Home > 2009 Kids Count Databook > SC Kids Count Essay

Young Children with Highest Readiness Risks

The 2009 Kids Count Data Book Essay makes a compelling argument for the creative development and use of better data to guide children's policies and programs toward greater effectiveness. At a time when the nationwide economic collapse has reduced expenditures on children drastically, it is more important now than ever that the power of data and data-based analysis be harnessed to make children's services accurately targeted, directed toward specific needs of individual children, and accountable for determining results.

Kids Count was created by the Annie E. Casey Foundation twenty years ago as a data-focused initiative. Over the past two decades, the growth of validated research and advanced computer techniques have produced huge, continuing breakthroughs in our understanding of the nature, causes, and consequences of children's development and of their specific problems.

SC Kids Count is housed in the Office of Research & Statistics (ORS) of the South Carolina Budget and Control Board. An outstanding strength of ORS is its Data Warehouse which integrates files from a very wide variety of public and private organizations. These files can be linked through unique child or adult identifiers which are totally confidential and untraceable back to any specific individual. By linking files from many organizations, dozens or even hundreds of data variables can be accessed as a common database for analysis. Files may be linked concurrently or longitudinally over many years, even decades.

To illustrate the practical application of data for policy development, as advocated in the 2009 Kids Count Data Book Essay (see quote at the end of this memorandum), SC Kids Count is presenting a highly revealing application of the Data Warehouse to a major children's issue: promoting school readiness through early childhood development, as a remedy for young children under the Abbeville case awaiting final ruling by the SC Supreme Court.

In December 2005, Circuit Court Judge Thomas Cooper ruled in response to Abbeville et. al. v. The State of South Carolina that South Carolina's constitution had been violated by inadequate intervention in early childhood to provide the "opportunity for each child to receive a minimally adequate education." The SC Supreme Court had previously defined "minimal adequacy" as "an opportunity to acquire the ability to read, write, and speak the English language, and knowledge of mathematics and physical science..." An appeal of Judge Cooper's ruling has been under consideration by the SC Supreme Court for a year. Judge Cooper emphasized children from poverty as the class deserving remedy, but did not examine their specific deficits requiring interventions. He was not prescriptive regarding the age range, mentioning "early childhood", "pre-kindergarten programs", and "pre-kindergarten through grade 3."

The Cooper decision raised to the highest level the issue of school readiness, especially determining which children with what deficits reach school performing far behind their classmates and seriously below established academic standards. In order to prevent these substantial readiness deficits, policy-makers, parents, and service providers need to know the causes of the most serious deficits. Over the past four years, SC Kids Count has utilized data warehouse files to determine the critical why, what, when, and who realities regarding children unready in the early years of school.

Why: The data warehouse files show that there are three major risk groups reaching school less than ready and subsequently testing below basic academic standards. These three groups are defined in terms of treatable risk factors causing their performance deficits rather than untreatable demographic descriptors such as gender, race, and income:
- Low language/literacy (their vocabulary, language skills, and literacy experiences developed primarily in their homes through interaction with their family members). Data variable = mother with less than 12 years of education at birth of the child.
- Disability (primarily speech and language disorders but also mental, emotional, vision, hearing, and learning disabilities). Data variable = disability identified for special education placement in kindergarten and 1st grade.
- Emotional-behavioral problems (externalizing and internalizing disorders of acting out conduct and oppositional-defiant behaviors or withdrawal, depression, and anxiety). Data variable = Kindergarten SC Readiness Assessment rating less than consistently ready on all 4 factors: self-control, social problem-solving, interaction with others, and self-concept. These problems are caused by genetic, neuro-biological, and family environmental influences such as abuse, neglect, and harsh, negative parenting.

What: The developmental problems of the three groups are associated with serious school readiness problems:
- Assessed on the SCRA in kindergarten as less than consistently ready were: 85% of those with emotional-behavioral problems; 53% of those identified as disabled, primarily with speech/language impairment; and 45% of those with low language and literacy, based on low maternal education (below 12 grades).
- Three years later on 3rd grade PACT, 47% of the emotional-behavioral group, 46% of the disabled, and 39% of the low language-literacy group tested below basic on English Language Arts (ELA) or math. In 5th grade, over half of all three groups were below basic: emotional-behavioral group (57%), disabled (51%), and low language-literacy (50%).
- The most critical readiness deficiency (far below basic) is reflected by Below Basic 1 PACT scores. These are the bottom half of below basic with performance so far below standards that few children tested BB1 ever reach basic in subsequent years. The grades 3 and 5 BB1 percentages for the three risk groups are: emotional-behavioral problems (27% and 35%), disabled (24% and 33%), and low language-literacy (21% and 28%).

Early Childhood Group

Risk Groups	Percent of the Cohort	% Below Standards			% Far Below Standards
Risk Groups	Percent of the Cohort	5K	Grade 3	Grade 5	5K	Grade 3	Grade 5
Any Disability	17	53	46	51	17	24	33
Mom with less than 12 years of education	24	45	39	50	11	21	28
All 4 Emotional-behavioral Problems	15	85	47	57	28	27	35
None of the 3 Risk Groups	59	16	14	22	1	6	9

When: All three risks groups have been identified for their deficits between birth and kindergarten. There is little doubt that most were already affected by these deficits during their first few years of life, well before entry into 4K pre-school or kindergarten. Children with the most severe deficits subsequently score BB1 on PACT: 16% and 22% in grades 3 and 5 for those having one risk factor, 32% and 42% for 2 risk factors, and 45% and 62% for all 3 risk factors. These highest risk children must be found and served as early as possible in their lives.

# of RISK FACTOR GROUPS	Percent of the Cohort	% Below Standards			% Far Below Standards
# of RISK FACTOR GROUPS	Percent of the Cohort	5K Less than Consistently Ready	Grade 3 BB	Grade 5 BB	5K Less than Sometimes Ready	Grade 3 BB	Grade 5 BB
None of the Three Risk Factors	58.6	16	14	22	1	6	9
Only one of the risk factors	31.2	43	32	42	8	16	22
Any two of the risk factors	8.9	78	55	64	28	32	42
All three of the risk factors	1.4	96	69	81	50	45	62

Who: Children failing to meet academic standards have long been identified as male, minority, and poor. Immigrant children with limited English proficiency have recently joined the known demographic groups with serious readiness risks. Data warehouse links have shown that young children who were disabled, abused, neglected, and placed in foster care or who were born to teen, low educated, and single mothers were likely to test below state standards. These demographic groups are listed below in order of their percent scoring below basic in 3^rd grade. Noteworthy is that low income (free lunch) ranking 13^th is not one of the most predictive risk factors.

(Highest to Lowest Percentage)

Rank	% BB		% BB1		% of Cohort	Indicator
Rank	Grade 3	Grade 5	Grade 3	Grade 5	% of Cohort	Indicator
1	88	96	56	89	1	Mental Disorders in Kindergarten
2	54	64	38	50	2	Very Low Birthweight: under 1500 grams
3	47	57	27	35	15	All 4 Emotional-Behavioral Problems (SCRA)
4	46	51	24	33	17	All Disabled (in Kindergarten Special Education)
5	44	54	24	32	3	Foster Care or Child Protective Service Ages 0-3
6	42	52	23	31	9	Mom Less than 10 Years Education
7	41	52	23	30	25	Self-Control (SCRA)
8	41	50	24	32	16	Speech Disability in Kindergarten
9	40	53	22	30	22	Minority Males
10	40	51	22	29	37	Self-Concept (SCRA)
11	39	49	21	28	31	Interaction with Others (SCRA)
12	38	50	20	27	15	Mom with 10 or 11 Years of Education
13	37	48	19	26	44	Free Lunch (under 130% of poverty)
14	37	46	18	25	8	Mom Less than 18 at Birth of Child
15	37	47	20	26	38	Social Problem-Solving (SCRA)
16	35	46	20	24	2	Low Birthweight: 1500-2000 grams
17	33	41	15	20	6	Low Birthweight: 2000-2500 grams
18	31	43	16	22	8	Mom Ages 18-20 at Birth of Child
19	30	40	14	19	21	Minority Females
20	20	29	10	13	76	Mom with 12 or More Years of Education
21	17	23	8	12	29	White Male
22	13	17	5	7	28	White Female
23	12	17	5	7	48	Full Pay Lunch (over 185% of poverty)

Conclusion: Early identification and intervention from birth through age 4 are imperative in order that 4K pre-school can have sufficient impact for the highest risk children to reach kindergarten ready to meet academic standards. Early identification and intervention for these highest risk children should be funded as part of the Abbeville early childhood remedy prescribed by Judge Cooper, though still pending a final decision from the SC Supreme court. These highest risk children, the "Cooper Kids," deserve our full support. As "good Samaritans", South Carolinians should not need a constitutional mandate to force our support enabling "the least of us" to reach school ready to succeed and become full contributors to the advancement of our state.

Early Childhood Group

Readiness to Meet Standards	Percent of the Cohort	% Below Standards			% Far Below Standards
Readiness to Meet Standards	Percent of the Cohort	5K Less than Consistently	Grade 3 BB	Grade 5 BB	5K Less than Sometimes	Grade 3 BB1	Grade 5 BB1
RACE/GENDER
African American and Other Male	22	47	40	53	12	22	30
African American and Other Female	21	35	30	40	7	14	19
White Male	29	30	17	23	6	8	12
White Female	28	20	13	17	3	5	7
INCOME
Free Lunch	44	43	37	48	10	19	26
Reduced Lunch	8	32	22	37	6	11	16
Full Pay Lunch	48	21	12	17	3	5	7
Free/Reduced Lunch	52	41	34	46	9	18	24
DISABLED
Any Disability	17	53	46	51	17	24	33
Speech Disabilities (94% of disability)	16	52	41	50	16	24	32
Learning Disability (9%)	2	85	67	74	37	40	54
Mental Disability (6%)	1	93	88	96	70	56	89
Emotionally (1%), Visually (1%), Hearing Disabled (2%) Autistic (1%)	-	-	-	-	-	-	-
LOW EDUCATED MOTHER
less than 10 years of education	9	47	42	52	12	23	31
10 and 11 years of education	15	44	38	50	11	20	27
less than 12 years of education	24	45	39	50	11	21	28
12 or more years of education	76	28	20	29	5	10	13
EMOTIONAL/ BEHAVIORAL PROBLEMS
Self -Concept	37	68	40	51	17	22	29
Self-Control	25	71	41	52	20	23	30
Interaction with Others	31	68	39	49	18	21	28
Social Problem-solving	38	63	37	47	16	20	26
All 4 Emotional-behavioral Problems	15	85	47	57	28	27	35

All Cohort Children	100	32	24	32	7	12	16

With appropriate permissions, data from the SC Data Warehouse housed at the Office of Research and Statistics (ORS) of the South Carolina Budget and Control Board have been used to produce the tables in this report. The data is from a 1995/96 birth cohort (one year of all SC births) linked to outcomes up to first time tested in 5th grade. The percentage of students reported below basic or far below standards on the state PACT test is for either ELA or math or both. Low education of the mother is taken from birth certificate files. Disability is for special education in kindergarten and 1st grade. Emotional-behavioral problems are taken from SCRA in kindergarten.

Quote from Essay in 2009 Kids Count Data Book:

Even when detailed information on participants and programs is compiled and computerized, human service and education agencies will derive little benefit unless they put the data to productive use. Unfortunately, most public agencies have neither the inclination nor capacity to do so. Few states and local jurisdictions rigorously analyze their data to identify key performance indicators or critical success factors—and too few have forged ties with universities or other potential research partners to help analyze the data for them.

One of the most important benefits of strong data is the opportunity to track each child's progress (or problems) over time—for example, from one level of school to the next, or from one instance of reported abuse to another, or from one delinquency arrest to the next. However, public agencies often lack this crucial capacity. In child welfare, not enough states track cases over multiple years, leaving them unable to capture the full range of experiences and outcomes for all children who pass through the foster care system. Instead, when child welfare agencies report on the average length of time in foster care, or the average time to adoption, they often base their figures on a point-in-time snapshot of children in care on a given date, or the subset of children who have exited care in the previous year—yielding a distorted portrait of their child welfare system's actual performance.

Likewise, most systems and agencies lack the ability to access important data from multiple sources. As a result, frontline workers (or teachers) in one system typically can't obtain information on the full range of their clients' (and students') needs and circumstances: Child welfare workers don't have children's education data; juvenile justice workers don't have child welfare records, or health records, and so on. Only a handful of jurisdictions nationwide integrate administrative data sets from several systems, even though this is crucial for understanding the complex needs of children and families with multiple issues and those who are involved with two or more systems simultaneously.

States and local jurisdictions must also build their capacity to integrate data sets and track the circumstances of youth involved in multiple systems. One option is to create virtual "data warehouses" with access to records from multiple state systems, as well as census, vital records, and other data streams. For instance, Florida has one data warehouse that combines pre-kindergarten through university-level education information and another that ties together a host of data sets related to employment and earnings. These two data warehouses can be linked and connected to administrative data from other state systems. South Carolina has used its extensive data warehouse to examine special health care needs facing children statewide, identify communities with large numbers of uninsured children, and profile the population of infants and toddlers at highest risk for school failure.