantage beyond individual and contextual levels through nine years of children's socialization. Children live within multiple contexts, with gamble factors at the family, school, and neighborhood level contributing to their development and wellbeing. Individual hazard factors seldom operate in isolation [83], and they are often strongly associated both within and across levels [84]. All risk factors captured in the latent grade analyses accept been independently associated with increased run a risk for academic problems [10, 71, 85, 86], and given that combinations of risk factors that cut across multiple domains explain the association between early on run a risk and later outcomes better than whatever isolated hazard gene [83, 84], the incorporation of person-centered and intersectionality approaches to the study of racial/indigenous, gender, and socioeconomic inequalities beyond school outcomes provides new insight into how children in marginalized social groups are socialized in the early life class.

Conclusions

The contrasting outcomes between racial/indigenous and gender minorities in self-cess and socioemotional outcomes, as compared to standardized assessments, provide support for the detrimental consequence that intersecting racial/ethnic and gender discrimination have in patterning academic outcomes that predict success in developed life. Interventions to eliminate achievement gaps cannot fully succeed as long as social stratification caused by gender and racial discrimination is not addressed [87, 88].

Supporting Information

S1 File. Supporting Tables.

Table A: Form characteristics. Table B: Associations betwixt race/ethnicity and gender groups and assigned class membership (membership to Classes 1, 2 or 4 every bit compared to Class 3: Individually and Contextually Wealthy).

https://doi.org/10.1371/journal.pone.0141363.s001

(DOCX)

Acknowledgments

This piece of work was funded by an ESRC grant (ES/K001582/one) and a Hallsworth Research Fellowship to LB. Nigh of this work was conducted while LB was a visiting scholar at the Institute for Social Enquiry, Academy of Michigan. She would like to thank them for hosting her visit and for the support provided.

Author Contributions

Conceived and designed the experiments: LB. Performed the experiments: LB. Analyzed the information: LB. Wrote the newspaper: LB NP.

References

1. ane. Jencks C, Phillips M. The Black-White Test Score Gap. Washington, D.C.: Brookings Institution Press; 1998.
2. 2. (NCES) NCfES. The Nation's Report Carte: Scientific discipline 2011(NCES 2012–465). Washington, D.C.: Constitute of Education Sciences, U.South. Department of Education, 2012.
3. 3. (NCES) NCfES. The Nation's Study Menu: A First Wait: 2013 Mathematics and Reading (NCES 2014–451). Washington, D.C.: Establish of Education Sciences, U.S. Department of Education, 2013.
4. iv. Ainsworth-Darnell J. Why Does Information technology Take a Hamlet? The Mediation of Neighborhood Furnishings on Educational Accomplishment. Social Forces. 2002;81(ane):117–52.
   • View Article
   • Google Scholar
5. 5. Duncan G, Magnuson K. Can family unit socioeconomic resource account for racial and ethnic test score gaps? The Future of Children. 2005;fifteen(1):35–54. pmid:16130540
   • View Article
   • PubMed/NCBI
   • Google Scholar
6. half dozen. Kao Yard, Tienda G, Schneider B. Racial and ethnic variation in academic functioning. Research in Sociology of Education and Socialization. 1996;11:263–97.
   • View Article
   • Google Scholar
7. 7. Noble Yard, Tottenham Northward, Casey B. Neuroscience perspectives on disparities in school readiness and cognitive accomplishment. The Time to come of Children. 2005;15:71–89. pmid:16130542
   • View Article
   • PubMed/NCBI
   • Google Scholar
8. viii. Roscigno V. Family/school inequality and African-American/Hispanic achievement. Social Problems. 2000;47(2):266–90.
   • View Article
   • Google Scholar
9. 9. Warren J. Educational inequality among White and Mexican-origin adolescents in the American Southwest: 1990. Sociology of Education. 1996;69:142–58.
   • View Article
   • Google Scholar
10. x. Brooks-Gunn J, Klebanov P, Duncan 1000. Indigenous differences in children's intelligence exam scores: role of economic deprivation, dwelling environment, and maternal characteristics. Child Evolution. 1996;67:396–408. pmid:8625720
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
11. xi. Quinn D. Kindergarten Blackness–White test score gaps: Reexamining the roles of socioeconomic condition and school quality with new data. Sociology of Education. 2015;88(2):120–39.
    • View Article
    • Google Scholar
12. 12. Ogbu J. Low performance as an adaptation: The case of blacks in Stockton, California. In: Gibson K, Ogbu J, editors. Minority Status and Schooling. New York: Thousand Publishing; 1991.
13. 13. Kao G, Tienda M. Educational aspirations of minority youth. American Journal of Education. 1998;106:349–84.
    • View Article
    • Google Scholar
14. fourteen. Ainsworth-Darnell J, Downey D. Assessing the oppositional civilisation explanation for racial/indigenous differences in school performance. American Sociological Review. 1998;63:536–53.
    • View Article
    • Google Scholar
15. xv. Cheng Southward, Starks B. Racial differences in the effects of significant others on students' educational expectations. Sociology of Didactics. 2002;75:306–27.
    • View Article
    • Google Scholar
16. 16. Qian Z, Blair S. Racial/ethnic differences in educational aspirations of high school seniors. Sociological Perspectives. 1999;42: 605–25.
    • View Article
    • Google Scholar
17. 17. Fagg J, Curtis Southward, Stansfield Southward, Congdon P. Psychological distress among adolescents, and its relationship to private, family and area characteristics in E London. Social Science and Medicine. 63:636–48. pmid:16584825
    • View Article
    • PubMed/NCBI
    • Google Scholar
18. eighteen. Maynard MJ, Harding S. Perceived parenting and psychological wellbeing in Great britain ethnic minority adolsecens. Kid Care Health and Development. 2010;36:630–8.
    • View Article
    • Google Scholar
19. 19. Maynard MJ, Harding S, Minnis H. Psychological well-being in Black Carribean, Blackness African, and white adolescentes in the UK Medical Inquiry Council DASH written report. Social Psychiary Psychiatric Epidemiology. 2007;42:759–69.
    • View Article
    • Google Scholar
20. 20. Priest N, Baxter J, Hayes L. Social and emotional outcomes of Australian children from Ethnic and culturally and linguistically various backgrounds. Australian and New Zealand Journal of Public Health. 2011. 8857.
    • View Article
    • Google Scholar
21. 21. Washbrook E, Waldfogel J, Bradbury B, Corak K, Ghanghro A. The development of young children of immigrants in Commonwealth of australia, Canada, the United Kingdom, and the United States. Child Evolution. 2012;83:1591–607. pmid:22966925
    • View Article
    • PubMed/NCBI
    • Google Scholar
22. 22. Zilanawala A, Sacker A, Nazroo J, Kelly Y. Indigenous differences in children's socioemotional difficulties: Findings from the Millennium Cohort Study. Social Science and Medicine. 2015;134:95–106. pmid:25931288
    • View Article
    • PubMed/NCBI
    • Google Scholar
23. 23. Mendelson T, Kubzansky L, Datta Grand, Buka S. Relation of female gender and low socioeconomic condition to internalizing symptoms amid adolescents: A instance of double jeopardy? Social Science and Medicine. 2008;66:1284–96. pmid:18248868
    • View Article
    • PubMed/NCBI
    • Google Scholar
24. 24. McLaughlin K, Hilt L, Nolen-Hoeksema S. Racial/indigenous differences in internalizing and externalizing symptoms in adolescents. Journal of Abnormal Child Psychology. 2007;35:801–sixteen. pmid:17508278
    • View Article
    • PubMed/NCBI
    • Google Scholar
25. 25. Kistner J, David C, White B. Ethnic and sex differences in children's depressive symptoms: Mediating effects of perceived and actual competence. Journal of Clinical Child and Adolescent Psychology. 2003;32:341–50. pmid:12881023
    • View Article
    • PubMed/NCBI
    • Google Scholar
26. 26. Hankin B, Abramson L, Moffitt T, McGee R, Silva P, Angell Grand. Development of depression from preadolescence to young adulthood: Emerging gender differences in a 10-year longitudinal study. Journal of Abnormal Psychology 1998;107:128–forty. pmid:9505045
    • View Article
    • PubMed/NCBI
    • Google Scholar
27. 27. Braveman P, Cubbin C, Egerter Southward, Williams D, Pamuk East. Socioeconomic disparities in health in the United States: What the patterns tell usa. American Journal of Public Wellness. 2010;100:S186–96. pmid:20147693
    • View Article
    • PubMed/NCBI
    • Google Scholar
28. 28. Williams D, Mohammed S, Leavall J, Collins C. Race, socioeconomic condition, and health: Complexities, ongoing challenges, and research opportunities. Annals of the New York University of Sciences 2010;1186:69–101. pmid:20201869
    • View Article
    • PubMed/NCBI
    • Google Scholar
29. 29. Nowell A, Hedges 50. Trends in gender differences in academic achievement from 1960 to 1994: An analysis of differences in mean, variance and extreme scores. Sex Roles. 1998;39:21–43.
    • View Article
    • Google Scholar
30. 30. Cvencek D, Meltzoff Northward, Greenwald A. Math–gender stereotypes in uncomplicated school children. Child Development. 2011;82(3):766–79. pmid:21410915
    • View Article
    • PubMed/NCBI
    • Google Scholar
31. 31. Eccles J. Gender roles and women's achievement-related decisions. Psychology of Women Quarterly. 1987;xi:135–72.
    • View Article
    • Google Scholar
32. 32. Keller J, Dauenheimer D. Stereotype threat in the classroom: Dejection mediates the disrupting threat effect on women's math performance. Personality and Social Psychology Bulleting. 2003;29:371–81.
    • View Commodity
    • Google Scholar
33. 33. Signorella M, Jamison Westward. Masculinity, femininity, androgyny, and cerebral operation: A meta-analysis. Psychological Bulletin. 1986;100:207–28.
    • View Article
    • Google Scholar
34. 34. Spencer South, Steele C, Quinn D. Stereotype threat and women's math performance. Periodical of Experimental Social Psychology 35, four–28 (1999). 1999;35:4–28.
    • View Article
    • Google Scholar
35. 35. Steele C. A threat in the air: How stereotypes shape the intellectual identities and performance American Psychologist. 1997;52(6):613–29. pmid:9174398
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
36. 36. Sinclair South, Hardin C, Lowery B. Self-stereotyping in the context of multiple social identities. Periodical of Personality and Social Psychology. 2006;90:529–42. pmid:16649853
    • View Article
    • PubMed/NCBI
    • Google Scholar
37. 37. Kelly Y, Bécares L, Nazroo J. Associations between maternal experiences of racism and early on child health and development: Findings from the UK Millennium Cohort Study. Journal of Epidemiology and Community Wellness. 2013;67:35–41. pmid:22760222
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
38. 38. Priest N, Paradies Y, Trenerry B, Truong Thousand, Karlsen S, Kelly Y. A systematic review of studies examining the human relationship between reported racism and health and wellbeing for children and young people. Social Science and Medicine. 2013;95:115–27. pmid:23312306
    • View Article
    • PubMed/NCBI
    • Google Scholar
39. 39. Catsambis S. The path to math: Gender and racial/ethnic differences in mathematics participation from center school to high school. Sociology of Education. 1994;67:199–215.
    • View Article
    • Google Scholar
40. xl. Catsambis Due south. Gender, race, ethnicity, and science didactics in the eye grades. Journal of Research in Science Teaching. 1995;32:243–57.
    • View Article
    • Google Scholar
41. 41. Coley R. Differences in the gender gap: Comparisons across racial/ethnic groups in education and work. Princeton, NJ: Educational Testing Service, 2001.
42. 42. McGraw R, Lubienski S, Strutchens Yard. A closer expect at gender in NAEP mathematics accomplishment and affect data: Intersections with achievement, race/ethnicity, and socioeconomic status. Periodical of Research in Mathematics Education. 2006;37:129–fifty.
    • View Article
    • Google Scholar
43. 43. Muller P, Phase F, Kinzie J. Scientific discipline achievement growth trajectories: Understanding factors related to gender and racial/ethnic differences in precollege science achievement. American Educational Research Journal. 2001;38:981–1012.
    • View Article
    • Google Scholar
44. 44. Hinshaw S. Externalizing behavior issues and academic underachievement in childhood and adolescence: causal relationships and underlying mechanisms. Psychological Message. 1992;111:127–55. pmid:1539086
    • View Article
    • PubMed/NCBI
    • Google Scholar
45. 45. Entwisle D, Alexander K, Olson L. Early on schooling: the handicap of beingness poor and male. Folklore of Education. 2007;80:114–38.
    • View Article
    • Google Scholar
46. 46. Bauer Yard. Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance wellness equity. Social Science and Medicine. 2014;110:10–7. pmid:24704889
    • View Article
    • PubMed/NCBI
    • Google Scholar
47. 47. Hankivsky O, Cormier R. Intersectionality: Moving women's health research and policy forward. Vancouver: Women's Wellness Research Network; 2009.
48. 48. Dhamoon G, Hankivsky O. Why the theory and exercise of intersectionality thing to health research and policy. In: Hankivsky O, editor. Health Inequities in Canada: Intersectional Frameworks and Practices. Vancouver: UBC Press; 2011. p. sixteen–50.
49. 49. Hankivsky O. Women's health, men'south wellness, and gender and health: Implications of intersectionality. Social Science and Medicine. 2012;74:1712–20. pmid:22361090
    • View Article
    • PubMed/NCBI
    • Google Scholar
50. 50. Crenshaw Thou. Demarginalizing the intersection of race and sexual activity: a blackness feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. Academy of Chicago Legal Forum. 1989:139–67.
51. 51. Browne I, Misra J. The intersection of gender and race in labor markets. Annual Review of Sociology. 2003;29:487–513.
    • View Article
    • Google Scholar
52. 52. Fiske Due south. Managing ambivalent prejudices: The smart-but-cold, and the warm-but dumb sterotypes. Annual American Academy Politcal Social Science. 2012;639(1):33–48.
    • View Article
    • Google Scholar
53. 53. Gillborn D, Rollock N, Vincent C, Ball SJ. 'You got a pass, so what more do you want?': race, grade and gender intersections in the educational experiences of the Black middle class. Race Ethnicity and Education. 2012;15(1):121–39.
    • View Article
    • Google Scholar
54. 54. Johnson K, Freeman J, Pauker K. Race is Gendered: How covarying phenotypes and stereotypes bias sex categorisation. Journal of Personality and Social Psychology. 2012;102(1):116–31. pmid:21875229
    • View Article
    • PubMed/NCBI
    • Google Scholar
55. 55. Mulvey KL. Challenging Gender Stereotypes: Resistance and Exclusion. Child Development. 2015;86(3):681–94. pmid:25382748
    • View Article
    • PubMed/NCBI
    • Google Scholar
56. 56. Penner A, Saperstein A. Engendering racial perceptions: An intersectional analysis of how Social status shapes race. Gender and Society. 2013;27:319–44.
    • View Article
    • Google Scholar
57. 57. Riegle-Crumb C, Humphries Chiliad. Exploring Bias in Math Teachers' Perceptions of Students' Ability by Gender and Race/Ethnicity. Cender and Guild. 2012;26(2).
    • View Article
    • Google Scholar
58. 58. Hughes D, Rodriguez J, Smith EP, Johnson DJ, Stevenson HC, Spicer P. Parents' Ethnic-Racial Socialization Practices: A Review of Research and Directions for Future Study. Developmental Psychology. 2006;42(5):747–70. 3870. pmid:16953684
    • View Article
    • PubMed/NCBI
    • Google Scholar
59. 59. Priest N, Walton J, White F, Kowal E, Baker A, Paradies Y. Understanding the complexities of ethnic-racial socialization processes for both minority and majority groups: A 30-year review. International Journal of Intercultural Relations. 2014;43:139–55. 10565.
    • View Article
    • Google Scholar
60. 60. Varner F, Mandara J. Discrimination Concerns and Expectations as Explanations for Gendered Socialization in African American Families. Child Development. 2013;84(three):875–90. pmid:23199216
    • View Article
    • PubMed/NCBI
    • Google Scholar
61. 61. Warner D, Brown T. Understanding how race/ethnicity and gender ascertain age-trajectories of disability: An intersectionality approach. Social Science and Medicine. 2011;72:1236–48. pmid:21470737
    • View Article
    • PubMed/NCBI
    • Google Scholar
62. 62. Tourangeau K, Nord C, Lê T, Sorongon A, Najarian Thousand. Early on Childhood Longitudinal Written report, Kindergarten Class of 1998–99 (ECLS-K), Combined User's Manual for the ECLS-K 8th-Course and G–8 Total Sample Data Files and Electronic Codebooks (NCES 2009–004). Washington, DC.: National Center for Teaching Statistics, Establish of Educational activity Sciences, U.S. Department of Education., 2009.
63. 63. Marsh HW. Self Clarification Questionnaire (SDQ) 2: A theoretical and empirical footing for the measurement of multiple dimensions of adolescent self-concept. An interim test manual and a research monograph. Sydney: University of Western Sydney: 1992.
64. 64. Asparouhov T, Muthén B. Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus. Mplus Spider web Notes [Internet]. 2013 April 2014. Available from: world wide web.statmodel.com/examples/webnotes/webnote15.pdf‎.
65. 65. Vermunt J. Latent Class Modeling with Covariates: 2 Improved Three-Step Approaches. Political Assay. 2010;18:450–69.
    • View Article
    • Google Scholar
66. 66. Schwarz G. Estimating the dimension of a model. Annals of Statistics. 1978;half dozen:461–4.
    • View Article
    • Google Scholar
67. 67. Dayton C. Latent course scaling assay. Thousand Oaks, CA: Sage, 1998.
68. 68. Lo Y, Mendell Due north, Rubin D. Testing the number of components in a normal mixture. Biometrika. 2001;88:767–78.
    • View Article
    • Google Scholar
69. 69. Celeux Thousand, Soromenho Thousand. An entropy criterion for assessing the number of clusters in a mixture model. Periodical of Classification. 1996;thirteen:195–212.
    • View Commodity
    • Google Scholar
70. 70. Muthén L, Muthén B. Mplus User'south Guide. Seventh Edition. Los Angeles, CA.: 2012.
71. 71. Brooks-Gunn J, Duncan G, Klebanov P, Sealand N. Practice Neighborhoods Influence Child and Adolescent Evolution? American Journal of Folklore. 1993;99(two):353–95.
    • View Article
    • Google Scholar
72. 72. Jackson P, Williams D. The intersection of race, gender, and SES: Health paradoxes. In: Schulz A, Mullings L, editors. Gender, Race, Course, & Health: Intersectional Approaches. San Francisco, CA: Jossey-Bass; 2006. p. 131–62.
73. 73. Paradies Y. A systematic review of empirical enquiry on self-reported racism and health. International Journal of Epidemiology. 2006;35:888–901. pmid:16585055
    • View Article
    • PubMed/NCBI
    • Google Scholar
74. 74. Gray-Little B, Hafdahl A. Factors influencing racial comparisons of self-esteem: A quantitative review. Psychological Bulletin. 2000;126:26–54. pmid:10668349
    • View Article
    • PubMed/NCBI
    • Google Scholar
75. 75. Twenge J, Crocker J. race and self-esteem: Meta-analyses comparing Whites, Blacks, Hispanics, Asians, and American Indians and annotate on Gray-Little and Hafdahl (2000). Psychological Message. 2002;128:371–408. pmid:12002695
    • View Article
    • PubMed/NCBI
    • Google Scholar
76. 76. Ennis Due south, Rios-Vargas Yard, Albert N. The Hispanic Population: 2010. United Census Bureau, 2011.
77. 77. Portes A, Truelove C. Making sense of multifariousness: Recent research on Hispanic minorities in the United States. Almanac Review of Sociology. 1987;13:359–85.
    • View Article
    • Google Scholar
78. 78. Steele C, Aronson J. Stereotype threat and the intellectual examination performance of African Americans. Journal of Personality and Social Psychology. 1995;69:797–811. pmid:7473032
    • View Article
    • PubMed/NCBI
    • Google Scholar
79. 79. Hudley C, Graham Southward. Stereotypes of achievement striving among early adolescents. Social Psychology of Instruction. 2001;five:201–24.
    • View Article
    • Google Scholar
80. fourscore. Wingfield AH. Racializing the glass escalator: Reconsidering men's experiences with women'southward piece of work. Gender and Society. 2009;23:five–26.
    • View Article
    • Google Scholar
81. 81. Blau F, Kahn Fifty. Ascent wage inequality and the U.S. gender gap. American Economic Review. 1994;84(ii):23–8.
    • View Article
    • Google Scholar
82. 82. Cotter D, Hermsen J, Vanneman R. Systems of gender, race and class inequality: Multilevel analyses. Social Forces. 1999;78:433–sixty.
    • View Article
    • Google Scholar
83. 83. Cicchetti D. Developmental psychopathology: Reactions, reflections, projections. Developmental Review. 1993;thirteen:471–502.
    • View Commodity
    • Google Scholar
84. 84. Lanza Southward, Rhoades B, Zero R, Greenberg M. Modeling the interplay of multilevel risk factors for futurity academic and behavior problems: A person-centered approach. Developmental Psychopathology. 2010;22:313–35.
    • View Article
    • Google Scholar
85. 85. Duncan K, Brooks-Gunn J, Klebanov P. Economic deprivation and early childhood development. Kid Development. 1994;65:296–318. pmid:7516849
    • View Article
    • PubMed/NCBI
    • Google Scholar
86. 86. Gutman 50, Sameroff A, Cole R. Academic growth bend trajectories from 1st grade to 12th grade: effects of multiple social risk factors and preschool child factors. Developmental Psychology. 2003;39:777–ninety. pmid:12859129
    • View Commodity
    • PubMed/NCBI
    • Google Scholar
87. 87. Hallinan M. Sociological perspectives on black-white inequalities in American schooling. Sociology of Education. 2001;74:fifty–seventy.
    • View Article
    • Google Scholar
88. 88. Rouse C, Brooks-Gunn J, McLanahan Due south. School Readiness: Closing racial and ethnic gaps. Washington, DC: Brookings Press, 2005.

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