Posted: July 17th, 2022

Prof Writing

B.Schrantz – SAMPLE – Methodology Plan

Problem Statement
Early childhood education history is often linked back to January 1965 when Lady Bird Johnson held a White House tea to announce federal funding for preschool classes that would break the vicious cycle of poverty (Lascarides & Hinitz, 2000). The federally funded Head Start early childhood program introduced the idea that early education of our young children was a public responsibility. After decades of early childhood education program Assessments, state legislators and educators both endorse the need to develop and fund high-quality early childhood programs. States’ policymakers have increased funding for early childhood education programs from $200 million in 1988 to $7.5 billion in 2018 (Education Commission of the States, 2019; National Center for Children in Poverty, 2000). “A robust body of research shows that children who participate in high-quality preschool programs have better health, social-emotional, and cognitive outcomes than those who do not participate” (U.S. Department of Education, 2015).
Greater awareness of early childhood as a critical developmental period has led to the aim of promoting high-quality children’s experiences in pre-kindergarten programs through a focus on healthy social/behavior development and academic/cognitive learning (Biddle, Crawford, & Seth-Purdie, 2017). Early childhood education is an essential foundation for developing learning behaviors and skills necessary for future success. Moss and Haydon (2008) defined education “as fostering and supporting the general well-being and development of children and young people, and their ability to interact effectively with their environment and to live a good life” (p. 2). Early childhood education programs have the potential to give all children a jump start to kindergarten by supporting both educational and social behaviors. High-quality early childhood education programs are the key to ensuring all children have equal access to learning opportunities and experiences.
Children from low-income and disadvantaged backgrounds enrolled in high-quality early childhood programs enter kindergarten academically ready (Ansari, Pianta, Whittaker, Vitiello, & Ruzek, 2019). The U.S. Department of Education (2015) continues to stress the need for “significant new investments in high-quality early education” to help close the school readiness gaps between disadvantaged children and their more advantaged peers. The Reauthorization of the Elementary and Secondary Education Act (ESEA) also highlighted the need for states to make early childhood education a priority, especially for children identified as at-risk for academic success. The substantial amount of public funding directed at early childhood education programs has increased from $200 million in 1988 to $7.5 billion in 2018 (Education Commission of the States, 2019; National Center for Children in Poverty, 2000). This increase in funding has increased the demand for additional research on the implications of structural programming requirements, student demographics (including race, gender, and socioeconomic levels), and composition of diversity on program quality and kindergarten readiness.
Most children in the United States have their first school experiences in four-year-old early childhood programs rather than in kindergarten (Hustedt & Barnett, 2011). Pre-kindergarten initiatives vary from state to state; however, they all share some common characteristics. First, all pre-kindergarten programs are voluntary. Second, programs are funded and directed by each states’ education department that identifies early learning standards that range from academic content knowledge, social/emotional development, motor development, and language development (Hustedt & Barnett, 2011). Also, states have identified required structural components to receive early childhood funding; these structural components include the location of service, length of the program, teacher certification, and class size. Most states have limited early childhood funding to only children meeting at-risk criteria such as socioeconomic level, ethnicity, or disability; also, some states provide preschool funding based on geographical locations. For example, South Carolina’s early childhood funding system segregates children in four-year-old kindergarten based on families’ socioeconomic conditions,
however, only funds these programs if the families reside in a rural, high-poverty county.
Currently, many states are solely funding four-year-old kindergarten programs for at-risk students, which limits the cultural and economic diversity needed for heterogeneous classrooms. Research studies centered around socioeconomic diversity and educational impact are necessary to justify the money spent on numerous segregated at-risk four-year-old kindergarten programs across the nation. Recent research highlights that the saturation of poverty in the classroom is related to lower classroom quality even though early childhood education programs aim to address the educational and socio-emotional needs of children from low-income backgrounds. Socioeconomic segregation of children may negatively impact the cognitive and social development of children, along with perpetuating the educational gap seen along socioeconomic lines. States’ policies and procedures, in regards to student selection and structural features of programs related to classroom, teacher, and child characteristics, may create unintended consequences. More research is needed to determine if the lack of racial and economic diversity is impacting the potential benefits of early childhood education programs.
High-quality preschool programs should enhance the early learning experiences for all children and develop the background knowledge and skills necessary for school readiness (Pelatti et al., 2016). Research is divided and often not conclusive on what constitutes essential components to create high-quality early childhood programs that impact academic and social outcomes. Numerous research studies have analyzed structural components and requirements of early childhood education programs and the impact on student achievement; however, all of these studies have been unable to specify which elements lead to measurable kindergarten readiness (Bainbridge et al., 2005; Bowne et al., 2017; Clifford et al., 2005; Magnuson et al., 2005; Pelatti et al., 2016). Recent research has suggested four possible mechanisms which impact the quality of early childhood education programs: 1) differences in structural components and curriculum/teaching; 2) peer effects on cognitive learning; 3) peer effects on social development; and 4) parent involvement (Reid & Ready, 2013). Current literature acknowledges that structural components are not the only variables in creating a high-quality early childhood education program; classroom diversity and sociocultural learning opportunities can positively impact the learning outcomes (Clifford et al., 2005; Pelatti et al., 2016; Pianta et al., 2005; Reid & Ready, 2013; Schechter & Bye, 2007).
Few research studies have focused on program design, classroom behaviors, and student achievement predictors of classroom quality for publicly supported at-risk pre-kindergarten programs with limited socioeconomic and ethnic diversity. Schechter and Bye’s (2007) research highlighted the importance of a diverse composition of students in early childhood classrooms and the requirement of these classes to incorporate activities where students can learn from each other’s experiences and background knowledge. Reid and Ready’s (2013) research study suggests that all children in an integrated early childhood education program learn more than a classroom primarily composed of children from low-income backgrounds with the same ethnic backgrounds. The research studies by Reid and Ready (2013) as well as Schechter and Bye (2007) show a correlation between achievement skills and integrated socioeconomic and ethnic classrooms; however, neither of these studies utilized a standardized achievement measure to determine the relationship between the diversity composition of a program and academic success.
The regular and consistent patterns of positive interactions between teachers and peers impact classroom experiences (Brown, Jones, LaRusso, & Aber, 2010). With current funding policies and procedures, South Carolina’s structural design of early childhood programs is trapping the youngest of South Carolina’s at-risk children in a cycle of educational poverty. South Carolina’s pre-kindergarten policy limits access to a heterogeneous grouping of students, which eliminates the sociocultural benefits of exposing children to a variety of cultures and environments to enhance problem-solving and critical thinking. Ultimately, in South Carolina, this design has led to not only socioeconomic segregation but also segregation of ethnic races in four-year-old kindergarten classrooms. By eliminating the cultural and economic diversity in these classrooms, South Carolina has diminished the “social and cultural nature of the developmental process and the role of peers Helping each other in learning” (Edwards, 2007, p. 84). Additional research is needed to evaluate the impact of kindergarten readiness in programs serving only at-risk four-year-old students as compared to a more diverse classroom population where children can learn from each other.
Due to the limited state funding available for early childhood education, programmatic and structural components of four-year-old pre-kindergarten programs must be providing the social-emotional, cognitive, and physical skills necessary for students to be kindergarten ready. South Carolina is in the early stages of implementation of the Child Early Reading and Development Education Program (CERDEP) for at-risk students and the requirement of a Kindergarten Readiness Assessment (KRA) for all 5K students. Data are being collected in South Carolina to determine the impact of pre-kindergarten programs on kindergarten readiness; however, no research study has evaluated all of these components. The primary goal of this quantitative study was to use the S.C. kindergarten data to investigate how kindergarten readiness scores compare between children attending a structured four-year-old kindergarten program or not. The next goal was to investigate how the kindergarten readiness scores compared based on the location (public or community-based) of CERDEP classrooms. Finally, the study was to compare the differences in kindergarten readiness assessment scores between white, African American, and Hispanic students who attended a four-year-old kindergarten program.
The research will provide school district leaders and state policymakers guidance and evidence of potential changes in funding or structural components needed to ensure all students receive a high-quality early childhood education program that prepares them for kindergarten success. Ultimately, the study would be a tool for parents and community members to identify the early childhood programs which positively impact kindergarten readiness and help minimize the educational achievement gaps between all populations. South Carolina parents deserve the right to know which types of early childhood programs will produce quality academic achievement and kindergarten readiness so that they can make informed decisions on the best program for their child.
State and local policymakers are searching for kindergarten readiness data to support the continued funding of early childhood programs. They are looking for features of interest, including whether the programs are full- or part-day, housed in school or community settings, universal or targeted groups of students, staffed by certified teachers or individuals with less formal training. Research has shown that children who have had high-quality preschool classroom experiences will enter kindergarten more school ready with better language development, reading skills, and math skills (LoCasale-Crouch, 2007). A cyclical pattern of inequality in education and income may be attributed to a lack of access to quality early childhood programs (Bainbridge et al., 2005) as well as a lack of access to an early childhood setting that incorporates opportunities for interactions with children from different backgrounds. Reid and Ready’s (2013) research found that children’s learning in classrooms with diverse ethnic and socioeconomic composition equals or even rivals the impact of children’s family backgrounds in a year of schooling. However, lawmakers have not had access to many research studies analyzing the impact of the ethnic and socioeconomic composition within the programs on academic readiness.
Current literature acknowledges that structural components are not the only variables in creating a high-quality early childhood education program; classroom diversity and sociocultural learning opportunities can positively impact the learning outcomes (Clifford et al., 2005; Pelatti et al., 2016; Pianta et al., 2005; Reid & Ready, 2013; Schechter & Bye, 2007). The purposes of this study were to test Vygotsky’s sociocultural theory (1978) by comparing enrollment in four-year-old kindergarten programs, comparing locations of CERDEP four-year-old kindergarten programs, and by comparing ethnicity in four-year-old kindergarten programs in terms of the Kindergarten Readiness Assessment scale scores in the domains of language/literacy, mathematics, social foundations, physical well-being/motor development and overall readiness of students in a rural, high-poverty South Carolina county.
Research Approach & Strategy
A quantitative approach is appropriate when a researcher seeks to understand comparisons and relationships between variables; whereas, a qualitative study seeks to understand and explain an experience through verbal narratives rather than numbers (McMillan, 2004). For this research study, a quantitative approach was the most appropriate choice. The research study’s purposes were to compare students’ prior four-year-old kindergarten program experiences and look at the relationship between ethnicity and socioeconomic composition of classrooms by utilizing the Kindergarten Readiness Assessment scale score data.
Explicitly, this study incorporated a nonexperimental quantitative research design using secondary data: 2018-2019 Kindergarten Readiness Assessment data, prior four-year-old kindergarten care experience data, and demographic data. According to McMillan (2004), a nonexperimental quantitative study is appropriate when “the investigator has no direct influence on what has been selected to be studied, either because it has already occurred or because it cannot be influenced” (p. 9). Since the 2018-2019 Kindergarten Readiness Assessment data were based on five-year-old kindergarten students, the prior four-year-old kindergarten care experience and assessment data had already occurred and could not be influenced.
This study explored the following research questions. 1) How did students who attended a structured four-year-old kindergarten program perform on the Fall 2018 Kindergarten Readiness Assessment (KRA) in the areas of language/literacy, mathematics, social foundations, physical well-being/motor development, and overall kindergarten readiness as compared to students who did not attend a four-year-old kindergarten program? 2) In Fall 2018, how did CERDEP qualified students who attended a four-year-old kindergarten program in a public school setting perform on the kindergarten readiness assessment as compared to CERDEP students who attended a four-year-old kindergarten program housed at Head Start or First Step daycares? 3) In Fall 2018, what were the differences in kindergarten readiness assessment scores between ethnic groups who attended four-year-old kindergarten programs?
All three research questions were a nonexperimental quantitative comparative study. The purpose was to “investigate the relationship of one variable to another by simply examining whether the value of the dependent variable in one group is the same as or different from the value of the dependent variable of the other group” (McMillan, 2004, p. 179). This method posed both advantages and disadvantages. The advantage of this type of study was that predictions, to a certain extent, could be made from the comparison data. The disadvantage of this type of study was that it does not reveal an underlying cause or explanation of how one variable affected or changed another variable.
Data Collection Tool and Sources
The South Carolina Department of Education’s CERDEP Guidelines manual (2018) was used to identify the counties who received CERDEP funds and were eligible for the study. Out of 85 school districts in the state, only 33 school districts received CERDEP funds. The 33 school districts represented 20 different counties in South Carolina. Only six of the 20 counties receiving CERDEP funds had more than one school district within the county. In order to investigate the comparison of prior four-year-old kindergarten care experiences, the study identified a county that had similar demographics, socioeconomic status of students, similar student enrollment, and similar geographical locations. In the fall of 2019, both superintendents from this county were approached to determine their level of interest in participating in a secondary study of their students’ Kindergarten Readiness Assessment data. The study had three targeted purposes focused on the Kindergarten Readiness Assessment scale scores in the domains of language/literacy, mathematics, social foundations, physical well-being/motor development, and overall readiness of students. First, the study compared the kindergarten readiness scores of students who attended structured four-year-old kindergarten programs and those who did not. Second, the study compared the kindergarten readiness scores of students who attended a CERDEP program in a public school setting and those who attended a community-based program. Third, the study compared the kindergarten readiness scores based on ethnicity in four-year-old kindergarten programs. Both superintendents elected to participate and agreed to support the study by providing all school Kindergarten Readiness Assessment data by domain and composite scale scores, non-identifiable student demographic data, and prior care experience data.
The study required dividing students into groups based on prior care experiences as well as ethnicity and socioeconomic status. The entire population was utilized in the study to ensure the data focused on kindergarten readiness for all students. A random sampling method would potentially eliminate relevant student data needed for each of the different breakout data points; therefore, no sampling methods were used. In order to collect the necessary information for all three research questions, a secondary analysis of existing data was collected through both districts’ Enrich student achievement database systems. This secondary data analysis received Institutional Review Board approval under exempt review in October 2019 (IRB #03-1019EX, Appendix A). In November 2019, the data for the study were collected from both school districts’ Director of Accountability and Testing. Data sets were collected from each director as an electronic Microsoft Excel 2018 file and contained relevant but non-identifiable information regarding five-year-old kindergarten students from the school year 2018-2019. The data set included the following: student gender, student ethnicity, student pupil in poverty status, 2017-2018 teacher, student prior child care, student prior provider, student prior program type, student prior class type, 2018 KRA school when tested, 2018 KRA overall score, 2018 KRA social foundations score, 2018 KRA language/literacy score, 2018, KRA mathematics score, and 2018 KRA physical development and well-being. Each district’s data file followed the same format, so the researcher could easily merge the two files into one data set for the study. The Enrich database system provided the Kindergarten Readiness Assessment scale score data on overall readiness and the four domain scores for language/literacy, mathematics, social foundations, and physical well-being/motor development. The Enrich data also provided student demographic data (i.e., gender, race, and pupil in poverty status) and prior four-year-old kindergarten care experience data. The prior four-year-old care data included whether the student received 4K services, the program type, the provider name, the teacher name, and the class type. The prior four-year-old kindergarten care experience data and student demographic information found in the districts’ Enrich database system was updated with data from the districts’ enrollment database system, PowerSchool.
Data Collection & Analysis Methods
For this study, kindergarten readiness was defined by five dependent variables: 1) language/literacy KRA scale score, 2) mathematics KRA scale score, 3) social foundations KRA scale score, 4) physical well-being/motor development KRA scale score, and 5) overall composite KRA scale scores. Data were collected in an electronic file from both school districts’ Enrich student achievement database systems. The file contained relevant but non-identifiable information regarding students from both school districts. After the data sets were compiled, descriptive statistics (i.e., mean, median, and mode) on each KRA domain and the overall kindergarten readiness composite were calculated for both gender and ethnicity.
This study also evaluated the relationship between student enrollment in a structured four-year-old kindergarten program and students not enrolled in any type of pre-kindergarten program on kindergarten readiness composite and domain (language/literacy, mathematics, physical well-being/motor development, and social foundations) scale scores. Through the comparison study, a prediction may be made about whether enrollment has a higher or lower kindergarten readiness score; however, relationships in this type of analysis cannot reveal any causal connections only whether there are significant differences between the groups (McMillan, 2004). This study also compared significant differences in students’ kindergarten readiness overall scores and domain scores based on the location of the CERDEP program (i.e., a public school setting or community based). This comparison study does not allow inferences about a causal relationship to be made (McMillan, 2004). Lastly, this study compared students’ kindergarten readiness based on the ethnic groups (white, African American, and Hispanic) within four-year-old kindergarten programs. A comparison study evaluated the performance data of the three ethnic groups in the four-year-old kindergarten programs to determine if there was a significant difference in kindergarten readiness in overall composite kindergarten readiness scores or domain scores. However, inferences about causal relationships cannot be established (McMillan, 2004).
For this research study, the Kindergarten Readiness Assessment 2.0 (KRA) was utilized to measure a child’s kindergarten readiness skills in the areas of language/literacy, mathematics, physical well-being/motor development, social foundations, and overall kindergarten readiness. In September 2013, the KRA 2.0 was developed through an Enhanced Assessment Grant (EAG) and partnerships between the Maryland State Department of Education, Ohio Department of Education, Johns Hopkins University Center for Technology in Education, and WestEd (WestEd Standards Assessment and Accountability Services, 2014). The KRA is a criterion-referenced assessment based on the Common Language Prekindergarten Standards. It incorporates the essential domains of school readiness (language and literacy development, early mathematics development, approaches toward physical well-being and motor development, and social and emotional development) as defined by the U.S. Department of Education (WestEd Standards Assessment and Accountability Services, 2019).
Each KRA item was composed of one question or observation that aligns with a specific essential skill from the Common Language Standards and results in one recorded score. The KRA has three item types: selected response, performance tasks, and observational rubrics. Selected response items were composed of a question or prompt with three possible answer options, in which there was only one correct answer. Performance tasks consisted of an activity a student completed in response to a question or prompt. Observational rubrics were provided to evaluate student’s specific behaviors or skills a student should demonstrate during typical classroom activities.
“The KRA utilizes a one-parameter item response theory (IRT) model, commonly referred to as the Rasch model, to define the relationship between the assumed latent trait (readiness for kindergarten) and the probability of a student correctly answering a given KRA item” (WestEd Standards Assessment and Accountability Services, 2019, p.4). Therefore, this model assumes that a student’s response is a function of a student’s knowledge about the content and the difficulty of the test item and allows the student’s score and the difficulty of an item to be placed on the same scale. The KRA test item’s IRT parameters were calculated using Winsteps Rasch measurement software. Raw scores (total points obtained) on the KRA were then converted to scale scores using the Rasch model since percent-correct scores would not provide a complete explanation of a student’s readiness for kindergarten (WestEd Standards Assessment and Accountability Services, 2019). The scale scores account for the difficulty of individual test items and provide consistency in the interpretation of results and allow for comparison of results. The KRA scale has a minimum score of 202 and a maximum score of 298 (WestEd Standards Assessment and Accountability Services, 2019). The KRA scale score determines each student’s performance level for the overall and four domain sections (language/literacy, mathematics, physical well-being, and social foundations). The performance levels were determined by a students’ demonstration of foundational skills and behaviors.
According to the Standards for Educational and Psychological Testing, validity refers to the degree to which evidence and theory support the interpretation of test scores for proposed uses of tests (Allen & Yen, 1979). The test content validity of KRA is evident through the item development process and the KRA blueprint of test question types for each domain and overall composite scores. The KRA is aligned to the Common Language Standards, which are based on the KRA states’ early learning standards and emphasizes all domains of school readiness and utilizes multiple item types to assess the skills and behaviors within each domain. Test validity and reliability were established for KRA starting with the item development process, which used detailed item specifications aligned to the Common Language Standards, moved to content experts reviewing questions and cognitive interviews, and ended with piloting and field testing of items. Every KRA item goes through a bias and content review board of early childhood educators. Numerous rounds of review and feedback were conducted to ensure fidelity to the standards and age-level appropriateness.
A test’s reliability would measure the consistency of students’ scores if the assessment were given multiple times. The most common measures of reliability include internal consistency, typically Cronbach’s alpha, and interrater reliability. The KRA used Cronbach’s alpha to evaluate the test’s reliability. The maximum value for Cronbach’s alpha is one that indicates perfect reliability. Higher values indicate that the items are closely related to each other and students’ scores consistently across all items. The standard error of measurement (SEM) is a function of the reliability measure (Cronbach’s alpha). It is defined as the standard deviation of error scores for a student under repeated independent testing with the same test (Allen & Yen, 1979).
The testing company not only ensured the reliability of the test questions and the scores but also focused on ensuring all KRA administrations were standardized by conducting intensive teacher professional development. All kindergarten teachers who administer the KRA must complete online training activities, including a simulator that models proper administration and scoring processes to support the reliability of item scores. All educators must pass a content assessment and a scoring scenario to ensure interrater reliability.
This study analyzed the data through multiple statistical tests testing the following hypotheses:
H10: There was no difference in the kindergarten readiness of students who were enrolled in a structured four-year-old kindergarten program and those who were not. A two-sample t-test was conducted on students enrolled in a structured four-year-old kindergarten program to those who were not to compare their performance on the Kindergarten Readiness Assessment overall composite scale score and each of the four domains (language/literacy, mathematics, physical well-being/motor development, and social foundations). According to Spatz (2019), “a two-tailed, two-sample t-test is a null hypothesis significance testing (NHST) technique that can detect differences among two population means and determine whether the difference is statistically significant in either the positive or negative direction” (p. 218). The level of significance was set at .05. If the t-stat was greater than the critical value and p < .05, then the test was significant, and the null hypothesis was rejected.
H20: There was no difference in kindergarten readiness of students who attended a CERDEP four-year-old kindergarten program located at a public school and those who attended a CERDEP four-year-old kindergarten program at a community-based location. A two-tailed, two-sample t-test was conducted on students who attended a CERDEP four-year-old kindergarten program in a public school setting compared to those who attended a CERDEP four-year-old program in a community-based setting and their performance on the Kindergarten Readiness Assessment overall composite scale score and each of the four domains (language/literacy, mathematics, physical well-being/motor development, and social foundations). The level of significance was set at .05. If the t-stat is greater than the critical value and p < .05, then the test is significant, and the null hypothesis is rejected.
H30: There was no difference in kindergarten readiness scores between ethnic groups who attended a four-year-old kindergarten program A one-way analysis of variance (ANOVA) was conducted between the three ethnic groups (white, African American, and Hispanic) to compare their performance on the Kindergarten Readiness Assessment overall composite scale score and the four KRA domains: language/literacy, mathematics, physical well-being/motor development, and social foundations. According to Spatz (2019), “a one-way analysis of variance (ANOVA) is a null hypothesis significance testing (NHST) technique that can detect differences among two or more population means” (p. 231). The study ran an ANOVA to receive a single (univariate) f-value. If the f-stat is greater than f-crit and p < .05, then the test is significant, and the null hypothesis is rejected. After completing the ANOVA statistical tests, significant differences between the ethnic groups were indicated for the overall readiness score and the domains for language and literacy, social foundations, and mathematics. However, the ANOVA tests did not identify which particular differences between pairs of means were significant. According to Spatz (2019), post hoc tests are used to explore differences between multiple groups means. A t-test was conducted between each group combination (white and African Americans, African Americans and Hispanics, and white and Hispanics) for the overall kindergarten readiness and the three domains that indicated significant differences to confirm where the differences occurred between the groups.
Ethical Consideration & Limitations
This study was conducted in compliance with the standards for research on human subjects, set forth by the Institutional Research Board at the University of the Cumberlands. Permission for an exempt study was requested and granted by the Institution Research Board at the University of the Cumberlands. Letters of support from both school districts’ superintendents were obtained and signed. All data collected for this secondary data analysis were non-identifiable and cannot be linked back to any participant. Due to the methodology of the study’s data collection, there was no potential harm to students. Upon receiving the two data files from each school district, the files were merged into one study file for analyses. The new data file was saved on a jump drive and stored in a locked file cabinet in the researcher’s office. Only the researcher had access to the jump drive. In compliance with the University of Cumberlands Institutional Research Board standards, the dataset and all statistical analysis files will be maintained for three years after the approval of the final defense. At the end of the three years, the dataset will be erased from the jump drive, and all data files and statistical analysis files will be destroyed following Institutional Research Board standards.
Despite the researcher’s best efforts, the results of the study were affected by the following limitations: 1) Two school districts and nine schools were studied, so test administrators’ training for the Kindergarten Readiness Assessment (KRA) may vary between schools and districts. Although, district KRA trainers received the same State Department of Education training material to use with their kindergarten teachers, and all teachers had to pass the KRA content and KRA inter-rater reliability assessment with an 80% before administering the assessment. 2) Testing environment conditions such as lighting, temperature, and noise distractions may have varied from classroom to classroom, school to school, and district to district. 3) This study only evaluated one rural South Carolina county’s kindergarten readiness scores. Therefore, results may not represent scores from other counties due to differences in four-year-old pre-kindergarten programs, geographic locations, and socioeconomic levels within the community.

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