By tracking longitudinally a sample of American children ( n = 1,097), this study examined the extent to which enrollment in private schools between kindergarten and ninth grade was related to students’ academic, social, psychological, and attainment outcomes at age 15. Results from this investigation revealed that in unadjusted models, children with a history of enrollment in private schools performed better on nearly all outcomes assessed in adolescence. However, by simply controlling for the sociodemographic characteristics that selected children and families into these schools, all of the advantages of private school education were eliminated. There was also no evidence to suggest that low-income children or children enrolled in urban schools benefited more from private school enrollment.

Among the focal points of efforts to reform the public education system in the United States and provide improved schooling experiences for vulnerable children, enrollment in private schools, largely through voucher or tax-credit financing, has been among the most frequently referenced as well as contentious (e.g., Dynarski, 2016; Lubienski & Lubienski, 2013; Urquiola, 2016). Policies and financing schemes that encourage enrollment in private schooling have been justified on the basis of increasing choice for low-income parents that cannot relocate to more affluent and better school districts, as a means of increasing pressure on public schools to compete in a market for parents’ selection, and as a remedy for the achievement gap, presumably because of the superior capacity of private schools to educate (poor) students. Many of these same arguments are made in support of expanded charter schools, which bear similarities with private school programs (greater flexibility in hiring or curricula) but tend to be subject to greater oversight, with most charters considered public schools in some form (Carpenter, Keith, & Catt, 2016; Levchenko & Haidoura, 2016; Mills & Wolf, 2017).

At the core of these justifications for reforms that utilize schools outside of the typical K–12 governance and operational structures is the assumption that private schools are more effective in educating students, producing higher levels of achievement, fostering positive social adjustment and citizenship, and decreasing risky behavior (Dynarski, 2016; Flanders & DeAngelis, 2017; Levchenko & Haidoura, 2016; Lubienski & Lubienski, 2013). If true, these features could be leveraged through policies that enable access, such as voucher systems coupled with parental choice, but ultimately, any such policy rests on the presumption that private schools perform better with respect to fostering students’ learning and development. In the present study, we take advantage of a unique longitudinal study of a large and diverse sample of children to examine the extent to which enrollment in private schools is predictive of achievement and social and personal outcomes at age 15 for students enrolled in the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SECCYD; NICHD Early Child Care Research Network [ECCRN], 2001). The SECCYD data set has been used to document longitudinal patterns in exposure to learning opportunities in classrooms and their impacts on student achievement (Pianta, Belsky, Vandergrift, Houts, & Morrison, 2008) as well as associations between experiences in child care prior to school entry and high school performance outcomes (Vandell, Burchinal, & Pierce, 2016). Because of its longitudinal nature and assessment of a broad range of outcomes, the use of the SECCYD data set in this study may add a unique and relevant perspective to consideration of recent federal policy initiatives to increase financial support through vouchers and tax credits for families to enroll their children in private schools.

The Current Study In the present study, we take advantage of a unique longitudinal study of a large and diverse sample of children to examine the extent to which enrollment in private schools is predictive of achievement and social and personal outcomes at age 15 for students enrolled in the NICHD SECCYD. The SECCYD offers a unique opportunity to examine private schooling effects with its comprehensive assessment of student outcomes at multiple intervals, a timeframe that is much longer than typical evaluations of private schooling, and the detailed and wide-ranging assessments of family background and contextual processes that can help in estimating (and reducing) selection bias, as has been done in the study’s evaluation of other policy-relevant factors, such as enrollment in child care or teacher quality (e.g., Belsky et al., 2007; Pianta et al., 2008; Vandell, Belsky, Burchinal, Steinberg, & Vandergrift, 2010; Vandell et al., 2016). And the SECCYD sample reflects a broad range of economic conditions, cultural beliefs, and childrearing practices, such as is the case in the United States. In sum, this study aims to evaluate the benefits of private school enrollment on a comprehensive set of student outcomes assessed in adolescence. We also consider the extent to which the the benefits of private school enrollment vary for children across the income distribution and children in urban and rural communities.

Method Participants The NICHD SECCYD is a multisite research project originally designed to determine the benefits of early child care on children’s development. Participants were recruited in 1991 from designated community hospitals at 10 university-based data collection sites: (1) Little Rock, Arkansas; (2) Irvine, California; (3) Lawrence, Kansas; (4) Boston, Massachusetts; (5) Philadelphia and (6) Pittsburgh, Pennsylvania; (7) Charlottesville, Virginia; (8) Seattle, Washington; (9) Hickory and Morganton, North Carolina; and (10) Madison, Wisconsin. Recruitment and selection procedures are described in detail (NICHD ECCRN, 2001), and study procedures are described on the study website (http://secc.rti.org). Children were followed from birth to 15 years with a common study protocol, including interview and home, school, and neighborhood observations that occurred on a yearly basis. For all study data collection protocols, human subjects institutional review boards at each university and the data coordinating center approved voluntary, written informed consents from participating families. Healthy newborns, discharged within one week of birth, of English-speaking mothers were recruited. When the target child was 2 weeks old, attempts were made to contact 3,015 families who met eligibility criteria to enlist their participation. Attempts to contact were unsuccessful for 512 families, and 151 families were deemed ineligible because the child remained in the hospital more than seven days or the family planned to move. An additional 641 families refused to participate, and 1-month interviews could not be scheduled for 185 families for other reasons. Out of 1,526 families scheduled, 1,364 families actually completed the 1-month home visit and became study participants. The resulting sample included (nonexclusively) 24% children of color, 15% single mothers, and 10% mothers without a high school diploma. At the 1-month home visit, mothers had an average of 14.23 years of education, and the average family income was 2.86 times the poverty threshold. There were no significant differences between these 1,364 families and the U.S. population (U.S. Census Bureau, 1990) based on ethnicity (80.3% White in U.S. population vs. 80.4% in cohort) and household income (household income information available on 1,271 families; $36,520 in U.S. population and $37,781 in cohort). However, the NICHD SECCYD cohort (missing marital status for 2 mothers) had a slightly higher percentage of parents who were married than the U.S. population (76.7% vs. 74.2%, p = .04). Of the original 1,364 study participants, 1,226 participated in Phase II (through first grade; 1995–1999), 1,061 participated in Phase III (through sixth grade; 2000–2004), and 1,009 participated in Phase IV of the study (through ninth grade; 2005–2007). And of the 1,364 children who were originally enrolled into the study, we: (a) excluded 207 children who had no record of school type between kindergarten and ninth grade and (b) 60 children who were ever homeschooled or ever enrolled in a public charter school. For sample descriptives for the 1,097 study children who made up our analytic sample, see Table 1. Table 1 Sample Descriptives for All Students and Separated for Students Who Ever Attended a Private School Versus Those Who Only Experienced Public School Education Between Kindergarten and Ninth Grade View larger version Measures and Procedures School sector Through administrative archives from schools, we had access to students’ school enrollment records for each year between kindergarten and ninth grade (all 10 sites had public school kindergarten). These school records were used to measure students’exposure to public and private school education in two different ways. Before discussing these two measures, it is important to note that for the purposes of the current investigation, children were allowed to have missing data on the school type variable over time in order to maximize the focal predictor (i.e., children were included if they had missing data at later waves on school type). On average, children had information on school sector available for 8.81 years (SD = 2.25) of the 10 years of study participation, and approximately 90% of children had at least six years of school type data. Roughly 7% to 17% of children had missing data for these measures at any given wave (7%, 13%, 17%, 7%, 9%, 12%, 12%, 11%, 13%, and 16% had missing data on school type in kindergarten and first, second, third, fourth, fifth, sixth, seventh, eighth, and ninth grades, respectively). However, as discussed earlier, because of our sample inclusion requirements, all 1,097 children had at least one wave of data on school type. With the aforementioned information in mind, we first measured any exposure to private school education (0 = no, 1 = yes), which captured in binary form whether participants ever attended a private school during their first 10 years of formal schooling. Second, we measured the number of years participants attended a private school. To create this indicator of years, we multiplied the proportion of waves children experienced private school by 10 (i.e., the years of data collection between kindergarten and ninth grade). As a precaution, we also estimated models that included an analytic weight that contained the number of years of data for which children had school type data. In doing so, children who had more data points received greater weight than children who had fewer data points (results discussed in more detail in the following). Ninth-grade outcomes Adolescents’ school performance and functioning in ninth grade was based on a variety of benchmarks collected through direct assessments, administrative records, self-report, and/or parent report (see Table 2 for descriptives). School records were pulled at the end of the year, and direct assessments and self- and parent reports generally occurred during the spring of the school year or right after the completion of ninth grade. Table 2 Age 15 Outcomes for All Students and Separated for Students Who Ever Attended a Private School Versus Those Who Only Experienced Public School Education Between Kindergarten and Ninth Grade View larger version Academic achievement and educational aspirations To begin, adolescents’ cognitive skills (Picture Vocabulary, alpha = 0.81), literacy achievement (Passage Comprehension, alpha = 0.81), and math achievement (Applied Problems, alpha = 0.87) were directly assessed with subtests from the Woodcock-Johnson Educational Battery–Revised (Woodcock & Johnson, 1989). Student’s working memory was also directly assessed with the Operation Span Task (OSPAN; Turner & Engle, 1989), which required that they complete a series of arithmetic problems, remember a list of letters, and then do these tasks at the same time. Through administrative records, we also had access to students overall grade point average at the end of ninth grade along with their math coursework (0 = no math course, 1= below Algebra I, 2 = Algebra I, 3 = geometry, 4 = Algebra II, and 5= advanced math) and science coursework (0 = no science coursework, 1 = survey science, 2 = earth science, 3 = biology, 4 = chemistry, 5 = physics, and 6 = advanced science). Students also reported on their mathematics (alpha = 0.84) and literacy self-concepts (alpha = 0.83) using 10 items that were adapted from the Self and Task Perception Questionnaire (1 = not at all good to 7 = very good; Jacobs, Lanza, Osgood, Eccles, & Wigfield, 2002), which captured the cognitive representation students had of themselves (e.g., “How good at SUBJECT are you?” and “How well do you expect to do in SUBJECT this year?”). Finally, students responded to three items that tapped into their educational aspirations, including the likelihood they would complete high school, attend college, and complete college (1 = not sure at all to 5 = very sure). Social behavior In addition to these academic-oriented outcomes, mothers also reported on their children’s externalizing behavior problems (33 items that captured delinquent and aggressive behavior; alpha = .91), internalizing behavior problems (31 items that captured withdrawn, somatic complaints, and anxious/depressed behavior; alpha = 0.86), and social skills (40 items that captured cooperation, assertiveness, responsibility, and self-control; alpha = 0.91) with the Child Behavior Checklist (Achenbach, 1991) and the Social Skills Rating System (Gresham & Elliot, 1990), which were both based on a 3-point Likert scale (0 = never or not true, 2 = very often or very true). Adolescents also used an audio computer-assisted self-interview to respond to a series of questions about risk taking over the course of the past year (0 = never, 2 = never more than once), which captured sexual risk taking (e.g., had sexual intercourse, diagnosed with an STD; 4 items; alpha =.58), other risky behavior (e.g., gotten into fights, drank, smoked; 36 items; alpha = .87), and victimization (e.g., been injured, harassed, mugged; 15 items; alpha = .76). For each measure of risk taking, we top coded the top 2% to 3% of responses to address issues of skew. Finally, students used the Future Outlook Inventory (1 = never to 4 = always; alpha = 0.72; Cauffman & Woolard, 1999) to respond to eight questions that captured their future outlook, which tapped into their ability to consider the longer term consequences and implications for others as a result of their decisions. Covariates To address concerns of omitted variable bias and reduce the possibility of spurious associations, our multivariate models adjust for a large number of covariates. It is important to note that for all time-varying factors, we take the average of children’s and families’ experiences during early childhood when children were 6, 15, 24, 36, and 54 months of age (unless otherwise noted). We discuss these covariates in blocks, but in general, these covariates reflect the measurement of a large and substantial set of factors implicated in possible selection bias relative to estimating private school effects. Family characteristics Our first block of covariates taps into families’ capacity and resources, namely: mothers’ age and years of education at birth of child, mothers’ psychological adjustment at 6 months of age (measured with the NEO Personality Inventory; Costa & McCrae, 1985), mothers’ vocabulary skills at 36 months of age (measured with the Peabody Picture Vocabulary Test–Revised; Dunn & Dunn, 1981), parenting quality (measured with the Home Observation for Measurement of the Environment Scale, Caldwell & Bradley, 1984; and videotaped interactions that captured maternal sensitivity), household income-to-needs ratio, maternal employment, maternal depressive symptoms (measured with the Center for Epidemiological Studies Depression Scale; Radloff, 1977), and an indicator of whether children lived in a two-parent household. Child characteristics Our next block of covariates taps into children’s own characteristics and experiences before kindergarten entry, which are likely correlated both with their later school performance and their parents’ school choice. More specifically, at the child level, our statistical models adjust for: gender, race/ethnicity, birthweight, birth order, and temperament at 36 months of age (measured with the Infant Temperament Questionnaire; Medoff-Cooper, Carey, & McDevitt, 1993). As part of the child-level characteristics, we also adjust for student’s academic achievement, working memory, and social-behavioral functioning as measured at 54 months of age, which is recognized as one of the strongest adjustments for omitted variable bias (NICHD ECCRN & Duncan, 2003). In addition to these characteristics of children themselves, we also control for children’s early education experiences between 6 and 54 months of age, including the proportion of time spent in center care, proportion of time spent in maternal care, and child care quality (as measured with Observational Record of the Caregiving Environment). Neighborhood characteristics Because there is likely to be geographic heterogeneity in private school offerings and access, we also controlled for a rich set of neighborhood characteristics. More specifically, through census data, we had access to block group data for children’s neighborhoods at 1, 15, 36, and 54 months of age, which we averaged together to capture neighborhood characteristics during the early childhood epoch. These variables include the percent of: households in poverty, single-parent households, households receiving government assistance, unemployed adults, adults with less than a high school education, and nonminority adults. As part of the neighborhood characteristics, we also adjusted for site fixed (i.e., Boston, Charlottesville, etc.), which captured the broader differences across communities. Analytic plan All analyses were estimated within the Stata program (StatCorp, 2009). To address missing data, we imputed 50 data sets via chained equations, and our focal research objectives were addressed within an ordinary least squares regression framework. Although two of our outcomes might best be thought of as ordinal categorical variables (i.e., math and science coursework), for simplicity, we specify all outcomes as continuous. As a precaution, however, we estimated parallel ordered logistic regression models for these two outcomes, and all conclusions were the same as those presented in the following (results available from authors). Within this general framework, we estimated a series of regression models in stages to demonstrate the influence of covariate adjustments on the simple bivariate (and likely biased) “effects” of private school enrollment. First, we begin with simple bivariate models (Models 1), which do not adjust for other factors that may be related to children’s school performance and their enrollment in public as compared with private schools. We then estimate a series of models that iteratively include different sets of covariates. In the first set of adjusted models (Models 2), we include family characteristics. In the second set of adjusted models, we add child characteristics and experiences during early childhood (Models 3), and then we include neighborhood characteristics and site fixed effects (Models 4). Although our primary model specification corresponds to Model 4, we test an additional set of models that includes covariates that capture family (parenting quality, income-to-needs ratio, proportion of mothers employed, maternal depression, and two-parent household) and neighborhood characteristics during middle childhood (a composite of first, third, and fifth grades) and adolescence (ninth grade) as a means of providing a more conservative estimate of the associations between school experiences and adolescent outcomes. It is important to note that if private school enrollment is associated with the covariates during the later years (i.e., during middle childhood and adolescence), then their inclusion in the model might result in a biased estimate for private school enrollment. We nonetheless estimate these alternative models given the 10-year window for private school enrollment and to ensure that our findings are robust to various possible specifications. With regard to whether private or public school is superior for certain children, we look at the experiences of lower income students and students in rural versus urban communities. To assess for heterogeneity across the income distribution, we recode our income variable to demarcate children and families who were: (a) at or below 300% of the federal poverty line (FPL) during the early childhood years (mean income-to-needs ratio of 1.76, SD = 0.75) and (b) those whose income-to-needs ratio exceeded 300% of the FPL (mean income-to-needs ratio of 5.65, SD = 2.84). We also tested continuous income by private school enrollment interactions and considered different income groupings (i.e., at or below 225% of the FPL; 225%–450% of the FPL; greater than 450% of the FPL), and in each case, our results were the same as those presented in the following. Finally, to capture heterogeneity as a function of urbanicity, we grouped the 10 study sites into either rural (39% of students) or urban (61% of students) locations. In the following analyses, we present a within-group examination of the benefits of private school enrollment (i.e., we estimate the benefits of private school enrollment within the different groups), and interaction terms were entered into full sample regressions to formally test for the moderation. It is important to note that these models that capture heterogeneity in the associations between private school enrollment and ninth-grade functioning correspond to the Model 4 specification outlined previously and adjust for child, family, and neighborhood factors derived from the early childhood years (but omit income or site, depending on the moderator).

Results We begin with a brief descriptive portrait of private school enrollment rates in the NICHD SECCYD. Then, we present bivariate and multivariate models that illustrate the differences in students’ ninth-grade outcomes as a function of school type. To begin, roughly 14% to 23% of students attended a private school between kindergarten and ninth grade, and 31% attended a private school for at least one year (see Table 3). We also find that across the first 10 years of schooling, on average, students attended private school for 1.75 years (SD = 3.30); among students who ever attended private school, they averaged 5.73 years (SD = 3.59). Table 3 Descriptive Statistics for Private Versus Public School Enrollment View larger version Next, our simple bivariate analyses of the associations between private school enrollment and ninth-grade outcomes, presented in Model 1 of Table 4, demonstrate that without adjusting for any selection or family background factors, students who ever attended a private school performed significantly better in ninth grade compared with students who only attended public schools on 14 of the 19 outcomes of interest, with absolute effect sizes of roughly 15% to 42% of a standard deviation. That is, students who ever attended a private school between kindergarten and ninth grade performed better academically, were more likely to take courses that were more rigorous, were more likely to expect to finish high school and attend (and complete) college, exhibited more optimal social-behavioral skills, and were less likely to engage in risky behaviors during ninth grade. The only exceptions to this general pattern was that adolescents who ever attended a private school did not demonstrate fewer internalizing behavior problems, they were not more (or less) likely to be victimized, nor did they exhibit higher self-concepts. Similar associations emerged when looking at the number of years students experienced private school education (see Model 1 of Table 5), with effect sizes of 8% to 18% of a standard deviation. In total, 24 of the 27 significant main effects of private school education had a p value of .01 or lower (and 15 of the 27 were significant at p < .001). Table 4 Bivariate and Multivariate Results of Ever Attending Private School Versus Public School Education for Students’ Ninth-Grade Outcomes View larger version Table 5 Bivariate and Multivariate Results for Years of Private Schooling Versus Public School Education for Students’ Ninth-Grade Outcomes View larger version Again, these results are not adjusted for the factors that account for families’ capacity or choice to enroll their child in a private school; as such, they are biased to the extent that unmeasured factors tap into selection effects. We present them here as the first step in a sequence of analyses intended to illustrate the confounding role of various factors as they pertain to students’ age 15 outcomes, one of which is enrollment in private school. Having established the bivariate associations between school type and adolescents’ ninth-grade functioning, we next estimated a series of models that iteratively included different blocks of covariates that are regularly implicated in students’ school performance and their likelihood of attending a public or private school (i.e., confounding or selection factors). Results from these adjusted models revealed that after accounting for any number of potential confounds, students who attended private schools did not perform better than their peers who experienced public school education on any of the outcomes of interest (see Models 2, 3, and 4 of Tables 4 and 5). In fact, after accounting for families’ income-to-needs ratio alone, only 1 of the 13 (when looking at the years of private school education) and 3 of the 14 (when looking at whether children ever enrolled in a private school) findings that were significantly different at a bivariate level remained significantly different. Put another way, the apparent “advantages” of private school education discussed previously in the simple bivariate comparisons that were not adjusted for confounds were almost entirely due to the socioeconomic advantages that selected families into these types of schools and were not attributed to private school education itself. And when we included middle childhood and adolescent covariates, we found no differences in our general conclusions (see Model 5 of Tables 4 and 5). Finally, when examining whether private schools are superior for lower income students and students in rural versus urban communities, we found that none of the 152 coefficients was statistically significant (see Table 6). When we entered interaction terms into the full sample, we found that only 1 of the 76 interactions was statistically significant, and therefore, it was not interpreted. That is, there was no evidence of differential “effects” of private school enrollment across different locations or the income distribution. Table 6 Heterogeneity in the Benefits of Private School Enrollment for Students’ Ninth-Grade Outcomes View larger version Robustness Check To ensure that our reported findings were robust, we estimated a series of supplemental models. First, as a means of addressing potential confounds, we estimated propensity score matching models (Rosenbaum & Rubin, 1983). It is important to note that these matching models are designed to be used with dichotomous predictors. Thus, as part of this algorithm, we matched children who never and ever attended private school and used the nearest neighbor method (with four matches) with a caliper of .01, ensuring a sufficient overlap between the two conditions on their propensity scores. Across the 50 imputed data sets, we successfully matched roughly 70% of the 1,097 students. We assessed the quality of the matches in two ways. We first checked that the standardized mean difference (SMD) between groups for all of the covariates were less than 10% of a standard deviation, a benchmark used in the literature to indicate negligible differences (Austin, 2011). We also regressed each of the covariates, individually, on the indicator variable that distinguished children by school sector within the matched samples. Before matching, the average SMD in covariates between students who ever and never attended private school was a little over 25% of a standard deviation, suggesting that children who attended private school were qualitatively different from public school attendees. However, after matching, the average SMD was approximately 3% of a standard deviation, suggesting that balance was achieved (see AppendixTable A1). As noted previously, propensity score matching is designed to be used with dichotomous predictors, meaning that this method could not be applied for our continuous predictor for years of private school enrollment. However, within the matched samples of ever and never attendees, we found that the SMD between the covariates and years of enrollment was also considerably smaller. When regressing the years of private school enrollment on the covariates, we found that the average SMD before matching was 10% of a standard deviation; after matching, the average SMD was only 3% of a standard deviation. Additionally, before matching, over 80% of the covariates were significantly different as a function of private school enrollment; after matching, there were no longer any significant group differences (see AppendixTable A1). Having successfully achieved balance, we ran a second set of regression models within these matched samples. To guard against any remaining bias, our analyses within the matched samples also controlled for all of the early childhood covariates listed in Table 1, which is recognized as doubly robust estimation (for more information on this methodology, see Funk et al., 2011). Results from these analyses within the matched samples confirmed our general conclusions discussed previously (see Model 6 of Tables 4 and 5): Among children who were equally likely to experience a public or private school education, there was no benefit through age 15 of enrollment in a private school. Finally, because not all children had school records available for each wave of data collection, we estimated additional models that included an analytic weight that contained the number of years of data for which children had school type data. In doing so, children who had more data points received greater weight than children who had fewer data points. When weighted in this manner, we found that overall, students attended 1.71 years (SD = 3.23) of private school (vs. 1.75 years in our primary specification), and among students who ever attended a private school, they attended a private school for 5.52 years (SD = 3.57 vs. 5.73 in our primary specification). Despite this minor fluctuation in the mean years of private school enrollment, results from these weighted regressions analyses examining the benefits of such enrollment (net of child, family, and neighborhood covariates) were also the same as those discussed earlier (see Model 7 of Tables 4 and 5).

Appendix Table A1 Sample Descriptives for Students After Propensity Score Matching View larger version

Notes This study was directed by a steering committee and supported by the National Institute of Child Health and Human Development (NICHD) through a cooperative agreement (U10), which calls for scientific collaboration between the grantees and the NICHD staff. Participating investigators, listed in alphabetical order, are: Jay Belsky, Birkbeck University of London; Cathryn Booth-LaForce, University of Washington; Robert Bradley, University of Arkansas, Little Rock; Celia A. Brownell, University of Pittsburgh; Margaret Burchinal, University of North Carolina, Chapel Hill; Susan B. Campbell, University of Pittsburgh; K. Alison Clarke-Stewart, University of California, Irvine; Sarah L. Friedman, CNA Corp., Alexandria, Virginia; Kathyrn Hirsh-Pasek, Temple University; Renate Houts, Research Triangle Institute; Aletha Huston, University of Texas, Austin; Jean F. Kelly, University of Washington; Bonnie Knoke, Research Triangle Institute, Research Triangle, North Carolina; Nancy Marshall, Wellesley College; Kathleen McCartney, Harvard University; Fred Morrison, University of Michigan; Marion O’Brien, University of North Carolina at Greensboro; Margaret Tresch Owen, University of Texas, Dallas; Robert Pianta, University of Virginia; Wendy Robeson, Wellesley College; Susan Spieker, University of Washington; Deborah Lowe Vandell, University of California, Irvine; Marsha Weinraub, Temple University. We express our appreciation to the study coordinators at each site who supervised the data collection, the research assistants who collected the data, and especially the families and child care providers who welcomed us into their homes and workplaces and cooperated willingly with our repeated requests for information.