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An Empirical Study of Coexisting Relationships

Between Area-Specific Support and Early Childhood Development

Jianjun Wang

Department of Advanced Educational Studies, California State University, Bakersfield

9001 Stockdale Highway, Bakersfield, CA 93311, Phone: 661-654-3048, Fax: 661-654-2016, jwang@csub.edu

Jamie Henderson

Executive Director, Kern County Children and Families Commission, 2724 L Street

Bakersfield, CA 93301

Judith Harniman 

Assistant Director, Kern County Children and Families Commission, 2724 L Street

Bakersfield, CA 93301

Authors’ Notes

The authors wish to thank the Institutional Review Board of California State University, Bakersfield for approving the human research protocol for this investigation. Appreciation is also extended to the First 5 Kern Commission and its Technical Advisory Committee. The research team received ongoing support from Theresa Martinez and Heather Schreiner. Nonetheless, opinions expressed here are those of the authors, and do not necessarily represent positions of California State University, Bakersfield (CSUB) or Kern County Children and Families Commission (First 5 Kern). Inquiries should be directed to the corresponding author at jwang@csub.edu.

Abstract

Early childhood development is inseparable from healthcare, parental education, and childcare services. Empirical data are gathered in this study from 131 children at age 3 to assess coexisting effects from those area-specific services at 21 preschool communities across Kern County, California. While muscle and bone growth occurs on scales of height and weight to support improvement of motor skills, more dramatic development has been found across communication, problem solving, and personal-social skills within the broad context of environmental interactions that are otherwise not detectable from an isolated investigation of a single service. To examine the well-rounded early childhood development at a county as large as the state of New Jersey, a comprehensive Context, Input, Process, and Product (CIPP) model is employed to identify pertinent variables from the current research literature and reconfirm the relationship through empirical data analyses. The result suggests additional needs of incorporating culture-dependent variables for future investigation.

Keywords: Coexisting Effect, Early Childhood Support, Service Integration, First 5 Kern

An Empirical Study of Coexisting Relationships

Between Area-Specific Support and Early Childhood Development

Long before implementation of federal legislations, such as No Child Left Behind Act of 2001, on child education, California has led the nation in providing subsidized child care and early childhood development for working parents (Arbegast, 2010). The first formal child care centers, referred to as day nurseries, were founded in California in 1913 (see National Research Council and Institute of Medicine, 1995). During World War II, the state government strengthened its social services by sponsoring the State Child Care Center program (Karoly, Reardon, & Cho, 2007). Two additional programs, California State Preschool Program and Aid to Families with Dependent Children Program, were subsequently launched in the cold war era with a clear focus on supporting children from disadvantaged homes (Reese, 1996). After the cold war end, Californians further incorporated broad-based scientific research on brain development to justify needs of state investment in early childhood services (Huttenlocher & Dabholkar, 1997; McLloyd & Wilson, 1991; Nash, 1997). As a result of the long-lasting public campaign, California Children and Families Act of 1998, also known as Proposition 10, was supported by the state voters to fund services for children ages 0-5 with an additional 50 cents-per-pack sales tax on cigarettes and other similar tobacco products.

Bodenhorn and Kelch (2001) recollected, “When California voters passed the Proposition 10 ballot initiative in 1998, they set in motion an unprecedented experiment to reshape how California communities address the needs of very young children and their families” (p. 151). With support from the tax revenue, Proposition 10 stipulated creation of First 5 Commissions to lead the service delivery at both state and county levels. Accordingly, Kern County Children and Families Commission (First 5 Kern) was established in 1998 to fund services in Kern County. While 20% of the tax revenue is retained by the state commission for state initiatives, the remaining 80% are distributed at the county level according to the local birth rate (First 5 California, 2012). Kern County represents an exemplary case for having the highest birth rate across California over the past decade (Wang, 2012). Since its inception, “First 5 [Kern] has allocated more than $100 million to 450 programs sponsored by 240 different organizations throughout Kern County” (Bedell, 2010, p. 1).

By making the county commissions a creative partner, not just a subordinate organization of the state commission, Proposition 10 has strengthened the local control and accountability on the revenue spending, and thus, promoted service access for the youngest children and their families across the state. To track the public accountability, the state commission identified specific focus areas through strategic planning. It was stipulated that “While counties design their programs to fit their specific local needs, they must provide services in each of the following four focus areas: Family Functioning, Child Development, Child Health, [and] Systems of Care” (First 5 California, 2010a, p. 15). A purpose of this investigation is to examine effectiveness of the area-specific support for early childhood development. Because the early years of a child’s life form a foundation for later growth, the system of care across these focus areas is likely to reduce the needs of extra funding for remedial education and welfare programs in the future.

Nonetheless, the recent state budget crisis has deeply eroded foundation of the public service for the local population. In March 2011, the state legislature passed Assembly Bill 99 (AB 99) with an attempt to take $11.7 million from First 5 Kern. This bill was part of the Assembly’s response to a fiscal emergency declared by Governor Brown on January 20, 2011. Prior to this proposed cut, Bodenhorn and Kelch (2001) admitted, “Although the resources of Proposition 10 represent a significant commitment to the early childhood years, they are insufficient to fill all of the existing service gaps” (p. 156). The threat from AB 99 has made it more imperative to examine the impact of First 5 Kern funding. In support of this timely investigation, empirical data have been gathered from 21 preschool communities across Kern County to analyze relationships between indicators of the area-specific service and early childhood development in Fiscal Year 2010-2011.

Research Questions

The Carnegie Corporation’s (1994) Task Force on Meeting the Needs of Young Children found that brain development during the first 5 years of life has been much more vulnerable to environmental influence than was suspected. Since each child is born with intrinsic dispositions that shape the individual experiences on multiple fronts, researchers suggested modeling of child development as a latent construct with manifest indicators (Bandstra, Vogel, Morrow, Xue, & Anthony, 2004). In addition, latent factors can be derived from multiple service areas to describe the coexisting contributions to child growth (Denham, Blair, & Demulder, 2003). Thus, research questions that guide this investigation are:

1. What indicators are pertinent to identification of child growth and its related services according to the current literature?

2. What are the empirical relationships between indicators of the area-specific service and early childhood development?

3. Does the statistical model fit the First 5 Kern database for this investigation?

While Questions 2 and 3 hinge on quantitative data analyses, answers to Question 1 depend on establishment of a theoretical framework to articulate empirical indicators across multiple service areas.

Literature Review

California counties received numerous state and federal grants in the past, but local

communities have never had such a large, area-specific funding on an ongoing basis (Cousineau, Stevens, & Pickering, 2007). As part of the funding requirement, “The Children and Families Act of 1998 [i.e., Proposition 10] mandates the collection of data for the purpose of demonstrating results” (First 5 Kern, 2011, p. 16). In March 2010, the state commission issued “Guidelines for Implementing the California Children and Families Act” to reaffirm “the support of local decision-making and the development of integrated strategies that are determined to be most appropriate for each county” (First 5 California, 2010b, p. 6). In response, First 5 Kern has developed a strategic plan to align the state focus areas with local priorities (Table 1).

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Local creativity was emphasized to address shortfalls of the government funding for child care and development. It was stated in AB 99 that

California is presently experiencing a severe fiscal crisis, which has resulted in funding shortfalls for many services at the state and local levels. Health and human services programs that serve children are among the most seriously affected by this lack of

funding. (p. 1)

Thus, a business as usual approach no longer works, and local innovations are needed to enhance effectiveness of the public spending.

Without a literature-based framework to streamline the knowledge accumulation, local explorations often rely on repeated discoveries of similar solutions through trial-and-error methods. To avoid the disorganized inquiry, John Tukey (1980) advocated confirmatory investigations through statistical modeling. He noted that “Ideas come from previous exploration more often than from lightning strokes. Important questions can demand the most careful planning for confirmatory analysis” (p. 23).  To facilitate new knowledge acquisition, data analyses in this study are guided by the research literature to model empirical results across different areas of early childhood service.

The Context, Input, Process, and Product (CIPP) Model

Over the first decade of the 21st century, First 5 Kern has evolved from a fledgling organization to an agency with strong accountability. According to the 2010 census findings, Kern County’s population has increased 27% over the past decade, making it the fastest growing county in California. Nearly 60% of the local residents live outside of the Bakersfield city boundary, and local communities of various sizes spread over a valley land as large as the state of New Jersey. Due to the important regional services, First 5 Kern has been recognized by the state commission for its leadership in engaging isolated communities, including those hosting migrant farm worker families (First 5 California, 2008). As tobacco consumption decreases, the state funding inevitably declines. Thus, accountability is particularly critical to address the extensive service demands with the shrinking resources.

While it takes a community to set a nurturing environment, parents still hold the responsibility to care for their children (Tomlin, 2007). “Although a consensus exists about the significant role that parents play in a child’s development, there exists neither a singular ‘one size fits all’ approach to parent education that has been promulgated statewide, nor any major local initiatives” (Zepeda & Morales, 2001, p. 5). Since no program is equally effective under any circumstances, the state statutes “emphasize local decision-making, to provide for greater local flexibility in designing delivery systems, and to eliminate duplicative administrative systems” (FindLaw, 2011, p. 1). To support the systematic approach, a context, input, process, and product (CIPP) model is employed to guide this investigation. As Sloane (2008) suggested, “We change the basic research question from what works to what works for whom and in what contexts” (p. 43).

Since new local context evolves from the previous product outcomes, the CIPP paradigm provides a sustainable platform to support the ongoing progress. In addition, the context phase readily incorporates the spirit of local control promoted by the state legislation. Since its adoption in the 1960s, the CIPP model supported evaluation of numerous projects to address needs of various stakeholders (Stufflebeam, 1983; Wang, 2011). Meanwhile, the model itself matured through a process of developing national evaluation standards over the past four decades (Program Evaluation Standards, 2010; Yarbrough, Shulha, Hopson, & Caruthers, 2010). As a proven example, White (1981) has acknowledged that the Context, Input, Process, and Product (CIPP) paradigm represents “the most comprehensive evaluation model” in the field (p. 217).

Contextual Support for Service Access

From the contextual perspective, concerted efforts have been made by First 5 Kern to improve service access for local children, including those of rural communities. As part of the effort on local capacity building, First 5 Kern funded mobile services, such as Kern County Children’s Dental Health Network and Children’s Mobile Immunization Program, to serve children and families in hard-to-reach communities (Wang, 2012). In addition, a support network has been established to assist children of Kern County on health insurance application. In the 2011-12 fiscal year, “99% of Kern County residents have available application assistance within ten miles of their community” (Wang, 2012, p. 14).

Considering parental education as an important attribute, Kern County is ranked among the lowest regions across the United States (Brookings Institution, 2010). At the county seat, Zumbrun (2008) concurred that Bakersfield was ranked as one of the least educated metropolitan areas across the nation. Thus, the application assistance paved the way for healthcare access within the local communities. In Fiscal Year 2010-2011, First 5 Kern funded 21 preschool programs across Kern County to enhance parent education and early childhood education. The site choice is grounded on a balanced consideration of transportation network and population distribution. As a result, “With First 5 Kern funding, greater Bakersfield and Kern County’s rural and mountain communities have local access to services that would otherwise be unavailable” (Henderson, 2011, p. 1).

Input Characteristics

Neural scientists indicated that brain development is adaptive to both deleterious and beneficial experiences that impact early childhood growth (Nelson, 2000; Shonkoff & Phillips, 2000). Hence, supportive and preventive measures are incorporated in the input phase to assist early child development. The earliest supports start from prenatal care. Low birth weight has been linked to prenatal care in the research literature (Rosenberg, 2004). As was cautioned by Lu, Bragonier, Silver, and Bemis-Hey (2000), “Nearly two thirds of neonatal and infant deaths in the United States and in California occur in the low birth weight population” (p. 3).

Bull (2003) added that “Smoking in pregnancy is a serious health risk to mother and baby that is associated with premature birth, low birth weight and respiratory disorders” (p. 661). Similar to prenatal care, “Quitting smoking before pregnancy may significantly reduce the risk of low birth weight in the infant” (Lu et al., 2000, p. 9). In this study, indicators of prenatal care and smoking prevention are incorporated to assess effectiveness of First 5 Kern services at the input phase.

Researchers found that children were at greater risk for growing up poor with inadequate parent education (Duncan, Yeung, & Brooks-Gunn, & Smith, 1998). More specifically, “failure to learn to read adequately is much more likely among poor children, among Non-white children, and among nonnative speakers of English” (Regalado, Goldenberg, & Appel, 2001, p. 4). Since more than 49.2% of the Kern County population has Hispanic or Latino origin, family income and parental education are the pertinent indicators of the input conditions prior to the service delivery.

In contrast to the threats encountered by poor children, children of affluent families could be "pressured but neglected" by their parents (Ansary & Luthar, 2009; Luthar & Latendresse, 2005). Although modern technology has replaced human labor on numerous fronts, parenting effort remains irreplaceable. “New research from Peabody College [Vanderbilt University] and the University of Virginia finds that infants learn little to nothing from popular educational videos and learn the most from face-to-face interactions with their parents and other familiar figures” (Troseth, 2011, p. 9). Unfortunately, shirking of parental duty has gained popularity in affluent families in recent year with more people admiring a “dual income, no kids” (DINK) lifestyle (United Nations Children’s Fund, 2007). The lack of parental commitment could create negligence in the home environment, causing an irrecoverable loss of learning opportunity for children (Lareau, 2002).

Facing the increasing demand on early childhood services, First 5 Kern has developed a vision to help all Kern County children enter school healthy and ready to learn. With its area-specific funding, school readiness is prepared through childcare, health care, and parental support, regardless of the individual differences in socioeconomic status. To reflect the holistic development of children on multiple fronts, First 5 Kern funded well-rounded services that are community-based, family-focused, and culturally appropriate for the youngest population of Kern County to grow physically, mentally, emotionally, and cognitively in a healthy environment.

Features of the Service Process

Through the First 5 Kern funding, the health care service covers enrollment, access, retention, and utilization of health insurance (First 5 Kern, 2011). Regular health screenings are included in the insurance coverage, and this practice has been found to reduce emergency room visits (Lasker, 1997). In addition, Schneiderman, McDaniel, Xie, and Clark (2010) identified “wait too long to see a doctor” as a most difficult access barrier. Thus, timely health checkups turn into a cost-saving indicator in the health care domain.

Another indicator of health service is dental care. Shobo (2002) pointed out, “although dental disease is preventable, dental decay is still the most common and costly oral health problem among children” (p. 1). More importantly, dental checkups do not preclude children at early ages. As Sears (1988) indicated,

Babies should receive their first dental checkup at six months, when their baby teeth start to erupt, … Early checkups enable dentists to evaluate a child's risk of dental disease based on hereditary, medical, and environmental factors; to begin preventive measures such as correct brushing techniques; and to prevent cavities caused by bottle feeding. (p. 32)

Similar to the needs of early health checkups, it is important to promote early childhood development. Armbruster, Lehr, and Osborn (2006) reported that “Learning to read and write can start at home, long before children go to school” (p. 1). To address the need of pre-schooling, First 5 Kern sponsored nursery education for children on a regular basis. Hannon and James (1990) recollected that “Nursery education has traditionally been used in developing specific pre-reading skills” (p. 259). Naturalistic studies also confirmed the linkage between child literacy tuition and parenting skill development (Heath, 1983; Taylor & Dorsey-Gaines, 1988).

In summary, it has been required that “the state and county commissions shall use outcome-based accountability to determine future expenditures” (Proposition 10 Statutes, p. 1). The CIPP model demonstrated the mechanism of justifying improvement in the product phase beyond baseline indicators. While service indicators have been identified from the literature review section, the indicator choice should be extended beyond the context, input, and process perspectives. Child growth outcomes need to be examined to justify the population accountability under the CIPP paradigm.

Method

Weiss (2010) noted that California has established “a comprehensive, integrated system of early childhood development services from the prenatal period to age five” (p. 243). To assess the integrated impact at the product phase, this section recaps indicator identification pertinent to First 5 Kern services, and incorporates outcome measures of early childhood development to examine effectiveness of the co-existing area-specific supports in the local context.

Local Service Indicators

To meet Proposition 10 requirements, effort has been made by First 5 county commissions to track explanatory variables in a Core Data Element (CDE) database. According to Harder+Company Community Research (2004),

CDEs are an important part of data collection for First 5. The CDEs are questions that

form the basis of data collection for the Commission ... The CDEs help the Commission understand the characteristics of clients that are served and also highlight areas of unmet needs in the County. (p. 19)

Under the auspices of Kern County Children and Families Commission, multiple indicators have been incorporated from the CDE data to support examination of the area-specific services. More specifically, children’s healthcare is identified by prompt (1) health checkups and (2) dental care services. Early childcare is reflected by issues of (1) late prenatal care and (2) smoke exposure. Support for those indicators of healthcare and childcare have been examined in the section of Literature Review (e.g., Bull, 2003; Lasker, 1997; Lu et al., 2000; Rosenberg, 2004; Schneiderman et al., 2010; Sears, 1988; Shobo, 2002).

The literature also endorsed parenting effort on strengthening reading literacy in early childhood development (Bartholomew, & Gustafsson, 1997; Furman, 1986; Wolff, 2002). The effort can be directly indicated by the time parents spent on reading books to their children (Armbruster, Lehr, & Osborn, 2006). In addition, it is desired to have parental support for nursery education at a center-based location (Hannon & James, 1990). As Hewes (1997) recalled,

Typical cooperative nursery schools in past years have required car-pooling mothers to work one morning a week, and the doors are locked by lunch time. Many are adapting to the changed environment by permitting “buy out” time or flexibility in participation, providing both half-day nursery school and extended hours, and making other modifications to fit today’s lifestyles. (p. 25)

Accordingly, the CDE instrument incorporated indicators of support from both aspects of home reading and nursery education (see Figure 1).

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With a computer software package of Linear Structure Relation (LISREL) that can handle indicators on dichotomous and/or ordinal scales (see Jöreskog, 1990), the factor of parenting effort is indicated by (1) parent support for regular attendance of nursery schooling by the children and (2) number of reading hours parents had for children in a week.

In examining the relationship between family resources and childcare services, researchers found support from Maslow's theory on the hierarchy of human needs (Nicholas, 2006; Norwood, 2009). While family income is essential to supporting the basic nutrition and body needs (Duncan, Yeung, Brooks-Gunn, & Smith, 1998; Gray, Byrd, & Cossman, 2007), child mental development is strongly link to parental education at the top of Maslow’s hierarchy (see Beatty, 2005; Heath, 1983; Taylor & Dorsey-Gaines, 1988). To cover these hierarchical needs, family resources are described by indicators of annual family income and parental education. The use of indicators has become a general practice to study latent variables (Loehlin, 1992). In fact, “The representation of latent variables based on their relation to observed indicator variables is one of the defining characteristics of SEM [Structural Equation Modeling]” (Garson, 2005, p. 3).

ASQ Measures

In this study, individual performance scores were gathered from an Ages & Stages Questionnaire (ASQ) to assess well-rounded outcomes of child growth. According to Crossley (2012), “Though many screening tools are in use today, few have been as rigorously researched as ASQ” (¶. 4). In particular, its current version (i.e., ASQ-3) has gone through field testing of 15,138 children from all 50 states and several U.S. territories that mirror the U.S. population in terms of race, ethnicity, and socio-economic groups (Assuring Better Child Health & Development, 2012). As Kerstjens et al. (2009) reported,

Amongst the parent-completed questionnaires for young children, the Ages and Stages Questionnaire (ASQ) is currently the most widely used. … It has been translated into Spanish, Korean, Chinese, French, Danish and Norwegian, and several other local translations exist. (p. 444)

In addition, “The original ASQ [in English] has been proven to be reliable and cost-effective with excellent psychometric properties” (Kerstjens et al., 2009, p. 444). In Branson’s (2009) study of the ASQ feasibility, “The majority of childcare providers (89%) demonstrated high agreement between their developmental impression of a child and ASQ results” (p. i). Nicol (2008) concurred that the ASQ measurement “has been shown to be reliable and valid” (p. 20). The instrument is also readable for most parents because “ASQ questions are written at the fourth to sixth grade reading level” (Branson, 2009, p. 43).

With approval from the Institutional Review Board (IRB) of California State University, Bakersfield (CSUB), the ASQ scores were gathered for this investigation during the first two quarters of Fiscal Year 2010-2011. The time choice coincided with the outbreak of California budget crisis that led to the passage of Assembly Bill 99. A total of 131 children were assessed across 21 preschool programs throughout Kern County.

As a screening instrument, “[ASQ] Scores beneath the cutoff points indicate a need for further assessment” (How ASQ Works, 2011). Thus, late assessments of the same type only occurred to those slow children at ages 4 or 5. Early results at ages 1 or 2 seemed too premature to examine school readiness. To strike for balance, this study focuses on the ASQ-3 findings at age 3. The age selection is concurred by rationale of Proposition 10:

It has been determined that a child's first three years are the most critical in brain development, yet these crucial years have inadvertently been neglected. Experiences that

fill the child's first three years have a direct and substantial impact not only on brain

development but on subsequent intellectual, social, emotional, and physical growth. (sec.

2c)

Similar to the CDE data collection, utilization of the ASQ measure has been a common practice of First 5 county commissions across California to assess early childhood development (Fitzgerald, Sormano, & Ramirez, 2010).

In summary, the ASQ choice is supported by the current research literature in early childhood services and the past practices of First 5 commissions across other counties of California. Following the ASQ-3 administration guideline, indicators of child growth are based on ASQ scores in five aspects – communication, gross motor, fine motor, problem solving, and personal-social skills. The use of multiple indicators is designed to better represent the latent variable of child growth from multiple aspects. In addition, it facilitates assessment of potential measurement errors in the statistical computing (Loehlin, 1992; Sharma, 1996). As Schumacker and Lomax (1996) observed, “Today, it is commonly accepted that multiple observed variables are preferred over a single variable in defining a latent variable” (p. 55).

Statistical Computing

Structural equation model (SEM) is a statistical technique that models relationships among latent variables through empirical data analyses. It embraces advantages of factor analysis and path analysis, and includes multiple regressions as a special case (Bollen, 1989; Kline, 2010). More importantly, “Structural equation models (SEMs) also called simultaneous equation models” (Fox, 2002, p. 1). The simultaneous nature fits the coexisting contributions of First 5 Kern services in multiple fronts, including childcare, health care, family resources, and parenting efforts. Although family resources, such as annual income and levels of parent education, are not directly modifiable through the First 5 Kern funding, these attributes are embedded in the context of family functioning to support the area-specific funding. Therefore, the SEM method endorses a holistic examination of early child development in the investigation.

The SEM analysis is supported the LISREL software (Jöreskog, Sörbom, Toit, & Toit, 2000). Relationships between child ASQ scores and area-specific factors are depicted in Figure 2. In justifying the accountability on state funding, path coefficients (γ) are estimated to assess what works in the local context. Factor loadings (λ) have been computed to delineate features of latent variables in terms of their manifest indicators.

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To assess whether the statistical findings have been supported by the First 5 Kern database, LISREL is employed to compute an overall Goodness-of-fit index (GFI) that describes the percent of observed covariances explained by the covariances implied by the model (Kline, 2010). In addition, the internal model fitting is represented by the Comparative Fit Index (CFI). A low CFI value is anticipated if the average correlation between the manifest indicators is not high (Kenny, 2010). The Root Mean Square Error of Approximation (RMSEA) index is used to examine the model fit against an external benchmark – good models are considered to have a RMSEA of .05 or less (Hu & Bentler, 1999). In this study, the CFI, RMSEA, and GFI indices are evaluated to justify the model confirmation.

Results

Figure 2 includes parameters from the measurement model and structural relation parts. Factor loadings (λ) are presented in Table 2 to model latent variable features using manifest indicators. In addition, the structural relations are described by path coefficients (γ) to reflect contributions of the latent variables toward outcomes of early childhood development. Those two parts are jointly presented in Table 2 to triangulate interpretations of the λ and γ values in the Discussion section.

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Meanwhile, statistical computing has been conducted to produce the model fitting indices. In particular, goodness of fit index (GFI) in Table 3 indicates overlap of covariances from fitting the observed data into the SEM model in Figure 2. Comparative fit index (CFI) is used to correct “underestimation of fit often noted in small samples” (Bentler, 1990, p. 238). The index of root mean square errors of approximation (RMSEA) is provided to assess the average of residuals not accounted for by the model. The high GFI and CFI indices and small RMSEA values reconfirm the model fitting results for this investigation.

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Discussion

Ong (2006) pointed out, “When thinking about children’s readiness for school, one must essentially consider the ‘whole child’ or all aspects of development” (p. 25). Recognition of the coexisting effects is a unique feature of this investigation. It directly reflects concurrent services of First 5 Kern on multiple fronts. “This distinction also recognizes that a single program, agency or service system cannot take sole responsibility (or credit) for achieving a desired condition of wellbeing for a whole population” (Gapstone Project Guide, 2008, p. 2).

First 5 Kern is a local commission that distributes Proposition 10 funding and oversees the population accountability in Kern County. Friedman (2011) clarified that “OBA [Outcome based Accountability] keeps population accountability separate from performance accountability. Population accountability belongs to partnerships” (p. 4). Although area-specific supports are not something new (Bodenhorn & Kelch, 2001), few researchers have examined concurrent impact of multiple services in early childhood development. In part, this is because program evaluations are often designed to address performance accountability within a funding area (Moodie-Dyer, 2011). To fill out the void, this investigation includes an exploratory part to articulate coexisting service outcomes across different funding areas.

In addition, guided by the tasks specified in the research question section, this study also features a confirmatory component according to the CIPP model. The model is grounded in an extensive literature base. Stufflebeam (1983) pointed out, “The CIPP approach is based on the view that the most important purpose of evaluation is not to prove but to improve” (p. 118). While accountability is important for state reporting, it is more important to examine empirical findings from the local setting to sustain the service improvement. Therefore, the confirmatory inquiry focuses on interpretation of statistical findings to verify indicator identification from the current literature.

Reconfirmation of Indicator Identifications

Under the CIPP paradigm, attributes of family resource have been incorporated in Figure 2 to support the indicator configuration in the product phase. Howes and Brown (2000) synthesized a general approach in examining the family context, and concluded that “researchers first accounted for family influences and then looked at the influences of child care beyond the contributions of the parents’ education, income, and behavior” (p. 3). Whereas behavior measures often involve various confounding variables (Carlo, Crockett, Randall, & Roesch, 2007), annual income and parental education are manifest indicators representing financial and human resources in the family context.

Despite individual differences since birth, research literature indicates that most children experience similar stages at approximately the same age (Case, Okamoto, & Griffin, 1996; Piek, Baynam, & Barrett, 2005). Table 2 includes the child growth indicators in communication, gross motor, fine motor, personal social, and problem solving domains. While development of the gross and fine motor skills is related to a control of large and small muscles, communication, personal social, and problem solving skills are more dependent on the environment. McManus (2009) pointed out, “children's development evolves toward competence through on-going transactions between themselves and their environments” (p. 17). Because of the inclusion of additional outcome measures involving environmental interactions, impact on child development can be compared in this study across those outcome indicators. The results showed higher factor loadings (λ) are derived from the communication, personal social, and problem solving indicators (Table 2). Without considering the joint impact from environment, an isolated study of child growth could have been delimited to the gross motor and fine motor aspects, and thus, failed to address major components of individual growth.

Hence, the ASQ outcome measures have supported an integrated approach of this investigation to reveal stronger influences from the environment. While muscle growth is visible, latent outcomes in the affective domain are intimately linked to the environmental exposure (Christensen, Montanez, Corr, & Medinger, 2005). As Barker (2001) observed,

Any time from eight months onwards your baby will begin to realise he is a separate person from you. He will find it exciting to be able to move away from you but scary when you move away from him. (p. 1)

Because “Identity is a social construct” (Walshaw, 2007, p. 93), social services play an important role in establishing a healthy environment to support early childhood development.

In alignment with the comprehensive child growth in multiple aspects, the parenting effort factor is not confined within the home setting. Across the county, First 5 Kern sponsored a total of 21 preschool programs to facilitate child growth in a center-based environment. Hence, parents need to incorporate child learning opportunities at home and in the community. Besides maintaining reading hours for children, they are expected to support nursery education at a preschool site. A positive path coefficient (γ=.13) has been found from the parenting effort factor on early childhood development when the effort is more or less balanced between weekly reading (λ=.38) and preschool support (λ=.35).

While significant associations have been found between poverty and child learning outcomes (Crooks, 1995), “new evidence shows low-income children in higher-quality settings can begin to catch up to their peers” (Klein & Knitzer, 2006, p. 13). Thus, the center-based support seems as important as the home-based reading effort. The statistical analysis in this aspect has confirmed the comparable factor loadings in identifying the parenting effort factor for the benefit of early childhood development. Without the integrated investigation on parenting effort, environmental indicators cannot be jointly disentangled between the aspects of home reading and nursery education.

Likewise, factor loadings (λ) are articulated with path coefficients (γ) to portray contributions from other service areas. For instance, the health care factor has a positive path coefficient (γ=.89) because of its positive factor loadings from the health checkups (λ=.28) and dental care (λ=.33). In comparison, the factor loading is slightly larger for the dental indicator. The additional emphasis is well-justified because “There are far more people without dental insurance than without medical insurance” (Platt & Cabezas, 2000, p. 4).

In addition, the childcare factor is reversely scaled by a negative factor loading on prenatal care (λ=-.31) and a positive factor loading on smoke exposure (λ=.12). Thus, a path coefficient of -.03 (i.e., γ=-.03) has been obtained to reflect the need of improving childcare in the local context. Both indicators reconfirmed Kern County needs suggested by other studies. For instance, Lucile Packard Foundation (2012) reported the rate of prenatal care for Kern County at 76% while the state rate has reached 83%. Smoke cessation has been placed as a priority of child protection, and Lu et al. (2000) urged that “We recommend that each county Proposition 10 Commission consider allocating a portion of its Proposition 10 monies to expanding local efforts on prenatal smoking cessation” (p. 29).

Part of the need for parental education is to create a healthy environment for child growth. Thus, “From a developmental perspective, any encouragement to parents to pay more attention to their children and their children’s responses is most likely a good thing” (Halfon, Shulman, & Hochstein, 2001, p. 16). In comparison, mothers take care of more childrearing tasks, such as prenatal care and breast feeding. Regalado, Goldenberg, and Appel (2001) discovered more influences on child development from maternal factors. Results from this investigation support the importance of parent education with high factor loadings. Consistent with the findings from research literature, mother’s education shows a higher factor loading (λ=.84) than the one on father’s education (λ=.78) in this integrated model of child growth (Figure 2).

Still, not all the indicators demonstrate a pattern of “the more the merrier”. One counterexample is illustrated by the negative path coefficient (γ=-.23) from the family resource factor. While traditionally underserved families need additional resources (Duncan, Yeung, Brooks-Gunn, & Smith, 1998), the funding from First 5 Kern cannot address the issue of affluenza. Koplewicz, Gurian, and Williams (2009) delineated,

The country music star Wynonna Judd amassed a fortune and squandered much of it, throwing money at her children out of guilt for missing hockey practices and buying more cars than she could ever drive. … Ms. Judd's remark epitomizes the no-win situation in which many American families have found themselves—victims of the all- consuming affliction—a condition known as affluenza. (p. 1053)

Hence, First 5 Kern resources appear to be less needed by families of high socioeconomic status. The greater the family resource, the less the need for subsidy. The opposite relationship has been reflected by the negative path coefficient from the family resource factor.

Impact on Early Child Development

Research Question 2 addresses coexisting relationships between factors of the area-specific service and early childhood development. To make a difference in the service outcomes, it is important to recognize that some indicators are not adjustable and others can be altered through First 5 Kern support. For instance, family resources represent a contextual attribute because First 5 Kern does not generate additional sources of family income. While poverty is often linked to a minority status (Duncan, Yeung, Brooks-Gunn, & Smith, 1998), the service cannot change ethnicity status. Nonetheless, “poverty and minority status pose significant barriers to gaining access to both primary care and dental care” (Platt & Cabezas, 2000, p. 5). In this study, the health care factor is indicated by timely health checkups and dental care, both being covered by grant-sponsored insurance from First 5 Kern. Because of the insurance coverage extended by Proposition 10, children in hard-to-reach communities have been granted service access to health care. As a result, paramount relationship (γ=.89) has been found between health care and child growth.

To reflect the joint effect on early childhood development, each path coefficient (γ) should be interpreted in the context of concurrent area-specific services, which makes the integrated model more realistic than an isolated model of a single aspect. For instance, when prenatal care is considered in isolation, Halfon, Shulman, and Hochstein (2001) reported that “the contribution a healthy pregnancy makes to optimal brain development might be comparable to that of appropriate interaction between parents and children once they are born” (p. 3). With inclusion of an additional smoke exposure indicator, the childcare factor has incorporated prenatal conditions to jointly reflect early childhood protection. Because of the additional influence channeled through the health care domain, the impact of childcare is not confined by its direct relationship to child growth with a relatively small effect size (|γ|=.03). Additional contribution has been channeled through health care to generate the largest path coefficient (γ=.89) across all service areas.

It should be noted that the health care need is reversely linked to family resources. Whereas the subsidy from Proposition 10 provides an important support for traditionally underserved children, particularly when the local unemployment rate has reached 17.1% in recent years (Rhoades, 2009), the state funding is less crucial for affluent families. Thus, a negative path coefficient (γ=-.23) has been obtained to reflect the reduced impact for children with more family resources.

An analysis of the coexisting effects is needed to enhance mutual supports among different service domains. While the contribution of health care is built on coexistence of impact from childcare, parenting effort, and family resource, early childhood development is reciprocally linked to the quality health care as a foundation. Wright et al. (2001) pointed out:

Without appropriate content and quality of health care, many cognitive, speech, language,

and other developmental problems and issues will go unidentified, parents will not receive important counseling to help them stimulate their children’s learning capacities, and children’s school readiness will be in jeopardy. (p. 1)

The integrated services sponsored by First 5 Kern are grounded on an assumption that the whole could be greater than the sum of its parts. While programs in the healthcare domain incorporate education components to disseminate current knowledge on childcare and protection, education programs reciprocally support healthcare agencies in expanding the service access, including working with parents on information sharing and insurance policy application (Wang, 2012). The joint effort addresses the fourth priority of First 5 Kern, i.e., establish a well-integrated system of services for children and families. The results from this investigation extend additional support for the inter-agency collaborations across those area-specific services that are essential for early childhood development.

Model Fitting Indices

Schumacker and Lomax (2004) further pointed out, “Model fit determines the degree to which the sample variance-covariance data fit the structural equation model” (p. 91). To evaluate the empirical support of the statistical model in Figure 2, multiple model fitting indices are examined in this section to triangulate the results.

Overall, the model fit indices are classified into three major categories of model fit, model comparison, and model parsimony fit indices (Hooper, Coughlan, & Mullen, 2008; Schumacker, & Lomax, 2004). On the model fit part, the goodness-of-fit (GFI) index has been computed from the LISREL software to indicate what proportion of the variance in the sample variance-covariance matrix is accounted for by the model (Jöreskog & Sorbom, 1993; Tabachnick & Fidell, 2007). The GFI value ranges from 0 to 1 with a larger value indicating a better fit of the model (Diamantopoulos & Siguaw, 2000). “Traditionally an omnibus cut-off point of 0.90 has been recommended for the GFI” (Hooper, Coughlan, & Mullen, 2008, p. 54). In this study, the GFI index is .92 (Table 3), indicating that Figure 2 provides a good platform to match the observed covariance matrix from the real database (Diamantopoulos & Siguaw, 2000).

In addition, Byrne (1998) verified that the Comparative Fit Index (CFI) performed well even when sample size is small. Today this index is an essential component of structural equation modeling, and becomes one of the most popularly reported fit indices (Fan, Thompson, & Wang, 1999; Hooper, Coughlan, & Mullen, 2008). As with the GFI, values for CFI range between 0.0 and 1.0 with values closer to 1.0 indicating good fit. A cut-off criterion of CFI ≥ 0.90 was initially advanced however, recent studies have shown that a value greater than 0.90 is needed in order to ensure that misspecified models are not accepted (Hu & Bentler, 1999). Again, the CFI obtained from this study is .91, which is indicative of good fit.

Furthermore, “the RMSEA [Root Mean Square Errors of Approximation] favours parsimony in that it will choose the model with the lesser number of parameters” (Hooper, Coughlan, & Mullen, 2008, p. 54). The RMSEA is regarded as “one of the most informative fit indices” (Diamantopoulos & Siguaw, 2000, p. 85) due to its sensitivity to the number of estimated parameters in the model. An RMSEA in the range of .05 to .10 was considered an indication of fair fit and values above 0.10 indicated poor fit (MacCallum, Browne, & Sugawara, 1996). In Table 3, the obtained RMSEA of .04 has positioned Figure 2 within a good fit range.

In summary, model fitting indices have been triangulated in all three major categories recommended by researchers (Hooper, Coughlan, & Mullen, 2008; Schumacker, & Lomax, 2004). The results consistently support the model fit, regardless of the criterion choices. The model evaluation has completely addressed the third research question for this investigation, i.e., empirical findings presented in this article are grounded on a solid fit to the First 5 Kern data, rather than unsubstantiated statistical artifacts.

Conclusion

This investigation features both quantitative and qualitative inquires to examine the coexisting relationships between the area-specific support and early childhood development. A well-established Context, Input, Process, and Product (CIPP) paradigm has been selected from the qualitative review of current literature to guide the quantitative data analyses. The literature review also revealed a void in the existing knowledge on articulation of coexisting impact from multiple service areas. The new exploration has expanded understanding of service outcomes in four fronts:

1) Context of Early Childhood Development

The ASQ indicators of early childhood development cover five aspects, i.e., gross motor, fine motor, communication, problem solving, and personal-social skills. While height and weight are typically used to measure muscle and bone growth in support of motor skills, development of communication, problem solving, and personal-social skills is inseparable from the context of environmental interaction (Piaget, 2000). This study revealed larger factor loadings from those context-based indicators in early childhood development (Table 2). According to Szanton (1992), "[A] newborn's brain is only about one-quarter the size of an adult's. It grows to about 80 percent of adult size by three years of age and 90 percent by age five” (¶. 2). It was a concurrent analysis of the multiple outcome measures that reconfirmed the more important contributions from the contextual support.

2) Attributes of Family Resources

Attributes of the family resources are the pre-existing conditions not to the credit of Proposition 10 funding. However, it is an unavoidable variable at the input phase of any local services. While affluent families have no problem to maintain healthcare coverage, children with limited family resources tend to show a strong need of First 5 Kern support. Thus, the family resource factor demonstrated a negative path coefficient to counterbalance the need of early childhood services funded by Proposition 10. This result won’t be available from an isolated investigation in which the service variables are confined among those that are alterable by Proposition 10 funding.

3) Process of Co-Existing Support

The support across different domains naturally occurred in daily life. As Wright, Kuo, Regalado, and Halfon (2001) observed, “child health providers who model reading behavior and provide books to their patients have been shown to increase the frequency and amount of reading behaviors that parents engage in with their children” (p. 6). In this example, the health care factor coexists with the contribution of parenting effort. The joint support makes it imperative to articulate the service process across multiple domains.

4) Product of Priority Settings

First 5 Kern funding has been strategically planned to address priorities in multiple focus areas (Table 1). While not all the variables carry the same weight, a predominant role has been found from the factor of healthcare when the results incorporates the joint impact from the process of prenatal service. Because of the alignments between the CIPP framework and the local priority setting, the integrated model in Figure 2 has been supported by the actual data according to critical model fit indices recommended by researchers (Hooper, Coughlan, & Mullen, 2008; Schumacker, & Lomax, 2004).

As First 5 Kern started its services in a large geographic area with rapid population growth, evaluation reports of its annual performance used to span more than 400 pages in the past (e.g., Corporation for Standards and Outcomes, 2009). This investigation represents the first effort to report concise research findings in a nationally refereed journal. Built on the model-based approach to articulating area-specific service impacts, more variables can be incorporated in future investigations to enrich the existing findings. For instance, cultural and ideological factors are often embedded in parental effort regarding whether it takes a community, such as nursery school settings, to raise children. Thus, parental attitude towar nursery education could have been incorporated in future studies.

In addition, the annual income indicator in this study did not include extra financial support from relatives or close friends. Still, the external support could have played a major role in some cultures. Teachman (1987) recollected that “In most research, family background has been measured by socioeconomic indicators (e.g., parents' education, family income), to the exclusion of other family characteristics” (p. 548). Although this investigation conformed to the indicator choice of most research, the inclusion of more sources of family support might help expand existing results across various cultural settings.

References

Ansary, N. A.. & Luthar, S. S. (2009).  Distress and academic achievement among adolescents of affluence: A study of externalizing and internalizing problem behaviors and school performance.  Development and Psychopathology, 21, 319-341.

Arbegast, H. W. (2010). Social-emotional development in early childhood as it relates to children’s early learning experiences and family development in San Diego county children from birth through age five. Malibu, CA: Pepperdine University

Armbruster, B., Lehr, F., & Osborn, J. (2006). A child becomes a reader: Proven ideas from research for parents, birth through preschool. Jessup, MD: National Institute for

Literacy.

Assembly Bill 99 (2011). Retrieved, July 4, 2012, from

Assuring Better Child Health & Development (2012). What’s new is ASQ-3? Retrieved, July 4, 2012, from

Bandstra, E., Vogel, A., Morrow, C., Xue, L. & Anthony, J. (2004). Severity of prenatal cocaine exposure and child language functioning through age seven years: a longitudinal latent growth curve analysis, Substance Use Misuse, 39, 25–59.

Barker, R. (2001). The mighty toddler: The essential guide to the toddler years. Sydney: Pan Macmillan Australia.

Bartholomew, L., & Gustafsson, U. (1997). Transition from nursery to primary school: Conducive to learning? The story of "O." International Journal of Early Childhood, 29, 1-7.

Beatty, B. (2005). The rise of the American nursery school: Laboratory for a science of child development. In D. B. Pillemer, & S. H. White (Eds.), Developmental psychology and social change: Research, history and policy (pp. 264-287). New York: Cambridge University Press.

Bedell, C. (2010, September 1). First 5 Kern names new executive director. The Bakersfield Californian. Retrieved, July 4, 2012, from [August 22, 2011]

Bentler, P. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.

Bodenhorn, K. & Kelch, D. (2001). Implementation of California’s Children and Families First Act of 1998. The Future of Children, 11, 150-157.

Bollen, K A (1989). Structural equations with latent variables. New York: Wiley.

Branson, D. (2009). The effect of the Ages and Stages Questionnaires on childcare providers’ developmental assessment ability. Reno, NV: University of Nevada.

Brookings Institution (2010). The state of metropolitan America: Educational attainment. Retrieved, July 4, 2012, from brookings.edu/metro/MetroAmericaChapters/education.aspx/

Bull, L. (2003). What can be done to prevent smoking in pregnancy? A literature review. Early Child Development and Care, 173, 661-667.

Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS and SIMPLIS: Basic concepts, applications and programming. Mahwah, New Jersey: Lawrence Erlbaum Associates.

Carlo, G., Crockett, L.J., Randall, B.A., & Roesch, S.C. (2007) A latent growth curve analysis of prosocial behaviors among rural adolescents. Journal of Research on Adolescence, 17, 301-324.

Carnegie Task Force on Meeting the Needs of Our Youngest Children (1994). Starting points: Meeting the needs of our youngest children. New York: Carnegie Corporation of New York.

Case, R. Okamoto, Y, & Griffin, S. (1996). The role of central conceptual structures in the development of children's thought. Monographs of the Society for Research in Child Development, 61, 1-295.

Christensen, D., Montanez, N., Corr, M., & Medinger, B. (2005). Nebraska early learning guidelines. Lincoln, NE: Nebraska Department of Education.

Corporation for Standards and Outcomes (2009). First 5 Kern annual report: Fiscal Year 2008-2009. Retrieved, July 4, 2012, from

Cousineau, M., Stevens, G., & Pickering, T. (2007). Children’s Health Initiatives have helped prevent over 1,000 unnecessary child hospitalizations annually. Alhambra, CA: USC Center for Community Health Studies.

Crooks, D. L. (1995). American children at risk: Poverty and its consequences for children’s health, growth, and school achievement. American Journal of Physical Anthropology, 21, 57-86.

Crossley, N. (2012). Ages and stages developmental screenings. Retrieved, July 4, 2012, from

Denham, S., Blair, K., & Demulder, E. (2003). Preschool emotional competence: Pathway to social competence? Child Development, 74, 238-256.

Diamantopoulos, A. & Siguaw, J.A. (2000). Introducing LISREL. London: Sage Publications.

Duncan, G. J., Yeung, W. J., Brooks-Gunn, J., & Smith, J. R. (1998). How much does childhood poverty affect the life chances of children? American Sociological Review, 63, 406 - 423.

Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling, 6, 56-83.

FindLaw (2011). Health and Safety Code Section 13100. Retrieved, July 4, 2012, from

First 5 California (2008). First 5 California 2007-2008 annual report. Sacramento, CA: Author.

First 5 California (2010a). First 5 California 2009-2010 annual report. Sacramento, CA: Author.

First 5 California (2010b). Guidelines for implementing the California Children and

Families Act. Sacramento, CA: Author.

First 5 California (2012). First 5 California annual report: Fiscal Year 2010-2011. Retrieved, July 4, 2012, from

First 5 Kern (2011). First 5 Kern strategic plan 2010-2011. Bakersfield, CA: Author.

First 5 Kern (2012). Core data elements. Retrieved, July 4, 2012, from

Fitzgerald, T., Sormano, S., & Ramirez, C. (2010). Special needs project: Final evaluation report. Sacramento, CA: First 5 California.

Fox, J. (2002). Structural equation models. Retrieved, July 4, 2012, from

Friedman, M. (2011). Turning the curve: Outcomes based accountability. York, UK: Yor-ok (Retrieved, July 4, 2012, from yor-.uk).

Furman, E. (1986). What nursery school teachers ask us about: Psychoanalytic consultations in preschools: The roles of parents and teachers in the life of the young child. Cleveland, OH: Cleveland Psychoanalytic Institute.

Gapstone project guide (2008). Retrieved, July 4, 2012, from [August 22, 2011]

Garson, G. D. (2005). Structural equation modeling. Retrieved, July 4, 2012, from

Gray, V. B., Byrd, S. H., & Cossman, J. S. (2007). Family characteristics have limited ability to predict weight status of young children. Journal of the American Dietetic Association, 107, 1204-1209.

Halfon, N., Shulman, E., & Hochstein, M. (2001). Brain development in early childhood. Los Angeles, CA: UCLA Center for Healthier Children, Families and Communities.

Hannon, P. & James, S. (1990) Parents' and teachers' perspectives on pre- school literacy development. British Educational Research Journal, 16, 259-271.

Harder+Company Community Research (2004). First 5 San Joaquin year three evaluation report: 2003 – 2004. San Francisco, CA: Author.

Heath, S. (1983) Ways with words: Language, life and work in communities and classrooms. NEW YORK: Cambridge University Press.

Henderson, J. (2010). A letter from the Executive Director. Retrieved, July 4, 2012, from

Hewes, D. W. (1997, March). California’s cooperative nursery schools: Perspectives from the past. Paper presented at the 48th annual convention of California Council of Parent Participation Nursery Schools, San Meteo, CA.

Hooper, D., Coughlan, J. & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. The Electronic Journal of Business Research Methods, 6, 53 – 60.

How ASQ Works (2011). Retrieved, July 4, 2012, from

Howes, C. & Brown, J. (2000). Improving child care quality: A guide for Proposition 10 Commissions. Los Angeles, CA: UCLA Center for Healthier Children, Families and Communities.

Hu, L.T. & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.

Huttenlocher, P. R., & Dabholkar, A. S. (1997). Regional differences in synaptogenesis in the human cerebral cortex. Journal of Comparative Neurology, 387, 167–178.

Jöreskog, K. G. (1990). New developments in LISREL: Analysis of ordinal variables using polychoric correlations and weighted least squares. Quality and Quantity, 24, 387-404.

Jöreskog, K. G. & Sorbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago, IL: Scientific Software International.

Jöreskog, K. G., Sörbom, D., Toit, S., & Toit, M. (2000). LISREL 8: New statistical features. Chicago, Scientific Software International.

Karoly, L. A., Reardon, E., & Cho, M. (2007). Early care and education in the golden state: Publicly funded programs serving california's preschool-age children. Retrieved July, 11, 2009 from .

Kenny, D. A. (2010). Measuring model fit. Retrieved, July 4, 2012, from [August 22, 2011].

Kerstjens, J. M., Bos, A. F., Vergert, E., Meer, G., Butcher, P., Reijneveld, S. (2009). Support for the global feasibility of the Ages and Stages Questionnaire as developmental screener. Early Human Development, 85, 443–447.

Kline, R. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York: The Guilford Press.

Klein, L. & Knitzer, J. (2006). Effective preschool curricula and teaching strategies. New York: National Center for Children in Poverty.

Koplewicz, H., Gurian, A., & Williams, K. (2009). The era of affluence and its discontents. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 1053-1055.

Lareau, A. (2002). Invisible inequality: Social class and childrearing in black families and white families. American Sociological Review, 67, 747–776

Lasker, R. (1997). Medicine & public health: The power of collaboration. New York: The New York Academy of Medicine.

Loehlin, J. C. (1992). Latent variable models: An introduction to factor, path, and structural

analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Lu, M.C., Bragonier, R., Silver, E.R., & Bemis-Heys, R.(2000). Where it all begins: The impact of preconceptional and prenatal care on early childhood development. Los Angeles, CA: UCLA Center for Healthier Children, Families and Communities.

Lucile Packard Foundation (2012). Infants whose mothers received prenatal care in the first trimester. Retrieved, July 4, 2012, from

Luthar, S. S., & Latendresse, S. J. (2005). Children of the affluent: Challenges to well-being. Current Directions in Psychological Science,14 , 49-53.

MacCallum, R.C., Browne, M.W., & Sugawara, H., M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-49.

McLloyd, V. C., & Wilson, L. (1991). The strain of living poor: Parenting, social support, and child mental health. In A. C. Huston (Ed.) Children in poverty (pp. 105–135). New York, NY: Cambridge.

McManus, S. (2009). Enhancing positive early childhood mental health outcomes. Eugene, OR: University of Oregon.

Moodie-Dyer, A. (2011). A policy analysis of child care subsidies: Increasing quality, access, and affordability. Children & School, 33, 37-45.

Nash, J. M. (1997, February 3). How a child’s brain develops. Time Magazine. 49–56.

National Research Council and Institute of Medicine. (1995). Integrating federal statistics on children: Report of a workshop. Committee on National Statistics and Board on Children and Families, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

Nelson, C.A. (2000). The neurobiological bases of early intervention. In J.P. Shonkoff &S.J. Meisels (Eds)., Handbook of Early Childhood Intervention (2nd ed.) (204-227). Cambridge, UK: Cambridge University Press.

Nicholas, A. (2006). An examination of the needs of mothers with infants in the neonatal intensive care unit. College Park, MD: University of Maryland, College Park.

Nicol, P. (2008). Evaluation of an undergraduate child development teaching program using a home visit and the Ages and Stages Questionnaire (ASQ). Neonatal, Paediatric and Child Health Nursing, 11, 19-24.

Norwood, G. (2009). Deeper mind: Maslow’s hierarchy of needs. Retrieved, July 4, 2012, from

Ong, C. A. (2006). The many faces of school readiness: How research intersects policy and practice. Doctoral dissertation. Los Angeles, CA: UCLA (AAT 3252390).

Piaget, J. (2000). "Commentary on Vygotsky". New Ideas in Psychology, 18, 241–259.

Piek, J., Baynam, G., & Barrett, N. (2005). The relationship between fine and gross motor ability, self-perceptions and self-worth in children and adolescents. Human Movement Science, 25, 65-75.

Platt, L. J. & Cabezas, M. C. (2000). Early childhood dental caries. Los Angeles, CA: UCLA Center for Healthier Children, Families and Communities.

Program evaluation standards (2010). [Online] Available at

Proposition 10 (No date). Retrieved, July 4, 2012, from

Proposition 10 Statutes (No date). Retrieved, July 4, 2012, from

Reese, E., (1996). Maternalism and political mobilization: How California's postwar child care campaign was won. Gender & Society, 10(5), 566–589.

Renk, K., & Phares, V. (2004). Cross-informant ratings of social competence

Regalado, M., Goldenberg, C., & Appel, E. (2001). Reading and early literacy. Los Angeles: UCLA Center for Healthier Children, Families, and Communities.

Rhoades, L. (2009). First 5 Kern 2008-2009 report to the community. Bakersfield, CA: First 5 Kern.

Rosenberg, J. (2004). Low birth weight is linked to timing of prenatal care and other maternal factors. International Family Planning Perspectives, 30, 101-103.

Schneiderman, J., McDaniel, D., Xie, B., & Clark, J. (2010). Child welfare caregivers: An evaluation of access to pediatric health. Children and Youth Services Review, 32, 698-703.

Schumacker, R. E. & Lomax, R. G. (1996). A beginner’s guide to structural equation modeling. Mahwah, NJ: Lawrence Erlbaum.

Schumaker, R. E., & Lomax, R. G. (2004). Beginner's guide to structural equation modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

Sears, C. (1988). Children’s crusade. American Health, 7, 32.

Sharma, S. (1996). Applied multivariate techniques. New York: John Wiley & Sons.

Shobo, Y. (2002). Too few visits to the dentist? The impact on children's health. Little Rock, Arkansas: Arkansas Advocates for Children and Families.

Shonkoff, J., & Phillips (2000). From neurons to neighborhoods: The science of early childhood development. Washington, DC: National Academy Press.

Sloane, F. (2008). Through the looking glass: Experiments, quasi-experiments, and the medical model. Educational Researcher, 37 (1), 41-46.

Stufflebeam, D. L. (1983). The CIPP model for program evaluation. In G. F. Madaus, M.

Szanton, E. S. (1992). Heart start: The emotional foundations of school readiness. Arlington, VA: Zero to Three National Center for Clinical Infant Programs. (ERIC document Reproduction Service No. ED352171)

Tabachnick, B.G. & Fidell, L.S. (2007). Using multivariate statistics (5th ed.). New York: Allyn and Bacon.

Taylor, D. & Dorsey-Gaines, C. (1988). Growing up literate: Learning from inner-city families. Portsmouth, NH: Heinemann.

Teachman, J. (1987). Family background, educational resources, and educational attainment. American Sociological Review, 52, 548-557.

Tomlin, C. (2007). It takes a community to raise a child. Jackson, TN: Excelligence Learning Corporation

Troseth, G. (2011, Spring). Babies learn best from parents, not video. Ideas in Action, p. 9.

Tukey, J. (1980). We need both exploratory and confirmatory. The American Statistician, 34, 23-25.

United Nations Children’s Fund (2007). A comprehensive assessment of the lives and well-being of children and adolescents in the economically advanced nations. Florence: Author.

Walshaw, M. (2007). Working with Foucault in education. Rotterdam, The Netherlands: Sense Publishers.

Wang, J. (2011). Evaluation highlights: The Robert Noyce Scholarship Program at CSUB. Retrieved, July 30, 2012, from

Wang, J. (2012). 2010-2011 annual report. Retrieved, July 30, 2012, from

Weiss, E. (2010). State investment in Pre-k Programs, 2001-2010: A mixed-methods evaluation of the influence of policy networks. Washington, DC: George Washington University.

White, J. G. (1981). Improving multicultural teacher in-service: A CIPP planning model. High School Journal, 64 (5), 217-221.

Wolff, M. (2002). The price of perfection. New York, 35, 32.

Wright, K., Kuo, A., Regalado, M., & Halfon, N. (2011). Developmental and behavioral health services for children: Opportunities and challenges for Proposition 10. Los Angeles, CA: UCLA Center for Healthier Children, Families and Communities.

Yarbrough, D. B., Shulha, L. M., Hopson, R. K., & Caruthers, F. A. (2010). The program evaluation standards (3rd ed.). Thousand Oaks, CA: Sage & the Joint Committee on Standards for Educational Evaluation.

Zepeda, M., and Morales, A. (2001). Supporting parents through parent education: Buildingcommunity systems for young children. Los Angeles, CA: UCLA Center for Healthier Children, Families and Communities.

Zumbrun, J. (2008, November 28). America's best- and worst-educated cities. Retrieved, July 30, 2012, from

Table 1.

Alignment Between the State Focus Areas and Local Priorities

|State Focus Area | First 5 Kern Priority Statements |

| | |

|Family Functioning |All parents and caregivers will be knowledgeable about early childhood development, effective parenting |

| |and community services. |

| | |

|Child Health |All children will have an early start toward good health. |

| | |

|Child Development |Quality early childcare and education services will be accessible. |

|Systems of Care |A well-integrated system of services for children and families will exist. |

Table 2.

Parameter Estimates for Modeling the Relationship Between First 5 Kern Support and Early Childhood Development

Latent Variables Indicators Parameter Estimates

Factor Loadings (λ)

Child Development communication (ASQ score) .75

gross motor (ASQ score) .41

fine motor (ASQ score) .56

personal social (ASQ score) .72

problem solving (ASQ score) .71

Parenting Effort hours of weekly reading .38

support for nursery school on regular basis .35

Health Care regular health checkup .28

regular dental care .33

Family Resource annual income .53

mother’s education .84

father’s education .78

Childcare timely prenatal care -.31

smoke exposure .12

Path Coefficients (γ)

Parenting Efforts ( Child Development .13

Health Care ( Child Development .89

Family Resource ( Child Development -.23

Childcare ( Child Development -.03

Table 3.

Model Fitting Indices for the First 5 Kern Database

Model Fitting Indices First 5 Kern Results

Goodness of Fit Index (GFI) .92

Comparative Fit Index (CFI) .91

Mean Square Error of Approximation (RMSEA) .04

[pic]

Figure 1. CDE items on parental support for home reading and nursery education

Figure 2. Relationships between child ASQ scores and area-specific factors.

-----------------------

Support Nursery

Schooling

Communication

Family Resource

Parenting

Effort

λx7

λx6

λ x4

Father’s

Education

Mother’s

Education

λx5

Annual

Income

Timely

Checkups

Dental Care

λ x3

λ x2

Read to

This Child

λ x1

Problem

Solving

λy5

Personal

Social

Gross

Motor

Fine

Motor

λy4

λy3

λy2

λ y1

Child

ASQ

Score

at 36th month

γ3

γ4

γ2

γ1

Smoke

Exposure

Timely ﵢ[pic]ﵤ[pic]ﵰ[pic]ﵲ[pic]ﵾ[pic]ﶀ[pic]ﶎ[pic]﶐[pic]ﶤ[pic]ﶺ[pic]ﶼ[pic]﷐[pic]﷦[pic]﷨[pic]ﷴ[pic]ﷶ[pic]︆[pic]︖[pic]︘[pic]︦[pic]︸[pic]︺[pic]﹜[pic]﹞[pic]﹬[pic]﹮[pic]ﹼ[pic]ﹾ[pic]ﺎ[pic]ýýýýPrenatal Care

λ x11

Childcare

λ x10

Health Care

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