PAGE Running head School Readiness and Later Achievement1.docx

PAGE
Running head: School Readiness and Later Achievement
1
School Readiness and Later Achievement
School Name
Course:
Date: 13th March 2013
The linkages between school-entry academic, attention, and
socioemotional skills and later school reading and math
achievement suggest that math skills have the greatest
predictive power, followed by reading and then attention skills
(Japel, 2007). In contrast, socioemotional behaviors, including
internalizing and externalizing problems and social skills, are
insignificant predictors of later academic performance, even
among children with relatively high levels of problem behavior
(Japel, 2007). These patterns are usually associated with boys
and girls from children from low and high socioeconomic
backgrounds.
Assessing “Research-Based” Curricula
Most early childhood programs are being asked to choose
curricula that are “research based.” This requirement is the
result of increased attention to children’s academic needs as
they enter kindergarten, and has the potential of improving our
delivery of curricula to young children (Japel, 2007). However,
the meaning of “research based” has not been delineated.
Therefore, most publishers of curricula for young children have
adopted the language, and identify their programs as “research
based.” A careful analysis of the underlying research of three
important math curricula could help practitioners make more
informed choices. This analysis will also provide a list of

criteria for selection of other early childhood curricula, which
will require practitioners to take a brief look at the type of
research that purports to provide the research base for the
curricula (Japel, 2007).
For Example, the Pre-K Mathematics is a scripted math program
for four year olds. Its primary goal has been to close the gap in
math achievement between low-income children and middle
class children. Much research has documented this gap, which
exists as children enter school and grows as children progress
through school. Such kind of research demonstrates years of
careful research that their curriculum can start to close this gap.
Pre-K Mathematics has a clearly delineated scope and sequence.
The scope and sequence is carefully connected to the
development of mathematical concepts that are needed in formal
math education in elementary school, concepts that low-income
children often lack. The lessons are designed to be presented to
very small groups of children for short periods of time. The
lessons are supported with daily math activities that are
plentiful in the children’s environment. These researchers have
many years’ experience and long list of published research that
document achievement gaps, math concept development, and
demonstration projects in math achievement.
Conclusion
The research-based curricula described here have the potential
to provide preschoolers with a math curriculum that can prepare
them for the more structured lessons of elementary school. Most
professionals agree that a curriculum is only the beginning of
the process. Children also need teachers who are sensitive,
responsive, and knowledgeable about development and the
concepts that children need to be successful in kindergarten.
Therefore, teachers who have a deep gratitude for
developmentally appropriate practices can be able to employ
these curricula to the advantage of the children in their

classrooms.
References
Japel, Crista (2007). School Readiness and Later Achievement.
Developmental Psychology., Vol. 43, No. 6, 1428 –1446
PAGE
Running head: Assessing “Research-Based” Curricula
1
School Name
Course:
Date: 13th March 2013
The linkages between school-entry academic, attention, and
socioemotional skills and later school reading and math
achievement suggest that math skills have the greatest
predictive power, followed by reading and then attention skills
(Japel, 2007). In contrast, socioemotional behaviors, including
internalizing and externalizing problems and social skills, are
insignificant predictors of later academic performance, even
among children with relatively high levels of problem behavior
(Japel, 2007). These patterns are usually associated with boys
and girls from children from low and high socioeconomic
backgrounds.
Most early childhood programs are being asked to choose
curricula that are “research based.” This requirement is the
result of increased attention to children’s academic needs as

they enter kindergarten, and has the potential of improving our
delivery of curricula to young children (Japel, 2007). However,
the meaning of “research based” has not been delineated.
Therefore, most publishers of curricula for young children have
adopted the language, and identify their programs as “research
based.” A careful analysis of the underlying research of three
important math curricula could help practitioners make more
informed choices. This analysis will also provide a list of
criteria for selection of other early childhood curricula, which
will require practitioners to take a brief look at the type of
research that purports to provide the research base for the
curricula (Japel, 2007).
Pre-K Mathematics (Klein, A., Starkey, P., and Ramirez, M.,
2003) is a scripted math program for four year olds. Its primary
goal has been to close the gap in math achievement between
low-income children and middle class children. Much research
has documented this gap, which exists as children enter school
and grows as children progress through school. These
researchers have demonstrated through several years of careful
research that their curriculum can begin to close this gap
(Starkey, Klein, and Wakeley, 2004). Pre-K Mathematics has a
clearly delineated scope and sequence. The scope and sequence
is carefully connected to the development of mathematical
concepts that are needed in formal math education in elementary
school, concepts that low-income children often lack. The
lessons are designed to be presented to very small groups of
children for short periods of time. The lessons are supported
with daily math activities that are plentiful in the children’s
environment. These researchers have many years’ experience
and long list of published research that document achievement
gaps, math concept development, and demonstration projects in
math achievement. Starkey, Klein and Wakeley (2004) have
field-tested this curriculum for at least five years. The
classroom teachers in their studies have had continuous
training, close supervision, and much success with children. In

one year of instruction children using the Pre-K Mathematics
Program have nearly closed the conceptual gap between
themselves and middle class children entering kindergarten
(Starkey, Klein and Wakeley, 2004).
Conclusion
The research-based curricula described here have the potential
to provide preschoolers with a math curriculum that can prepare
them for the more structured lessons of elementary school. Most
professionals agree that a curriculum is only the beginning of
the process. Children also need teachers who are sensitive,
responsive, and knowledgeable about development and the
concepts that children need to be successful in kindergarten.
Teachers who have a deep appreciation for developmentally
appropriate practices will be able to employ these curricula to
the advantage of the children in their classes (Bredekamp &
Copple, 1997).
References
Klein, A., Starkey, P., and Ramirez, A. (2003). Pre-K
Mathematics Curriculum. Glendale, Il:
ScottForesman.
Starkey, P., Klein, A. and Wakeley, D. (2004). Enahancing
young children’s mathematical
knowledge through a pre-kindergarten mathematics
intervention. Early childhood
research Quarterly, 19, 99-120.
Japel, Crista (2007). School Readiness and Later Achievement.
Developmental Psychology., Vol. 43, No. 6, 1428 –1446

PAGE
Running head: TYPE ABBREVIATED TITLE HERE
1
Title of the Research Proposal in Full Goes Here
Group Names Go Here
Southwestern Oregon Community College
HDFS 247 Preschool Child Development
Date
Title of the Paper
Double space, indent each paragraph l ½ inch, and start typing.
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topic per paragraph, and avoid long paragraphs so you can hold
the reader’s attention. A paragraph should be a minimum of
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hypothesis? Research questions you want to answer?
LOGICAL RELATIONSHIP

TRANSITIONAL EXPRESSION
Similarity
also, in the same way, just as … so too, likewise, similarly
Exception/Contrast
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nevertheless, nonetheless, notwithstanding, in contrast, on the
contrary, still, yet
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Time
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immediately, later, meanwhile, now, recently, simultaneously,
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there
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From http://writingcenter.unc.edu/handouts/transitions/
Next section Here, answering the next question or questions
from the list
It I OK to combine questions from the list below into sections,
but just be sure that each section heading clearly labels what the
section contains. For Example, it might say: Type of
Resaearch and Population. The next section could include

Sample, Groups, etc. Here is the list of what needs to be
included in your proposal
What is your hypothesis? Research questions you want to
answer?
What type of research will you use to answer the research
questions? (Quantitative or qualitative? Experiment, case
study, longitudinal, etc.?)
What is your population?
What is your sample?
Define your experimental group.
Define your control group.
How will you assign participants to groups?
What is your independent variable?
What is your dependent variable?
How will you collect data?
How will you operationalize these terms?
Are there any confounds or nuisance variables operating?
What ethical concerns may be present to consider in doing this
study?
Another Heading….
And so forth until the conclusion.
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made in your work. Click here for information on writing a
conclusion.
References
Berger, R., Miller, A., Seifer, R., Cares, S., & Lebourgeois, M.
(2012). Acute sleep restriction effects on emotion responses in
30- to 36-month-old children. Journal Of Sleep Research,
21(3), 235-246. doi:10.1111/j.1365-2869.2011.00962.x
Feak, C. and Swales, J. (2009). Telling a research story:
Writing a literature review. Ann Arbor, MI: University of
Michigan Press.
Kavanagh, K., Absalom, K., Beil, W., & Schliessmann, L.
(2008). Connecting and becoming culturally competent: A
Lakota example. Advances in Nursing Science, 21, 9-31.
Retrieved March 26, 2012 from ProQuest/Nursing Journals
database.
Learn how to write a review of literature. (n.d.). Retrieved
September 12, 2012 from The Writer’s Handbook Website. site:
http://writing.wisc.edu/Handbook/ReviewofLiterature.html
"Review." The Oxford Pocket Dictionary of Current English.
2009. Retrieved September 18, 2012 from Encyclopedia.com:
http://www.encyclopedia.com/doc/1O999-review.html

Minnesota School Readiness Study:
Developmental Assessment at Kindergarten Entrance
Fall 2010

Acknowledgements
Minnesota School Readiness Study:
Developmental Assessment at Kindergarten Entrance
The Minnesota School Readiness Study: Developmental
Assessment at Kindergarten
Entrance Fall 2010 was planned, implemented, and the report
prepared by the Minnesota
Department of Education (MDE).
Special thanks to the 108 elementary schools involved in the
study, their principals,
kindergarten teachers, support staff and superintendents. The
observation and collection
of developmental information by teachers on kindergarten
children in the classroom was
essential to the study and is much appreciated.
All analyses in this report were conducted by the Human Capital
Research Collaborative
(HCRC), a partnership between the University of Minnesota
and the Federal Reserve

Bank of Minneapolis.
For more information, contact Avisia Whiteman at
[email protected] or
651-582-8329 or Eileen Nelson at [email protected] or 651-
582-8464. Ama nda
Varley, University of Minnesota Graduate School intern, also
provided significant
support to t he project.
Date of Report: November 2011
mailto:[email protected]
mailto:[email protected]
Background
Minnesota School Readiness Study: Developmental
Assessment at Kindergarten Entrance - Fall 2010

Research has shown, and continues to show, that t here is a
critical relationship between
early childhood experiences, school success, and positive life-
long outcomes. This
research has been a focal point for many states as they strive to
reduce the growing
achievement gap between less advantaged students and their
same-aged peers in the
educational system.
With no systematic process in place to assess children’s
school readiness, the Min nesota Department of Education
(MDE) in 2002 initiated a series of three yearly studies
focused on obtaining a picture of the school readiness of a
representative sample of Minnesota entering
kindergartners. Also, the series of studies was to evaluate
changes in the percentage of children fully prepared for
school at kindergarten entrance. The studies were well-
received by the public, and during the 2006 Minnesota
state legislative session, funding was appropriated for the
study to be continued on an annual basis.

This report describes findings from the assessment of
school readiness usin g a representative sample of children
entering kindergarten in Minnesota in Fall 2010. The data
provide a picture of the ratings of entering kindergartners
across five domains of child
development. The study provides information on school
readiness for parents; school
teachers and administrators; early childhood education and care
teachers, pr oviders and
administrators; policymakers; and the public.
Definition of School Readiness
For purposes of the study, “school readiness” is defined as the
skills, knowledge,
behaviors and accomplishments that children should know and
be able to do as they enter
kindergarten in the following areas of child development:
physical development; the arts;
personal and social development; language and literacy; and
mathematical thinking.

Assessing School Readiness
The study is designed to capture a picture of the readiness of
Minnesota children as they
enter kindergarten and track readiness trends over time. To
ensure that results are reliable
and can be generalized to the entire population of Minnesota
kindergartners, the study
uses a 10 percent sample of schools with entering
kindergartners. This sample size
generates data from approximately 6,000 kindergartners
annually.
Minnesota School Readiness Study 2010
The study uses the Work Sampling System (WSS®), a
developmentally appropriate,
standards-based observational assessment that allows children
to demonstrate their
knowledge and skills in various ways and across
developmental domains.

WSS® is aligned with the state’s early learning standards,
Minnesota Early Childhood
Indicators of Progress, and the K-12 Academic Standards. S ee
Appendix A.
Each domain and developmental indicator within the WSS®
Developmental Checklist
includes expected behaviors for children at that age or grade
level. For each indicator,
teachers used the following guidelines to rate the child's
performance:
Proficient — indicating that the child can reliably and
consistently demonstrate the skill,
knowledge, behavior or accomplishment represented by the
performance indicator.
In Process — indicating that the skill, knowledge, behavior
or accomplishment
represented by the indicator are intermittent or emergent, and
are not demonstrated
reliably or consistently.
Not Yet — indicating that the child cannot perform the
indicator (i.e., the performance

indicator represents a skill, knowledge, behavior or
accomplishment not yet acquired).
Because children’s rate of development is variable, the study
assesses children’s
proficiency within and across the developmental domains.
Rubrics for each rating level were distributed to teachers at the
start of the study. The
rubrics, provided by the publisher and revised in 2009, provide
additional detail for each
indicator for a Not Yet, In Process or Proficient rating.
Partnership with the Human Capital Research Collaborative
Throughout 2010, MDE worked in
partnership with the Human Capital
Research Collaborative (HCRC) to better
understand the relationship between
kindergarten entry results and future
academic achievement. HCRC is a
partnership of the University of Minnesota

and the Federal Reserve Bank of
Minneapolis. It was important to assess the
predictive validity of Minnesota’s school
readiness indicators and determine the
degree to which the School Readiness Study checklist added
additional weight beyond
demographics towards the likelihood of passing Grade 3 MCAs.
Work was conducted to
determine which type of measure from the checklist best
predicted Grade 3 MCA results.
Findings centered on children who reach 75 percent of the total
possible points on the
checklist having a greater likelihood in passing Grade 3 MCAs.
W hile national research
2
over decades has pointed to the relationship between early
experiences and academic

success, it is instructive to have a reference standard within the
existing checklist.
Based on data from Kindergarten cohorts in 2003, 2004, and
2006 who had available
achievement test scores in third grade or information on
remedial education, HCRC
found that the School Readiness Study checklist, including the
75 percent standard,
significantly and consistently predicted third-grade MCA
reading and math test scores
and the need for school remedial services (special education or
grade retention) above
and beyond the influence of child and family background
characteristics. The strength of
prediction was consistent across a range of child and family
characteristics (e.g., family
income, gender, and race/ethnicity). For more information on
this report, go to:
http://www.humancapitalrc.org/mn_school_readiness_indicators
.pdf
2010 Recr uitment

MDE contacted superintendents, principals and teachers
beginning mid-winter to build
the sample for the coming fall. A list of all public schools
with kindergartners as of
October 1 the previous year was compiled. The list was
divided into eight strata which
accounts for proximity to population centers and population
density and separated
charter and magnet schools. A representative sample of
schools within each strata was
invited to participate via a mailed invitation to the
superintendent and principal of each
site. Follow-up calls were made to each site to answer
questions. In 2010, 55 percent
(495/900) of all schools were invited to participate.
Approximately 24 pe rcent (120/495)
of those invited responded positively to the initial invitation.
In late spring, schools are
selected to be released from the cohort when student counts
exceed the sample amount.
In 2010, no s chools were released. By November, 12 pe rcent
of all elementary schools
(108/900) submitted child-level data.

The following table shows the total kindergarten population
compared to the sample
population. The sample seeks to be representative of all public
schools including charters
and magnets across federally mandated demographic categories.
(See Table 1.)
3
.pdf
Table 1 - Kindergarten Population Compared to the
Sample
State
Study
Kindergarten
Sample
Enrollment
American Indian 2.3% 5.4%
Asian 7.1% 5.6%
Hispanic 8.5% 7.0%

Black 10.9% 8.8%
White 71.1% 71.7%
Limited English Proficiency 11.7% 6%
Special Education 10.4% 7%
2010 Res ults
A total of 5,838 kinder gartners from 108 se lected elementary
schools across the state
were included in the Fall 2010 cohort. This reflects 9.2 pe rcent
of the entering
kindergartners for the 2010-2011 sc hool year. Of
these children, 5,654 students had all WSS
indicators completed for analysis. For the Fall of
2010, 60 percent of Minnesota’s kindergartners
reached the 75 percent standard. For selected
categories, see Chart 1. The selected categories
in Chart 1 are based on the statistically
significant categories from the regression. The
regression is discussed in more detail on page 9.

The domain rankings by proficiency for the 2010 cohort are
reordered with previous
years of the study. (See Table 2 and Chart 2.) Physical
Development had the highest
percentage of children assessed Proficient on average, followed
in order by Language &
Literacy; The Arts; Personal and Social Development and
Mathematical Thinking.
Indicator order within each domain changed only slightly from
2009 in Mathematical
Thinking; Personal and Social Development and Language and
Literacy. ( See Table 3.)
Proficiency by domain is defined as the average percent
proficient across indicators
within each domain.
It is important to note that while there are trends towards
increases in estimates of
Proficient results, the trends are not outside the margin of
error. Also, the existing data
set does not allow for examination of potential reasons for
shifts.

4
Table 2 - Results By Domain
Margin
Domain/Result Proficient of Error
Physical Development 70%
2.7%
Language & Literacy 59% 2.9%
The Arts 56% 2.9%
Personal & Social
Development 56% 2.9%
Mathematical Thinking 52% 2.9%

Note categories are adjusted for stratified cluster sampling.
75 Percent Standard 60% 2.9%
Chart 1 – Percent of Students Reaching 75 Percent Standard by
Selected Sub-Cate
The 75 percent standard is defined as the percent reaching at
least 75
percent of the possible points on the checklist, a predictor of
grade 3
MCAs.
gories
5

Table 3 Domain & Indicator Results
Ranked by Proficiency
Percent
Physical Development Proficient
Physical Development
Average Score Summary 70%
Performs some self-care tasks independently. 73%
Coordinates movements to perform simple tasks. 71%
Uses eye-hand coordination to perform tasks. 67%
The Arts
The Arts Domain
Participates in group music experiences. 63%
Participates in creative movement, dance and drama. 60%
Uses a variety of art materials for tactile experience and
exploration. 59%

Responds to artistic creations or events. 56%
Personal and Social Development
Personal and Social Development Domain
Interacts easily with familiar adults. 63%
Shows eagerness and curiosity as a learner. 62%
Interacts easily with one or more children. 62%
Shows empathy and caring for others. 60%
Follows simple classroom rules and routines. 58%
Manages transitions. 57%
Shows some self-direction. 56%
Seeks adult help when needed to resolve conflicts. 53%
Attends to tasks and seeks help when encountering a
problem. 52%
Approaches tasks with flexibility and inventiveness. 50%
-
6

Language and Literacy
Language and Literacy Domain Average Score Summary 59%
Shows appreciation for books and reading. 66%
Speaks clearly enough to be understood without contextual
clues. 65%

Shows beginning understanding of concepts about print.
61%
Comprehends and responds to stories read aloud. 60%
Begins to develop knowledge about letters. 60%
Gains meaning by listening. 59%
Represents ideas and stories through pictures, dictation and
play. 57%
Follows two- or three-step directions. 55%
Uses expanded vocabulary and language arts for a variety
of purposes. 52%
Uses letter-like shapes, symbols and letters to convey
meaning. 52%
Demonstrates phonological awareness. 21%
Mathematical Thinking
Mathematical Thinking Domain Average Score Summary 52%
Begins to recognize and describe the attributes of shapes.
60%
Shows beginning understanding of number and quantity.
58%
Shows understanding of and uses several positional words.
57%

Begins to use simple strategies to solve mathematical
problems. 50%
7
Chart 2 – Proficiency Rates by Domain
Descriptive Results
The 2010 c ohort was also analyzed for descriptive results
based on single demographic
categories. For example, to report under the income charts, all
parents are included in the
under 100 percent Federal Poverty Guidelines grouping
without controlling for education
status, home language or race/ethnicity. The family survey asks
parents to select all
race/ethnicity categories that are relevant for their child. If
multiple categories are
selected, the child will be represented in the

appropriate categories. A similar process was
followed for primary home languages. The
percent within each demographic category
reaching the 75 percent standard are reported in
Appendix B.
Family Survey Results
As part of the study process, families are asked to
complete a voluntary survey. This information is
8
combined with the Work Sampling System® checklist results
(see Appendix C). I n total,
4,932 pa rents (84 p ercent) completed the survey. Of this
group, 4,695 responses (95

percent) were usable for analysis. (A parent survey may not
be usable for analysis
because it was incomplete, the student information strip was
incomplete or the survey
lacked coordinating information in Work Sampling Online
(WSO).) After matching the
family survey data with Work Sampling Online results, 4,168 re
cords remained for
regression analysis. This is 85 pe rcent of all submitted parent
surveys and 89 pe rcent of
those available to match.
Logistic Regression Results
The analysis of the data included examining how a particular
child or family
characteristic may affect that child’s ratings while controlling
for the effects of other
demographic variables with which it may be confounded (e.g.,
a child from a family with
a lower household income is more likely to have a parent with
a lower education level).
The result of reaching the 75 percent proficiency standard
across all domains was

analyzed with respect to the demographic characteristics of
gender, parent education
level, household income, primary home language and race and
ethnicity collected from
parent surveys. (See Table 4 and Appendix D.) For
comparison to previous years, see
Appendix E.
All 2010 a nalyses reported involved statistical estimation
procedures that reflect the
stratified cluster sampling design used (with school as the
primary sampling unit), and
include correction for finite population sampling. Observations
within each stratum were
weighted to reflect the statewide proportion of students in the
stratum.
Table 4 - Statistically Significant Factors in Reaching the 75
Percent Standard
Household Income
Parent Education Level
Gender
Note: predictors significant at p < .05

Household Income
The odds of reaching the 75 percent standard for a student
whose household income was
at or above 400 percent of the Federal Poverty Guidelines
(FPG) were more than one and
a half times as great as compared to a
student whose household income was less
than 250 pe rcent FPG when holding all
other variables constant. The odds of
reaching the 75 percent standard for a
student whose household income was 250
400 percent FPG are nearly one and half
times as great as compared to a student
whose household income is up to 250
-
9

percent FPG. This result is statistically
significant.
Parent Education Level
Parent education level was found to be
statistically significant in reaching the 75 percent
standard. Students whose parents have a high
school degree a re twice as likely to reach the 75
percent standard as compared to students whose parents have
less than a high school
degree. Students with parents who have a an Associate degree,
Bachelor or graduate
degree are approximately one and a half ti mes as likely to
reach the 75 percent standard
as compared to students whose parents who have a high school
diploma or GED.
Primary Home Language
Primary home language was not found to be statistically
significant in reaching the 75

percent standard when holding all other variables constant.
Race and Ethnicity
Parent-report of race and ethnicity was not a statistically
significant factor in reaching the
75 percent standard when holding all other variables constant.
Minority status as an
overall category was marginally significant.
Gender
Gender continues to be a statistically significant factor. The
odds of reaching the 75
percent standard for females were up to one and a third times g
reater, as compared to
males.
Principal and Teacher Surveys
As in previous years, the success of the study rested with the
willingness of school
principals and kindergarten teachers to participate.
Participating school principals and

kindergarten teachers w ere again given surveys to complete
regarding their decision to
participate, barriers to participation, and the associated
workload and benefits. The
following information is based upon the response of 35 pr
incipals (108 possible
responses or 32 p ercent) and 165 kinder garten teachers (288
potential responses or 57
percent).
Principal Perspectives
Principals reported two primary benefits of participating in the
study: helping influence
statewide policy (100 percent) and gaining information about
where students are at the
beginning of the school year (69 percent). Reported barriers
for participation included
10

adding to existing teacher workloads (63 p ercent). Principals
balanced the need of the
project with competing needs by having more experienced
teachers mentor newer
teachers, paying teachers for their extra time and shifting staff
development resources.
Principals will use the information gained from the study to
identify children’s needs
earlier in the year (50 pe rcent). Principals using Work
Sampling Online (WSO) reported
that the online training was easy to access. A m ajority of
principals (84 pe rcent) reported
receiving the appropriate amount of information prior to and
during their participation.
Teacher Perspectives
A vast majority of teachers (86 pe rcent) responded that
contributing to a study that will
influence statewide early childhood policy was of benefit to
them. The same percent
reported receiving a $200 stipend as a benefit. Others reported
the benefit of gaining
information about where students are at the beginning of the

school year (68 percent). A
little over one-third of the teachers reported that collecting the
parent surveys was a
challenge for them (37 percent). On a follow-up question, 80 pe
rcent responded that they
were able to implement the parent survey with great to
moderate ease. Thirty-one percent
had no challenges implementing the study. Teachers reported
that the study took a
minimal (12 pe rcent) to average (72 pe rcent)
amount of work for a special project.
Teachers report planning to use the
information to identify children’s needs
earlier in the year (46 pe rcent) and helping
them target instruction (47 percent).
Regarding the use of technology, 96 percent
report great to moderate ease in accessing
WSO and the Web-based orientation.
Teachers report receiving adequate levels of information prior
to (95 pe rcent) and during

the study (98 p ercent). They also report receiving adequate
support from MDE (92
percent) throughout the study period. Currently, 28 percent of
teachers use Work
Sampling in their schools, 35 pe rcent report planning to
continue using WSO after the
study period. Approximately one-third of all teachers report
using locally designed
assessment tools in additional to the Work Sampling System®.
Limitations
Because children develop and grow along a continuum but at
varied ra tes, the goal of the
study is to assess children’s proficiency within and across these
developmental domains
over time and not establish whether or not children,
individually or in small groups, are
ready for school with the use of a “ready” or “not ready”
score. Nor is the study’s goal to
provide information on the history or the future of an
individual student.
Recent national reports have discussed the complexities in the

development of state-level
accountability systems. Taking Stock: Assessing and Improving
Early Childhood
11
Learning and Program Quality (2007) and The National
Academy of Science report Early
Childhood Assessment: Why, What and How? (2008) details
the necessary steps to use
authentic assessment results, also referred to as instructional
assessments, in
accountability initiatives. The National Academy of Science
reports that even in upper
grades, e xtreme caution is needed in relying exclusively on
child assessment and that for
children birth to five “even more extreme caution is needed.”
Discussion

In line with national research, family household income and
parent education was found
to be predictive in reaching the 75 percent standard.
Race/Ethnicity as an overall category
was marginally significant but not significant for individual
groups and G ender is
predictive in reaching the 75 percent standard.
Recommendations
1. Continue to work toward improving the quality of early
childhood education and care
programs in Minnesota by emphasizing the importance of
teacher-child interactions and
content-driven, intentional curriculum and instruction. Build on
the 10 Essential Elements
of Effective Early Childhood Programs and Governor Dayton’s
7-Point Plan for
Achieving Excellence.
2. Target intervention strategies to children assessed as Not
Proficient, especially in the
areas of literacy and mathematics. Implement compensatory
strategies as soon as a

child’s need is identified. Work with the Governor’s Early
Learning Council to identify
staged implementation strategies to maximize resources.
3. Support more children in their efforts to read well by third
grade by focusing state
policies on young children’s language and literacy
development.
4. Strengthen teacher-child interactions to improve
learning by implementing professional development
that includes teacher observation and development.
5. Individualize instruction by using assessment
information to design classroom experiences.
6. Use child progress assessment information when
teachers talk with parents about setting goals for
children.
7. Increase collaborations from early childhood
through Grade 3 at the teacher, director, principal and

superintendent levels. Identify district and state
policy opportunities to promote this work.
12
8. Consider collecting information on prior early care and
education experiences and
incorporating that information into the early childhood
longitudinal data system. Results
from the 2010 prior experience data pilot need to be considered
when planning for the
future.
Early Learning Council
T he Early Childhood Advisory Council (ECAC), seated from
December 2008 to January
2011, looked to the a nnual School Readiness study as one

measure of state progress on
early learning. The Council was reauthorized and renamed the
Early Learning Council by
Governor Dayton’s Executive Order 11-05. Read the Executive
Order on the Governor’s
website. The newly formed Early Learning Council (ELC)
may continue to look to the
results of the study to guide school readiness policy.
13
http://mn.gov/governor/multimedia/pdf/Executive-Order-11-
05.pdf
http://mn.gov/governor/multimedia/pdf/Executive-Order-11-
05.pdf
For further reading
Campbell, F. A., Ramey, C. T., Pungello, E., Sparling, J.,
& Miller-Johnson, S. (2002).Early childhood

education: Young adult outcomes from the Abecedarian
project. Applied Developmental Science, 6(1), 42
57.
Coley, R. J. (2002). An uneven start: Indicators of
inequality in school readiness. Princeton, NJ:
Educational Testing Service.
Dichtelmiller, M. L., Jablon, J. R., Marsden, D. B., &
Meisels, S. J. (2001). Preschool-4 developmental
guidelines (4th Ed.). New York: Rebus.
Gershoff, E. (November 2003). Living at the edge research
brief no.4: Low income and the development of
America’s kindergartners. New York: National Center for
Children in Poverty.
Meisels, S.J. & Atkins-Burnett, S. (2006). Evaluating early
childhood assessments: A differential Analysis.
In K. McCartney & D. Phillips (Eds.), The Blackwell
handbook of early childhood development (pp. 533
549). Malden, MA: Blackwell Publishing.
Minnesota Department of Education (2003). Minnesota

School Readiness Initiative: Developmental
Assessment at Kindergarten Entrance. Roseville: Minnesota
Department of Education.
Minnesota Department of Education. (2004). Minnesota
School Readiness Year Two Study: Developmental
Assessment at Kindergarten Entrance Fall 2003. Roseville:
Minnesota Department of Education.
Minnesota Department of Education. (2005). Minnesota
School Readiness Year Three Study:
Developmental Assessment at Kindergarten Entrance Fall
2004. Roseville: Minnesota Department of
Education.
School Readiness Study: Developmental
School Readiness Study: Developmental

Minnesota Department of Education and Minnesota
Department of Human Services. (2005). Early
childhood indicators of progress: Minnesota’s early learning
standards. Roseville: Minnesota Department
of Education.
National Early Childhood Accountability Task Force. (2007)
Taking Stock: Assessing and Improving Early
Childhood Learning and Program Quality. Washington DC:
The Pew Charitable Trusts.
National Research Council. (2008). Early Childhood
Assessment: Why, What, and How. Committee on
Developmental Outcomes and Assessments for Young
Children, C.E. Snow and S.B. Van Hemel, Editors.
Board on Children, Youth, and Families, Board on Testing
and Assessment, Division of Behavioral and
Social Sciences and Education. Washington, DC: The
National Academies Press.
National Research Council & Institute of Medicine. (2000).
From neurons to neighborhoods:
The science of early childhood development. Washington,
DC: National Academy Press.
Reynolds, A., Englund, M., Hayakawa, C., Hendricks, M.,

Ou, S., Rosenberger, A., Smerillo, N., Warner-
Richter, M. Assessing the Validity of Minnesota School
Readiness Indicators: Summary Report. Human
Capital Research Collaborative. January 2011. Retrieved
May 2011,
.pdf
-
-
.pdf
Reynolds, A. J., Temple, J. A., Robertson, D. L., &
Mann, E. A. (2001). Long-term effects of an early
childhood intervention on educational achievement and
juvenile arrest: A 15-year follow-up of low-income
children in public schools. Journal of the American Medical
Association, 285(18), 2339-2346.
Schweinhart, L. J., Montie, J., Xiang, Z., Barnett, W. S.,

Belfield, C. R., & Nores, M. (2005). Lifetime
effects: The high/scope perry preschool study through age 40.
Ypsilanti, MI: High/Scope Press.
U.S. Department of Education, U.S. National Center for
Education Statistics, Home Literacy Activities and
Signs of Children’s Emerging Literacy, 1993, NCES 2000-
026, November 1999; and the Early Childhood
Program Participation Survey, National Household Education
Surveys Program, 2005, unpublished data.
http://www.census.gov/compendia/statab/tables/09s0229.xls
U.S. Department of Health and Human Services. (2009). The
2009 HHS Poverty Guidelines. Retrieved
January 8, 2011, from
http://aspe.hhs.gov/poverty/09Poverty.shtml.
Wertheimer, R., & Croan, T. (December 2003). Attending
kindergarten and already behind: A statistical
portrait of vulnerable young children. Washington, DC: Child
Trends.
Zill, N., & West, J. (2000). Entering kindergarten: A
portrait of American children when they begin school.
Washington, DC: U.S. Department of Education, National

Center for Education Statistics.
http://www.census.gov/compendia/statab/tables/09s0229.xls
http://aspe.hhs.gov/poverty/09Poverty.shtml
A. Sample Work Sampling System® Developmental
Checklist (Minnesota P4)
B. Work Sampling System Subgroup Analysis with
Sampling Weight (2010)
C. Family Survey (English)
D. Logistic Regression Predicting Proficiency at the 75
Percent Standard
(Weighted)
E. Statistically Significant Factors from Logistic
Regression
Appendices

Appendix B
Work Sampling System Subgroup Analysis with Sampling
Weight (2010)
75% Overall
Proficiency
(weighted)
All children 59.9
Race/ethnicity
White (N=2841) 62.7
Asian/ Native Hawaiian/Pacific Islander (N=221) 62.0
Black/African/African American (N=349) 57.0
Other (N=64) 53.8
American Indian/Alaskan Native (N=203) 44.4
Hispanic/Latino (N=278) 43.6

Gender
Female (N=2754) 65.4
Male (N=2902) 54.5
IEP Status (Special education)
No (N=5258) 61.9
Yes (N=398) 29.9
Family Income
Over 250% Federal Poverty Guideline (N=1554) 69.2
250% Federal Poverty Guideline and under
52.3
(N=1735)
Parent Education
Less than high school (N=200) 32.4
High School Diploma/GED (N=671) 48.7
Trade school or some college (N=1013) 55.7
Associate’s degree (N=581) 61.2
Bachelor’s degree (N=1024) 67.6
Graduate or professional degree (N=466) 70.7

Strata
1 – Minneapolis and St. Paul (N=655) 57.4
2 – 7 country metro excluding MSP
1
(N=1551) 69.3
3 – Outstate enrollment 2,000+ (N=1306) 51.5
4 – Outstate enrollment 1,000-1,999 (N=1092) 45.9
5 - Outstate enrollment 500-999 (N=605) 52.4
6 - Outstate enrollment <500 (N=445) 63.6
* Note, 250% FPG for a family of four for this time period is
$55,125.
1
The seven county metro area includes Anoka, Carver,
Dakota, Hennepin, Ramsey, Scott and Washington Counties.
Appendix C
Parent Survey - Minnesota School Readiness Study

1. Please indicate whether you are this child’s:
___ Mother ___ Father ___ Other
2. Your highest level of school completed? Mark only
one.
___ Less than high school
___ High school diploma/GED
___Trade school or some college beyond high school
___ Associate degree
___ Bachelor’s degree
___ Graduate or professional school degree
3. Your household’s total yearly income before taxes from
January-December last year? Round to
the nearest thousand.
$________________________
4. How many people are currently in your household?

1 2 3 4 5 6 7 8 Indicate:_____________
5. Race/ethnicity of your kindergarten child? Mark all
that apply.
___ Black/African/African American
___ American Indian/Alaskan Native
___ Asian
___ Native Hawaiian or other Pacific Islander
___ Hispanic or Latino
___ White/Caucasian
___ Other
6. What language does your family speak most at home?
___ English __ Vietnamese
___ Spanish __ Russian
___ Hmong __ Other
___ Somali

Thank you for your time in working with us on this
study.
For school use only:
Dist #_______ School #________ Gender: M F DoB:
____/____/____ MARSS:
_______________________________________
(include all 13 digits, including leading zeros)
Appendix D
Logistic Regression Predicting Proficiency at the 75 Percent
Standard
(Weighted)
VARIABLES b se(b) Wald df p Odds Ratio

Parent Education 38.12*** 5 0.000
Less than High School -0.67*** 0.23 8.09 1 0.004 0.51
High School or GED #
Some Post High
School 0.14 0.13 1.28 1 0.258 1.16
Associate Degree 0.37** 0.15 6.47 1 0.011 1.45
Bachelor Degree 0.54*** 0.14 15.12 1 0.000 1.71
Grad/Prof Degree 0.60*** 0.17 12.54 1 0.000 1.82
Percent of Federal
Poverty Guidelines 20.23*** 2 0.000
0-250 #
>250-400 0.37*** 0.11 11.49 1 0.001 1.44
>400 0.49*** 0.12 16.95 1 0.000 1.63
Home Language 1.24 1 0.266
Non-English #
English Only 0.21 0.19 1.24 1 0.266 1.24
Minority Status 5.07* 2 0.079
Minority Only -0.18 0.12 2.22 1 0.136 0.84

White and Minority 0.21 0.15 1.98 1 0.160 1.24
White Only #
Gender 15.15*** 1 0.000
Male #
Female 0.32*** 0.08 15.15 1 0.000 1.37
Intercept -0.35 0.22 2.52 1 0.113
Number of observations: 3246
# indicates reference category
*** p<0.01, ** p<0.05, * p<0.1
Appendix E

Statistically Significant Factors from Logistic Regression
Domain/Year
Parent Percent Primary Race and Gender
Education of FPG* Home Ethnicity
Language
Physical Development and
Health
2006 -- *** -- -- ***
-- *** -- -- ***
-- *** *** -- ***
*** *** -- -- --
*** -- -- -- ***
-- *** -- -- ***
-- *** -- -- ***
-- *** -- *** --
*** *** -- -- ***

--- *** -- -- ***
-- *** -- *** ***
-- *** -- -- ***
*** *** -- -- --
-- *** *** -- ***
-- *** *** -- ***
-- *** -- -- --
*** *** -- -- ***
*** *** *** -- ***
-- *** *** -- ***
-- *** -- -- ***
2007
2008
2009
The Arts

2006
2007
2008
2009
Personal and Social
Development
2006
2007
2008
2009
Mathematical Thinking
2006
2007
2008
2009
Language and Literacy

2006
2007
2008
2009
75 Percent Standard
2010 *** *** -- -- ***
*** Noted demographic is significant for specified domain
and year.
* Federal Poverty Guideline is used from 2007 forward.
2006 income asked categorically.
Note – Analysis 2010 forward focused on 75 percent
standard.
- - -
- - -
- -
- - -
- - -

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

Greg J. Duncan
Northwestern University
Chantelle J. Dowsett
University of Texas at Austin
Amy Claessens
Katherine Magnuson
University of Wisconsin–Madison
Aletha C. Huston
University of Texas at Austin
Pamela Klebanov
Princeton University
Linda S. Pagani
Université de Montréal
Leon Feinstein
University of London
Mimi Engel
Jeanne Brooks-Gunn
Columbia University
Holly Sexton
University of Michigan
Kathryn Duckworth

University of London
Crista Japel
Université de Québec à Montréal
Using 6 longitudinal data sets, the authors estimate links
between three key elements of school
readiness—school-entry academic, attention, and
socioemotional skills—and later school reading and
math achievement. In an effort to isolate the effects of these
school-entry skills, the authors ensured that
most of their regression models control for cognitive, attention,
and socioemotional skills measured prior
to school entry, as well as a host of family background
measures. Across all 6 studies, the strongest
predictors of later achievement are school-entry math, reading,
and attention skills. A meta-analysis of
the results shows that early math skills have the greatest
predictive power, followed by reading and then
attention skills. By contrast, measures of socioemotional
behaviors, including internalizing and exter-
nalizing problems and social skills, were generally insignificant
predictors of later academic perfor-
mance, even among children with relatively high levels of
problem behavior. Patterns of association were
similar for boys and girls and for children from high and low
socioeconomic backgrounds.
Keywords: school readiness, socioemotional behaviors,
attention, early academic skills
Supplemental materials: http://dx.doi.org/10.1037/[0012-
1649.43.6.1428].supp
Greg J. Duncan, Amy Claessens, and Mimi Engel, School of
Education

and Social Policy, Northwestern University; Chantelle J.
Dowsett and
Aletha C. Huston, Department of Human Ecology, University of
Texas at
Austin; Katherine Magnuson, Department of Social Work,
University of
Wisconsin–Madison; Pamela Klebanov, Center for Research on
Child
Wellbeing, Princeton University; Linda S. Pagani, Department
of Psycho-
education, Université de Montréal, Québec, Canada; Leon
Feinstein and
Kathryn Duckworth, Department of Quantitative Social Science,
Institute
of Education, University of London, London, England; Jeanne
Brooks-
Gunn, Department of Pediatrics, Columbia University; Holly
Sexton, Re-
seach Center for Group Dynamics, University of Michigan;
Crista Japel,
Département d’éducation et formation spécialisées, Université
de Québec
à Montréal, Québec, Canada.
A preliminary version of this article was presented at the
biennial
meetings of the Society for Research on Child Development,
Atlanta,
Georgia, April 2005. We are grateful to the National Science
Foundation-supported Center for the Analysis of Pathways from
Child-
hood to Adulthood (CAPCA; Grant 0322356) for research
support. We
thank Larry Aber, Mark Appelbaum, Avshalom Caspi, David
Cordray,

Herbert Ginsburg, David Grissmer, Mark Lipsey, Derek Neal,
Cybele
Raver, Arnold Sameroff, Robert Siegler, Ross Thompson,
Sandra Jo
Wilson, Nicholas Zill, and other members of CAPCA and the
MacArthur Network on Families and the Economy for helpful
com-
ments.
Correspondence concerning this article should be addressed to
Greg J.
Duncan, School of Education and Social Policy, Northwestern
University,
2046 Sheridan Road, Evanston, IL 60208. E-mail: greg-
[email protected]
Developmental Psychology Copyright 2007 by the American
Psychological Association
2007, Vol. 43, No. 6, 1428–1446 0012-1649/07/$12.00 DOI:
10.1037/0012-1649.43.6.1428
1428
Early childhood programs and policies that promote academic
skills have been gaining popularity among politicians and re-
searchers. For example, President George W. Bush (2002) en-
dorsed Head Start reforms in 2002 that focus on building early
academic skills, observing that “on the first day of school,
children
need to know letters and numbers. They need a strong
vocabulary.
These are the building blocks of learning, and this nation must
provide them” (p. 12). The National Research Council’s
Commit-
tee on the Prevention of Reading Difficulties in Young Children

recommends providing environments that promote preliteracy
skills for all preschool children (Snow, Burns, & Griffin, 1998).
Similarly, the National Association for the Education of Young
Children and the National Council of Teachers of Mathematics
(2002) issued a joint statement that advocated for high-quality
mathematics education for children ages 3–6.
Others, however, maintain that a broad constellation of behav-
iors and skills enables children to learn in school. Asked to
identify
factors associated with a difficult transition to school,
kindergarten
teachers frequently mentioned weaknesses in academic skills,
problems with social skills, trouble following directions, and
dif-
ficulty with independent and group work (Rimm-Kaufman,
Pianta,
& Cox, 2000). Researchers too have made this point. The
National
Research Council and Institute on Medicine argued that “the
elements of early intervention programs that enhance social and
emotional development are just as important as the components
that enhance linguistic and cognitive competence” (Shonkoff &
Phillips, 2000, pp. 398–399).
These two views have emerged in the current debate about what
constitutes school readiness and in particular about what skills
predict school achievement. Many early education programs, in-
cluding Head Start, are designed to enhance children’s physical,
intellectual, and social competencies on the grounds that each
domain contributes to a child’s overall developmental
competence
and readiness for school. However, if early acquisition of
specific
academic skills or learning-enhancing behaviors forecasts later
achievement, it may be beneficial to add domain-specific early

skills to the definition of school readiness and to encourage
inter-
ventions aimed at promoting these skills prior to elementary
school. Thus, understanding which skills are linked to
children’s
academic achievement has important implications for early edu-
cation programs.
We adopted a child-centered model of school transition, which
is nested within a broader ecological framework but focuses on
the
set of individual skills and behaviors that children have
acquired
prior to school entry (Rimm-Kaufman & Pianta, 2000). A
child’s
individual characteristics contribute to the environments in
which
the child interacts and the rate at which the child may learn new
skills; in turn, the child receives feedback from others in the
environment (Meisels, 1998). Thus, because they affect both the
child and the social environment, early academic skills and
socio-
emotional behaviors are linked to subsequent academic achieve-
ment because they provide the foundation for positive classroom
adaptation (Cunha, Heckman, Lochner, & Masterov, 2006;
Entwisle, Alexander, & Olson, 2005).
For example, a child who enters kindergarten with rudimentary
academic skills may be poised to learn from formal reading and
mathematics instruction, receive positive reinforcement from
the
teacher, or be placed in a higher ability group that facilitates the
acquisition of additional skills. Similarly, a child who can pay
attention, inhibit impulsive behavior, and relate appropriately to
adults and peers may be able to take advantage of the learning

opportunities in the classroom, thus more easily mastering
reading
and math concepts taught in elementary school. For these
reasons,
the skills children possess when entering school might result in
different achievement patterns in later life. If achievement at
older
ages is the product of a sequential process of skill acquisition,
then
strengthening skills prior to school entry might lead children to
master more advanced skills at an earlier age and perhaps even
increase their ultimate level of achievement.
Although there are strong theoretical reasons to expect that
individual differences in children’s early academic skills and
be-
havior are linked to subsequent behavior and achievement, sur-
prisingly little rigorous research has been conducted to test this
hypothesis. Consequently, the purpose of this article is to assess
as
precisely as possible, using six longitudinal, nonexperimental
data
sets, the association between skills and behaviors that emerge
during the preschool years and later academic achievement. As
with Robins’s (1978) classic study of adult antisocial behavior,
our
approach consists of comparable analyses of a number of
different
longitudinal developmental studies.1 We are especially
interested
in identifying academic, attention, and socioemotional skills
and
behaviors that may be learned or improved through experiences
prior to school entry. In the following sections, we draw from
developmental literature to identify key dimensions of school
readiness and to derive theoretical predictions about how chil-

dren’s school-entry skills and behaviors contribute to short- and
long-term academic success.
Associations Between Early Skills and Later Achievement
Academic achievement is a cumulative process involving both
mastering new skills and improving already existing skills
(Entwisle & Alexander, 1990; Pungello, Kuperschmidt,
Burchinal,
& Patterson, 1996). Information about how children acquire
read-
ing and math skills points to the importance of specific
academic
skills but also indicates that more general cognitive skills,
partic-
ularly oral language and conceptual ability, may be increasingly
important for later mastery of more complex reading and mathe-
matical tasks. Basic oral language skills become critical for un-
derstanding texts as the level of difficulty of reading passages
increases (NICHD Early Child Care Research Network, 2005b;
Scarborough, 2001; Snow et al, 1998; Storch & Whitehurst,
2002;
Whitehurst & Lonigan, 1998). Likewise, mastery of
foundational
concepts of numbers allows for a deeper understanding of more
complex mathematical problems and flexible problem-solving
techniques (Baroody, 2003; Ferrari & Sternberg, 1998; Hiebert
&
Wearne, 1996).
Although children’s academic achievement is largely stable
throughout childhood, children do demonstrate both transitory
fluctuations and fundamental shifts in their achievement
trajecto-
ries (Kowaleski-Jones & Duncan, 1998; Pungello et al., 1996).
Nonexperimental data show that children’s achievement test

1 Robins (1978) justified her approach as follows: “In the long
run, the
best evidence for the truth of any observation lies in its
replicability across
studies. The more the populations studied differ, the wider the
historical
eras they span; the more the details of the methods vary, the
more
convincing becomes that replication” (p. 611).
1429SCHOOL READINESS AND LATER ACHIEVEMENT
scores are related to prior cognitive functioning and the
attainment
of basic skills in math and literacy such as number and letter
recognition (Stevenson & Newman, 1986). In their meta-
analysis,
La Paro and Pianta (2000) found middle-range correlations in
cognitive/academic skills both from preschool to kindergarten
(.43) and from kindergarten to first or second grade (.48).
Attention-related skills such as task persistence and self-
regulation are expected to increase the time during which
children
are engaged and participating in academic endeavors. Research
has
shown that signs of attention and impulsivity can be detected as
early as age 2.5 but continue to develop until reaching relative
stability between ages 6 and 8 (Olson, Sameroff, Kerr, Lopez, &
Wellman, 2005; Posner & Rothbart, 2000). Studies linking
atten-
tion with later achievement are less common, but consistent evi-
dence suggests that the ability to control and sustain attention

as
well as participate in classroom activities predicts achievement
test
scores and grades during preschool and the early elementary
grades (Alexander, Entwisle, & Dauber, 1993; Raver, Smith-
Donald, Hayes, & Jones, 2005). These attention skills, which
are
conceptually distinct from other types of interpersonal
behaviors,
are associated with later academic achievement, independent of
initial cognitive ability (McClelland, Morrison, & Holmes,
2000;
Yen, Konold, & McDermott, 2004) and of prior reading ability
and
current vocabulary (Howse, Lange, Farran, & Boyles, 2003).
Ex-
amining attention separately from externalizing problems has
clar-
ified the role of each in achievement, suggesting that attention
is
more predictive of later achievement than more general problem
behaviors (Barriga et al., 2002; Hinshaw, 1992; Konold &
Pianta,
2005; Ladd, Birch, & Buhs, 1999; Normandeau & Guay, 1998;
Trzesniewski, Moffitt, Caspi, Taylor, & Maughan, 2006).
Children’s socioemotional skills and behaviors are also ex-
pected to affect both individual learning and classroom
dynamics.
Inadequate interpersonal skills promote child–teacher conflict
and
social exclusion (Newcomb, Bukowski, & Pattee, 1993; Parker
&
Asher, 1987), and these stressors may reduce children’s
participa-
tion in collaborative learning activities and adversely affect

achievement (Ladd et al., 1999; Pianta & Stuhlman, 2004). Cor-
relational evidence linking problem behaviors to academic
achievement is found in the Beginning School Study. First-
grade
ratings on items describing a cheerful, outgoing temperament
(roughly the opposite of internalizing problems) predicted adult
educational attainment better than preschool or first-grade
achieve-
ment scores (Entwisle et al., 2005). Other studies yield similar
results. For example, children with consistently high levels of
aggression from ages 2–9 were more likely than other children
to
have achievement problems in third grade (NICHD Early Child
Care Research Network, 2004).
Experimental Evidence and Crossover Effects
Many nonexperimental studies find associations between early
achievement, attention, and behavior and later achievement, yet
few of these studies are designed to determine which of these
skills
can be modified prior to school entry or whether these changes
predict later achievement. In theory, intervention research
should
shed light on this gap by demonstrating ways to improve
children’s
skills and by testing whether improvements in early skills are
associated with better adjustment in the long term. Indeed, a
small
number of experimental interventions provide encouraging evi-
dence that high-quality programs for preschool children “at
risk”
for school failure produce gains in cognitive and academic
skills
and reduce behavior problems (Conduct Problems Prevention

Re-
search Group, 2002; Karoly, Kilburn, & Cannon, 2005; Love et
al.,
2003). Early educational interventions have also been found to
result in long-term reductions in special education services,
grade
retention, and increases in educational attainment (Campbell,
Ramey, Pungello, Sparling, & Miller-Johnson, 2002; Lazar et
al.,
1982; Reynolds & Temple, 1998).
As is the case with nonexperimental studies, few intervention
studies are designed to isolate the relative contributions of
changes
in achievement, attention, and behavior to later school achieve-
ment. A first problem is that behavioral interventions tend to
measure behavioral but not achievement outcomes, whereas
read-
ing and math interventions tend to measure achievement but not
behavioral outcomes. Interesting exceptions are a small number
of
experimental behavior-based interventions that tested for
achieve-
ment impacts (Coie & Krehbiel, 1984; Dolan et al., 1993). For
example, a random-assignment evaluation of a behavioral inter-
vention targeting both aggressive and shy behaviors among first
graders found short-run improvements in both teacher and peer
reports of aggressive and shy behavior but no crossover impacts
on
reading achievement (Dolan et al., 1993; Kellam, Mayer,
Rebok,
& Hawkins, 1998). Given evidence, albeit limited, that
behavioral
interventions succeed at improving behavior but not
achievement,
behavior would appear to play a limited role in academic

success.
A second problem is that many intervention programs target
both children’s academic skills and their socioemotional behav-
iors, rendering it impossible to assess their separate impacts
through simple experimental contrasts. For example, the Fast
Track prevention program provided a number of services to
chil-
dren who were identified as disruptive in kindergarten,
including
direct tutoring in reading skills in first grade (Conduct
Problems
Prevention Research Group, 1992; 2002). It is possible to
estimate
nonexperimental mediated models to determine whether
program
effects are more likely to be due to children’s improved
achieve-
ment, attention, or behavior skills (e.g., Reynolds, Ou, &
Topitzes,
2004). This is rarely done, however.
The Present Study
This study builds on previous school readiness research in
several ways. First, the scope of the study is unprecedented. We
estimated a carefully specified set of models with data from six
large-scale longitudinal studies, two of which were nationally
representative of U.S. children, whereas two were drawn from
multisite studies of U.S. children, with one each focusing on
children from Great Britain and Canada. Second, we included as
predictors a wide representation of school readiness indicators
used in previous research and carefully distinguished between
related but conceptually distinct skills (e.g., oral language vs.
preliteracy skills, attention vs. externalizing problems)
wherever

possible. Third, we examined multiple dimensions of academic
achievement outcomes, including grade completion and math
and
reading achievement as assessed by both teacher ratings and test
scores. Fourth, we implemented rigorous analytic methods that
attempted to isolate the effects of school-entry academic,
attention,
and socioemotional skills by controlling for an extensive set of
prior child, family, and contextual influences that may have
been
1430 DUNCAN ET AL.
related to children’s achievement. Finally, we assessed whether
the
predictive power of school readiness components differs by
gender
or socioeconomic status, which would indicate that some
children
are at heightened risk of low achievement.
We tested a number of hypotheses related to how school-entry
academic, attention, and socioemotional skills are associated
with
later school achievement. Developmental theory suggests that
chil-
dren’s informal, intuitive knowledge of early language and math
concepts plays an important role in the acquisition of more
com-
plex skills formally taught in elementary school (Adams,
Treiman,
& Pressley, 1998; Baroody, 2003; Griffin, Case, & Capodilupo,
1995; Tunmer & Nesdale, 1998). Theoretically, children’s atten-
tion and socioemotional skills should also affect achievement

because they influence children’s engagement in learning
activities
and facilitate (or disrupt) classroom processes (Ladd, Birch, &
Buhs, 1999; Pianta & Stuhlman, 2004). However, some scholars
point out that it is important to distinguish between behaviors
that
are directly relevant for learning, such as attention, and those
that
may be correlated with attention but are less likely to be
directly
linked with achievement, such as interpersonal skills and
problem
behavior (Alexander et al., 1993; Cooper & Farran, 1991;
McClel-
land et al., 2000; McWayne, Fantuzzo, & McDermott, 2004).
Therefore, we expected early academic and attention-related
skills
to predict subsequent test scores and teacher achievement
ratings,
and we expected attention skills to predict achievement more
consistently than do socioemotional behaviors.
In seeking a better understanding of the extent to which our
broad set of early skills is associated with later achievement, it
is
important to consider how outcomes are being measured.
Although
test performance provides a key independent assessment of aca-
demic achievement, teacher ratings also lend insight into chil-
dren’s everyday performance in the classroom. Teachers’
evalua-
tions are probably based on a broad picture of children’s
accomplishments, which include their academic skills as well as
whether they complete assignments on time, work
independently,
get along with others, and show involvement in the learning

agenda of the classroom. Moreover, previous research has found
that children’s behavior can play a role that is equal to, if not
greater than, prior cognitive ability in predicting teacher-rated
attainment or achievement (Lin, Lawrence, & Gorrell, 2003;
Schaefer & McDermott, 1999) and academic skills (National
Cen-
ter for Education Statistics, 1993). Consequently, we expected a
stronger relationship between school-entry socioemotional
behav-
iors and subsequent teacher-rated achievement than with subse-
quent test scores.
Although many previous studies have examined the association
between early academic, attention, and socioemotional skills
and
subsequent achievement, few have systematically considered the
extent to which these associations differ by gender
(Trzesniewski
et al., 2006). On average, boys receive poorer grades and have
more difficulties related to school progress (e.g., grade
retention,
special education, and drop out) than do girls (Dauber,
Alexander,
& Entwisle, 1993; McCoy & Reynolds, 1999), and these differ-
ences are especially pronounced among low-income children
(Hin-
shaw, 1992). Children from low-income families enter school
with
lower mean academic skills, and the gap tends to increase
during
the school years (Lee & Burkam, 2002). These groups also have
higher rates of problems with attention and externalizing
behavior
(Entwisle et al., 2005; Miech, Essex, & Goldsmith, 2001; Raver,
2004).

Despite differences in children’s behavior linked to gender and
family socioeconomic status, few studies have considered
whether
gender and socioeconomic status moderate the association be-
tween these early skills and behaviors and subsequent achieve-
ment. We expected early academic skills, attention, and
socioemo-
tional behaviors to matter more for these subgroups, particularly
when children enter school with very low levels of these skills.
To estimate the associations between early academic skills and
socioemotional behaviors and later school achievement, we
sum-
marize results from a coordinated series of analyses across six
longitudinal data sets in two ways. First, we relate early
academic,
attention, and socioemotional skills to later achievement in each
of
the six data sets and provide a basic summary of these results.
Second, we formally summarize the findings from these studies
in
a meta-analysis, again focusing on the extent to which this
collec-
tion of early skills predicts later achievement.
Method
In this section, we describe the data sets used in this study and
the common analytic procedures that were implemented across
studies. Detailed information about the measures, descriptive
sta-
tistics, and regression results from each study is presented in
Appendices A–F, which can be found online. As the goal of our
study was to relate early academic, attention, and
socioemotional

skills and behaviors to later achievement, each data set has mea-
sures of these constructs, although there is variation across the
studies with respect to when and how each skill or behavior is
assessed.
Table 1 provides an overview of data sources and measures
available in each study. All six data sets provide measures of
children’s academic skills as well as assessments of attention
and
socioemotional behaviors at about age 5 or 6. Because most
children enter elementary school at this age, we refer to the
timing
of these measures as school entry but alert the reader that the
precise timing varies considerably across studies. To facilitate
comparison of findings across studies, we standardized all mea-
sures to have a mean of 0 and standard deviation of 1.
We measured achievement outcomes using teachers’ reports,
test scores, and grade retention in early elementary school and,
in
some studies, middle childhood. In terms of the timing of the
measurement of achievement outcomes, the children of the Na-
tional Longitudinal Survey of Youth (NLSY) measures are as-
sessed as late as early adolescence, the National Institute of
Child
Health and Human Development Study of Early Child Care and
Youth Development (NICHD SECCYD) as late as fifth grade,
and
the 1970 British Birth Cohort Study (BCS) at age 10, whereas
none
of the other studies measures achievement beyond third grade.
As
for measurement methods, two studies have both test-score-
based
and teacher reports of reading and mathematics achievement
(the

Early Childhood Longitudinal Study–Kindergarten Cohort
[ECLS-
K] and NICHD SECCYD).
We measured attention and socioemotional behaviors on the
basis of mothers’ reports, teachers’ reports, and observation.
Table
1 provides an overview of the similarities and differences in
measurement across the six studies. One of our data sets, the
Infant
Health and Development Program (IHDP), has observer reports
of
T
ab
le
1
St
ud
y
M
ea
su
re
s
of

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en
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ci
oe
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io
rs
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ea

su
re
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L
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ra
de
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L
SY
ag
e
13
–1
4
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IC
H
D
SE

C
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Y
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de
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D
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ag
e
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es
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es
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ea
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ite
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sp
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(I
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)
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xt
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ab
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ch
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en
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ts
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IA
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)
R
ea
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ec
og
ni
tio
n:

M
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ch
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tte
rs
,
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m
in
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na
m
es
,a
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ad
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le
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st
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oo
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hn
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n
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yc
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—
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ev
is
ed
(W
J-
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)
R

ea
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:
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et
te
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or
d,
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or
d
A
tta
ck
A
ca
de
m
ic
Sk
ill

s
R
at
in
gs
Sc
al
e:
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ea
ch
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R
ep
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t
W
oo
dc
oc
k–
Jo
hn
so

n
T
es
ts
of
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en
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0s
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er
ba
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ki
lls
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se
/
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de
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ta
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of
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en
ch

,
ch
ar
ac
te
ris
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of
or
al
co
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ic
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(�
�
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4)
:

T
ea
ch
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R
ep
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t
E
di
nb
ur
gh
R
ea
di
ng
T
es
t:
Se
lf
-c
om

pl
et
io
n,
w
or
d
re
co
gn
iti
on
,u
se
an
d
un
de
rs
ta
nd
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g
of

sy
nt
ax
,s
eq
ue
nc
in
g,
co
m
pr
eh
en
si
on
,a
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(�

�
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6)
M
at
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A
ch
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m
en
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es
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at
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IR
T
“a
dv

an
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d”
su
bs
ca
le
s:
M
ul
tip
lic
at
io
n/
di
vi
si
on
,
Pl
ac
e-
va

lu
e,
W
or
d
pr
ob
le
m
s
A
ca
de
m
ic
R
at
in
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Sc
al
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(�
�

.9
4)
:
T
ea
ch
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R
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t
PI
A
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M
at
h:
A
pp
lic
at
io
n
of

m
at
he
m
at
ic
al
co
nc
ep
ts
,n
um
be
r
re
co
gn
iti
on
,c
ou
nt
in

g,
m
ul
tip
lic
at
io
n,
di
vi
si
on
,f
ra
ct
io
ns
,a
nd
ad
va
nc
ed
al

ge
br
a
an
d
ge
om
et
ry
(te
st
/re
te
st
�
.7
4)
W
J-
R
M
at
h:
A

pp
lie
d
pr
ob
le
m
s
A
ca
de
m
ic
Sk
ill
s
R
at
in
gs
Sc
al
e:
T

ea
ch
er
R
ep
or
t
W
oo
dc
oc
k–
Jo
hn
so
n
T
es
ts
of
A
ch
ie
ve

m
en
t—
R
ev
is
ed
:
B
ro
ad
M
at
h
(�
�
.9
0s
)
N
um
be
r
K

no
w
le
dg
e
T
es
t:
N
um
be
r
se
qu
en
ce
,
ad
di
tio
n,
su
bt
ra

ct
io
n,
m
ul
tip
lic
at
io
n,
di
vi
si
on
,
fr
ac
tio
ns
,d
ec
im
al
s

U
ni
ve
rs
ity
of
B
ris
to
lM
at
h
T
es
t:
R
ul
es
of
ar
ith
m
et
ic

,
pl
ac
e
va
lu
e,
fr
ac
tio
ns
,
m
ea
su
re
m
en
t,
al
ge
br
a,
ge

om
et
ry
,a
nd
st
at
is
tic
s
(�
�
.9
3)
Sc
ho
ol
en
tr
y
sk
ill
s
Fa

ll
of
ki
nd
er
ga
rt
en
A
ge
5–
6
A
ge
4.
5
A
ge
5
Ju
ni
or
an
d

se
ni
or
ki
nd
er
ga
rte
n
A
ge
5
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ch
ie
ve
m
en
t
R
ea
di
ng
A

ch
ie
ve
m
en
tT
es
tR
ea
di
ng
IR
T
“e
ar
ly
”
su
bs
ca
le
s:
L
et

te
r
re
co
gn
iti
on
,
be
gi
nn
in
g
an
d
en
di
ng
w
or
d
so
un
ds

PI
A
T
R
ea
di
ng
R
ec
og
ni
tio
n
W
J-
R
R
ea
di
ng
:L
et
te
r-

W
or
d
Id
en
tif
ic
at
io
n
(�
�
.8
4)
L
an
gu
ag
e/
ve
rb
al
ab
ili

ty
Pr
es
ch
oo
l
L
an
gu
ag
e
Sc
al
e–
3:
E
xp
re
ss
iv
e
co
m
m

un
ic
at
io
n
W
ec
hs
le
r
Pr
es
ch
oo
l
an
d
Pr
im
ar
y
Sc
al

e
of
In
te
lli
ge
nc
e
(W
PP
SI
):
V
er
ba
l
IQ
(�
�
.9
4)
Pe
ab
od

y
Pi
ct
ur
e
V
oc
ab
ul
ar
y
T
es
t(
PP
V
T
),
Fo
rm
s
A
an
d

B
,F
re
nc
h
ad
ap
ta
tio
n:
Sp
lit
-
ha
lf
re
lia
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lit
y
.6
6
an
d

.8
5
fo
r
A
an
d
B
,r
es
pe
ct
iv
el
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te
st
–r
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ta
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ee

k
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2
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ng
lis
h
Pi
ct
ur
e
V
oc
ab
ul
ar
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T
es
t:
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er
ba

l
in
te
lli
ge
nc
e
M
at
h
A
ch
ie
ve
m
en
t
T
es
t
M
at
h

IR
T
“e
ar
ly
”
su
bs
ca
le
s:
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ou
nt
in
g,
or
di
na
lit
y
an
d
re

la
tiv
e
si
ze
PI
A
T
M
at
h
W
J-
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at
h:
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pp
lie
d
pr
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le

m
s
(�
�
.8
4)
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um
be
r
K
no
w
le
dg
e
T
es
t:
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fo
rm
al
nu

m
be
r
kn
ow
le
dg
e,
co
un
tin
g,
ad
di
tio
n,
nu
m
be
r
se
qu
en
ce

A
tte
nt
io
n
sk
ill
s
A
tte
nt
io
n
sk
ill
s
A
pp
ro
ac
he
s
to
L

ea
rn
in
g
(�
�
.8
9)
:T
ea
ch
er
R
ep
or
t
C
on
tin
uo
us
Pe
rf
or

m
an
ce
T
as
k:
A
tte
nt
io
n
A
tte
nt
io
n:
C
hi
ld
co
nc
en
tr
at

es
,
lis
te
ns
at
te
nt
iv
el
y,
et
c.
(�
�
.8
2)
:
T
ea
ch
er
R
ep

or
t
A
tte
nt
io
n
pr
ob
le
m
s
H
yp
er
ac
tiv
ity
(d
if
fi
cu
lty
co

nc
en
tr
at
in
g,
re
st
le
ss
,e
tc
.):
M
at
er
na
l
R
ep
or
t
(I
)

C
hi
ld
B
eh
av
io
r
C
he
ck
lis
t
(C
B
C
L
),
A
tte
nt
io
n
pr

ob
le
m
s
(�
�
.9
3)
:F
al
l
of
K
in
de
rg
ar
te
n
T
ea
ch
er
R

ep
or
t
(I
I)
C
on
tin
uo
us
Pe
rf
or
m
an
ce
T
as
k:
Im
pu
ls
iv
ity

A
ch
en
ba
ch
C
hi
ld
B
eh
av
io
r
Pr
of
ile
,
A
tte
nt
io
n
pr
ob

le
m
s
(�
�
.6
1)
:M
at
er
na
l
R
ep
or
t
H
yp
er
ac
tiv
e:
C
hi

ld
se
em
s
ag
ita
te
d,
im
pu
ls
iv
e,
et
c.
(�
�
.9
0)
:
T
ea
ch
er

R
ep
or
t
R
ut
te
r
Sc
al
e:
In
at
te
nt
io
n
(�
�
.6
7)
:
M
at

er
na
l
R
ep
or
t
1432 DUNCAN ET AL.
T
ab
le
1
(c
on
ti
nu
ed
)
Sc
ho
ol
en

tr
y
sk
ill
s
Fa
ll
of
ki
nd
er
ga
rt
en
A
ge
5–
6
A
ge
4.
5
A
ge

5
Ju
ni
or
an
d
se
ni
or
ki
nd
er
ga
rt
en
A
ge
5
So
ci
oe
m
ot
io

na
l
be
ha
vi
or
s
E
xt
er
na
liz
in
g
pr
ob
le
m
s
E
xt
er
na
liz

in
g
Pr
ob
le
m
s
(�
�
.9
0)
:
T
ea
ch
er
R
ep
or
t
(I
)
H
ea

d
St
ro
ng
(s
tu
bb
or
n,
st
ro
ng
te
m
pe
r,
et
c.
):
M
at
er
na
l

R
ep
or
t
(I
I)
A
nt
is
oc
ia
l
(c
he
at
s,
bu
lli
es
,e
tc
.):
M
at

er
na
l
R
ep
or
t
C
B
C
L
,A
gg
re
ss
iv
e
be
ha
vi
or
(�
�

.9
3)
:
Fa
ll
of
K
in
de
rg
ar
te
n
T
ea
ch
er
R
ep
or
t
A
gg
re

ss
io
n:
C
hi
ld
fi
gh
ts
,
bu
lli
es
ot
he
rs
,e
tc
.(
�
�
.7
2)
:

T
ea
ch
er
R
ep
or
t
R
ut
te
r
Sc
al
e,
E
xt
er
na
liz
in
g:
C
hi

ld
bu
lli
es
ot
he
rs
,i
s
di
so
be
di
en
t,
et
c.
(�
�
.7
2)
:M
at
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y.
attention, another (NICHD SECCYD) has both test-based and
teacher-rated measures of attention, and three (NLSY, IHDP,
and
BCS) have parent rather than teacher reports of socioemotional
behaviors. In addition, two of the studies (NICHD SECCYD and
the Montreal Longitudinal-Experimental Preschool Study
[MLEPS]) measure both attention skills and problems, whereas
three (NLSY, IHDP, and BCS) have measures of attention prob-
lems but not skills, and one study (ECLS-K) has a measure of
attention skills but not attention problems. In addition, with one
exception, all of the studies provide measures of academic,
atten-
tion, and socioemotional skills prior to the point of school
entry,
which we used as key control variables in our analyses.
The Studies and Samples
The Early Childhood Longitudinal Study–Kindergarten Cohort
(ECLS-K). The ECLS-K follows a nationally representative
sam-
ple of 21,260 children who were in kindergarten in 1998–1999.
We used data from kindergarten, first grade, and third grade.
Data
were collected from multiple sources, including direct achieve-
ment tests of children and surveys of parents, teachers, and
school
administrators (see Table 1; National Center for Education
Statis-
tics, 2001).

Achievement tests were administered in the fall of kindergarten
and in the spring of kindergarten, first grade, and third grade.
We
used teacher reports of children’s “approaches to learning”
(which
measure both attention skills and achievement motivation) and
socioemotional behaviors, including internalizing and
externaliz-
ing problems, self-control with peers, and interpersonal skills,
collected in the fall and spring of kindergarten.
The battery of achievement tests given as part of the ECLS-K
kindergarten and first-grade assessments covered three subject
areas: language and literacy, mathematical thinking, and general
knowledge. For third grade, the achievement tests included
math-
ematics, reading, and science. We used item response theory
scores for the first two of these as key dependent variables.
These
third-grade assessments required students to complete
workbooks
and open-ended mathematics problems. As detailed in Appendix
A, a host of family- and some child-level controls are available
in
the data.
The children of the National Longitudinal Survey of Youth
(NLSY). The NLSY is a multistage stratified random sample of
12,686 individuals age 14 to 21 in 1979 (Center for Human
Resource Research, 2004). Black, Hispanic, and low-income
youth
were overrepresented in the sample. Annual (through 1994) and
biennial (between 1994 and 2000) interviews with sample mem-
bers and very low cumulative attrition in the study contribute to
the

quality of the study’s data.
Beginning in 1986, the children born to NLSY female partici-
pants were tracked through biennial mother interview
supplements
and direct child assessments. Given the nature of the sample, it
is
important to note that early cohorts of the child sample were
born
disproportionately to young mothers. With each additional
cohort,
the children become more representative of all children, and
NLSY
children younger than age 14 in 2000 share many demographic
characteristics of their broader set of age mates.
The sample used in the present analysis consists of 1,756 chil-
dren whose academic achievement was tracked from age 7–8 to
age 13–14 and whose achievement and behavior was assessed at
age 5–6. Consequently, our sample comprises children who
were
age 5 or 6 in 1986, 1988, 1990, or 1992. The age 13–14
achieve-
ment and behavior of these children were assessed in the
respec-
tive 1994, 1996, 1998, and 2000 interviews.
School readiness measures, including math and reading test
scores (Peabody Individual Achievement Test; Dunn & Mark-
wardt, 1970) and maternal reports of children’s behavior
problems
(adapted from the Achenbach Behavior Problems Checklist;
Baker, Keck, Mott, & Quinlan, 1993) were collected at age 5 or
age 6. Academic achievement outcome measures were collected
biennially for children between the ages of 5 and 14. In

addition,
key control variables include children’s receptive vocabulary
(Pea-
body Picture Vocabulary Test—Revised; Dunn & Dunn, 1981)
and children’s temperament (compliance and sociability) at age
3
or 4. Additional family- and child-level control variables are
described in Appendix B.
The NICHD Study of Early Child Care and Youth Development
(SECCYD). Longitudinal data from the NICHD SECCYD are
drawn from a multisite study of births in 1991 (NICHD Early
Child Care Research Network, 2005a). Participants were
recruited
from hospitals located at 10 sites across the United States.
During
24-hr sampling periods, 5,265 new mothers met the selection
criteria and agreed to be contacted after returning home from
the
hospital. At 1 month of age, 1,364 healthy newborns were
enrolled
in the study. Although it is not nationally representative, the
study
sample closely matches national and census tract records with
respect to demographic variables such as ethnicity and
household
income. The majority of children in the sample are White, 12%
are
African American, and 11% are Hispanic or of another
ethnicity.
About 30% of mothers had a high school education or less, and
14% were single parents (NICHD Early Child Care Research
Network, 1997). The analysis sample had valid data on the
achievement outcome measures and at least three sources of in-
formation on the key independent variables (approximately 981
at

first grade, 928 at third grade, and 907 at fifth grade).
School readiness measures, including achievement tests and
attention/impulsivity tasks, were administered in a controlled
lab-
oratory setting at age 4.5, and attention problems, aggression,
internalizing behavior, and social skills were measured by
teacher
report in the fall of the kindergarten year. Outcomes at first,
third,
and fifth grades include achievement in math and reading
accord-
ing to teacher ratings and Woodcock–Johnson Tests of Achieve-
ment—Revised test scores (Woodcock & Johnson, 1990; see
Table
1). Key control variables at age 3 include children’s cognitive
ability, language skills, impulsivity, externalizing problems,
and
internalizing problems. The NICHD SECCYD also collects
infor-
mation from infancy about children’s early environments,
includ-
ing child-care type and quality, home environment, and
parenting;
these and other child- and family-level covariates are described
in
Appendix C.
The Infant Health and Development Program (IHDP). The
IHDP is an eight-site randomized clinical trial designed to
evaluate
the efficacy of a comprehensive early-intervention program for
low birth weight (LBW) premature infants. Infants weighing
2,500
g (5.51 lb) or less at birth were screened for eligibility if their
postconceptional age between January and October 1985 was 37

weeks or less and if they were born in one of eight participating
medical institutions. A total of 985 infants was randomly
assigned
either to a medical follow-up only or to a comprehensive early
1434 DUNCAN ET AL.
childhood intervention group immediately following hospital
dis-
charge.
Infants in both the comprehensive early childhood intervention
and medical follow-up only groups participated in a pediatric
follow-up program of periodic medical, developmental, and
famil-
ial assessments from 40 weeks of conceptional age (when they
would have been born if they had been full term) to 36 months
of
age corrected for prematurity. The intervention program, lasting
from hospital discharge until 36 months, consisted of home
visits,
child-care services, and parent group meetings. A coordinated
educational curriculum of learning games and activities was
used
both during home visits and at the center.
The primary analysis group consisted of 985 infants. Of these
985 infants, cognitive assessments are available for 843
children at
age 3, 745 children at age 5, and 787 children at age 8. In
addition,
76 children who were born at an extremely low birth weight
(ELBW; 1,000 g [3.27 lb] or less) were excluded from the
sample

because ELBW children differ markedly from other LBW
children
in cognitive and behavioral functioning (Klebanov, Brooks-
Gunn,
& McCormick, 1994a, 1994b). Thus, this study focuses on a
subsample of 690 children who were not born ELBW and for
whom cognitive assessment and family background data were
available.
Data come from a variety of sources: questionnaires, home
visits, and laboratory tests (see Table 1). School readiness mea-
sures include preschool performance and verbal test scores,
paren-
tal reports of children’s mental health and aggressive behavior,
and
observer reports of children’s attention and task persistence. We
assessed reading and math achievement using the Woodcock–
Johnson Tests of Achievement—Revised broad reading and
math
tests and the Wechsler Intelligence Scale for Children—Third
Edition (Wechsler, 1991) performance and verbal tests at 8
years
of age. Key control variables include cognitive ability,
sustained
attention, and behavior problems at age 3. Additional family-
and
child-level control variables are described in Appendix D.
The Montreal Longitudinal-Experimental Preschool Study
(MLEPS). The MLEPS comprises several consecutive cohorts
launched from 1997 to 2000. The original sample of 4- and
5-year-old children (N � 1,928), representing one third of its
population base, was obtained after a multilevel consent process
involving school board administrators, local school committees,
parents, and teachers. Given that its final cohort (2000) does not
meet all the data requirements for the research objective

examined
here, we limited ourselves to the sample of children beginning
kindergarten in the fall of 1998 and the fall of 1999.
Incomplete data reduced the sample from 1,369 to 767 children.
Students in the final sample had a valid value on any of the four
outcome measures of interest (first- and third-grade
achievement
measures) and on at least four of the six socioemotional
measures.
Of the 767 participants in the final sample, 439 began
kindergarten
in 1998 and 328 began kindergarten in the fall of 1999.
Addition-
ally, for 350 of the 767 students, initial data were collected
during
the fall of junior kindergarten (332 who began junior
kindergarten
in 1997 and 18 who began junior kindergarten in 1998).
Initial and follow-up data were collected from multiple sources,
including direct cognitive assessments of children and surveys
of
parents and teachers. Early academic assessments include
individ-
ually administered number knowledge and receptive vocabulary
tests at the end of senior kindergarten. Teachers rated children’s
behavioral development, including physically aggressive,
anxious,
depressive, hyperactive, inattentive, and prosocial behavior.
Third-
grade assessments include a group-administered math test and
teacher ratings of children’s French language skills (see Table
1).
Key control variables include number knowledge and

vocabulary
measured on entry into junior kindergarten (age 4) for Cohort 1
and on entry into senior kindergarten (age 5) for Cohort 2.
Addi-
tional family- and child-level control variables are detailed in
Appendix E.
The 1970 British Birth Cohort Study (BCS). The U.K. 1970
BCS, a nationally representative longitudinal study, has
followed
into adulthood a cohort of children born in Great Britain during
1
week in 1970 (Bynner, Ferri, & Shepherd, 1997). The birth
sample
of 17,196 infants was approximately 97% of the target birth
population. Attrition has reduced the original sample to 11,200
participants. Nevertheless, the representativeness of the original
birth cohort has largely been maintained, although the current
sample is disproportionately female and highly educated (Ferri
&
Smith, 2003). Missing data on key variables reduce the sample
size
for most analyses to between 9,000 and 10,000 cases.
At each wave, cohort members were given a battery of tests of
intellectual and behavioral development (see Table 1). School
readiness measures include vocabulary and copying skills tests,
and maternal reports of attention, externalizing behavior, and
internalizing behavior were collected when the children were 5
years of age. Reading and mathematics achievement tests were
administered at age 10. Key control variables include measures
of
basic skills and behavior at ages 22 and 42 months for a 10%
subsample of the data. Additional family- and child-level
controls
are described in Appendix F.

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