Economically Disadvantaged Children’s Transitions Into Elementary School: Linking Family Processes, School Contexts, and Educational Policy (2024)

Economic Disadvantage, Children, and Family Life

Economically disadvantaged children enter school with less developed cognitive skills than their peers and then make lower grades and test scores, take lower level course work, and ultimately obtain fewer degrees (; ; ; ). Thus, economic disadvantage can derail the trajectories of educational attainment on which long-term socioeconomic attainment is predicated. Many structural mechanisms for these patterns have been identified (Arum, 2000; ; ). Developmentalists have elucidated how the risks of economic disadvantage are also rooted in interpersonal contexts (McLoyd, 1998). Such research is exemplified by the family socialization (or process) model, which theorizes that the effects of economic stratification are filtered through family dynamics. In short, the stresses of economic hardship disrupt parents’ lives in ways that alter the organization of the home, family relations, and the psychological well-being of family members. Such changes are then manifested in child maladjustment (Elder, 1974; McLoyd, 1998). Although occasionally criticized as blaming poor parents for children’s troubles, proponents of the family socialization model have argued that it captures how parents and children are both victims of economic stratification (Huston, 1991).

Traditionally, the family socialization model has focused on interpersonal processes. For example, research on a variety of data sources has indicated that maternal sensitivity and parental discipline are powerful mediators of income effects on early childhood outcomes (; ). Moreover, parental management of education and marital/partner interactions (e.g., conflict) also appear to be mediators for academic outcomes (; Conger et al., 2002; Raver et al., 2007). Over time, family member adjustment has become central to the model. For example, a constellation of psychological responses to economic hardship, such as depression and stress, alters parents’ organization of their own and their children’s lives (; Mistry et al., 2002). The corresponding psychological conditions of economically disadvantaged children have not been consistently incorporated into the family socialization model as mediators because they typically serve as outcomes. Yet, because of research documenting that psychosocial problems can disrupt learning (Crosnoe, 2006; ), the social and emotional adjustment of children might be considered a mediator of economic effects on academic outcomes just as their parents’ adjustment is.

According to recent research, this family socialization model is especially relevant to the transition into elementary school (; Raver et al., 2007). Family dynamics are most implicated in academic disparities during this period because, as children move through school, the formal (e.g., curriculum) and informal (e.g., peer influences) processes of education account for increasingly larger shares of such disparities. Moreover, because the educational system is cumulative, small group differences tend to widen as initial advantages select children into better opportunities to learn over time (; Farkas, 1996; ). Thus, the transition into school is when family dynamics related to economic disadvantage likely have a pronounced impact on learning in ways that forecast long-term disparities. As such, it may be a critical intervention point.

A Family Socialization Model of Early Education

Building on past research, this study reconfigures and expands the family socialization model as it is applied to early education. The first expansion, alluded to above, involves the consideration of child adjustment as part of the set of potential mediators linking economic disadvantage and learning in elementary school.¹ A second change concerns the conceptualization of economic disadvantage. Typically, it is captured by income, with other social and economic statuses (e.g., parent education, family structure) controlled to isolate income effects (Duncan et al., 1998). Yet, isolating income may be counterproductive if what is really disadvantaging children is the accumulation of risks, not any one risk (McLanahan, 2004). In this spirit, we replicate research () based on the theoretical work of Elder by viewing disadvantage as the clustering of social and economic risks, including income poverty, single-parent structure, teen fertility, low parent education, and welfare receipt.

Yet another expansion of the family socialization model in this study is the treatment of selection as a conceptual issue, not just a methodological problem. Specifically, the positions of parents in larger structures and institutions of society tend to shape their parenting behavior while also organizing their children’s opportunities to learn (Duncan et al., 2004). Theoretically grounded, empirically supported examples include race/ethnicity, neighborhood settings, migration and employment histories, and job characteristics, as well as the more immediately felt consequences of global socioeconomic statuses, such as food insecurity (Crosnoe, 2006; Gershoff et al., 2007; Mayer, 1997). At the same time, children’s traits and early experiences that support or disrupt learning over time may also elicit different kinds of parenting. Children’s experiences in early child care and their cognitive and personality characteristics (many of which are genetically inherited) likely play such roles (). Thus, we build on the family socialization model by contextualizing its paths within the transactions among structural, institutional, interpersonal, and intraindividual systems in the developmental ecology of early childhood. By allowing for better understanding of what the “effects” in the family socialization model may be, this expansion improves the potential of this conceptual model to inform policy.

A final expansion of the family socialization model concerns what, from an educational standpoint, is one of its biggest weaknesses: the absence of schools. The school is the primary extrafamilial context of childhood, and economically disadvantaged and advantaged youth differ in the quality of their schools and in the benefits they derive from school quality (; ). Furthermore, compared to families, schools are less difficult to change on the large scale and efforts to do so are less controversial (Coleman, 1990; Huston, 1991). From a policy perspective, then, incorporating schools into the family socialization model adds value. Consequently, we connect this model to education research and increase its policy relevance by considering whether school resources can buffer against the learning risks associated with the family disruptions of economic disadvantage.

Of course, a multitude of school and classroom factors could be conceptualized as buffers in this expanded family socialization model. We focus on teachers’ qualifications (e.g., training, credentials) and classroom practices (e.g., instructional foci) in part because they have been empirically related to achievement disparities and are frequent targets of educational policy (Darling-Hammond, 2006). Theoretically, such teacher characteristics also align with the family socialization model’s emphasis on parents because they tap resources (and risks) children can accrue through their connections to adult figures at school. Specifically, following the functional substitution idea that resources in one context will make more of a difference to individuals who lack similar resources in other contexts (), economically disadvantaged children who have less social capital, cognitive stimulation, and stability at home (relative to peers) may benefit more when they can tap such resources at school.

Evidence from national and localized investigations documents that teacher experience, advanced credentials, and education factor into student learning (; E. Fuller, 1998; ; ; Wenglinsky, 2000). Other studies report more limited effects of teacher qualifications relative to teacher attitudes and teaching styles (Hanushek, 1997; ). On the whole, a better trained, more experienced teaching staff focusing on academic skills seems to be part of building a school context with a storehouse of information and knowledge, a consistent curriculum, and a positive environment (; ; ). Unfortunately, schools serving economically disadvantaged youth tend to have teaching staffs with fewer credentials, fewer in-subject degrees, and shorter tenures than do other schools, and teachers in these schools tend to spend less time on academic instruction (Clotfelter et al., 2007; ; ). Indeed, these very disparities motivated the highly-qualified-teacher provision of No Child Left Behind.

These teacher-related dimensions of school context, therefore, are hypothesized to buffer against some academic risks related to family life that young economically disadvantaged children face. Teachers cannot change how families are hurt by economic disadvantage, but they may protect child learning from associated risks during a critical window. Yet, just as family mediation should be conceptualized as part of a system of selection processes, school moderation must be embedded in an array of institutional, social, and individual forces that help to match children to teachers. In addition to the selection factors described above for the family piece of the conceptual model, the location, structure, and composition of economically disadvantaged children’s schools affect the kinds of teachers they have as well as their general opportunities to learn (Darling-Hammond, 2006; ; ).

The Present Study

The basic structure of the family socialization model, therefore, can be enriched by expanding the conceptualization of economic disadvantage and family mediation and then incorporating selection processes and school context more fully into this conceptualization. Following this argument, the three research questions of this study are:

• Do parenting behaviors, family relations, parent adjustment, and child adjustment mediate the link between parents’ general level of economic disadvantage and children’s learning early in elementary school?

• To what extent is this family-based mediation a proxy for the impact of related parental statuses, family circ*mstances, and children’s traits and experiences on the early learning of economically disadvantaged children?

• Can the qualifications and classroom practices of elementary school teachers condition family-based mediation of economic disadvantage above and beyond the forces selecting disadvantaged children into different kinds of classrooms in schools?

Data and Sample

Run by the National Center for Education Statistics (NCES), the ECLS-K is a study of a nationally representative sample of American kindergartners, created through a multistage sampling frame—the selection of 100 primary sampling units (typically counties), then 1,000 schools, and then 22,782 students. All students were in kindergarten at the first wave (fall 1998), with subsequent waves in the spring of 1999, fall of 1999, and the spring of 2000, 2002, and 2004. Data collection consisted of interviews with parents and school personnel and diagnostic tests for children (http://nces.ed.gov/ecls). We retained the full 2002 data release (N = 17,401) in order to compare the focal kindergarten/ first-grade period to the end of primary grades. Such a comparison allowed us to assess the potential relative fade, noted earlier, of family dynamics in economic disparities as children move through the educational system (Alexander et al., 2007) as well as the potential decay of teacher effects after children leave a classroom (Rothstein, 2008). Longitudinal sample weights were employed to account for differential attrition across waves.

Measures

Plan of Analyses

The analytical plan included a series of models with the kindergarten/ first-grade math/reading change scores as outcomes. The use of change scores was motivated by two factors. First, children’s academic skills and behaviors can elicit parenting (Duncan et al., 2004). As a result, we needed to measure child factors before (or at the same time as) family factors. Unfortunately, ECLS-K child assessments began in kindergarten. Thus, the best alternative to studying the transition into school was to examine what happened to children in the first 2 years of school, while holding constant their starting level. In other words, did family or school processes exacerbate extant economic disparities in child learning after elementary school had begun? Second, results were identical for change score models and lagged models (predicting the first-grade test scores controlling for the kindergarten scores). We chose the former because the interpretation of the outcome— point increases on the tests from year to year—is more straightforward for readers of diverse methodological backgrounds.

For the first research question, the math/reading change scores were regressed on economic disadvantage and, to calibrate from what starting level over-time change occurred, the corresponding kindergarten test scores. Next, the three sets of family socialization factors were added to the model. Following Baron and Kenny (1986), we assessed mediation by determining whether the family factors were predicted by economic disadvantage, whether they predicted the outcomes, and whether their inclusion attenuated the economic disadvantage coefficient, with the significance of this attenuation gauged by a test of indirect effects (Sobel, 1982).

These models were estimated with the mixed procedure, the SAS version of multilevel modeling (Singer, 1998). Because all predictor variables at this point were individual level, they were entered on Level 1. Still, multilevel modeling was useful because it corrected the school-based clustering of the ECLS-K to produce robust standard errors. In these models, weights were employed to account for the bias of differential attrition and planned oversamples. To reduce additivity violations (e.g., when alternate samples are underrepresented) that undermine causal inference, we employed the multiple imputation procedure in SAS to estimate all item-level missing data, thereby maintaining the large, heterogeneous longitudinal sample and avoiding the statistical bias of listwise deletion (Allison, 2001). This procedure used full information from available data to create multiple data sets in which missing values were replaced with different equally plausible values.⁴ Results from the mixed models were generated from each data set and then combined across data sets to produce valid statistical inferences for parameters of interest.

For the second research question, the child and family controls were added to the models as predictors of the math/reading change scores in an attempt to account for observable selection processes. Of course, some hypothesized selection processes (e.g., neighborhood conditions, genetic effects) could not be studied in the ECLS-K, and we acknowledge that other selection processes may have been omitted from our conceptual model. These unobserved confounds posed clear threats to tests of the conceptual model. To explore these threats, we drew on robustness indices. Rather than controlling for the impact of unobserved confounds on a focal association, the impact threshold for confounding variables (ITCV) index quantifies just how powerfully an unknown confound would have to be correlated with both variables in that association for its control to wash out the association. The ITCV equation is r_xy − r^#_xy / 1 − r^#_xy, where r^#_xy = t / SQRT[(n − q − 1) + t²], t is the critical t value, n is the sample size, and q refers to the number of model parameters (excluding the intercept). This equation can be extended to models with multiple covariates by ITCV_{with covariates} = ITCV_{no covariates} × [SQRT (1 − R²_xg)(1 − R²_yg)], where g is the set of covariates, R²_xg is the R² value from a regression predicting the focal independent variable by the covariates, and R²_yg is the R² value from a regression predicting the outcome by the covariates (Frank, 2000). The ITCV represents the minimum product of the correlation between the predictor and confound and the correlation between the outcome and the confound (r_xc × r_yc) needed to make the focal association from the regression (e.g., family factor → math change score) nonsignificant. Thus, higher ITCV values indicate more confidence that a coefficient in a multivariate model would remain statistically significant even if all unobserved confounds could be observed and entered as controls.

For the third research question, all teacher qualification and classroom practice variables were entered into the models as predictors of the math/reading change scores, with and without the full set of school and classroom controls. Technically, school variables and teacher/classroom variables were on different levels. Yet, results did not differ when two- and three-level models were estimated, and so we entered all school and teacher/classroom variables on Level 2. Next, cross-level interactions were estimated between all significant family mediators of economic disadvantage effects and all of the focal teacher variables.

Table 2

Descriptive Statistics for Selected Variables by Markers of Family Economic Disadvantage in Kindergarten

Economic Disadvantage, Family Disruption, and Math

All three research questions of this study center on the link between economic disadvantage and children’s learning. Thus, we first regressed the kindergarten/first-grade math change score on economic disadvantage and the kindergarten math score. Model 1 in Table 3 presents the results of this weighted multilevel model. For an assessment of effect sizes, we have included standardized β coefficients in the parentheses below the unstandardized b coefficients.

With each marker of disadvantage, children lost just under 1 point of increase on the test between periods (b = −0.81, p < 001). In terms of effect size, every standard deviation increase in disadvantage was associated with just under one tenth of a point difference in test score change (β = −.09). Extrapolating from the b coefficient, the average child with no marker of disadvantage would gain an extra 4 points on the test (about 40% of a standard deviation in the outcome) than the average child with all 5. As a side analysis, we gauged the degree to which these economic disadvantage effects were accounted for by factors besides our family socialization mediators by adding the full set of child and family controls to Model 2. The economic disadvantage coefficient was reduced by about 20%, especially by the control for race/ethnicity. The new coefficient (b = −0.64, β = −.07, p < .001) was smaller but significant.⁵

Of course, the economic disadvantage scale encompasses several family circ*mstances. To unpack its effect, we broke down the scale into its constituent markers (results not shown). All five were inversely associated with math change scores, especially low parent education. Its standardized coefficient doubled the magnitude of all other disadvantage markers. Different combinations of markers were also assessed to determine which configuration best captured the effect size for the full economic disadvantage scale in Model 2. These ancillary analyses revealed that summing scores for poverty status, low maternal education, and teen parenthood effectively created the general economic disadvantage coefficient.

With the magnitude of this association between economic disadvantage and child learning established, we turned to the first research question of this study, which concerned the potential for family socialization factors to mediate this association (Model 3 in Table 3). For the sake of parsimony, only the six significant predictors of the math change score were retained in the final model. Three (parenting stress, child externalizing problems, and child internalizing problems) tapped family members’ adjustment. They were inversely associated with the outcome. The remaining three (organized activities, parental involvement, cognitively stimulating materials) were parenting factors positively associated with the outcome. Of these, internalizing problems (b = −1.48, β = −.07, p < .001) and cognitively stimulating materials (b = 0.78, β = .07, p < .001) had the biggest effect sizes. A 1 standard deviation change in each (0.51 for internalizing, 0.96 for stimulation materials) was associated with a 0.07 change on the math test over time. Child externalizing problems had the next largest effect size, followed by the other three factors.

Although effect sizes gauge the statistical magnitude of the observed impact of some factor, we recognize that they do not necessarily gauge the meaningfulness of this impact. To that end, we can compare the effect sizes of the family socialization main effects to the various items in the economic disadvantage scale, given that the importance of these specific items for children have long been viewed as real public concerns worthy of millions, even billions, of dollars of federal and state investment (). The effect sizes for child internalizing problems, child externalizing problems, and cognitively stimulating materials exceeded the effect sizes for all markers of economic disadvantage except for low parent education. In other words, these family socialization effect sizes—which controlled for economic disadvantage—exceeded those for low-income status (one of the major disparities targeted by No Child Left Behind) and single-parent family structure (the focus of the federal government’s Healthy Marriage Initiative). The other family socialization factors had smaller effect sizes than teen parenthood but essentially the same effect size as low-income status and single-parent family structure. Thus, to the extent that something like income is accepted as having a meaningfully large association with child learning, then the corresponding associations for the family socialization factors should be viewed as meaningful too.

As for mediation, the six significant family factors attenuated the association between economic disadvantage and the math change score by 42% relative to Model 2 and 54% relative to Model 1. Moreover, ancillary analyses revealed that each accounted for some attenuation in the association between economic disadvantage and the math change score, when entered separately, and was itself significantly predicted by economic disadvantage and passed the Sobel test. Thus, the six family socialization factors all met the basic criteria for mediation.⁶

Turning to the second research question of this study, which concerned the robustness of these family socialization patterns in the face of selection processes, we estimated the family mediation model just described with (Model 3) and without (not shown) the full set of child and family controls. Comparing the results indicated that these child and family factors accounted for about 2% of the observed associations with the outcome for child internalizing problems and school-based parental involvement and 9% of the association for cognitively stimulating materials. The other family socialization factors remained stable or slightly increased when the additional child and family factors were controlled.

To explore the selection processes that could not be measured in the ECLS-K, we calculated the ITCV for all significant family mediators in Model 3. The highest ITCV (.10) was for cognitively stimulating materials. Assuming that the two focal correlations (Materials × Unknown Confound, Math Change Score × Unknown Confound) were equivalent, each correlation would have to exceed .32 (absolute value) to produce a confounding effect big enough to reduce the association between cognitively stimulating materials and the math change score in Table 3 to nonsignificance. For comparison’s sake, only a few demographic factors even approached this level of correlation with the math change score, and none of them were correlated with the family factor at the same level. This ITCV boosts confidence that the coefficient for this family factor would be robust if other unobserved confounds could be measured and controlled.

The ITCV was .09 (.30 × .30) for child internalizing problems and school-based parental involvement, .08 (.28 × .28) for organized activities, and .06 (.24 × .24) for child externalizing problems. The lowest ITCV (.04 = .20 × .20) was for parenting stress, indicating that it was the most vulnerable to some unobservable confound. Still, given that no variable examined in the ECLS-K was correlated with both parenting stress and the math change score at this magnitude, the level of robustness for even this family factor appeared to be acceptable.

Economic Disadvantage, Family Disruption, and Reading

The steps taken to answer the first and second research questions were repeated for reading. The initial association between family economic disadvantage and the reading change score (b = −1.44, p <.001, in Model 1 in Table 3) indicated that a child with no markers of economic disadvantage would gain, on average, 7-plus more points on the reading test over time than a child with all five markers. This difference equaled two thirds of a standard deviation in the outcome. The effect size (β = −.13) was slightly larger than what was seen in the math model. This association was attenuated (by about 15%)by the control of observed child and family confounds in Model 2, but it persisted and remained highly significant.⁷ Again, when the disadvantage scale was decomposed into its constituent items, all five were significantly and inversely associated with the reading change scores. The associations for poverty status, receipt of public assistance, and low parent education (the marker with the biggest effect size) were at least two times the magnitude of those for teen parenthood and single parenthood. Indeed, the combination of the first three markers accounted for the coefficient for the full scale in Model 2.

For the first research question, Model 3 included six family socialization factors that significantly predicted the reading change score when examined independently and together (as well as the child and family controls). Three tapped family adjustment (parent depression, child externalizing problems, and child internalizing problems) and were inversely associated with the reading change score. Three tapped parenting factors (organized activities, cognitively stimulating materials, rules/routines) and were positively associated with the reading change score. Child externalizing behaviors (b = −1.48, β = −.07, p < .001) and cognitively stimulating materials (b = 0.95, β = .07, p < .001) had the largest effect sizes. A 1 standard deviation change in each (0.64 for externalizing, 0.96 for stimulating materials) was associated with a 0.07 change in reading test scores between kindergarten and first grade. The remaining effect sizes were, in order, internalizing problems, rules and routines, parent depression, and organized activities. The effect sizes for child externalizing problems and cognitively stimulating materials exceeded the effect sizes for all of the constituent items in the economic disadvantage scale, and the effect sizes for the remaining family socialization factors met or exceeded the effect sizes for all of the disadvantage items besides low-income status and low parent education. The inclusion of these six factors attenuated the economic disadvantage coefficient by 28% relative to Model 2 and 39% relative to Model 1. All six factors met the criteria (including the Sobel test) for mediation.

The second research question, comparing the reading results from Model 3 with and without the child and family controls, indicated that the child and family controls accounted for about 10% of the association of parent depression with child learning (3% for child internalizing problems and 12% for child externalizing problems and organized activities). The remaining family socialization factors remained the same or increased slightly when the child and family controls were added to the model. The ITCV was also calculated for the six family socialization mediators. Child externalizing problems and cognitively stimulating materials had the highest values (.12), meaning that an unobserved confound would have to be correlated with both the family predictor and the outcome at absolute values of .35 or higher for its inclusion in Model 3 to reduce the association between predictor and outcome to non-significance. The other family factors had ITCV values between .07 (.26 × .26) and .10 (.32 × .32). These values help to boost confidence, at least somewhat, that the results in Model 3 were robust to threats to causal inference from confounds that could not be or were not measured.

Bringing in the School

Our third research question concerned the potential for teacher qualifications and practices to condition the link between child learning and the family socialization factors that mediated economic disadvantage effects, above and beyond the school and classroom settings of different kinds of teacher recruitment, investment, and practice. In an attempt to answer this question, we added measures tapping various dimensions of teacher qualifications and practices to our comprehensive family socialization model of kindergarten/first-grade math/reading change scores (Table 4) as well as their interactions with the focal family socialization factors.

Net of the child and family controls, as well as the kindergarten test score, economic disadvantage, and the significant family socialization mediators, teacher qualifications and practices did little to predict math/reading change scores (Model 1). Indeed, only mechanics instruction was significantly associated with the math outcome. Four (elementary certification, tenure in grade, tenure in school, whole-language instruction) were associated with the reading outcome.⁸ These patterns did not change when the full set of school and classroom controls were included as covariates (Model 2). With a few exceptions, most school and classroom variables did not predict the outcomes in the full model. Moreover, their inclusion did not affect the pattern of results for the focal family variables. Lastly, calculating the ITCV for the significant teacher factors in these models revealed sufficiently high values (greater than .04) for elementary certification and teacher grade tenure but low values for all other factors (< .01). Thus, the school-based selection processes in our conceptual model did not appear to be all that important.

Testing for interactions between each teacher factor and each significant family socialization factor revealed that the only significant interactions concerned teacher tenure. For math, the only such interaction was between parenting stress and teacher tenure in grade (b = −0.70, ns, for parenting stress; −0.04, ns, for grade tenure; 0.04, p < .05 for Stress × Tenure). For reading, these interactions were

• Parenting Stress × Teacher Grade Tenure (b = −0.96, ns, for parenting stress; − 0.003, ns, for grade tenure; 0.04, p < .05, for interaction)

• Child Internalizing Problems × Teacher Grade Tenure (b = −2.13, p < 05, for internalizing; −0.01, ns, for grade tenure; 0.05, p < .05, for interaction)

• Child Externalizing Problems × Teacher Grade Tenure (b = −1.96, p < .001, for externalizing; −0.01, ns, for grade tenure; 0.05, p < .05, for interaction)

• Cognitively Stimulating Materials at Home × Teacher Grade Tenure (b = 0.68, ns, for materials; −0.03, ns, for grade tenure; 0.04, p < .01, for interaction)

• Cognitively Stimulating Materials at Home × Teacher School Tenure (b = 0.68, ns, for materials; 0.03, ns, for grade tenure; −0.04, p < .05, for interaction)

These interactions were interpreted by calculating predicted math and reading change scores for children in the sample varying on the focal family and teacher factors (1 standard deviation below and above the mean for both), with all other variables in the model held to their sample means. For all teacher grade tenure interactions, doing so revealed evidence of buffering, or protection—the negative association between the family factor and the achievement change scores appeared to decrease in magnitude in the presence of the teacher factor.

To illustrate, Figure 1 graphs the interactions between the child adjustment factors and teacher tenure in grade for reading. Children with externalizing problems had smaller gains in reading across the 2-year span than their peers did, regardless of whether their teachers had experience in their grade level or not, but this gap between high- and low-externalizing children was smaller when their teachers had such experience (a 1.7 point difference in test score gains equaling 13% of a standard deviation in the test change score) than when their teachers had less grade-level experience (a 2.75 point difference equaling 21% of a standard deviation). The same pattern held, with slightly smaller effect sizes, for internalizing problems. The other family risks associated with economic disadvantage—parenting stress (both subjects) and a lack of cognitively stimulating materials at home (reading only)—demonstrated the same buffering pattern of interaction with teacher tenure in grade.

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Figure 1

Change in reading test scores between kindergarten and first grade by child adjustment and teacher grade tenure.

Interestingly, the only other significant Family × Teacher interaction that emerged (Teacher Tenure in School × Cognitively Stimulating Materials for the reading change score) did not fit the buffering pattern. Controlling for teacher tenure in grade, the learning gap between children from families with few cognitively stimulating materials at home (who tended to be economically disadvantaged) and children from families with more cognitively stimulating materials at home was more pronounced when teachers had more experience at a school. This learning gap was less pronounced when teachers had less experience at a school.

Shifting the Time Frame

To examine the degree to which our model was subject to the expected fade in family effects (relative to school) as children progress through the system and the decay in benefits of having a certain teacher after that school year has ended, we reestimated our models for math/reading change scores between first and third grade, measuring the family and school factors in the spring of first grade. Six of the 13 family socialization factors (marital conflict, parental warmth, parent depression, parenting stress, cultural activities, physical discipline) were not considered, as they were not measured after kindergarten. Fortunately, only two had been significant mediators of economic disadvantage effects on kindergarten/first-grade change scores (parenting stress for math, parent depression for reading). All teacher measures were repeated in first grade, as were all of the school controls. We summarize the results of these models here.

Overall, economic disadvantage had stronger effects on first-grade to third-grade change scores than it had on the corresponding kindergarten/first-grade change scores. Yet, the extent to which family socialization factors mediated these effects was diminished in the later time frame. Three factors (cognitive stimulating materials at home, child internalizing problems, and child externalizing problems) mediated the association between economic disadvantage and the math change score, and five (cognitive stimulating materials at home, organized activities, parental involvement at school, child internalizing problems, and child externalizing problems) mediated the association between economic disadvantage and the reading change score. ITCV values for the child adjustment factors, however, revealed scant evidence of robustness. Moreover, the degree of attenuation of the economic disadvantage coefficient by the significant family mediators was low relative to the kindergarten/first-grade period. Lastly, none of the teacher factors interacted with any family mediators to predict the change score in math or reading between first and third grade.

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Robert Crosnoe is an associate professor of sociology and a faculty associate in the Population Research Center at the University of Texas at Austin, 1 University Station, Austin, TX 78712; ude.saxetu.nitsua@eonsorc. A sociologist of education, his work focuses on the connection between young peoples’ general health and development and their educational trajectories and how this connection contributes to socioeconomic and immigration-related inequalities in educational attainment.

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Carey E. Cooper is a postdoctoral research associate at the Center for Research on Child Wellbeing at Princeton University; ude.notecnirp@repoocec. Her work takes ecological and life course approaches to the developmental significance of family poverty, with a specific focus on how policy-amenable factors in school and home settings buffer against the deleterious impact of poverty on young children.

¹Attempts to tease out the ordering of the three main mediators (e.g., parent adjustment linking income to parenting behavior; see Conger et al., 2002; ) have yielded inconsistent, causally indeterminate evidence.

²Children who scored below a set threshold on an oral language test were screened out of reading tests. Math tests were given in Spanish when appropriate. These numbers decreased substantially from year to year. A binary marker for test language status was added as a control.

³If a child had more than one teacher, the National Center for Education Statistics selected a “primary” teacher according to who had primary academic responsibility for the child and could best report the needed information.

⁴On average, 5% to 10% of the sample had missing data on the child, family, or school variables, and 15% to 22% had missing data on teacher variables.

⁵Results for other child and family factors are available from the authors. Predictors of math change scores with significant positive coefficients included being White, having immigrant parents, later timing of assessment, and Southern region. Predictors with significant negative coefficients included female gender, food insecurity, inadequate medical care, and residential instability.

⁶Group-specific side analyses revealed a slight tendency for family mediation to be stronger among White children than among children from other race/ethnic groups.

⁷Few controls predicted the reading change score beyond economic disadvantage. Exceptions included significant positive coefficients for female gender, age, and timing of assessment and significant negative coefficients for food insecurity and inadequate medical care.

⁸See the 2007 report by Barker and Coley for a similar pattern of how family factors trump school and classroom factors in predicting achievement outcomes. See the article by Palardy and Rumberger (2008) for similar weak findings on certain teacher qualifications in ECLS-K.

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