Dosage effects

LWW/IYC IYC200068 March 2, 2011 5:23 Char Count= 0

Infants & Young ChildrenVol. 24, No. 2, pp. 117–132Copyright c© 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins

“Dosage” Effects on DevelopmentalProgress During Early ChildhoodInterventionAccessible Metrics for Real-Life Researchand Advocacy

Stephen J. Bagnato, EdD; Hoi K. Suen, EdD;Antonio V. Fevola, PhD

The accountability movement in education, in general, and early childhood intervention (ECI),

specifically, have fueled the debate about the quality, benefits, and limitations of various types of

publicly funded ECI and human service programs (PEW Charitable Trusts, 2008; National Research

Council/National Academy of Sciences, 2009) not only in the United States but also internation-

ally. However, policymakers, government regulatory bodies, and philanthropies are confused often

by the complexity of our research methods and have proposed the concept of “dosage” (time-in-

program) as a simpler way to depict child progress during program participation as an account-

ability model. Despite its technical and programmatic limitations, the dosage concept can be made

uniform and rigorous to inform and to advocate. We have proposed and field-validated an “ECI

minimum dosage” methodology that uses performance (ie, effect size) criteria from national ECI

studies and regression metrics to establish a minimum comparative standard for state and national

accountability and real-life program evaluation research efforts and advocacy in ECI for children

at developmental risk. Practitioners and researchers can access a Web site to employ an excel

program to input and analyze their data. In this article, we present dosage and progress data on

n = 1350 children in a high-profile ECI initiative in the Pennsylvania to demonstrate the effective-

ness of the proposed minimum-dosage metrics. Implications and lessons learned for practitioners,

researchers, and policymakers are presented. Guide points to help programs to conduct applied

research in real-life community settings to show “how good they are at what they do”are offered.

With more accessible metrics, we can be more persuasive to advocate and influence public policy

in ECI in desired directions for the benefits of all children, families, and programs—especially our

most vulnerable ones. Key words: accountability, applied community research, dosage effects,dosage metrics, early childhood intervention, time-in-program

PROGRAM IMPACT, OUTCOMES, ANDACCOUNTABILITY

Policymakers and practitioners are inter-

ested increasingly in accounting for the stated

Author Affiliations: Schools of Medicine andEducation, Pediatrics & Applied DevelopmentalPsychology and Office of Child Development (DrBagnato), School of Education, EducationalPsychology, Penn State (Dr Suen), and School ofMedicine, Psychiatry, University of PittsburghMedical Center (Dr Fevola).

Correspondence: Stephen J. Bagnato, EdD, Early Child-hood Partnerships (ECP), Office of Child Develop-

benefits of diverse types of educational and

psychosocial programs for all individuals, but

particularly, individuals who are at develop-

mental risk or who have delays, disabilities, or

disorders. In the United States, No Child Left

Behind (US Department of Education 2001) is

perhaps the most widely known educational

accountability effort to demonstrate the profi-

ciency of students in school-based programs.

ment, 400 N. Lexington Avenue, Pittsburgh, PA 15208([email protected]).

DOI: 10.1097/IYC.0b013e3182104896

Copyright © 2011 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

117


118 INFANTS & YOUNG CHILDREN/APRIL-JUNE 2011

Within early intervention, the Office of Spe-

cial Education Programs (2006) of the US De-

partment of Education has mandated the col-

lection of entry and exit data on the progress

of children with developmental disabilities

during participation in diverse types of early

childhood intervention (ECI) programs and

related services. In recent years, the National

Reporting System (Head Start Bureau 2002)

was employed as a uniform measurement

method to gather status and progress data on

children in Head Start programs. Increasing

numbers of states such as Colorado, Califor-

nia, Pennsylvania, and Kentucky, themselves,

have begun to mandate the collection of on-

going child progress data linked to their early

learning standards to document the benefits

of preschool and prekindergarten programs

for children with typical abilities, and for

those at developmental risk of “kindergarten

failure” due to poverty. Clearly, the account-

ability movement in education, in general, and

ECI, specifically, have fueled the debate about

the quality, benefits, and limitations of vari-

ous types of publicly funded programs (Na-

tional Research Council/National Academy of

Sciences, 2009; PEW Charitable Trusts, 2008).

EFFECTIVENESS OF ECI: EFFECT SIZE

The designation of “early childhood inter-

vention” or ECI has been proposed and jus-

tified by Shonkoff and Meisels (2000) as the

generic reference to connote any type of early

learning intervention or service program that

encompasses young children birth to 8 years

of age and their families, including early care

and education, Head Start, and early interven-

tion. Thus, ECI refers both to children at de-

velopmental risk due to experiencing the neg-

ative effects of poverty and to children with

developmental delays/disabilities. In this arti-

cle, we use the term ECI in similar fashion

but primarily apply the dosage concept to

young children at developmental risk and/or

with mild developmental delays (ie, early liter-

acy and learning, speech/language, and social-

behavioral difficulties). The dosage method-

ology for children with significant disabilities

would not likely capture the essence, impact,

and outcomes of individualized intervention

programs and would require more multivari-

ate methods and individualized methods. In

fact, our sample research study to demon-

strate the dosage concept did not include chil-

dren with significant disabilities. Our review

of the effect size literature, however, cast a

wide net by including studies with children

at risk and with mild delays or disabilities to

derive a more representative effect size and

dosage metric. With this logic and for pur-

poses of brevity, we refer to early childhood

intervention as ECI for the remainder of this

article.

Regarding the efficacy of ECI programs,

Guralnick (1991) wrote eloquently about the

need to devote the next decade to more rigor-

ously designed and executed research to high-

light the various child, family, age-time, and

intervention factors that are associated with

positive and enduring child outcomes. Since

that time, researchers have conducted innova-

tive studies to form this evidence base (Gural-

nick, 1997; Shonkoff & Meisels, 2000). More-

over, since experimental-control group de-

signs are not always possible or even recom-

mended in such research into the impact of

community-based “natural experiments”(Bag-

nato, Grom, & Haynes, 2004), alternative de-

signs and statistical methodologies are neces-

sary to isolate program impact and outcomes

(Bagnato, Suen, Brickley, Smith-Jones, & Det-

tore, 2002; McCall, 2004; McCall & Green,

1999; Yoshikawa, 2004). Guralnick (1991)

proposed that in such ECI programs, effect

size “is an especially useful metric to eval-

uate effectiveness because it allows data to

be aggregated across diverse studies.”(p.21) Ef-

fect size refers to the estimated magnitude of

the impact of the treatment or intervention

expressed in standard score units; effect size

also serves as a useful summary measure of

effectiveness for individual research studies.

Our own analysis of the myriad of published

effective ECI outcomes research (to be dis-

cussed in step 1) concludes that an effect size

range of between 0.41 and 0.75 of a standard



Dosage Effects Metrics for ECI Research 119

deviation (6-12 standard score points) under-

pins “effective intervention.”

THE DOSAGE QUESTION AND EFFECTSIZE IN ECI

Researchers and practitioners in the fields

of ECI understand clearly that the impact of

a program or service on a child’s develop-

mental progress involves a myriad of com-

plex factors including parent engagement, age

at entry, length of participation, intervention

match, intensity of related services, treatment

fidelity; program quality, teacher’s instruc-

tional behaviors, and type of delay/disability.

In our experience, despite this admonition

about complexity, unfortunately, policymak-

ers and funders, especially private founda-

tions and businesses (Bagnato, 2002), have ad-

vocated for simpler and more functional and

accessible markers of accountability and have

embraced the concept of dosage to under-

score both evidence-based models and those

with promising features in the absence of

clear outcomes.

This conceptual reference to dosage, as

has been expressed by policymakers and fun-

ders, is ill-defined. It is based on a nebulous

metaphor drawn from the medical field, in

general, and from pharmacology in specific.

The general concept is one of the “amount of

treatment.” In medicine, the amount of treat-

ment, or dosage, is calibrated via such mea-

surement units as the number of tablets, the

number of teaspoons, weight in milligrams,

volumes in cubic centimeters, or number of

cups. These metrics are sensible measures for

medicine because the content and chemical

structure of the medicine are known, fixed,

and standardized; and the drugs are tangi-

ble physical objects that can be measured in

weight, volume, or number of pills or tablets

of fixed sizes.

Unfortunately, while the generalized con-

cept of dosage, or amount of treatment, might

be loosely borrowed from medicine, the mea-

surement metrics and methods of calibration

used in medical dosages cannot be trans-

ferred to ECI to serve accountability. This is

because, unlike medicine, ECI “treatments”

are dynamic, flexible, interactive, situational,

individualized, and often relatively unstruc-

tured; ECI treatments are not tangible objects

that can be weighed or counted in the same

manner as medicines.

The closest parallel situation to ECI that

might offer some feasible guidance for a prac-

tical operationalization of dosage is perhaps

found in the psychotherapy literature. A num-

ber of studies in psychotherapy have defined

and employed a concept of “dose-effect anal-

ysis” (Barkham et al., 1996; Howard, Kopta,

Krause, & Orlinsky, 1986; Kadera, Lambert,

& Andrews, 1996). Treatments in psychother-

apy are also dynamic, flexible, and interactive,

and cannot be weighed or counted. For the

determination of dose-effect analysis, these

studies have typically operationalized dose as

the “number of therapy sessions.” In other

words, the concept of dosage has been cali-

brated through the concept of time or dura-

tion of treatment; rather than amount or inten-

sity of treatment. A similar operationalization

can be employed in ECI by measuring dosage

in terms of number of days engaged or par-

ticipating in a particular program.

The operationalization and calibration of

dosage through the metric of “number of

days” are by itself necessary but insufficient

to serve the purpose of accountability. It only

provides a standardized unit to measure treat-

ment or cost. It does not by itself provide use-

ful information for policymakers or funders,

who are more interested in such interrelated

measures as cost per dose, dose-effect, mini-

mum dosage needed, or minimum cost. These

interrelated measures can be directly or indi-

rectly expressed through a benchmark con-

cept of ECI minimum dosage, which we have

defined as the minimum amount of time that

an individual child must engage and partici-

pate in an ECI program or service to show

measurable functional progress (eg, by ei-

ther meeting a minimum progress standard

of 6.8 standard score units derived from na-

tional ECI research or demonstrating progress

that approaches age-expected performance—

standard score range of 90-110).




The empirical benchmarks such as mini-

mum dosage are showing an increasing use

in applied developmental psychology as a

method for interpreting effect sizes and treat-

ment effects (Hill, Bloom, Black, & Lipsey,

2008). Recent dosage studies using this con-

cept in ECI have been conducted to exam-

ine the effects of multisession prevention

programs for young boys with aggressive be-

haviors (Charelebois, Brenden, Vitaro, Nor-

mandeau, & Boudreau, 2004); the extent to

which time in a child care program predicts

socioemotional adjustment during kinder-

garten transition (National Institute of Child

Health and Human Development Early Child

Care Research Network, 2003); attendance in

Head Start programs related to cognitive and

social gains and family risk status (Tait et al.,

2002); and the impact of time-in-program

on the early learning and social-behavioral

progress of young children at developmental

risk due to poverty in diverse early care and

education and prekindergarten settings (Bag-

nato, 2002; Bagnato et al., 2002; Bagnato, Sal-

away, & Suen, 2009; Reynolds, 1995).

Despite the initial array of dosage studies, it

is clear that researchers and policymakers can

benefit by using some uniform methods and

standards to determine minimum ECI dosage

needed in state and national accountability ef-

forts so that government regulators and fun-

ders can have assurances about the rigorous

and precise nature of the methodology em-

ployed and the promising nature of the in-

tervention model. Thus, we regard the au-

dience for our dosage method to be broad

encompassing not only researchers but also

policy makers, government representatives,

business leaders, and program directors.

We propose a 2-step process to determine

minimum functional dosage: First, determine

what is the minimum expected progress; and

then find the relationship between progress

and number of days of program engagement

to determine the minimum number of days

needed to attain the minimum progress. Also,

by using this methodology, practitioners and

researchers can immediately employ these

metrics by accessing an indicated Web site

and excel program to input and analyze their

data.

A PROPOSED UNIFORM METHODOLOGYFOR ECI DOSAGE RESEARCH

This article describes the proposed 2-step

process for calculating ECI minimum dosage;in the following sections, we will describe

these dosage procedures and metrics; iden-

tify a Web site to analyze data; and, then, il-

lustrate the application of the dosage met-

rics within the database and results of a large-

scale, longitudinal ECI study in Pennsylva-

nia. Implications of using the ECI minimum

dosage methodology will be discussed in the

form of “lessons learned.”

METHODOLOGY

Establish evidence-based minimumprogress target standards: Step 1

A thorough examination was conducted of

the research literature across the fields of

ECI pertaining specifically to the impact, out-

comes, and efficacy studies for children who

were at risk or with mild delays/disabilities.

It is beyond the intent or scope of this re-

search to present an exhaustive profile of each

intervention, but rather to justify the dosage

benchmark derived (interested readers are re-

ferred to the study citations to review the

research details). However, our search em-

phasized those effective interventions involv-

ing randomized trials, quasi-experimental de-

signs, and longitudinal, multivariate program

evaluation research studies that focused pri-

marily on successful models for children at de-

velopmental risk; many studies did not iden-

tify type of research design. In addition, few

studies explicitly documented their fidelity

of intervention, which should be a focus in

additional comparative studies in this area

since the confidence to be placed in the re-

sults can be affected significantly. Again, we

regard our ECI minimum dosage metric to

be most applicable for high-risk children and

those with mild delays/disabilities who may

be the most responsive to intervention. Thus,




our exploration of the literature included only

those meta-analyses and individual studies of

children between birth and 6 years of age;

these studies were deemed representative of

that literature and excluded those studies

that focused primarily on children with sig-

nificant disabilities such as autism spectrum

disorder and other neurodevelopmental dis-

abilities. The cited studies involved a wide

array of types of ECI interventions includ-

ing home-based, center-based, parent train-

ing, and small-group models in mostly inclu-

sive but also some segregated settings. For

the most part, the studies reflect full-day (eg,

6 hour) models. Methods for measuring treat-

ment fidelity were inconsistently presented or

used in the studies.

Table 1 summarizes the review of this ev-

idence base involving mostly meta-analytic

studies over the past 25 years. The meta-

analytic studies reported effect size statistics

regarding mostly programmatic effects (ie,

intensity, duration) on child developmental

progress. Not all studies consistently reported

the standard deviation for their effect sizes.

Table 2 profiles the highest to lowest effect

sizes to generate an effect size range as a proxy

confidence interval for the general lack of

standard deviations.

The first step to determine minimum

dosage is to determine the typical minimum

progress. This is done by determining the

most representative effect size for ECI out-

comes research. On the basis of the review

of the studies in Table 1, we judged the

average effect size to be 0.46. This would

serve as a reasonable indicator of expected

progress and was therefore chosen as the tar-

get/standard or benchmark for minimum ex-

pected progress in ECI. An effect size of 0.46

translates into a progress metric of 6.8 stan-

dard score units (eg, 15 standard score points

is the common standard deviation of most out-

come measures). It is instructive, however,

that the standard error of most measures is

6 standard score points and that this is the

effect size of some of the most well-known

and respected ECI studies. This minimum ef-

fect size chosen on the basis of the litera-

ture also coincidentally corresponds approxi-

mately to Cohen’s (1988) criterion of a “mod-

erate” effect size value (ie, 0.5). Next, we de-

cided to establish a more challenging and rig-

orous target/standard to document the impact

of program engagement or dosage. Using our

research database, we posed the question of

whether children who participated in our

Heinz ECI longitudinal research study demon-

strated sufficient skill gains so that they even-

tually attained a typical level of functioning

(eg, a standard score of 100) irrespective of

whether they experienced documented de-

velopmental delays based on PA state criteria

at program entry or not.

The fact that the value of 0.46 as a bench-

mark is close to the value described as

“medium” by Cohen (1988) is coincidental

but arguably valid. Cohen exhorted that the

context of the intervention influences the va-

lidity of the classification of the magnitude of

the treatment effect. In this regard, Table 1

provides this context and supports its valid-

ity since 0.46 is the derived average effect

size among the applied research studies re-

viewed. Moreover, the choice of this average

effect size as a benchmark is reasonable for

2 reasons. First, since this minimum bench-

mark is recommended for use for all interven-

tions, it would be an oxymoron to demand

every program to perform “minimally above

average.”Second, and more important, this av-

erage is derived from applied empirical stud-

ies within meta-analyses of peer-reviewed and

published research reports. Given the well-

known file-drawer or publication bias prob-

lem inherent in meta-analyses, the results can

be expected to be based on a biased sample

of almost exclusively effective interventions.

Therefore, the benchmark of 0.46 actually

represents the average of primarily effectiveinterventions—with ineffectual interventions

excluded (ie, it is the expected size of effect

when an intervention is effective). As such,

it is actually rather stringent as a “minimum”

dosage. This ECI minimum dosage benchmark

demands that an intervention must not only




Table 1. National Studies to Establish Minimum “Dosage” Effect Size Standards for Early Child-

hood Intervention Research

Number ofResearch Article Studies Age of Subjects Study Description

Bagnato et al. (2002) 1 0-6 y (M: 3.01 y) Longitudinal study of collaborative

consultation aimed at program quality

for 1350 children at-risk or with mild

delays.

White and Casto

(1985)

230 <66 mo Efficacy of intervention with children

who are at-risk (disadvantaged) and

with disabilities

Casto and

Mastropieri (1986)

74 3-5 y Studies testing intervention for

preschoolers with disabilities

Shonkoff and

Hauser-Cram

(1987)

31 <36 mo Effect of programs serving infants and

toddlers based on the outcomes

measured used. (selected from White

& Casto, 1985)

Gorey (2001) 35 <5 y Studies focused on early preventative

cognitive intervention for

disadvantaged children >18 000

without medical or organic conditions

and not receiving intervention.

Goldring and

Presbrey (1986)

11 3 mo through 5 y Studies on early care and education for

children in poverty and at-risk

Blok, Fukkink,

Gebhardt, and

Leseman (2005)

19 Pre-birth to 64 mo

(M: 37 mo)

Studies measuring the effectiveness of

early intervention published between

1985 and 2005 on 3 child-based

outcomes

Spiker and

Hopmann (1997)

12 0-15 y Studies of children with Down Syndrome

Mahoney, Boyce,

Fewell, Spiker, and

Wheeden (1997)

4 0-4 y, M: 31.9 mo Studies of parent-child interaction for 422

children at-risk or with mild disabilities

Harris (1988) 9 0-preschool Studies examining the efficacy of early

developmental therapy for children

with disabilities.

Farran (1997) 74 preschool At-risk and mild disabilities

Kavale and Forness

(1999); Forness,

Kavale, Blum, and

Lloyd (1997)

74 0-preschool Studies reporting or analyzing the impact

of early intervention for children at-risk

or with disabilities

be effective but also be as effective as the top

half of previous effective interventions. On

the basis of this logic, we regard the ECI mini-

mum dosage of 0.46 as a logical and represen-

tative benchmark for outcome and account-

ability research in ECI.

Establish evidence-based minimumdosage: Step 2

Using an effect size of 0.46 as the minimum

target, the question of dosage became one of

what dosage is needed to attain a progress

benchmark of at least such an effect size.




Table 2. Effect Size Estimates of Cited Studies

Effect Size EstimatesNumber of

Author(s) Studies Mean ES Highest ES Lowest ES

Bagnato et al. (2002) 2 0.46 (0.29) 0.85 0.33

White and Casto (1985) 316 M (SEM)

Disabilities: 0.43

(.04), At-Risk:

0.51 (0.15)

0.56 0.42

Casto and Mastropieri (1986) 74 0.66 (0.76) Longer

interventions

500 h or 10 h/wk

= 0.86

0.88 0.40

Shonkoff and Hauser-Cram (1987) 31 0.62 (0.49) 2.08 0.22

Gorey (2001) 35 0.27a 1.21a 0.32

Goldring and Presbrey (1986) 11 Math 0.25 Reading

0.19 IQ 0.42

.63 .03

Blok et al. (2005) 19 Median range:

0.32-0.44

1.88 0.01

Spiker and Hopmann (1997) 4 0.36 1.21 0.23

Mahoney, Boyce, Fewell, Spiker, and

Wheeden (1997)

4 0.42 0.93 0.23

Harris (1988) 9 0.31 0.41 0.11

Farran (1997) 74 Mean range =025-0.55

0.95 0.21

Kavale and Forness (1999) 74 0.67 (0.73) 2.13 0.18

a Converted from Cohen’s U3 statistic.

Hence, minimum dosage in ECI is best ex-

pressed as the minimum number of days (or

months) in which the child engages and par-

ticipates in a particular program, intervention,

and/or service to attain a gain in developmen-

tal skills equivalent to an effect size of 0.46 or

6.8 standard score units.

Since children naturally mature and grow,

the measurement of this effect size needs to

be gauged on an age-neutral metric such as

a linear standard score or a normalized stan-

dard score. In a typical linear or normalized

standard score system in which a mean of 100

and a standard deviation of 15 are used, the ef-

fect size of 0.46 would be equivalent to about

6.8 points. Thus, the minimum dosage is

the minimum number of days of intervention

needed for a child to gain at least 6.8 points

on the standard score scale.

To determine this minimum dosage, we

propose that the relationship between child

outcome scores expressed in linear or nor-

malized standard scores and the number of

days of intervention engaged in a particu-

lar program be evaluated using either sim-

ple regression or polynomial regression meth-

ods. Since the best representation of child

growth under treatment is unlikely to be lin-

ear, we recommend the use of a quadratic

term in polynomial regression. However, the

exact model to be used should be based on

best data fit and whether the quadratic term

is statistically significant. Also, the multiple

R should be substantial enough (say, at least

0.4) to justify the use of the resulting re-

gression equation to determine the effect of

treatment as a function of number of days in

intervention.




This polynomial regression equation can

then be used in either 1 of 2 ways to deter-

mine the minimum dosage and the dosages

needed to attain minimum or optimal per-

formance. One way to determine dosage is

to do so algebraically. After estimating the

beta values (ie, the regression coefficients) in

the polynomial regression function, use these

beta values to solve algebraically for the age

difference that correspond to a gain of 6.8 in

the child outcome standard scores. This age

difference is the minimum dosage expressed

in the number of days needed to attain an in-

crease of 6.8 standard score units, or 0.46 in

the effect size metric.

This minimum number of days can be

estimated by the minimum number of days

=√

β2

1 + 27.2β2 − β1

2β2

(1)

where β1 is the regression weight for the first

term (ie, number of days) in the quadratic

equation, β2 is that for the second term (ie,

squared number of days) in the equation, and

27.2 is a constant value that is derived in the

process of solving the underlying quadratic

equations.

Next, to derive the dosage required for

a child with a mild developmental delay to

reach the typical level of functioning (ie, a

standard score of 100 points), we first subtract

the standard score of the child at the point of

program entry, x1, from 100. The result is the

target standard score units to be gained by this

child to be at the typical level of functioning.

Again, with the beta weights from the polyno-

mial equation, we can derive the value of this

dosage as follows:

Number of days needed to attain typical

functioning =√

β2

1 + 4(100 − x1)β2 − β1

2β2

(2)

The values 4 and 100 are constant values

derived by solving the underlying quadratic

equations.

If the beta weight for the quadratic (ie, 2nd)

term of the polynomial equation is not statis-

tically significant, the regression equation re-

duces to a simple linear equation. In that case,

the minimum dosage in the number of days

of treatment would be calculated through the

following equation:

6.8

β1

(3)

Instead of equation (1) and, instead of equa-

tion (2), the dosage needed to bring a child to

the typical level of functioning of other chil-

dren in the norm group is as follows:

100 − x1

β1

(4)

To ease the application of equations (1)

through (4), we have prepared an Excel

macro routine for program administrators and

researchers to use to estimate dosages, based

on input of regression weights. The spread-

sheet macro can be downloaded from http://

suen.educ.psu.edu/∼hsuen/dosage.xls.

When calculating dosages via any of the

above equations, potential regression error or

the standard error of estimate is purposely

ignored. This is because the goal of calcu-

lating these values is to derive a metric to

describe needed dosages for the overall pro-

gram. The goal is not to evaluate how many

days are needed for a given child. At the in-

dividual child level, the number of days of

intervention needed will vary from child to

child.

A complementary strategy is to determine

these dosages graphically. We can plot the

graph representing the polynomial or lin-

ear relationship as appropriate between the

number of days of intervention and standard

scores. From this graph, we can find the value

of the number of days corresponding to an in-

crease of 6.8 standard score units. This value

is the minimum dosage needed. We can also

find the value of the number of days that cor-

respond to the standard score of 100. This

is the dosage associated with children at risk

or children who demonstrated measurable

preintervention developmental delays who




demonstrate progress toward a typical level of

functioning after a period of intervention.

DESCRIPTION OF THE HEINZPENNSYLVANIA EARLY CHILDHOODINITIATIVE (USA)

To apply and illustrate the ECI minimum

dosage method “in action,” we use longitudi-

nal outcome data from the Heinz Pennsylvania

Early Childhood Initiatives. A detailed analysis

of ECI and its 3-year research outcomes can be

found in Bagnato (2002) and also download-

able at www.earlychildhoodpartnerships.org

under Scaling Progress in Early Childhood Set-

tings core program.

In 1994, the Heinz Endowments, a na-

tionally renowned philanthropy in Pittsburgh,

Pennsylvania, organized the business, cor-

porate, agency, and foundation sectors in

an ambitious effort to expand quality early

care and education programs and options for

nearly 2000 unserved children in 25 high-

risk neighborhoods. The overarching mission

of the Heinz ECI model has been to foster

preschool and early school success for chil-

dren of poverty in communities and school

districts whose typical grade retention and

special education placement rates in kinder-

garten have ranged between 18% and 40% in

Pittsburgh.

The Heinz Early Childhood Initiative has

been a unique, collaboratively designed and

privately funded joint venture with vari-

ous Pittsburgh urban neighborhoods to nur-

ture the development of diverse early care

and education options for young children.

The design features, logic model, and im-

plementation of the Heinz model plan were

based on various core features of effec-

tive ECI programs summarized in previous

meta-analytic efficacy research conducted by

Ramey and Ramey (1998): on-site consulta-

tion and mentoring to promote National As-

sociation for the Education of Young Children

program quality standards, curriculum, and

standards alignment focused on early school

success competencies: parent involvement,

community-based decision-making, linkages

to schools and other community resources,

child-centered programming, and involve-

ment in an ongoing program evaluation pro-

cess to improve quality.

The Heinz ECI model was conceived as

a local “natural experiment” whose ultimate

objective is to enroll all unserved children

in high-risk urban neighborhoods into high-

quality early care and education programs that

reflected diverse options. Thus, the Heinz ECI

research is a type of population study in which

all children are included in the evaluation

and no unserved or “untreated”group was al-

lowed; each child and program were its own

control.

Children, families, and programs

In the current descriptive and statistical

study, developmental outcome data on urban

preschool children and families who partic-

ipated in the Heinz Early Childhood Initia-

tive for the longest periods of time were an-

alyzed. The detailed regression analysis was

conducted on n = 1350 children and fami-

lies with complete data sets on all dependent

outcome measures; preinvention assessments

were conducted in September of their entry

into the Heinz ECI and then after 3 years of

intervention with the final postintervention

evaluation in May. Average length of interven-

tion was 31.3 months (SD = 9.7; range =7.4-37.8 months). Approximately 86% of the

children enrolled in ECI programs were clas-

sified as “at-risk”using Annie E. Casey Founda-

tion (1994) guidelines for distressed commu-

nities. In the Heinz ECI sample, at-risk was

defined as a standard score range of 86 to

89. Mild developmental delays were identi-

fied in 14% of the ECI children on the basis

of the Commonwealth of Pennsylvania Early

Intervention and Special Education Standards

(1991) (eg, 1.0 SD below average in 1 or more

domains) and using preintervention develop-

mental rates on the Developmental Observa-

tion Checklist System (DOCS). It should be

noted that these children in the Heinz mod-

els were for the most part educated in inclu-

sive classroom and family child care settings




in early care and education programs in

their home communities and based on par-

ent choice were not enrolled in early inter-

vention services. National incidence rates of

developmental delay related to poverty range

from 3% to 18% (Fujiura & Yamaki, 2000).

Average chronological age of the boys (53%)

and girls (47%) enrolled in the Heinz ECI for

this analysis is 3.01 years (SD = .82 years,

range = 0.59-5.30 years). Ethnic mix of chil-

dren in the urban ECI communities included

African American (73.4%), white (24.3%), and

Asian and Hispanic (2.3%) representation. Me-

dian yearly family income for the ECI chil-

dren is $13 024. Reviews of school district

Web data within the urban Heinz ECI com-

munities show the following averages: low

income (61%); free or reduced-price lunches

(68%); and limited English proficiency (66%).

Overall, all Heinz ECI programs incorporated

a developmental curriculum aligned with the

Pennsylvania Early Learning Standards and

used a developmentally appropriate practices

model that matched the standards of the

National Association for the Education of

Young Children (2008). Each of the 25 Heinz

program sites adhered to the core effective

program features outlined previously but em-

phasized small group and some individual-

ized programming for children focused on

fostering the acquisition of early literacy and

social-behavioral competencies as prerequi-

sites for early school success in the transition

to kindergarten. For the most part, children in

the Heinz ECI programs were engaged in full-

day programs (eg, 5 days per week; 6 hours

per day). Uniformly, each program included 1

leader teacher and 1 teacher’s aide.

Developmental outcome measure

The DOCS (Hresko, Miguel, Sherbenou, &

Burton, 1994) is the uniform child devel-

opmental observation assessment instrument

used in the Heinz ECI dosage study. DOCS is a

unique norm-based measure whose 475 devel-

opmental competencies are naturally occur-

ring child skills in everyday routines (eg, rec-

ognizes the McDonald’s sign; finds the correct

toy at the bottom of the toy box; can read sim-

ple signs) that are teachable curricular com-

petencies and predictive of school success

and sensitive to the effects of intervention.

Thus, the DOCS is one of the best national

consumer-rated examples of an “authentic as-

sessment” instrument with norms (Bagnato,

Neisworth, & Pretti-Frontczak, 2010). DOCS

samples child skills in the following domains:

cognitive, language, social, motor, and over-

all. DOCS was nationally (33 states) normed

on nearly 1100 children from birth to 6 years

of age. Concurrent, criterion, and predic-

tive validity data are strong (0.78–0.94) as

well as interobserver (parent-teacher = 0.81)

and rating-rerating reliability (0.95). Only the

DOCS total standard scores over six progress

evaluation time-points was used in the current

analysis of minimum dosages.

RESULTS

To apply the proposed ECI minimum

dosage method, the children in the Heinz ECI

program were divided into 4 groups on the ba-

sis of their initial, preintervention DOCS stan-

dard scores. Graphed in Figure 1, the 4 groups

were as follows:

1. Above average group: initial DOCS stan-

dard scores above 110;

2. Average group: initial DOCS standard

scores between 90 and 110;

3. Below-average group: initial DOCS stan-

dard scores between 85 and 89; and

4. Delayed group: initial DOCS standard

scores less than 85.

It was determined that no particular min-

imum dosage should be set for the above-

average group as the objective for this group

is, at least, maintenance of progress. For the

remaining 3 groups, a separate polynomial re-

gression analysis was performed to identify

the best model to describe the relationship

between the number of days of treatment

and DOCS standard scores. For the average

group, which consisted of 1110 children, the

quadratic term in the polynomial analysis was

found to be not statistically significant. The

best fitting model was the following linear




Figure 1. Regression curves on time-in-intervention “dosage”effects for 3 groups of children within early

childhood intervention (ECI). DOCS indicates Developmental Observation Checklist System.

model:

Expected DOCS = 101.266 + 0.0175 (days)

(5)

For this model, multiple R = 0.26; R2 = 7%,

and F1,1108 = 83.44, P < .01. Since this model

is linear, Equation (3) was applied to estimate

the minimum dosage needed to gain 6.8 stan-

dard scores points. Equation (4) was not ap-

plied since this group is already performing at

the typical level of functioning. For Equation

3, in this case, the value of β1 is 0.0175 and

minimum dosage to gain 6.8 points was found

to be 388 days.

The below-average group consisted of 164

children and the best fitting polynomial func-

tion was

Expected DOCS = 87.5129 + 0.07014 (days)

− 0.0000766 (days2) (6)


and F2,161) = 23.56, P < .01. Equations (1)

and (2) were applied to estimate the minimum

dosage needed to attain 6.8 points and the

dosage needed to attain a typical level of func-

tioning. For these equations, β1 is 0.07014,

β2 is −0.0000766, and x1 is 87.5129. When

applied, these equations yielded a minimum

dosage value of 110 days and the dosage

needed to attain a typical level of functioning

was 241 days.

The delayed group consisted of 129 chil-

dren and the best fitting polynomial function

was

Expected DOCS=79.1697+0.081228 (days)

− 0.0000656 (days2) (7)


and F2,126) = 34.91, P < .01. β1, β2, and

x1 is respectively 0.081228, −0.0000656, and

79.1697. When Equations (1) and (2) were ap-

plied to these values, the minimum dosage

was found to be 91 days while the dosage

needed to attain a typical level of functioning

was 362 days.

Again, the results for the above calculations

can be obtained via the dosage spreadsheet

macro routine downloadable at http://suen.

educ.psu.edu/∼hsuen/dosage.xls.

To apply the complementary graphic

method, Equations 5 to 7 were applied to

plot the 3 trajectories of changes in DOCS

standard scores changes as a function of the

number of days of intervention. The Figure

shows the resulting graphs for the 3 groups




respectively. For the delayed group, improv-

ing 6.8 standard score points corresponds

to a standard score of 86. As can be seen in

the graph for the delayed group that a DOCS

score of 86 corresponds approximately

around 90 days, which is the approximate

minimum dosage. This same graph intersects

with the score of 100 around 360 days.

Therefore, this is the dosage needed to attain

a typical level of performance. Using the

same visual examination of the graph for

the below-average group, we can determine

that the minimum dosage is approximately

110 days, which corresponds to the target

standard score of about 94. The graph crosses

a score of 100 at approximately 240 days,

which is therefore the dosage needed to

attain a typical level of performance. For

the average group, the target standard score

for a gain of 6.8 points is around 108. This

corresponds approximately to 390 days,

which would then be the minimum dosage

for this group. The precision of results of the

graphic method can be improved by using

more detailed tick marks on the graph.

LESSONS LEARNED OF THE ECIMINIMUM DOSAGE METHODOLOGYFOR APPLIED RESEARCHAND ADVOCACY

Technical considerations with the ECIminimum dosage methodology

It must be made clear that the ECI minimum

dosage methodology is most relevant to group

interventions (ie, curricular approaches based

on developmentally appropriate practices)

because it requires the modeling of trends

based on group data. Users are cautioned

against the use of the dosage methodology

for individual children; single-subject analyses

provide the only justifiable methodology to

monitor the impact and outcomes of inter-

ventions with individual children who display

widely varying functional capabilities and ser-

vices needs.

Similarly, as stated earlier, the issue of con-

text is important involving the external valid-

ity and generalization of outcome data based

upon both the congruence between the chil-

dren and programs referenced in the national

studies and also the Heinz ECI data. For this

very reason, we identified national outcomes

studies for our dosage benchmark that in-

volved the type of program models that are

most often used in the United States for chil-

dren at developmental risk. Thus, we be-

lieve that the strength of our model resides

in the fact that it balances logic, rigor, and

practicality.

Determining a minimum dosage is like de-

ciding on the “passing score” of a certifica-

tion examination. As one would not provide

a range as a passing score, one should not

provide a range or a confidence interval as

the “minimum dosage.”As for determining the

confidence interval for the number of days

for an individual, we have difficulty seeing

the actual purpose of such a confidence inter-

val. It would appear that such a confidence

interval would only encourage programs to

shorten the length of intervention when a pro-

gram can make, for instance, such nonsensical

statements as “well, we have not provided the

minimum number of days of intervention, but

we are well within 2 standard errors of that

minimum—so we are 95% confident that we

have provided statistically the same number

of days as the minimum.”

Users are cautioned to use the ECI min-

imum dosage method and macro in good

faith to fairly represent and portray their pro-

gram’s child outcome data to various stake-

holders such as parents, funders, and govern-

ment agencies. The quality of the data is in-

fluenced strongly by the quality of the pro-

gram itself. Important factors include using

developmentally appropriate practices sanc-

tioned by the national professional organiza-

tions, implementing a strong authentic assess-

ment measure over several time points to doc-

ument child progress, and ensuring the qual-

ity of the program through ongoing monitor-

ing and mentoring of teacher’s instructional

and management behaviors and also program

administrative procedures. Perhaps, most

importantly, policymakers and government




representatives are cautioned strongly against

using this dosage method to “pan” programs

that do not meet the minimum dosage stan-

dard. Rather, any research should be used

for program improvement by engaging the

program leaders and to examine all of the

earlier factors such as the use of evidence-

based and developmentally appropriate prac-

tices and to continue to support and monitor

the expected improvements in program prac-

tices and outcomes when programs are men-

tored to higher quality.

Implications in applying the ECIminimum dosage method

The notion of “dosage” effects for ECI out-

comes research appears at odds with the phi-

losophy and recommended practices in the

early childhood education fields. As develop-

mentalists and behaviorists, we understand

that the impact of a program and its interven-

tions and supports is a complex matter be-

yond the mere concept of the amount of time

that a child spends in the program.

Nevertheless, policymakers, government

regulatory bodies, and philanthropies are con-

fused by this complexity; in response, they

often propose simple and questionable meth-

ods that are often inappropriate and mislead-

ing, particularly for children with develop-

mental differences. The ongoing debate about

the best methods to document accountabil-

ity in ECI is most germane in this regard (Na-

tional Research Council/National Academy of

Sciences, 2009).

We have proposed a methodology that

uses performance or success (ie, effect size)

criteria from national ECI studies to estab-

lish a comparative standard for state and

national accountability efforts. Similarly, our

model poses that recommended statistical

techniques using regression formulae can en-

able evaluators to conduct their account-

ability studies in a uniform manner. Thus,

some basic cross-program comparisons can

be made. In this article, we presented mini-

mum dosage and progress data on n = 1350

children to demonstrate the effectiveness of

the proposed dosage metrics.

As applied developmentalists, we need to

be able to portray and explain our research

outcomes in less esoteric terms that are more

communicable to the public, particularly our

public and private funders, and the business

sector. With more communicable and sensi-

ble metrics, we can be more persuasive and

influence public policy in ECI in desired di-

rections for the benefits of children, families,

and programs.

In the process of developing and applying

the ECI minimum dosage methodology with

associated alternative strategies (ie, hierarchi-

cal linear modeling; growth curve analyses)

in the evaluation of the Heinz Pennsylvania

Early Childhood Initiative, we have learned

several important lessons that have important

implications for applied program evaluation

research and government policy, practices,

and advocacy.

1. In our work with the foundation and

business sectors, “dosage” has be-

come a ubiquitous, catchy, and simple

marker for nonresearchers such as

philanthropies, the business sectors,

and the general public to understand

the effectiveness of human service

programs. Obviously, understandable

metrics often hide the complexities

of documenting program impact and

outcomes. Researchers recognize that

time-in-program is only the most basic

feature of potential effectiveness. An

analysis of the impact and outcomes

of interventions depends upon such

factors as parent engagement, inter-

vention match, intensity of related

services, treatment fidelity; program

quality, teacher’s instructional behav-

iors, and type and severity of disability.

Nevertheless, we have found that the

dosage metric, while simplistic, enables

researchers to gain the attention and

understanding of the public, particu-

larly the business community, and to

increase their receptivity to other ways

of portraying effectiveness data.

2. The concept of dosage, while simplis-

tic on its face, since it does not include




other specific programmatic variables in

its formulation, adds the critical marker

of a programmatic element to the over-

all discussion of child progress and pro-

gram outcomes in both state and federal

accountability efforts. Unfortunately, our

government mandates too often over-

look the mediating program variables

in their short-sighted focus on ultimate

child progress. Children do not progress

in the absence of effective teaching and

high-quality programs. The dosage con-

cept and methodology, at least, ensure

that length of program engagement be-

comes an essential explanatory variable

in the interpretation of children’s de-

velopmental and early learning progress

within ECI programs.

3. Applied researchers must develop new

methodologies that estimate such com-

plex programmatic variables as interven-

tion intensity, parent engagement, and

the match between the intervention and

the child’s needs to build upon the

dosage concept and metric. Such multi-

variate metrics can then be used in com-

bination to display the value-added effect

of community-based interventions.

4. Cautions are very important in using the

dosage methodology. We want to make

clear that that minimum dosage metric

is a simple, yet powerful way of mak-

ing a comparative statement about the

relative effectiveness of an intervention.

However, the dosage metric should be

used for research and program evalua-

tion purposes and not individual child

progress studies. Moreover, we admon-

ish policymakers against the tendency

to use the dosage metric to compare

programs negatively and to “pan” them

for not apparently fostering minimum

progress in children. Like any metric, the

most productive use resides in using the

tool to help programs make collabora-

tive decisions with supportive mentors

to identify strong and weak features of

their programs and to reach consensus

about how to improve their practices;

then, the dosage metric can then be used

to document program improvement, im-

pact, and outcomes.

5. Applied researchers must become more

skilled at public presentations of their

research results to various sectors. The

Heinz ECI involved the heavy engage-

ment of the corporate, business, and

philanthropic sectors of the community.

Our use of the ECI minimum dosage met-

ric showed a systematic way to enable

the public sector to understand the value

of increased time and participation in

ECI programs for young children at de-

velopmental risk that influenced the ad-

vocacy process for our programs.

6. We have found that the lay public regard-

ing psychosocial concepts and research

understandably tends to reduce uncer-

tainty in their decision-making processes

by ignoring it or simplifying it. The

dosage concept and minimum dosage

metric helped to reduce this uncertainty

and increase their receptivity for more

complex variables such as task engage-

ment. Our series of community presen-

tations to the business community, gov-

ernment representatives, and commu-

nity stakeholders helped to build pub-

lic support for tax increases to fund in-

clusive ECI programs and networks; as

a result of these community stakehold-

ers’ meetings that presented the Heinz

ECI research outcomes, the legislature

in the Commonwealth of Pennsylvania

created a more universal funding stream

for young children in the very success-

ful Pre-K Counts program (see Bagnato

et al. 2009).

The dosage concept and methodology have

proven to be both simple and powerful, but

grounded in comparative “functional” bench-

marks, sound statistical methods, and applied

research brought to scale in real-life commu-

nity settings. This has provided the effective

advocacy base to demonstrate clearly that ECI

works!




REFERENCES

Annie E. Casey Foundation. (1994). U.S. youth at risk.

The Futurist, 28(6), 60.

Bagnato, S. J. (2002). Quality early learning: Key toschool success—A first phase 3-year evaluation re-search report for Pittsburgh’s Early Childhood Ini-tiative (ECI). Pittsburgh, PA: Early Childhood Part-

nerships, Children’s Hospital/University of Pitts-

burgh and Heinz Endowments.

Bagnato, S. J., Neisworth, J. T., & Pretti-Frontczak, K.

(2010). LINKing authentic assessment and earlychildhood intervention: Best measures for bestpractices. (4th ed.). Baltimore, MD: Paul Brookes

Publishing, Inc.

Bagnato, S. J., Salaway, J., & Suen, H. K. (2009). Pre-K counts in Pennsylvania for youngsters’ earlyschool success: Authentic outcomes for an inno-vative prevention and promotion initiative. Pitts-

burgh, PA: Early Childhood Partnerships, Heinz En-

dowments.

Bagnato, S. J., Suen, H., Brantley, K., Smith-Jones, J.,

& Dettore, E. (2002). Child developmental impact

of Pittsburgh’s Early Childhood Initiative (ECI) in

high-risk communities: First-phase authentic eval-

uation research. Early Childhood Research Quar-terly, 17(4), 559–580.

Barkham, M., Rees, A., Stiles, W. B., Shapiro, D. A., Hardy,

G. E., & Reynolds, S. (1996). Dose-effect relations in

time-limited psychotherapy for depression. Journalof Consulting and Clinical Psychology, 64(5), 927–

935.

Blok, H., Fukkink R. G., Gebhardt, E. C., & Leseman,

P. M. (2005). The relevance of delivery mode and

other programme characteristics for the effective-

ness of early childhood intervention. InternationalJournal of Behavior Development, 29(1), 35–47.

Casto, G., & Mastropieri, M. A. (1986). The efficacy of

early intervention programs: A meta analysis. Excep-tional Children, 52(5), 417–424.

Charelebois, P., Brenden, M., Vitaro, F., Normandeau,

S., & Boudreau, J. F. (2004). Examining dosage

effects on prevention outcomes: Results from a

multi-modal longitudinal preventive intervention

for young disruptive boys. Journal of School Psy-chology, 42(4): 201–220.

Cohen, J. (1988). Statistical power analysis for the be-havioral sciences (2nd ed.). Hillsdale, NJ: Lawrence

Erlbaum Associates.

Farran, D. (1997). Effects of intervention with disadvan-

taged and disabled children. In M. Guralnick (Ed.),

The effectiveness of early intervention (pp.501–

539). Baltimore, MD. Paul Brookes Publishing.

Forness, S. R., Kavale, K. A., Blum, I. M., & Lloyd J. W.

(1997). Mega-analysis of meta analyses: What works

in special education and related services. TeachingExceptional Children, 29(6), 4–9.

Fujiura, G. T., & Yamaki, K. (2000). Trends in demog-

raphy of childhood poverty and disability. Excep-tional Children, 66(22), 187–199.

Goldring, E. B., & Presbrey, L. S. (1986). Evaluation of

preschool programs: A meta-analytic approach. Ed-ucational Evaluation and Policy Analysis, 8(2),

179–188.

Gorey, K. M. (2001). Early childhood education: A meta-

analytic affirmation of the short- and long-term ben-

efits of educational opportunity. School PsychologyQuarterly, 16(1), 9–30.

Guralnick, M. J. (1991). The next decade of research on

the effectiveness of early intervention. ExceptionalChildren, 58(2), 174–178.

Harris, S. R. (1988). Early Intervention: Does develop-

mental therapy make a difference? Topics in EarlyChildhood Special Education, 7(4), 20–32.

Head Start Bureau (2002). Overview of the National Re-porting System. Washington, DC: US Department of

Health and Human Services, Administration on Chil-

dren and Families.

Hill, C. J., Bloom, H. S., Black, A. R., & Lipsey, M. W.

(2008). Empirical benchmarks for interpreting ef-

fects sizes in research. Child Development Perspec-tives, 2(3), 172–177.

Howard, K. I., Kopta, S. M., Krause, M. S., Orlinsky, D. E.

(1986). The dose-effect relationship in psychother-

apy. American Psychologist, 41(2), 159–164.

Hresko, W. P., Miguel, S. A., Sherbenou, R. J., & Burton,

S. D. (1994). Developmental observation check-list system: A systems approach to assessing veryyoung children. Austin, TX: Pro-Ed.

Kadera, S. W., Lambert, M. J., & Andrews, A. A. (1996).

How much therapy is enough? A session-by session

analysis of the psychotherapy dose-effect relation-

ship. The Journal of Psychotherapy Practice andResearch, 5, 132–151.

Kavale, K. A., & Forness, S. R. (1999). The handbookof school psychology: Effectiveness of special edu-cation. Washington, DC: American Association on

Mental Retardation.

McCall, R. B., C. S., & Green, B. L. (1999). Some

non-randomized constructed comparison groups

for evaluating age-related outcomes of intervention

programs. American Journal of Evaluation, 2(20),

213-226.

Mahoney, G., Boyce, G., Fewell, R., Spiker, D., & Whee-

den, C. A. (1997). The relationship of parent-child

interaction to the effectiveness of early intervention

services for at-risk children with disabilities. Topicsin Early Childhood Special Education, 18(1), 5–

17.

McCall, R. (2004). Beyond the methodological gold stan-

dards of behavioral research: Considerations for

practice and policy. Society for Research in ChildDevelopment: Social Policy Report, 18(2).

National Association for the Education of Young




Children (2008). Early childhood curriculum, as-sessment, and program evaluation: building aneffective, accountable system in programs for chil-dren birth through age 8. Washington, DC: NAEYC.

National Research Council/National Academy of

Sciences (2009). Early childhood assessment:Why, what, and how? Washington, DC: National

Academies Press.

NICHD Early Child Care Research Network (2003).

Does amount of time spent in child care predict

socio-emotional adjustment during the transition

to kindergarten? Child Development, 74(4), 976–

1005.

Office of Special Education Programs (2006). Report-ing OSEP outcome indicators for early interven-tion programs. Washington, DC: US Department of

Education.

PEW Charitable Trusts. (2008). Taking stock: Assessing

and improving early childhood learning and pro-

gram quality—Report of the national early child-

hood accountability task force. Chicago, IL: PEW

Charitable Trusts.

Ramey, C., & Ramey, S. (1998). Early education and early

experience. American Psychologist, 53(2), 4–20.

Reynolds, R. J. (1995). One year in preschool interven-

tion or two? Does it matter? Early Childhood Re-search Quarterly, 10(1), 1–31.

Shonkoff, J. P., & Hauser-Cram, P. (1987). Early interven-

tion for disabled infants and their families: A quan-

titative analysis. Pediatrics, 80(5), 650–658.

Shonkoff, J. P., Meisels, S. M. (2000). Handbook of

early childhood intervention. New York, NY: Ox-

ford Press.

Spiker, D., & Hopmann, M. (1997). The effectiveness

of early intervention for children with down syn-

drome. In M. Guralnick (Ed.), The Effectiveness ofEarly Intervention (chap. 13). Baltimore, MD: Paul

Brookes Publishing.

Tait, L. H., McDonald-Culp, A., Huey, E., Culp, R., Starost,

H. J., & Hare, C. (2002). Relation of Head Start

attendance to children’s cognitive and social out-

comes: Moderation by family risk. Early ChildhoodResearch Quarterly, 17(2), 539–558.

US Department of Education (2001). No Child Left Be-hind (NCLB). Washington, DC: Author.

White, K., & Casto, G., (1985). An integrative review of

early intervention efficacy studies with at-risk chil-

dren: Implications for the handicapped. Analysisand Intervention in Developmental Disabilities, 5,7–31


Documents

Dosage effects