Literature review . Articles enclosed in PDF as well as instructions.. NO OTHER OUTSIDE SOURCES FOR LIT REVIEW just the Articles enclosed

http://www.policyforchildren.org/pdf/school_readiness_study

 Literature Review.

 

instructions in class was each article 2 pages double spaced…  the rest of instructions are in the attached section

 

NO OUTSIDE SOURCES.. Just the articles that are enclosed ( thats the literature review)

 

Research statment then developed off of the articles and school readiness and the enclosed Research Statement of :

Pre Kindergartner students who participate in early childhood education programs will have a higher school readiness screening scores than Pre Kindergartner students who did not participate in any early childhood education programs…. ( the research portion of the assignment will use our research statement above and enclosed in Word Doc… all instructions from the actual class are in here. Follow the instructions, otherwise it will be sent back.

Http://connect.mcgraw-hill.com/selfstudy    ( click option labeled register now ) access code will be released once assignment is accepted and directions are actually read. To many repeat offenders do not read the material before accepting the assignement.  once again you can not use other references for the Literarture review ( a literarture review for this assignement is the articles I have enclosed )  Text book is

A CHILD’S WORLD

infancy through adolescence

12th edition

McGraw-Hill (published by)

IV. When you have found the articles, and they have been approved – Use the format below and work with your group to write the review. Each group will submit one paper, which is a group effort.

A.) Introduction: Introduce your topic and briefly explain why this is a significant or important area for study. Define terms if necessary. Give a quick idea of the topic of the literature review, such as the central theme or organizational pattern.

B.) Body: Summary of articles: Each article has a separate summary section.

The source – bibliographical information, using APA or MLA format. APA is preferred for education.

· The purpose of the research

· The hypothesis

· The methodology – Qualitative, Quantitative, or mixed method….

· experimental

· non-experimental

· longitudinal

· cross-sectional, etc.

· The results of the study, the significance of the findings.

Hints: When referring to an article, use the last name of author or authors and date of publication in the text. Example: Ben-Joseph (2004) states that chicken pox is most common in children under 15, but anyone can get the disease. Or use quotations if you are using the exact words from the article. Chicken pox is highly contagious: “If exposed to an infected family member, about 80% to 90% of those in a household who haven’t had chickenpox will get it (Ben-Joseph, 2004).”

C.) Conclusion: Briefly summarize the major points. One conclusion summarizes all the individual reviews. In the conclusion, you should:

· Summarize major contributions of these significant studies to the topic, maintaining the focus established in the introduction.

· Answer: What type of theory might this research support? How might the resulting information be used?

· Discuss conclusions from reviewing this literature. Where might the research proceed in the future?

D.) References: The sources – bibliographical information APA or MLA – repeat the references from each section, and add any other references used to write the introduction and conclusion.

Hints: For information on APA formatting, visit


http://owl.english.purdue.edu/owl/resource/560/01/

For information on MLA formatting, visit


http://owl.english.purdue.edu/owl/resource/557/01/

APA Format for Websites: (If you use MLA format, you will have to research how to reference websites)

Author, A. A., & Author, B. B. (Date of publication). Title of article. Title of Journal, volume number. Retrieved month day, year, from

http://www.someaddress.com/full/url/

PAGE

Running head: TYPE ABBREVIATED TITLE HERE
1

Title of the Literature Review 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. Your introduction does not have a heading – just the title of the paper.

You will need to write a thesis statement in your introduction, the main idea of a paper. Click

here

to visit a website about thesis statements.

Once you’ve developed the thesis, you can then begin composing your introduction. Click
here
for information on writing introductions.

Part One: Title of Study Article

This will be the beginning of the body of the essay. Even though it has a new heading, you want to make sure you link this to your previous section so your reader can follow and understand. Remember to make sure your first sentence in each paragraph both transitions from your previous paragraph and summarizes the main point in your paragraph. Stick to one topic per paragraph, and avoid long paragraphs so you can hold the reader’s attention. A paragraph should be a minimum of three sentences.

LOGICAL RELATIONSHIP

TRANSITIONAL EXPRESSION

Similarity

also, in the same way, just as … so too, likewise, similarly

Exception/Contrast

but, however, in spite of, on the one hand … on the other hand, nevertheless, nonetheless, notwithstanding, in contrast, on the contrary, still, yet

Sequence/Order

first, second, third, … next, then, finally

Time

after, afterward, at last, before, currently, during, earlier, immediately, later, meanwhile, now, recently, simultaneously, subsequently, then

Example

for example, for instance, namely, specifically, to illustrate

Emphasis

even, indeed, in fact, of course, truly

Place/Position

above, adjacent, below, beyond, here, in front, in back, nearby, there

Cause and Effect

accordingly, consequently, hence, so, therefore, thus

Additional Support or Evidence

additionally, again, also, and, as well, besides, equally important, further, furthermore, in addition, moreover, then

Conclusion / summary

finally, in a word, in brief, briefly, in conclusion, in the end, in the final analysis, on the \whole, thus, to conclude, to summarize, in sum, to sum up, in summary

From

http://writingcenter.unc.edu/handouts/transitions/

First, include the reference of the article like this (APA shown):

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

Each review section should be a page or page and a half long. This paragraph should contain the purpose of the research, the hypothesis, and the methodology of the research study.

This paragraph should include a summary of the findings (results of the study).

This paragraph should be a statement about the value of this article for your research agenda or your profession generally (the significance of the findings).

Next Article Here, Following the Format of Paragraphs Above…

Again, the topic sentence of this section should explain how this is related or a result of what’s been discussed in the previous section. Each review section should be a page or page and a half long. This paragraph should contain the purpose of the research, the hypothesis, and the methodology of the research study.

This paragraph should include a summary of the findings (results of the study).

This paragraph should be a statement about the value of this article for your research agenda or your profession generally (the significance of the findings).

Another Heading for Another Article…

Another section of a page or page and a half long if you have three in your group. This paragraph should contain the purpose of the research, the hypothesis, and the methodology of the research study.

This paragraph should include a summary of the findings (results of the study).
This paragraph should be a statement about the value of this article for your research agenda or your profession generally (the significance of the findings).

And so forth until the conclusion.

Conclusion

Your conclusion section should recap the major points you have 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

School Readiness and Later Achievemen

t

Greg J. Dunca

n

Northwestern Univers

ity

Chantelle J. Dowsett
University of Texas at Austin

Amy Claessen

s

Northwestern University

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
Northwestern University

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 pri

or

to school entry, as well as a host of family background measures. Across all 6 studies, the stronge

st

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 wer

e

similar for boys and girls and for children from high and low socioeconomic backgrounds

.

Keywords: school readiness, socioemotional behaviors, attention, early academic skil

ls

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, Cybe

le

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-
duncan@northwestern.edu

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 indicato

rs

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

1431SCHOOL READINESS AND LATER ACHIEVEMENT

T
ab

le
1

St
ud

y
M

ea
su

re
s

of
O

ut
co

m
es

,
Sc

ho
ol

E
nt

r

y
an

d
P

re
sc

ho
ol

A
ch

ie
ve

m
en

t

,
A

tt
en

ti
on

an
d

So
ci

oe
m

ot
io

na
l

B
eh

av
io

rs

M
ea

su
re

E
C

L
S-

K
G

ra
de

3
N

L
SY

ag
e

13
–1

4

N

IC
H

D
SE

C
C

Y
D

G
ra

d

e
5

I

H
D

P
ag

e
8

M
L

E
PS

G
ra

de
3

B
C

S
ag

e
10

O
ut

co
m

e

s
R

ea
di

ng
A

ch
ie

ve
m

en
t

T
es

t
R

ea
di

ng
ite

m
re

sp
on

se
th

eo
ry

(I
R

T
)

“a
dv

an
ce

d


su

bs
ca

le
s:

E
xt

ra
po

la
tio

n,
E

va
lu

at
io

n
A

ca
de

m
ic

R
at

in
g

Sc
al

e
(�


.9

5)
:

T
ea

ch
er

R
ep

or
t

Pe
ab

od
y

In
di

vi
du

al
A

ch
ie
ve
m

en
tT

es
ts

(P
IA

T
)

R
ea

di
ng

R
ec

og
ni

t

io
n:

M
at

ch
in

g
le

tte
rs

,
na

m
in

g
na

m
es

,a
nd

re
ad

in
g

si
ng

le
w

or
ds

ou
tl

ou
d

(

te
st

–r
et

es
t�

.8
9)

W
oo

dc
oc

k–
Jo

hn
so

n
Ps

yc
ho


E

du
ca

t

io
na

lB
at

te
ry


R

ev
is

ed
(W

J-
R

)
R

ea
di

ng
:

L
et

te
r-

W
or

d,
W

or
d

A
tta

ck
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
R

ea
di

n

g
(�


.9

0s
)

V
er

ba
lS

ki
lls

:U
se

/
un

de
rs

ta
nd

in
g

of
Fr

en
ch

,
ch

ar
ac

te
ris

tic
s

of
or

al
co

m
m

un
ic

at
io

n
(�


.9

4)

:

T
ea
ch
er
R
ep
or
t

E
di

nb
ur

g

h
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
in

g
of

sy
nt

ax
,s

eq
ue

nc
in

g,
co

m
pr

eh
en

si
on

,a
nd

re
te

nt
io

n
(�

.9

6)

M
at

h
A

ch
ie
ve
m
en
t
T
es

t
M

at
h

IR
T

“a
dv
an
ce
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

g
Sc

al
e

(


.9
4)

:
T

ea
ch

er
R

ep
or

t

PI
A

T
M

at
h:

A
pp

l

ic
at

io
n

of
m

at
he

m
at

ic
al

co
nc

ep
ts

,n
um

be
r

r

e
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

u

m
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

it

y
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

l

l
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

A
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

bi
lit

y
.6

6
an

d
.8

5
fo

r
A

an
d

B
,r

es
pe

ct
iv

el
y;

te
st
–r
et

es
ta

t1
w

ee
k


.7

2

E
ng

lis
h

Pi
ct
ur
e
V
oc
ab
ul
ar
y
T
es

t:
V

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:
C

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-
R
M
at
h:
A
pp
lie
d
pr
ob
le
m

s
(�


.8

4)
N

um
be

r
K

no
w

le
dg

e
T

es
t:

In
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

c

e
A

t

te
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

a

t
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
er

na
lR

ep
or
t
In
te

rn
al

iz
in

g
pr

ob
le
m
s
In
te
rn
al
iz
in

g
Pr

ob
le
m
s
(�

.8
0)

:
T
ea
ch
er
R
ep
or
t
A

nx
io

us
/d

ep
re

ss
ed

(f
ee

ls
un

lo
ve

d,
sa

d)
:

M
at
er
na
l
R
ep
or
t
C
B
C
L
:
In

te
rn

al
iz

in
g
(�

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

A
nx

io
us

/D
ep

re
ss

ed
:

C
hi

ld
w

or
ri

es
,c

ri
es

of
te

n,
et

c.
(�

.8
0)
:
T
ea
ch
er
R
ep
or
t
R
ut
te
r
Sc
al

e,
In

te
rn
al
iz
in
g:
C
hi
ld
w
or
ri

es
,s

ee
m

s
m

is
er

ab
le

,e
tc

.(


.5

4)
:

M
at
er
na
l
R
ep
or

t
So

ci
al

sk
ill

s
(I

)
Se

lf
C

on
tr

ol
(�


.7

9)
:

T
ea
ch
er
R
ep
or
t
(I
I)
In
te

rp
er

so
na

l
Sk

ill
s

(�

.8
9)
:
T
ea
ch
er
R
ep
or
t
So
ci

al
Sk

ill
s
R
at
in
g

Sy
st

em
(S

SR
S)

:

C
oo

pe
ra

tio
n,

as
se

rti
on

,s
el

f-
co

nt
ro

l(


.9

3)
:F

al
lo

f
K

in
de

rg
ar

te
n

T
ea
ch
er

Pr
os

oc
ia

l:
C

hi
ld

is
he

lp
fu

l,
sy

m
pa

th
et

ic
to

ot
he
rs
,e

tc
.

(�

.9
2)

:
T
ea
ch
er
R
ep
or
t

Pr
io

r
ch

ild
co

nt
ro

ls
A

ge
3–

4
A

ge
3

A
ge

3
Ju

ni
or
an
d
se
ni
or
ki
nd
er
ga
rt
en
A

ge
42

m
on

th
s

Pr
io

r
co

gn
iti

ve
/

ac
hi

ev
em

en
t
Pe
ab
od
y
Pi
ct
ur
e
V
oc
ab
ul
ar
y
T
es

t—
R

ev
is

ed
(P

PV
T

-R
;

sp
lit

-h
al

f
re

lia
bi

lit
y

of
.8

0)

B
ra

ck
en

B
as

ic
Sk

ill
s

(c
ol

or
s,

le
tte

rs
,n

um
be

rs
,

et
c.

,�

.9
3)

R
ey

ne
ll

D
ev

el
op

m
en

ta
l

L
an
gu
ag
e
Sc
al
e

(v
oc

ab
ul
ar
y
co
m

pr
eh

en
si

on
,�


.9

3;
ex

pr
es

si
ve

la
ng

ua
ge

,�

.8
6)

St
an

fo
rd

-B
in

et
IQ

T
es

t
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
bi
lit
y

.6
6

an
d

.8
5

fo
r

A
an
d
B

,r
es

pe
ct

iv
el

y;
te

st
–r

et
es

ta
t1

w
ee

k

.7
2

N
um
be
r
K
no
w
le
dg
e
T
es

t:
In

fo
rm

al
nu

m
be

r
kn

ow
le

dg
e,

sh
ap

es
,c

ol
or

s,
co

un
tin

g,
nu

m
be
r
se
qu
en
ce
,
ad
di

tio
n

C
ou

nt
in

g
(�

.9

5)
Sp

ea
ki

ng
&

V
oc
ab
ul
ar
y
(�

.7
9)

C
op

yi
ng

de
si

gn
s

(�

.8
3)

A
ge

22
m

on
th

s:
C

ub
e

st
ac

ki
ng

,L
an

gu
ag
e
(�

.9

3)
,C

op
yi

ng
de

si
gn

s
te

s

t
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1433SCHOOL READINESS AND LATER ACHIEVEMENT

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.

Analysis Plan

We begin our analysis by estimating a similar set of regression
models across all six studies, in which school-entry academic,
attention, and socioemotional skills are related to later academic
achievement. For example, in ECLS-K data, the school-entry skills
and behaviors are measured in the fall of kindergarten (referred to
hereafter as FK), whereas math and reading achievement are
measured in the spring of third grade (referred to hereafter as 3rd).
The resulting equation is as follows:

ACH i3rd � a1 � �1ACADiFK � �2ATTNiFK � �3SEiFK

� �1FAMi � �2CHILDi � eit, (1)

where ACHi3rd is the math or reading
2 achievement of child i in

the spring of third grade; ACADiFK is the collection of math,
reading, and general knowledge skills that child i has acquired at
school entry, assessed by achievement tests in the fall of the
kindergarten year; ATTNiFK is a teacher-reported measure of
attention; SEiFK is the collection of socioemotional skills that child
i’s teacher reports; FAMi and CHILDi are sets of family back-
ground and child characteristics, respectively, included in analyses
to control for individual differences that might influence child
achievement before and after school entry; a1 is a constant; and eit
is a stochastic error term.

2 We use reading as shorthand for the set of reading, language, and
verbal ability skills measured in our data sets.

1435SCHOOL READINESS AND LATER ACHIEVEMENT

Our interest is in estimating �1, �2, and �3, which, if correctly
modeled, can be interpreted as the impact of school-entry aca-
demic, attention, and socioemotional skills on subsequent achieve-
ment. A key challenge in this approach is ensuring that we have
accounted for the possibility of omitted variable bias, which is
likely to arise if unobserved family or child characteristics are
correlated with both children’s school entry skills and their later
achievement. Our principal strategy for securing unbiased estima-
tion of �1, �2, and �3 is to estimate a model of the form of
Equation 1 that includes as many prior measures of relevant child
and family characteristics as possible.

All of the studies contain important measures of child and
family characteristics that may be confounded with children’s
achievement, attention, and behavior. Although the specific set of
covariates varies across studies, most studies include measures of
the child’s race and ethnicity, maternal education, family structure,
and family income or economic well-being. In some studies,
measures of child health, maternal depressive symptoms, parent-
ing, and quality of the home environment, as well as children’s
participation in early child care and education during early child-
hood were also included as controls.3 Details about the specific
controls used in each study are provided in the appendices, and a
complete list of covariates for each study can be found in Tables
A6, B6, C6, D5, E6, and F5.

Our analysis was designed to examine the relations between
early skills and later achievement, irrespective of the characteris-
tics of the classroom/school the child attends. The ECLS-K is an
exception, owing to a sample design that selects an average of 4
students per kindergarten classroom to be enrolled in the study. We
took advantage of this classroom clustering by adjusting our
ECLS-K estimates for classroom fixed effects. Thus, all of the
variation used in the regression stems from within-classroom dif-
ferences, which holds constant school and classroom characteris-
tics.

Of course, we cannot be certain that even a comprehensive set
of control variables captures all of the important confounds, which
leaves open the possibility that this approach will still produce
biased estimates of �1, �2, and �3. For example, an obvious bias
of this sort would arise if scores on a kindergarten mathematics test
reflected both math skills and underlying cognitive ability.

To further reduce the possibility of biases, we include measures
of a child’s attention and socioemotional behaviors and either
cognitive ability or preacademic skills assessed prior to school-
entry, which are available in all but two studies (ECLS-K and
MLEPS).4 With these prior measures, our model becomes

ACH i3rd � a1 � �1ACADiFK � �2ATTNiFK � �3SEiFK

� �4ACADiPre-FK � �5ATTNiPre-FK � �6SEiPre-FK

� �1FAMi � �2CHILDi � eit. (2)

ACADiPre-FK, ATTNiPre-FK, and SEiPre-K refer to child i’s respec-
tive achievement, attention, and socioemotional behavior prior to
school entry, respectively. This constitutes a particularly powerful
version of Equation 1, because controlling for the child’s cognitive
and behavioral skills before school entry should reduce, if not
eliminate, omitted-variable bias in �1, �2, and �3.

One concern about Equation 2 is that by controlling for school
entry achievement, we might reduce the deserved explanatory

power of attention and socioemotional skills. This would occur if
one of the ways in which attention and socioemotional skills
affected later achievement were to raise children’s school entry
academic skills. We investigate this possibility by estimating ver-
sions of Equation 2 that omit ACADiFK.

In the second step of our analysis, we use meta-analytic tech-
niques to summarize coefficients obtained from our six studies’
estimates of Equation 2 and seek to determine whether particular
study characteristics are associated with larger (or smaller) coef-
ficients. More specifically, the meta-analysis treats the standard-
ized regression coefficients from Equation 2 as observations in a
regression predicting academic achievement measured as late in
childhood as possible. Independent variables in the meta-analytic
regression include (a) the type of school-entry measure,5 (b)
elapsed time (scaled in years) between measurement of school-
entry characteristics and the outcome, (c) whether the outcome is
math or reading achievement, and (d) whether the outcome is
based on a test or a teacher report. In keeping with standard
meta-analytic practices, we weighted each regression coefficient
observation by the inverse of its variance (Hedges & Olkin, 1985).

Results

Regression Results

To consider whether school entry6 achievement, attention,
and socioemotional skills are predictive of subsequent achieve-
ment, we first estimated a comparable set of regressions (Equa-
tion 2) across all of the studies. For each study, reading and
math outcomes measured as late in the data set as possible were
regressed on school-entry achievement, attention, and socio-
emotional behaviors, with controls for important family and
child characteristics also included in the regression. In all but
two cases, our regressions include measures of both cognitive
ability and either attention or socioemotional behaviors.

3 Our list of child and family control variables is more extensive than in
most developmental studies. In selecting these variables, we were careful
to include only variables measured prior to or concurrently with our
school-entry measures of achievement and behavior. We were also mindful
that added controls might introduce multicolinearity into our regression
estimation, but there was no indication that this might be the case. And
finally, our appendix tables compare models run with and without our child
and family controls and show that the results of our analyses depend little
on adjustments for these factors; concurrent controls for the other achieve-
ment and behavioral measures matter much more.

4 The MLEPS provides preschool cognitive measures but not attention
or socioemotional behaviors (see Table 1).

5 The decision of which type of measure should serve as the omitted
dummy variable category is noteworthy, because the coefficients on the
included measure categories represent differences from the omitted cate-
gory. We selected internalizing behavior problem coefficients as the omit-
ted category because the simple average of their regression coefficients
was very close to zero (–. 01 for reading outcomes and –. 01 for math
outcomes).

6 We remind the reader we use the term school entry somewhat loosely.
It refers to age 5 in four cases, age 5–6 in one case, and the fall of the
kindergarten year in only one case.

1436 DUNCAN ET AL.

Standardized regression coefficients and standard errors from
models predicting achievement from the school-entry academic,
attention, and socioemotional behaviors are presented in Table
2. Complete regression results using all available reading and
math outcomes are presented in appendix tables and are sum-
marized below in our meta-analysis.

As expected, the regression results indicate that school-entry read-
ing and math skills are almost always statistically significant predic-
tors of later reading and math achievement, with standardized coef-
ficients ranging from .05 to .53. Not surprisingly, school-entry reading
skills predict subsequent reading achievement better than subsequent
math achievement, just as early math skills are more predictive of later
math than reading achievement.

In the case of attention skills and attention problems, coeffi-
cients are usually smaller than those for math skills, but they are
statistically significant for more than half of the coefficients. In
contrast, coefficients for socioemotional behaviors—externalizing
and internalizing behavior problems and social skills—rarely pass
the threshold of statistical significance.

This general pattern—relatively strong prediction from
school-entry reading and math skills, moderate predictive
power for attention skills, and few to no statistically significant
coefficients on socioemotional behaviors—is also found for
reading and math achievement measured at earlier points in the
studies and in logistic regressions in which grade retention is
the dependent variable (results shown in Tables A3, B3, C3,
D3, E3, and F3 but not in Table 2).

To consider whether the effects of school-entry skills differ by
children’s gender or socioeconomic status (SES), we ran regressions
(Equation 2) but also included gender interactions with school-entry
achievement, attention, and socioemotional skills for all six data sets
and SES interactions for all but the NICHD SECCYD and MLEPS
(results shown in Appendices A–F).7 In the case of gender interac-
tions, 10 of 76 relevant interaction coefficients were .05 or larger and
statistically significant, but there was no consistent pattern in the
direction of effects. In the case of SES interactions, only 2 of the 30
interaction coefficients were .05 or greater and statistically significant.
It appears that the influences of school-entry achievement, attention,
and socioemotional skills are broadly similar for both boys and girls
and for children from both low- and high-SES families.8

Meta-Analytic Results

To summarize findings across the six studies more system-
atically, we first averaged the 102 bivariate correlations be-
tween school-entry achievement, attention, and socioemotional
skills and the latest available reading and math achievement
available in each of the data sets. (Detailed correlation tables
are shown in Tables A2, B2, C2, D2, E2, and F2.) As shown in
the first column of Table 3, the absolute value of these corre-
lations average between .40 and .50 for school-entry reading
and math achievement, average .25 for the collection of atten-
tion measures, and average between .10 and .21 in absolute
value for the three sets of socioemotional measures.

Next, we conducted a formal meta-analysis of the standardized regres-
sion coefficients emerging from the individual study regressions. We
used two sets, with the first comprising the 102 coefficients shown in
Table 2, and drew from regressions based on Equation 2 and achievement

outcomes measured as late in childhood as possible. These results are
shown in the second column of Table 3. The second meta-analytic
regression is based on the 228 coefficients taken from regressions with
outcomes measured at all possible points in a given study. These coeffi-
cients are shown in the appendix tables and produce the results shown in
the third, fourth, and fifth columns of Table 3.

A clear conclusion from the first meta-analytic regression is
that only three of the school-entry skill categories predict
subsequent reading and math achievement: reading/language, math,
and attention. Moreover, rudimentary mathematics skills appear to
matter the most, with an average standardized coefficient of .33.9 The
association of reading skill with later achievement was less than half
as large (.13), and, at .07, the average standardized coefficients on the
attention-related measures was less than one quarter the size of the
mean math-skills coefficient. As expected from Table 2, the meta-
analysis results confirm that behavior problems and social skills are
not associated with later achievement, holding constant achievement
as well as child and family characteristics. Indeed, none had a stan-
dardized coefficient that averaged more than .01 in absolute value.

Turning to the other coefficients listed in the second column
of Table 3, one can see that the school-entry skills coefficients
decreased a little (.010 per year) with each additional year
between school entry and the point of assessment of the math or
reading outcome. As for whether teacher-report outcomes or
direct skill assessments are more likely to be predicted by early
skills, our meta-analytic results suggest that both types of
measures performed about the same.

Our appendices (Tables A3, B3, C3, D3, E3, and F3) provide
standardized coefficients from regressions of achievement outcomes
measured at different ages, which constitute the 228 observations used
for the meta-analytic regression results shown in the remaining col-
umns of Table 3. The advantage of using outcomes at several ages is
that it enables us to control for the study from which a given stan-
dardized coefficient was estimated, which we do by including a set of
study indicator variables.10 The third column of Table 3 summarizes
the results across domains, and the fourth and fifth columns show

7 Coefficients were excluded from our summary calculations if the
standard errors for the gender or SES interactions were too large to detect
differences of .15.

8 The ECLS-K, NLSY, and IHDP studies provided enough observa-
tions on Black and White children to enable us to test for race inter-
actions as well. We found no consistent evidence of race-based inter-
actions.

9 Technically speaking, the .33 coefficient reflects the regression-
adjusted difference between the average school-entry math and the
omitted-group internalizing problem behavior standardized coefficient.
Recall that the simple average of regression coefficients on internaliz-
ing behavior problems was –.01 for both math and reading outcomes.
The large sample sizes available in the ECLS-K push weighted meta-
analytic results closer to the ECLS-K study coefficients than when the
coefficient-variance weights are not used. Unweighted, the coefficients
on school-entry reading and math are .12 and .20, respectively; the
coefficient on attention is .05, the coefficient on externalizing is .00,
and the coefficient on social skills is –.01.

10 With study dummies in the regression, we have what amounts to a
fixed-effects regression in which coefficients are averaged within rather
than across studies. Dropping the study dummies produced few changes in
the remaining coefficients.

1437SCHOOL READINESS AND LATER ACHIEVEMENT

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1438 DUNCAN ET AL.

results separately for reading and math outcomes. To provide a visual
representation of the data underlying the meta-analysis, we plot the
114 coefficients for reading outcomes in Figure 1 and the 114 coef-
ficients for math outcomes in Figure 2.

The general pattern of results found for outcomes measured later
in childhood also holds when we also consider the broader set of
regression coefficients, which includes outcomes measured earlier
in childhood (column 3 of Table 3). With an average standardized
coefficient of .34, school-entry math skills are most predictive of
subsequent achievement outcomes, followed by reading skills
(.17) and attention-related measures (.10). None of the socioemo-
tional behavior categories show predictive power.

Results in columns 4 and 5 of Table 3 confirm that early reading
skills are stronger predictors of later reading achievement than of later
math achievement. Less expected are the fourth column’s results
showing that early math skills are as predictive of later reading
achievement as are early reading skills. Children’s attention skills
appear to be equally important (and socioemotional behaviors equally
unimportant) for reading and math achievement. The separate regres-
sions for reading and math skills also show that the association
between school-entry skills and later achievement declines more
quickly over time for reading than for math outcomes.

As before, we find no overall difference in the size of the standard-
ized coefficients depending on whether the outcome being predicted

is based on teacher reports or direct skill assessments. To answer the
more specific question of whether shared method variance might lead
to more pronounced associations between school-entry achievement
test scores and later achievement (as opposed to school-entry teacher-
reported achievement) outcomes, we added to the meta-analytic re-
gressions interactions between the categories of school entry skills
and dummy variables for type of achievement measure (results not
shown). Surprisingly, we found no evidence that the impact of early
reading and math skills mattered more for test-based than for teacher-
reported outcomes.11 Thus, shared method variance does not appear
to be biasing our results.

Because some of our skills groupings are quite broad, we
explored whether they might be concealing systematic differences
among more specific skills. For example, our reading category

11 None of the interactions between type of achievement assessment and
school-entry reading and math skills was statistically significant. We did
find one statistically significant interaction—between the school-entry
assessments of attention and the mode of outcome assessment. The average
coefficient on attention skills was nearly twice as large for teacher reports
of reading and math achievement (.13) as for reading and math test scores
(.07). However, this result does not bear on the issue of whether the
explanatory power of school-entry achievement test scores is artificially
high owing to shared method variance.

Table 3
Average Correlations and Meta-Analytic Regression Results for the Standardized Coefficients From the Six Data Sets

Independent variable

Zero-order
correlation
coefficients

Most recent
reading and

math outcomes Reading and math

All observed outcomes

Reading Math

School-entry measure
Reading .44 .13*** (.01) .17*** (.03) .24*** (.03) .10*** (.02)
Math .47 .33*** (.06) .34*** (.04) .26*** (.02) .42*** (.04)
Attention skills .25 .07* (.02) .10*** (.01) .08*** (.02) .11*** (.02)
Externalizing problems �.14 .01 (.00) .01 (.01) .01 (.02) .01 (.01)
Internalizing problemsa �.10 — — — —
Social skills .21 �.01 (.01) �.01 (.01) �.00 (.02) �.01 (.01)

Time (years between school entry
measure and outcomes)

�.010*** (.001) �.009 (.005) �.012** (.005) �.005 (.005)

Outcome source
Test score .00 (.01) �.00 (.02) �.01 (.02) .01 (.02)
Teacher reporta — — — —

Outcome subject
Math .01** (.00) �.00 (.02)
Readinga — — — —

Data set
ECLS-Ka — — —
NLSY �.01 (.03) .00 (.03) �.02 (.03)
NICHD SECCYD �.01 (.02) �.01 (.02) �.01 (.02)
IHDP .03 (.05) �.01 (.03) .08 (.09)
MLEPS �.03 (.02) �.05 (.03) �.03 (.02)
BCS �.00 (.02) �.01 (.03) .01 (.02)

Observations 102 102 228 114 114
R2 .75 .74 .80 .86

Note. All coefficients used in these analyses come from the individual study regressions that include full controls. Column 1 shows the simple average
correlation between the given measure and the most recent math and reading outcomes. Model 2 standard errors are corrected for within-study clustering
using Huber–White methods. Regression coefficient observations are weighted by the inverse of their variances. Robust standard errors are in parentheses.
Dashes indicate that this category was omitted. ECLS-K � Early Childhood Longitudinal Study–Kindergarten Cohort; NLSY � National Longitudinal
Survey of Youth; NICHD SECCYD � National Institute of Child Health and Human Development Study of Early Child Care and Youth Development;
IHDP � Infant Health and Development Program; MLEPS � Montreal Longitudinal-Experimental Preschool Study; BCS � British Birth Cohort Study.
a Omitted in regression models.
* p � .05. ** p � .01. *** p � .001.

1439SCHOOL READINESS AND LATER ACHIEVEMENT

includes measures of school-entry reading achievement as well as
language and verbal ability. When we reran the meta-analytic
regression in the third column of Table 3 with separate groups for
reading achievement and the collection of other language-related
measures, we found that we could not reject the hypothesis of
equal effects ( p � .11).

Extensions

Beyond shared method variance, there are a number of other
reasons to worry that we may have stacked the deck in favor of our
school-entry achievement measures: (a) Attention and socioemo-
tional skills may be more difficult to measure than achievement-
related skills; (b) maternal reports available in three of our data
sets may be less predictive than teacher reports of later academic
achievement, in part because parents do not observe their children
in school settings; (c) our models may overcontrol for
achievement-related impacts of attention and socioemotional
skills; (d) socioemotional skills may matter more for important
school-related outcomes such as drop out than do test scores,
because drop out reflects some combination of achievement and
behavior; (e) most of our outcomes are measured in middle child-
hood, and the relative importance of school-entry factors may
change as schools encourage older children to become independent
learners; (f) a number of our socioemotional measures are counts
of students’ problems, which restricts their range and perhaps
explanatory power relative to the full-scale achievement measures;
and (g) substantial attrition in some of our studies may bias results.

A first potential threat to our general conclusion is that chil-
dren’s behavior is more difficult to measure than their early
achievement. Perhaps the lower reliability or validity of the be-
havioral measures accounts for their weaker explanatory power. It
is certainly true that school-entry tests have high internal consis-
tency (e.g., the alphas were at least .74). But the internal consis-
tency of most of the attention and socioemotional skills was also

fairly high, particularly in the case of teacher reports, which were
all .79 or higher.

To investigate the potential impact of unreliable measurement
on our results, we used the reported internal consistencies in the
ECLS-K and NLSY data to estimate regression models, using the
errors-in-variables reliability adjustment in the EIVREG procedure
in Stata (Stata Corporation, 2004). To accord the behavioral mea-
sures maximum explanatory power, we included in our regressions
school-entry academic test scores as well as family and child
control variables but only the given measure of attention or socio-
emotional behaviors.

For third-grade reading outcomes and with no reliability adjust-
ments in the ECLS-K, the respective standardized coefficients on
approaches to learning, self-control, interpersonal skills, external-
izing problems, and internalizing problems were .05, .02, .02, –.03,
and .00, respectively. Reliability adjustments produced very sim-
ilar coefficients: .06, .02, .03, –.04, and .00, respectively.
Reliability-related changes to the coefficients on these measures
predicting math achievement were similarly modest.

For the NLSY early-adolescence reading test score outcome,
respective coefficients associated with hyperactivity, headstrong,
antisocial behavior, and anxiety/depression were –.06, –.02, –.05,
and –.01, respectively. Adjusting for reliability generally increased
(the absolute value of) these coefficients somewhat to –.09, –.03,
–.08, and –.01, respectively. Reliability-related changes in coeffi-
cients predicting early adolescents’ math scores were similar.
Although the proportionate increases in these coefficients are
substantial in some cases, none of the reliability-adjusted coeffi-
cients begins to rival the size of the coefficients on early reading
and math skills. In sum, it is unlikely that lower internal reliability
explains the low explanatory power of our attention and socio-
emotional behavior measures. Although test–retest correlations
may provide a richer understanding of the reliability of these
measures, these data were not readily available. Nevertheless, with

Figure 1. Standardized coefficients from models of reading achievement estimated from six data sets. Filled
squares indicate statistically significant coefficients.

1440 DUNCAN ET AL.

average effect sizes ranging from –.01 to .01 (see Table 3), it is
unlikely that even substantial reliability adjustments to our behav-
ior and social skills measures would change our conclusions.

The overall validity of the attention and socioemotional behav-
ior measures is much more difficult to assess. Correlations shown
in the first column of Table 3 between later achievement and the
attention and socioemotional behavioral measures have the ex-
pected signs and range from .10 to .25 in absolute value, suggest-
ing at least some degree of validity. However, there remains the
possibility that low validity might lead us to underestimate their
predictive power. Of course, the validity of kindergarten-level
achievement tests has also been questioned (Hirsh-Pasek,
Kochanoff, Newcombe, & de Villiers, 2005; Meisels & Atkins-
Burnett, 2004), so validity-based downward bias is also a concern
with respect to the coefficients on the early achievement measures.

A second concern is that we relied on maternal reports of
socioemotional behaviors in three studies (NLSY, IHDP, and
BCS). Because maternal ratings are comparatively (siblings vs.
classmates) and contextually (family vs. school) different from
teacher ratings, it is possible that our reliance on maternal reports
in these data sets leads to a downward bias in the estimated effects
of the attention and socioemotional behavior measures (Gagnon,
Vitaro, & Tremblay, 1992).

To investigate whether this might be the case, we took data from
the ECLS-K and NICHD SECCYD, both of which gathered com-
parable ratings from parents and teachers of several components of
socioemotional behaviors, and substituted parent reports for the
teacher-report-based measures of these skills. Using our full set of
controls and averaging across latest reading and math outcomes,
we found that standardized coefficients on externalizing and in-
ternalizing problems and social skills averaged, respectively, .05,
.03, and .03 for teacher reports and –.00, .01, and .01 for parent
reports. Noting the unexpected direction of effects for the teacher
reports of problem behaviors and the very modest coefficients in
general, reporter bias does not appear to be driving our results.

The third issue, overcontrol, is complicated. Our regression
models control for school-entry achievement, but if early attention
and socioemotional skills affect later achievement primarily by
affecting school-entry achievement skills, are we not robbing the
school-entry nonachievement measures of some of their explana-
tory power?12 The pattern of average correlations presented in the
first column of Table 3 bears on this issue. Bivariate associations
between school-entry attention and socioemotional skills are con-
siderably larger than their regression-adjusted partial associations,
suggesting that this might possibly be the case.

To investigate this possibility more systematically, we reesti-
mated our full-control models, using the latest outcomes in each
study and omitting our school-entry measures of reading and math
skills (but retaining all other control variables). Standardized co-
efficients on attention, externalizing problems, internalizing prob-
lems, and social skills averaged .13, .03, .02, and .03, respectively,
without school-entry reading and math skills. Corresponding av-
erages for coefficients from models that included school-entry
academic skills were .07, .03, .02, and .02, respectively. Thus,
even without including school-entry reading and math skills, only
attention skills appear to relate to later reading and math achieve-
ment, and this may be due in part to their correlation with the
omitted school-entry achievement measures rather than to a true
mediation through achievement. That said, it remains the case that
our analysis is focused on behavior during the years just before and
at the point of school entry. If some types of socioemotional skills
are well established before the preschool years, and unchanging
during these years, then we will not be able to detect their effects.

Fourth, attention and socioemotional skills may matter more for
outcomes such as special education classification or dropping out

12 Note that an overcontrol argument applies equally to the achievement
as to the nonachievement measures, because early success in learning
reading and math skills may alter preschool behavior.

Figure 2. Standardized coefficients from models of math achievement estimated from six data sets. Filled
triangles indicate statistically significant coefficients.

1441SCHOOL READINESS AND LATER ACHIEVEMENT

of school than for the test scores and teacher-reported achievement
outcomes used in our studies. The outcomes of our analyses are
indeed limited, and it may well be that these types of measures of
school completion and success are more strongly linked to chil-
dren’s socioemotional behavior and attention skills than to aca-
demic skills. Our test for this possibility was to estimate models of
the effects of early academic and self-regulatory skills on grade
retention, an outcome that includes elements of both academic and
behavioral competence. Results in Tables A3, B3, C3, D3, E3, and
F3 were quite similar to those from models with purer
achievement-related outcomes.13 Nevertheless, the possibility re-
mains that the predictive power of school-entry skills may differ
for other, even more behavior-based educational outcomes.

A fifth concern is that most of the outcomes were measured
during children’s elementary-school years. This is important for
two reasons. First, teachers and classrooms differ across the extent
to which they support learning academic, attention, and behavior
skills. Our analysis does not consider how the associations among
these skills may differ as a function of classroom and teacher
contexts. Moreover, the associations among these skills may
change over time as the contexts of classrooms change. Achieve-
ment in the middle- and high-school years involves increasingly
complex reading and mathematical tasks, and it may be that
general cognitive skills, particularly oral language and conceptual
abilities, are crucial for comprehension and advanced problem
solving (Baroody, 2003; Ferrari & Sternberg, 1998; Hiebert &
Wearne, 1996; NICHD Early Child Care Research Network,
2005b; Scarborough, 2001; Snow et al., 1998; Storch & White-
hurst, 2002; Whitehurst & Lonigan, 1998). It is also possible that
once children are past learning the basics in the early grades, the
relative importance of early attention and socioemotional skills
grows as children are increasingly called on to be independent
learners, allocate their own time, and complete group work and
assignments.

For a general look at the evidence about whether any of the
impacts of academic, attention, and socioemotional skill measures
are growing over time, we reran our meta-analytic regressions
including interactions between each of the school-entry measures
and the time between school entry and the outcome assessment.
Coefficients on the interactions with early reading, early math,
attention, and socioemotional behavior were all negative and
ranged from –.023 to –.039 annual decrements in effect sizes,
providing no support for hypotheses of increasing importance.

Because most of our outcomes are assessed in elementary
school, the interactions shed relatively little light on what would
happen if more outcomes were measured during middle or high
school. The NLSY data are most telling on this point, because the
same skill assessment was given at both age 7–8 and age 13–14.
In regressions of outcomes based on these two time points, we find
that although school-entry hyperactivity retained its modest ex-
planatory power (effect sizes around –.07) between these two
points, the explanatory power of reading and math fell. In the case
of NICHD SECCYD outcomes measured in third and fifth grade,
there was inconclusive evidence on the direction of coefficient
change.

A sixth worrisome methodological concern is that a number of
our socioemotional measures are counts of students’ problems,
which restricts the range of behaviors they capture and might
therefore reduce their predictive power. We explored this possi-

bility by estimating spline regressions, which allow for nonlinear
effects (results not shown). In these analyses, two linear segments
per measure were fit to the data. The first segment was estimated
for the most problematic third of the sample distribution, and the
second segment was estimated for the other two thirds. If, say,
externalizing behavior problems matter a great deal for the chil-
dren with very high levels of problem behavior but owing to the
restricted range, much less for the others, then the slope of the line
fit to the most problematic group should be significantly larger (in
absolute value) than the slope for the rest of the sample.

We found little systematic evidence that this was the case. In the
ECLS-K data, there was some evidence that improving early math
skills mattered more for low math achievers, whereas the NLSY
showed that hyperactivity mattered more for the most hyperactive
children. No significant nonlinearities emerged in the analysis of
NICHD SECCYD data. These spline analyses confirm that there
are few nonlinear associations between the socioemotional mea-
sures included in this study and the outcomes they predict. It does
not rule out, however, the possibility that other measures that
capture a broader range of behaviors may be more strongly asso-
ciated with later achievement.

A seventh and final concern is that sample attrition in some of
our studies may bias our results. The extent of attrition is docu-
mented in our appendices. All of the coefficients used in our
meta-analyses come from models in which missing data are ac-
counted for with missing data dummy variables. In the appendices,
we present results for three of our data sets (ECLS-K, NICHD
SECCYD, and MLEPS) in which missing data are handled with
multiple imputation and listwise deletion.

In two data sets (ECLS-K and MLEPS), we also used nonre-
sponse weighting adjustments. In the ECLS-K, both multiple im-
putation and listwise deletion estimates are slightly smaller in
magnitude than the results using missing data dummy variables,
although the pattern of results is consistent across all the tech-
niques. In the MLEPS, the pattern of results does not change
across the different methods; however, listwise deletion produces
few statistically significant estimates given the reduction in sample
size from 500 to approximately 150. In the NICHD SECCYD, the
overall pattern of results is similar across the two missing data
techniques; however, the coefficient estimates are most consistent
between missing data dummy estimates and multiple imputation.
Across the three data sets, the respective coefficients on school
entry reading, math, attention, externalizing, internalizing, and
social skills averaged .13, .23, .06, .04, .02, and .02, respectively,
when we used missing data dummies. Corresponding coefficient
averages for multiply-imputed models were .12, .17, .06, .01, .01,
and .02, respectively. Corresponding coefficient averages for list-
wise deletion models were .09, .27, .07, .00, .02, and .00, respec-
tively. Corresponding coefficient averages for nonresponse
weighted models were .12, .31, .10, –.00, .01, and –.00, respec-
tively.

13 Only the BCS has followed study participants long enough to measure
their completed schooling and early-career labor market earning and, in
results not reported in the appendices, we found that school-entry attention
problems were a significant predictor of school completion but not labor
market success.

1442 DUNCAN ET AL.

Discussion and Conclusions

We have presented results from a coordinated analysis of six
longitudinal data sets relating school-entry skills to later teacher
ratings and test scores of reading and math achievement, holding
constant children’s preschool cognitive ability, behavior, and other
important background characteristics. Our meta-analytic results
indicate that such early math concepts as knowledge of numbers
and ordinality were the most powerful predictors of later learning
(the average effect size of school-entry math skills was .34 and
every bit as large as early reading skills in predicting later reading
achievement). Less powerful, but also consistent, predictors across
studies were early language and reading skills such as vocabulary;
knowing letters, words, and beginning and ending word sounds
(the average effect size across our studies was .17); and attention
skills (average effect size .10). The average effect sizes of exter-
nalizing and internalizing problem behaviors and social skills were
close to zero.

Despite our extensive investigation of the robustness of our key
results, any nonexperimental analysis using imperfectly measured
cognitive, achievement, and behavioral constructs such as ours
cannot rule out all threats to its conclusions. First, although shared
method variance, reporter bias, overcontrol, restricted range, and
measurement reliability cannot account for the differential predic-
tive power of school-entry achievement and socioemotional mea-
sures, we are unable to rule out bias from the lower validity of
socioemotional measures. Second, despite our ability to control for
cognitive ability prior to school entry in five of our six studies, and
despite our controls for concurrent reading skills in all six studies,
it remains possible that our surprisingly large school-entry math
coefficients overstate causal impacts.

One of our noteworthy results is that early math is a more
powerful predictor of later reading achievement than early reading
is of later math achievement. Despite our controls for cognitive
ability, it remains possible that some of the apparent effects of
early math skills is spurious. To the extent that the effects are real,
it is important to discover why. Math is a combination of both
conceptual and procedural competencies. Although our data do not
allow us to examine these competencies separately, future research
could focus on this direction. Another productive avenue of re-
search would be to examine efforts to improve math skills prior to
school entry. Random-assignment evaluations of early math pro-
grams that focus on the development of particular mathematical
skills and track children’s reading and math performance across
elementary school could help to illuminate missing causal links
between early skills and later achievement.

Another finding from our analysis is that attention skills are
modestly but consistently associated with achievement outcomes.
One explanation for this predictive power is that attention skills
increase the time children are engaged and participating in aca-
demic endeavors and learning activities. Other studies have shown
that attention skills have important associations with school suc-
cess, independent of cognitive and/or language ability (Alexander
et al., 1993; Howse et al., 2003; McClelland et al., 2000; Yen et al.,
2004), but few of these studies have controlled for prior levels of
academic skills as well as prior levels of behavior. Our results
suggest that attention skills, but not problem behavior or social
skills, predict achievement outcomes, even after the effects of

early achievement knowledge and cognitive ability have been
netted out.

Although all of the studies we analyzed were drawn from
normative populations, all contain at least some children falling in
the clinical ranges of behavior problems. We were surprised that
our spline-regression models produced no consistent evidence of
nonlinear effects of problem behaviors on later achievement. We
caution, however, that it remains possible a more focused analysis,
perhaps with clinical samples, might reach different conclusions.

Given that teachers emphasize the importance of attention skills
and socioemotional behavior for school readiness and the possi-
bility that these skills shape classroom learning processes, it might
be expected that these early skills would have crossover effects on
subsequent reading and math achievement. With the important
exception of attention skills, we did not find evidence that changes
in these skills during the preschool years predict later achievement.
However, as noted earlier, academic skills are only one facet of
educational success, and improvements in problem behavior or
social skills may better predict other important school outcomes,
such as a child’s engagement in school and motivation for learn-
ing, relationships with peers and teachers, and overall self-concept
and school adjustment (Greenberg et al., 2003). It might also be the
case that early-grade teachers are somehow able to prevent prob-
lem behaviors from interfering with student learning but that
problem behaviors would be linked with lower achievement if
teachers were less capable. Despite the uniformly small and often
insignificant coefficients on these measures in our regressions, we
caution against completely dismissing the potential academic ben-
efits of environments or programs that promote positive socioemo-
tional development.

An additional caveat is that any one child’s socioemotional
behavior, in particular externalizing problem behaviors, may affect
other students’ achievement more than the child’s own individual
achievement. For example, problem behaviors may disrupt class-
room activities such that even well-behaved children spend less
time engaged in instructional and learning activities. Our analyses
do not consider this possibility because it requires more complete
data about classmates’ behavior than the studies provide. We raise
this point, however, because we believe that the topic of peer
effects deserves further attention in future research on socioemo-
tional behavior.

Our analyses focus on skills and behaviors that emerge at the
time of school entry and not on the effects of socioemotional
behaviors that emerge after children enter school. This is impor-
tant, because it may be that reading achievement and problem
behavior develop in tandem during the early elementary years
(Trzesniewski et al., 2006). Additional research is necessary to
further elucidate the potentially complex and reciprocal relation-
ships between children’s socioemotional behaviors and their aca-
demic achievement.

Our conclusions about the importance of early academic and
attentions skills are consistent with the recommendations endorsed
by the National Association for the Education of Young Children
and the National Council of Teachers of Mathematics (2002) and
the National Research Council’s Committee on the Prevention of
Reading Difficulties in Young Children (Snow et al., 1998). How-
ever, our results say nothing about the types of curricula that would
be most effective in promoting these skills. Play-based, as opposed
to “drill-and-practice,” curricula designed with the developmental

1443SCHOOL READINESS AND LATER ACHIEVEMENT

needs of children in mind can foster the development of academic
and attention skills in ways that are engaging and fun. Taking early
math skills as an example, the Big Math for Little Kids program
has been designed to capitalize on children’s interest in exploring
and manipulating numbers (Greenes, Ginsburg, & Balfanz, 2004).
In addition, play-based curricula may also have the added benefit
of fostering attention-related skills (Berk, 1994).

Our findings support three key conclusions for developmental
research. First, math and reading skills at the point of school entry
are consistently associated with higher levels of academic perfor-
mance in later grades. Particularly impressive is the predictive
power of early math skills, which supports the wisdom of exper-
imental evaluations of promising early math interventions. Second,
among attention-related and socioemotional behaviors, only the
attention-related skills predicted later academic achievement with
any consistency. We find no noteworthy regression-adjusted asso-
ciations between either interpersonal skills (or problems) or ag-
gression and later achievement. Finally, all of our data sets suggest
that reading and math tests that were individually administered to
children by trained personnel around the point of school entry can
be a highly reliable way of assessing early skills. That said, it was
also the case that we could not attribute most of the variation in
later school achievement to our collection of school-entry factors,
so the potential for productive interventions during the early
school grades remains.

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Received April 18, 2006
Revision received April 30, 2007

Accepted May 16, 2007 �

1446 DUNCAN ET AL.

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 Avisia.Whiteman@state.mn.us or

651-582-8329 or Eileen Nelson at Eileen.Nelson@state.mn.us 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:Avisia.Whiteman@state.mn.us

mailto:Eileen.Nelson@state.mn.us

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

Minnesota School Readiness Study 2010

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

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

http://www.humancapitalrc.org/mn_school_readiness_indicators

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 cate

gories

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.

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

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

Average Score Summary 56%

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

Average Score Summary 56%

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

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

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.

Minnesota School Readiness Study 2010

13

http://mn.gov/governor/multimedia/pdf/Executive-Order-11-05

http://mn.gov/governor/multimedia/pdf/Executive-Order-11-05

Minnesota School Readiness Study 2010

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.

Minnesota Department of Education (2007). Minnesota School Readiness Study: Developmental

Assessment at Kindergarten Entrance Fall 2006. Roseville: Minnesota Department of Education.

Minnesota Department of Education (2008). Minnesota School Readiness Study: Developmental

Assessment at Kindergarten Entrance Fall 2007. Roseville: Minnesota Department of Education.

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,

http://www.humancapitalrc.org/mn_school_readiness_indicators

­
­

http://www.humancapitalrc.org/mn_school_readiness_indicators

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

Appendices

Minnesota School Readiness Study 2010

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.

Minnesota School Readiness Study 2010

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

Minnesota School Readiness Study 2010

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.

Minnesota School Readiness Study 2010

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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. Your introduction does not have a heading – just the title of the paper.

You will need to write a thesis statement in your introduction, the main idea of a paper. Click

here

to visit a website about thesis statements.

Once you’ve developed the thesis, you can then begin composing your introduction. Click
here
for information on writing introductions.

Part One: Hypothesis

This will be the beginning of the body of the essay. Even though it has a new heading, you want to make sure you link this to your previous section so your reader can follow and understand. Remember to make sure your first sentence in each paragraph both transitions from your previous paragraph and summarizes the main point in your paragraph. Stick to one topic per paragraph, and avoid long paragraphs so you can hold the reader’s attention. A paragraph should be a minimum of three sentences. In this section you will address:

What is your 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

but, however, in spite of, on the one hand … on the other hand, nevertheless, nonetheless, notwithstanding, in contrast, on the contrary, still, yet

Sequence/Order

first, second, third, … next, then, finally

Time

after, afterward, at last, before, currently, during, earlier, immediately, later, meanwhile, now, recently, simultaneously, subsequently, then

Example

for example, for instance, namely, specifically, to illustrate

Emphasis

even, indeed, in fact, of course, truly

Place/Position

above, adjacent, below, beyond, here, in front, in back, nearby, there

Cause and Effect

accordingly, consequently, hence, so, therefore, thus

Additional Support or Evidence

additionally, again, also, and, as well, besides, equally important, further, furthermore, in addition, moreover, then

Conclusion / summary

finally, in a word, in brief, briefly, in conclusion, in the end, in the final analysis, on the \whole, thus, to conclude, to summarize, in sum, to sum up, in summary

From

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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.

Conclusion

Your conclusion section should recap the major points you have made in your work. Click
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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

PAGE

Running head: Assessing “Research-Based” Curricula
1

Assessing “Research-Based” Curricula

School Name

Course:

Date: 13th March 2013

Assessing “Research-Based” Curricula

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).

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:

School Readiness and Later Achievement

1

School Readiness and Later Achievement

School Name

Course:

Date: 13th March 2013

School Readiness and Later Achievement

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

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