1. Ecological Determinants of School Readiness: A Longitudinal Study Mark Wade, Dillon Browne, Heather Prime, & Jennifer Jenkins Department of Human Development & Applied Psychology; University of Toronto Introduction: Background/ Rationale: Children develop within multiple layers of context (i.e. family, school, neighbourhood) which interact with one another to influence child outcomes (e.g. Bronfenbrenner, 1979). Many of these child outcomes are associated with one another. For example, academic achievement is both concurrently (e.g. Nelson et al., 2004) and longitudinally (e.g. Masten et al., 2005) associated with internalizing and externalizing problems. One of the strongest predictors of later academic achievement is school-entry reading and language ability (e.g. Duncan et al., 2007). Thus, in order to promote positive child outcomes, it is important to identify multilevel influences on children’s reading and language ability since these school-readiness skills may predict later academic and non-academic (e.g. emotional, behavioural, etc.) outcomes. Identification of these multilevel risk and resiliency factors may provide targets for intervention and policy development. Past Research Numerous factors are related to children’s school readiness skills. Child-level influences include early language skills (Duncan et al., 2007); Family-level influences include stimulation from the home environment (Elardo et al., 1977) and maternal sensitivity (Tamis- LeMonda et al., 2001); Macro-environmental influences include socioeconomic status (SES) (Bradley et al., 2001), maternal education, and ethnicity (Christian et al., 1998). Also, aspects of neighbourhood (e.g. social cohesion and control) are associated with warm, affectionate, and stimulating parenting, and may thus exert indirect effects on children’s language and reading development. Present Study: While the influence of these multilevel factors on language is well documented, their relationship to reading is less well known. Here, we take factors known to be important for language development to examine effects for school readiness in language (i.e. receptive vocabulary) and reading (i.e. print recognition). 1) Examine the direct effect of neighbourhood (assessed during infancy) on reading and language at 3 years. 2) By examining multilevel influences within a single model, we can elucidate the proportion of variability accounted for by individual- level, family-level, and macro-level factors. This will be useful in making policy decisions about where to allocate effort/resources. 3) By understanding how factors at different levels combine to compensate for one another, we can learn more about how children living in similar circumstances are differentially advantaged. For example, how might a child with poor early language skills show good reading at school entry? Also, is there any way a mother with low education can advantage her child? Methods Participants The sample included 501 families from the Kids, Families, and Places (KFP) study - an ongoing longitudinal study taking place in Toronto and Hamilton. Compared to 2006 Census data, study families were similar on family size, income, immigration status, and marital status. Education levels of KFP families were higher than census families. Data on the youngest children from each family are reported. Data were collected at three times (infancy, 18 months and 3 years) with the use of questionnaires, child testing, taped interactions of mothers with children, interviewer observations, and aggregated census data. Measures Canadian-Born Status a. Mother-report of birthplace. Socioeconomic Status a. Composite of household assets and income, reported by the mother. Neighbourhood Quality a. Composite of social cohesion, trust, and control in the neighbourhood, reported by the mother. Maternal Education a. Years of maternal education, reported by the mother. Child Language b. MacArthur CDI was used as a mother-reported measure of children’s expressive language, standardized for sex and age. Maternal Sensitivity b. 5-minute mother-child interactions were coded using the Coding of Attachment-Related Parenting (CARP; Matias, 2006) measure, and the Parchisy (PAR; Deater-Deckard et al., 1997). Early Reading Ability c. Print recognition assessed with Get Ready To Read Receptive Vocabulary c. Standard scores on the PPVT were used. **Note: a = measured during infancy, b = measured at 1.5 years, c = measured at 3 years There are multilevel influences on children’s language and reading ability at 3 years. These effects are independently small, but when examined together account for an appreciable amount of variability. For reading, there are important contingencies between child-, family-, and macro-level variables: (1) only children with adequate language skills and sensitive mothers show gains in reading at age 3; kids without sensitive mom’s do not show such gains regardless of language level. (2) Neighbourhood quality does not affect the kid’s of mothers who are well educated, whereas kids of less educated mom’s may benefit from living in a better neighbourhood. The latter is in line with results from Christian et al. (1998), who showed that children of less educated mother’s had improved academics when engaged in child care. Our results suggest that neighbourhood quality may also predict better reading in these kids. The positive interactive effect of maternal sensitivity and language on children’s reading suggests that these may be appropriate targets for intervention. In general, policy decisions that focus on family- level influences and mother education may prove most effective. Discussion: Results: References: Step 1: Bivariate associations between all predictor and outcome variables were examined using Pearson’s correlation coefficients. Significant predictors were retained and combined into exploratory multiple regression models. Step 2: A number of common, significant predictor variables of both Early Reading and Receptive Vocabulary were identified and selected. These variables were entered hierarchically in to a regression model as follows: Child-level effects Family-level effects Macro-level effects. The final models for reading and language are presented in Table 1 and Table 2. The total variance predicted by each level is presented in Table 3 and Table 4. Step 3: Several contingency (interaction) effects were also explored. Term B SE β p Intercept9.260.410.00 Child Language (Age 1.5 years) 0.540.230.160.02 Maternal Sensitivity (Age 1.5 years) 1.220.330.270.00 Maternal Education-0.240.11-0.170.03 SES0.290.360.070.42 Canadian Born Parent-0.930.54-0.120.09 Neighbourhood Quality1.010.430.180.02 Maternal Sensitivity * Child Language 0.600.260.160.02 Maternal Education * Neighbourhood Quality -0.350.14-0.170.01 Term B SE β p Intercept89.4441.9100.00 Child Language (Age 1.5 years) 2.6461.043.166.012 Maternal Sensitivity (Age 1.5 years) 5.0961.437.247.000 Maternal Education-.394.793-.061.620 SES3.2831.650.164.048 Canadian Born Parent3.7662.563.109.143 Neighbourhood Quality2.9933.402.117.380 Table 1: Final Model for Early Reading AbilityTable 2: Final Model for Receptive Vocabulary Note: Only common, significant predictors of Early Reading Ability and Receptive Vocabulary are included in the final models. Model % of Variance p Child Effects (Language)5.20.001 Family Effects (Mat Sens)7.00.000 Mat Ed + SES Effects2.00.109 CA Born + Neighbourhood Quality 4.20.009 Maternal Sensitivity * Child Language 1.80.043 Maternal Education * Neighbourhood Quality 2.60.014 Model % of Variance p Child Effects (Language)5.40.001 Family Effects (Mat Sens)12.10.000 Mat Ed + SES Effects6.70.000 CA Born + Neighbourhood Quality 4.20.006 Figure 1: Early Reading as a function of Maternal Sensitivity and Child Language Figure 2: Early Reading as a function of Maternal Education and Neighbourhood Quality The final model for Receptive Vocabulary accounted for 28% of the variance, R 2 =.28, F (6, 182) = 12.02, p <.001. Table 3: Variance Predicted by Each Level for Early ReadingTable 4: Variance Predicted by Each Level for Vocabulary The final model for Early Reading ability accounted for 23% of the variance, R 2 =.23, F (8, 192) = 6.82, p <.001. 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