1. Protective Effects of Language Development Among Children in Head Start: A Person-Centered Approach Christine Meng Curriculum and Instruction University of Wisconsin-Madison INTRODUCTION Children in Head Start programs who come from economically disadvantaged families are likely to lag behind in language development when they enter formal schooling (e.g., Brody, Stoneman, & McCoy, 1994). Research has used the variable-centered approach, rather than the person-centered approach, to identify risk factors (e.g., poverty) and protective factors (e.g., preschool program quality) for these children. A study (Booth Kreisman, 2003) that used the person-centered approach identified heterogeneous literacy developmental trajectories among children in Head Start programs, but it was unclear whether family factors, child factors, and Head Start classroom factors would serve as protective factors to differentiate these developmental patterns. Identifying the protective factors that help children develop language skills in Head Start programs is crucial because intervention programs may promote these factors for low-performing children. This study used the person-centered approach to examine (1) Head Start children’s patterns of vocabulary developmental trajectories, and (2) whether family literacy environment, child motivation, and Head Start classroom quality as protective factors could differentiate the patterns of vocabulary developmental trajectories among children in Head Start programs. RESULTS The growth mixture modeling results showed two distinct patterns of language developmental trajectories: a low- performing group and a decrease-increase group whose language skills decreased in the first year in the Head Start programs and then increased thereafter. The multinomial logistic regression results showed that family literacy environment (shared book reading and the amount of reading materials at home) was a significant predictor of the vocabulary class membership. Children with higher levels of persistence were more likely to be in the decrease-increase group. Children had teachers who encouraged independence were more likely to be in the decrease-increase group. Finally, children in a classroom with more social interactions were likely to be in the decrease-increase group. DISCUSSION The purpose of this study was to understand (1) the number of vocabulary class among Head Start children; and (3) whether family literacy environment, children’s motivation and persistence, and classroom process quality would be predictive of the vocabulary class membership. Overall, this study demonstrates a number of significant direct paths between positive parenting and home aggression and social skills. A number of control variables were also significantly related to home aggression, social skills, and school aggression. The results underscored the importance of family literacy environment, child persistence, the teachers’ autonomy support, and social interactions in a classroom. These factors can support the children in Head Start programs to develop vocabulary skills by exposing them with literacy materials at home, teaching them the approaches toward learning, fostering positive teacher-student relationships, and creating social classroom environments. DATASET The data used for this project came from the Head Start Family and Child Experiences Survey (FACES—2003 Cohort). The FACES is a national longitudinal study that examines the cognitive, emotional, social, and physical development of the Head Start children in the United States. The FACES study also includes variables that examine the characteristics and well-being of families, the observed quality of Head Start classrooms, and the characteristics and opinions of Head Start teachers and other program staff. The FACES included a sample of 1,778 children who were three and four years old and their parents in a stratified national probability sample of 43 Head Start programs. The sample was stratified by three variables: region of the country, urbanicity, and percentage of minority families in the program. The following Head Start programs were excluded: Migrant and Seasonal Head Start programs, American Indian/Alaska Native Head Start programs, Early Head Start programs, programs in the United States territories, and programs that do not serve children directly. The children were assessed at the initial entry of the Head Start programs (Fall 2003). They were then assessed twice during the Head Start programs (Spring 2004 and Spring 2005). The children were last assessed during their first year in the kindergartens (Spring 2005/2006). REFERENCES Arnett, J. (1989). Caregivers in day-care centers: Does training matter? Journal of Applied Developmental Psychology, 10, 541-552. Booth Kreisman, M. (2003). Evaluating academic outcomes of head start: An application of general growth mixture modeling. Early Childhood Research Quarterly, 18, 238-254. doi:10.1016/s0885-2006(03)00027-9 Brody, G. H., Stoneman, Z., & McCoy, J. K. (1994). Contributions of protective and risk factors to literacy and socioemotional competency in former Head Start children attending kindergarten. Early Childhood Research Quarterly, 9, 407-425. doi:10.1016/0885-2006(94)90017-5 Dunn, L. M., & Dunn, L. M. (1997). Peabody Picture Vocabulary Test, Third Edition. Examiner’s manual and norms booklet. Circle Pines, MN: American Guidance Service. Harms, T., Clifford, R., & Cryer, D. (1998). Early childhood environment rating scale. New York: Teachers College Press. McDermott, P. A., Green, L. F., Francis, J. M., & Stott, D. H. (2000). Preschool Learning Behaviors Scale. Philadelphia: Edumetric and Clinical Science. Data Sources *The Office of Planning, Research and Evaluation, the Administration for Children and Families, the U.S. Department of Health and Human Services *Child Care & Early Education Research Connections ABSTRACT This study examined whether the family literacy environment, children’s characteristics, and classroom environment would function as protective factors against the negative effect of poverty on language development among Head Start children. Growth mixture modeling was used to address the research questions. The results showed two classes of vocabulary development: a high-performing class and a low-performing class. Results showed children with parents who frequently read to them and provided reading materials at home were more likely to belong to the resilient group. Children with higher levels of persistence and had teachers who encouraged independence were more likely to be in the resilient group. Finally, children in a classroom with more social interactions were likely to be in the resilient group. METHOD SAMPLE The sample included a total of 2,612 children and their parents or the primary caregivers (mean annual family income = $16,457.75, SD = 12,396.828). The family structure was diverse (48% two-parent household, 47% mother-only single-parent household, 2% father-only single-parent household, and 3% had neither a mother nor a father as the head of the household). The children’s mean age at program entry was four years old (49% male, 51% female; 29% Caucasian, 31% African American, 33% Hispanic, 7% others). The mothers’ mean age in fall 2003 was 37.63 years old (SD = 6.24) with 67% having more than high school education. The fathers’ mean age in fall 2003 was 40.53 years old (SD = 7.38) with 64% having more than high school education. The parents had diverse race and ethnicity backgrounds (mothers: 34% Caucasian, 30% African American, 32% Hispanics, and 4% others; fathers: 30% Caucasian, 34% African American, 31% Hispanics, 5% others). MEASURES The Peabody Picture Vocabulary Test (PPVT-III), the English vocabulary test, was used to assess children’s vocabulary for the four measurement occasions (Dunn & Dunn, 1997). The FACES 2003 adapted a shortened version of the PPVT-III based on Item Response Theory (IRT) analyses. The shortened version had 48 questions. Each question asked the children to look at a series of pictures. The children were asked to point to the picture that corresponded to the word. The questions became increasingly difficult. The reliabilities of the shortened version of the PPVT-III ranged between.84 and.91 across the four measurement occasions. Home literacy environment was measured by shared book reading and the amount of reading materials at home. Shared book reading in fall 2003 was measured by parental report on the frequency of reading to their child in the past week. The rating scale ranged from 0 (not at all in last week) to 7 (daily). Parents also reported the amount of reading materials at home in fall 2013. Parents were asked whether they had the following reading materials at home: comic books, magazines for children, magazines for adults, newspapers, catalogs, religious books, and dictionaries or encyclopedias. The parents responded the seven questions with yes (coded as 1) and no (coded as 0). The questions were summed, so that higher scores represented more print exposure at home. The reliability for the amount of reading materials at home was.53. The teacher-student relationship in fall 2003 was measured by the Arnett Caregiver Interaction Scale to assess five areas of teacher behavior: sensitivity, harshness, detachment, permissiveness, and independence with 30 items (Arnett, 1989). Classroom environment was measured by the revised Early Childhood Environment Rating Scale (ECERS-R) in fall 2003 (Harms, Clifford, & Cryer, 1998). Process-related classroom quality was rated by an independent observer with 37 questions on the scale of 1 (inadequate) to 7 (excellent). The subscales of the ECERS-R in the areas of language skills (e.g., providing books) and social skills (e.g., staff-child interactions) were used. Children’s motivation in fall 2003 was measured by the subscales of the Preschool Learning Behavior Scale, competence motivation and persistence (McDermott, Green, Francis, & Stott, 2000). Child gender, family poverty status, mother education, child ethnicity, and mother marital status were included as control variables. ANALYSIS The analyses involved two steps. First, this study used growth mixture modeling (GMM) implemented in Mplus version 6.12 to statistically identify latent trajectory classes of vocabulary development. The intercept, the linear slope, and the quadratic slope as the three latent growth factors were free to estimate. To determine the number of classes, unconditional models without predictors and covariates with one to five classes were first estimated. The same process was repeated to estimate conditional models. Second, I performed multinomial logistic regression to test whether the vocabulary class membership would be predicted by family literacy environment, classroom process quality, and children’s learning behaviors while controlling for child gender, poverty, mother education, mother marital status, and child ethnicity.