DSRT 837 Methodology Plan RubricName _________________________

DSRT 837: Methodology Plan Rubric
Name: _____________________________________________
Topic
Distinguished
Proficient
Apprentice
Novice
Problem Statement
(20pts possible)
The problem statement is concise, includes descriptor variables
and informs the reader of the exact purpose of the study
The problem statement is concise and informs the reader of
purpose of the study
The problem statement is stated
The problem statement is not stated
Research Approach & Strategy
(30pts possible)
Describes research approach used with rationale for addressing
the research questions, citing appropriate methodological
literature
Describes research approach used with rationale for addressing
the research questions
Lists research approach used
Does not list research approach used
Data Collection Tool and Sources
(30pts possible)
Clearly explains and justifies type of sample to be used and, if
using human participants, how human rights will be protected.
Describes and justifies data collection methods and procedures,
including how, when, where, and by whom data were collected.
Explains and justifies type of sample to be used and, if using
human participants, how human rights will be protected;
Describes and justifies data collection methods and procedures
Lists the type of sample to be used; Briefly describes the data

collection methods and procedures
Does not list the type of sample to be used; Does not describe
the data collection methods and procedures
Data Collection and Analysis Methods
(30pts possible)
Clearly describes and justifies methods and statistical tools (if
applicable) used for analysis. Clearly discusses measures taken
to enhance study validity and reliability.
Describes and justifies methods and statistical tools (if
applicable) used for analysis. Discusses validity and reliability
measures.
Briefly describes methods and/or statistical tools (if applicable)
used for analysis; Briefly discusses validity and reliability
measures
Does not lists methods and statistical tools (if applicable) used
for analysis; Does not discuss validity or reliability.
Ethical Consideration & Limitations
(20pts possible)
Clearly identifies and discusses ethical considerations and
limitations of research
Discusses ethical considerations and/or limitations of research
Briefly describes ethical considerations and/or limitations of
research
Does not describe ethical considerations and limitations of
research
APA Format and Citations, Mechanic, Grammar, and Proofing
(20pts possible)
Full citation using proper APA format with no errors;
Methodology Plan is well written from start to finish, with no
spelling, grammar or use of English errors; The methodology
plan is well organized, clear and presents ideas so another
researcher could replicate the study.
Full or partial citation with minor APA formatting errors;
Methodology Plan is moderately written, with minimal spelling
errors, grammar or use of English errors; Methodology Plan is

moderately organized, clear and presents ideas in somewhat
coherent way.
Partial citation with major formatting errors or no citation;
Methodology Plan is not well written, and contains many
spelling errors, and/or grammar errors and/or use of English
errors; Methodology Plan is poorly organized, lacks clarity
and/or does not present ideas in a coherent way.
Does not properly use APA format; Methodology Plan is
missing components and does not flow; Contains numerous
grammatical errors which impacts readability.
Hi, I am Henry Mathis and wow my first assignment is here, I
am 41 with two kids and I am a Full-time detention officer I
choose the picture below because it means the world to me, and
also it was the first time I was able to be around my mom
without a mask due to being labeled as highrisk I managed to
remain thankful for all thanks and have a nice night I look
forward to working with my new classmates,
Henry Mathis
Running head: EQUITY OF SC FUNDED 4K CLASSROOMS
1
EQUITY OF SC FUNDED 4K CLASSROOMS 19
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 evaluations, 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 structur al
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 assisting 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 opportuniti es 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 kindergarte n 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.
References
Administration on Children, Youth, and Families. (2001). Head
Start program
performance measures: Third progress report. Washington,
DC: U.S. Department
of Health and Human Services.

Administration on Children, Youth, and Families. (2003). Head
Start FACES 2000:A
whole child perspective on program performance fourth
progress report. Washington, DC: U.S. Department of Health
and Human Services, 46.
Alakeson, A. (2004). A 2020 vision for early years: Extending
choice; improving life
chances. London: The Social Market Foundation.
Allen, M. J., Yen, W. M. (1979). Introduction to measurement
theory. Monterey, CA:
Brooks/Cole.
Ansari, A., Pianta, R. C., Whittaker, J. V., Vitiello, V. E., &
Ruzek, E. A. (2019). Starting
early: The benefits of attending early childhood education
programs at age 3. American Educational Research Journal, 51,
403-434. doi:10.3102/0002831218817737
Bainbridge, J., Meyers, M. K., Tanaka, S., & Waldfogel, J.
(2005). Who gets an early
education? Family income and the enrollment of three- to five-
year-olds from 1968 to 2000. Social Science Quarterly, 86(3),
724-745. https://doi.org/10.1111/j.0038-4941.2005.00326.x
Biddle, N., Crawford, H., & Seth-Purdie, R. (2017). Risk
burden, participation in early
childhood education and care, and child outcomes. Australasian
Journal of Early Childhood, 42(1), 49-59.
Bowne, J. B., Magnuson, K. A., Schindler, H.S., Duncan, G. J.,
& Yoshikawa, H. (2017).
A meta-analysis of class sizes and ratios in early childhood
education programs: Are thresholds of quality associated with
greater impacts on cognitive, achievement, and socioemotional
outcomes? Educational Evaluation and Policy Analysis, 39(3),
407-428. DOI:10:3102/0162373716689489
Brown, J. L., Jones, S. M., LaRusso, M. D., & Aber, J. L.
(2010). Improving classroom
quality: Teacher influences and experimental impacts of the 4rs
program. Journal of Educational Psychology, 102(1), 153-167.

DOI:10.1037/a0018160
Click, C., & Hinshaw, D. (2014, November 14). SC Supreme
Court finds for poor
districts in 20-year-old school equity suit. The State, pp. 1A,
2A.
Clifford, R. M., Barbarin, O., Chang, F., Early, D., Bryant, D.,
Howes, C., … Pianta, R.
(2005). What is pre-kindergarten? Characteristics of public pre-
kindergarten programs. Applied Developmental Science, 9(3),
126-143. https://doi.org/10.1207/s1532480xads0903_1
Coley, R. L., Lombardi, C. M., Sims, J., & Votruba-Drzal, E.
(2013). Early education and
care experiences and cognitive skills development: A
comparative perspective between Australian and American
children. Family Matters, 93, 36-49. Retrieved from
https://search.informit.com.au/fullText;dn=768724417777514;re
s=IELHSS
Currie, J. (2001). Early childhood education programs. Journal
of Economic Perspectives
15(2), 213-238. Retrieved from
https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.15.2.213
Education Commission of the States. (2019). Trends in pre-k
education funding in 2017-
2018. Denver, CO: Parker, E., Keily, T., Atchison, B., &
Mullen, J.
Edwards, S. (2007). From developmental-constructivism to
socio-cultural theory and
practice: An expansive analysis of teachers’ professional
learning in early childhood education. Journal of Early
Childhood Research, 5(1), 83-106. DOI:
10.1177/1476718X07072155
Furstenberg, F., Brooks-Gunn, J., & Morgan, S. P. (1987).
Adolescent mothers in later
life. New York, NY, US: Cambridge University Press.
http://dx.doi.org/10.1017/CBO9780511752810
Glassman, M. (2001). Dewey and Vygotsky: Society,

experience, and inquiry in
educational practice. Educational Researcher, 30(4), 3-14.
https://doi.org/10.3102/0013189X030004003
Helburn, S., & Howes, C. (1996). Child care cost and quality.
The Future of Children,
6(2), 62-82. DOI:10.2307/1602419
Hustedt, J. T., & Barnett, W. S. (2011). Private providers in
state pre-k: Vital partners.
Young Children, 66(6), 42-48.
Johnson, J. J., Gallagher, R. J., Montagne, M. J., Jordan, J. B.,
Gallagher, J. J., Hutiner, P.
L., & Karnes, M. B. (Eds.) (1994). Meeting early intervention
challenges: Issue from birth to three (2nd ed.). Baltimore, MD:
Brookes.
John-Steiner, V., & Mahn, H. (1996). Sociocultural approaches
to learning and
development: A Vygotskian framework. Educational
Psychologist, 31(3/4), 191-206. Retrieved from
https://www.researchgate.net/profile/Holbrook_Mahn/publicatio
n/233858618_Sociocultural_Approaches_to_Learning_and_Dev
elopment-
A_Vygotskian_Framework/links/0fcfd50c3d30ccc22e000000.pd
f
Lascarides, V., & Hinitz, B. (2000). History of early childhood
education. New York,
NY: Routledge.
LoCasale-Crouch, J., Konold, T., Pianta, R., Howes, C.,
Burchinal, M., Bryant, D.,
… Barbarin, O. (2007). Observed classroom quality programs
and associations with teacher, program, and classroom
characteristics. Early Childhood Research Quarterly, 22, 3-17.
doi:10.1016/j.ecresq.2006.05.001
Magnuson, K., Ruhm, C., & Waldfogel, J. (2007). Does
prekindergarten improve school
preparation and performance? Economics of Education Review,
26, 33-51. doi:10.1016/j.econedurev.2005.09.008

Marcon, R. A. (2002) Moving up the grades: Relationship
between preschool model and
later school success. Early Childhood Research and Practice,
5(1). Retrieved from http://ecrp.uiuc.edu/v4n1/marcon.html
Maryland State Department of Education. (2019). Ready for
kindergarten: Maryland’s
Early Childhood Comprehensive Assessment System. Baltimore,
MD: Author. Retrieved from
https://earlychildhood.marylandpublicschools.org/system/files/f
iledepot/4/kra_2018-19_technical_report.pdf
McMillan, J. (2004). Educational research: Fundamental s for
the consumer. Boston,
MA: Pearson Education, Inc.
Moss, P. (2008). What future for the relationship between early
childhood education and
care and compulsory schooling? Research in Comparative and
International Education, 3(3), 224-234.
http://dx.doi.org/10.2304/rcie.2008.3.3.224
Moss, P., & Haydon, G. (2008). Every child matters and the
concept of education.
London: Institute of Education University of London.
National Center for Children in Poverty. (2000). Map and track
2000: State initiatives for
young children and families. New York: Author.
National Center for Education Statistics. (2019). The condition
of education 2019.
Washington, DC: Author.
Ohio Department of Education. (2019). State Kindergarten
Readiness Assessment (KRA)
data 2018-2019 [Data file]. Retrieved
fromhttps://reportcard.education.ohio.gov/download
Peizener-Feinberg, E. S., Burchinal, M. R., Clifford, R. M.,
Culkin, M. L., Howes, C.,
Kagan, S. L., … Rustici, J. (1999). The children of the cost,
quality, and outcomes study go to school: Public report. Chapel
Hill: University of North Carolina at Chapel Hill, FPG Child

Development Center.
Pelatti, C. Y., Dynia, J. M., Logan, J. A. R., Justice, L. M., &
Kaderavek, J. (2016).
Examining quality in two preschool settings: Publicly funded
early childhood education and inclusive early childhood
education classrooms. Child Youth Care Forum, 45, 829-849.
DOI:10.1007/s10566-016-9359-9
Pianta, R., Howes, C., Burchinal, M., Bryant, D., Clifford, R.,
Early, D., & Barbarin, O.
(2005). Features of pre-kindergarten programs, classrooms, and
teachers: Do they predict observed classroom quality and child-
teacher interactions? Applied Developmental Science, 9(3),
144-159. https://doi.org/10.1207/s1532480xads0903_2
Ready at Five. (2019). Readiness matters 2019 [PowerPoint
slides]. Retrieved from
https://www.readyatfive.org/school-readiness-data/readiness-
matters-2019/1655-readinessmatters-2019-powerpoint.html
Reid, J., & Ready, D. (2013). High-quality preschool: The
socioeconomic composition of
preschool classrooms and children’s learning. Early Education
and Development. 24, 1082-1111. DOI:
10.1080/10409289.2012.757519
Reynolds, A. J., Mann, E., Miedel, W., & Smokowski, P.
(1997). The state of early
childhood intervention: Effectiveness, myths and realities, new
directions. Focus 19(1). University of Wisconsin-Madison:
Institute for Research and Poverty.
Schechter, C., & Bye, B. (2007). Preliminary evidence for the
impact of mixed-income
preschools on low-income children’s language growth. Early
Childhood Research Quarterly, 22(1), 137-146. DOI:
10.1016/j.ecresq.2006.11.005
Shonkoff, J., & Phillips, D. (Eds.). (2000). From neurons to
neighborhoods: The science
of early childhood development. Washington, DC: National
Academy Press.

South Carolina Department of Education. (2018a). CERDEP
guidelines.
Columbia, SC: Author.
South Carolina Department of Education. (2018b). School
headcount by gender and
ethnicity d45_2018_19 [Data File]. Retrieved from
https://ed.sc.gov/data/other/student-counts/active-student-
headcounts/
South Carolina Department of Education. (2018c). School
headcount by grade
d45_2018_19 [Data File]. Retrieved from
https://ed.sc.gov/data/other/student-counts/active-student-
headcounts/
South Carolina Department of Education. (2019). Precode
manual (DRAC No. SCDE-
08-0005). Columbia, SC: Office of Assessment.
South Carolina Education Oversight Committee. (2019).
Analysis of Kindergarten
Readiness Assessment (KRA) results: School year 2018-2019.
Columbia, SC: Author.
Spatz, C. (2019). Exploring statistics: Tales of distributions.
Conway, AR: Outcrop
Publishers.
U.S. Department of Education. (2015). A matter of equity:
Preschool in America.
Washington, DC: Author.
U.S. Department of Education. (n.d.). Every Student Succeeds
Act (ESSA). Retrieved
October 12, 2019, from https://www.ed.gov/essa
Vasagar, J. (2012, June 27). Michael Gove’s Masai inspiration.
[blog post]. Retrieved
from
https://www.theguardian.com/education/mortarboard/2012/jun/2
7/michaelgove-kenya
Vygotsky, L. S. (1978). Mind in society: Development of higher
psychological processes.

(M. Cole, V. John-Steiner, S. Scribner, & E. Souherman, Eds.
and Trans.). Cambridge, MA: Harvard University Press.
Vygotsky’s Sociocultural Theory. (n.d.) Retrieved from
http://www.ceebl.manchester.ac.uk/events/archive/aligningcolla
borativelearning/Vygotsky.pdf
WestEd Standards Assessment and Accountability Services.
(2014). Ready for
kindergarten: Kindergarten Readiness Assessment technical
report fall 2014. Retrieved from https://ed.sc.gov/tests/tests-
files/pre-k-and-kindergarten-readiness-assessments/kra-
technical-report-2014/
WestEd Standards Assessment and Accountability Services.
(2019). Kindergarten
Readiness Assessment South Carolina: Technical report 2018-
2019. Retrieved from https://ed.sc.gov/tests/tests-files/pre-k-
and-kindergarten-readiness-assessments/kra-sc-technical-report-
2018-19/
Wood, E. (2007). Reconceptualising child-centred education:
Contemporary directions in
policy, theory, and practice in early childhood. FORUM
49(1&2), 119-134. http://doi.org/10.2304/forum.2007.49.1.119

DSRT 837 Methodology Plan RubricName _________________________

DSRT 837 Methodology Plan RubricName _________________________

Recommended

Recommended

More Related Content

Similar to DSRT 837 Methodology Plan RubricName _________________________

Similar to DSRT 837 Methodology Plan RubricName _________________________ (20)

More from AlyciaGold776

More from AlyciaGold776 (20)

Recently uploaded

Recently uploaded (20)

DSRT 837 Methodology Plan RubricName _________________________