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A Multivariate Investigation of Maternal Risks and The

Relationship to Low-Income, Preschool Children's

CompetenciesMarlo A. Perry

a& John W. Fantuzzo

b

aAdagio Health, Inc.,

bUniversity of Pennsylvania,

Available online: 25 Jan 2010

To cite this article: Marlo A. Perry & John W. Fantuzzo (2010): A Multivariate Investigation of Maternal Risks and Their

Relationship to Low-Income, Preschool Children's Competencies, Applied Developmental Science, 14:1, 1-17

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A Multivariate Investigation of Maternal Risks andTheir Relationship to Low-Income, PreschoolChildrens Competencies

Marlo A. Perry

Adagio Health, Inc.

John W. Fantuzzo

University of Pennsylvania

Utilizing a developmental-ecological framework, the purpose of this study was tounderstand the unique impact of multiple maternal risks across time on ethnicallydiverse, low-income, preschool childrens cognitive skills, pro-social behaviors, andbehavior problems. Additionally, this study sought to understand the variability ofmaternal risks within a low-income population. Data from the national impactevaluation of the Comprehensive Child Development Program (CCDP) was used(N3,852). Variable-centered analyses demonstrated that maternal educationaccounted for the most variance in childrens cognitive outcomes, whereas chronicityof maternal depression accounted for the most variance in childrens pro-social andproblem behaviors. Person-centered analyses revealed eight distinct profiles of maternalrisks, demonstrating the heterogeneity of this low-income population. Further, theseprofiles related differentially to childrens preschool skills, indicating that different com-binations of maternal risks were associated with varying outcomes for young children.

Implications of study findings for early childhood practice, policy, and future researchare discussed.

Early school success for young children has become anational priority, especially for young children livingin poverty. Achievement gaps between poor andnon-poor children at young ages have highlighted thesignificance of a childs early years. The National Center

for Education Statistics has noted achievement gapsbetween Black, Hispanic, and White children as earlyas kindergarten, with minority children showing pooreroutcomes. Achievement gaps have also been notedbetween young children experiencing many family risks(including, but not limited to, poverty) versus youngchildren experiencing few family risks (U.S. Departmentof Education, 2006; Wirt et al., 2004). Such findingsemphasize the importance of early childhood, especially

for poor Black and Hispanic children.National reports, such as Eager to Learn (National

Research Council [NRC], 2001) and From Neurons toNeighborhoods (NRC, 2002), have drawn attention toa host of biological and social risk factors thatsignificantly impact the development of cognitive andsocial-emotional competencies that are necessary forchildren to succeed in school. Both biological andsocial risks are more pronounced among low-income

The data utilized in this project were made available by the DataArchive of the Head Start Performance Measures Center (HSPMC)

and have been used by permission. Data from the ComprehensiveChild Development Program (CCDP) were originally collected by

Abt Associates, Inc. and supported under the contract for the Evalu-ation of the Comprehensive Child Development Program (ContractNo. 105-90-1900) by Administration on Children, Youth, and Famil-

ies, U.S. Department of Health and Human Services. Neither the col-lector of the original data, the funder, the Data Archive of the

HSPMC, Westat, nor its agents or employees bear any responsibilityfor the analyses or interpretations presented here.

Address correspondence to Marlo A. Perry, PhD, Associate Direc-tor of Applied Research, Adagio Health, Inc., 960 Penn Ave, Suite600, Pittsburgh, PA 15222, USA. E-mail: [email protected]

APPLIED DEVELOPMENTAL SCIENCE, 14(1), 117, 2010

Copyright# Taylor & Francis Group, LLCISSN: 1088-8691 print=1532-480X online

DOI: 10.1080/10888690903510281


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families (McLoyd, 1990; NRC, 2002; Parker, Greer, &Zuckerman, 1988), putting this group of children atfurther risk for poor educational outcomes. Biologicalrisks include prenatal threats to the fetus as well ascongenital characteristics. Maternal substance use, poorprenatal care, low birthweight, and premature birth areall examples of biological risks that place infants in dan-

ger of developmental delays (NRC, 2002; Shonkoff &Marshall, 2000). Social risks are those found in thechilds immediate environment. For young childrenwhose most proximal environment consists primarilyof their mother, these are typically maternal risks thatalso affect the childs development (NRC, 2002). Thesemay include risks such as chronicity of depression orpersistence of unemployment. Social risks are generallymutable and, thus, lend themselves well to intervention.Therefore, it is critical that researchers and policymakers understand the impact and variability of theserisks within poor populations and develop effectiveinterventions for low-income children and families.

A developmental-ecological model (Bronfenbrenner,1986) provides a useful conceptual framework forunderstanding the relations between a diverse set ofmaternal risks and the development of cognitive andsocial-emotional competencies that contribute to earlyschool success for vulnerable, low-income children. Thisframework recognizes the importance of characteristicsthat the child brings to the world (such as sex, race=ethnicity, and biological risks) and an understandingof the influence of the most proximal caregiver, typicallythe mother, on early child development. The influence ofthis proximal caregiver consists of aspects associatedwith maternal characteristics (microsystem risks) as well

as aspects associated with the mothers relationshipsand transactions with other systems (exosystem risks;Garbarino & Ganzel, 2000).

A variable-centered approach is traditionally taken inorder to investigate maternal risks and their relationshipto childrens early school skills. There are many studiesthat take this variable-centered approach, though theyare typically univariate in nature. For example, Dollaghanand colleagues (1999) found that maternal education ispositively associated with childrens receptive vocabulary,and Youngblut and associates (2001) found that maternalemployment was positively related to childrens achieve-ment and inversely associated with childrens externalizingbehavior problems. Such studies have identified maternalcharacteristics such as young age, low education level,and depression as more proximal maternal risks, andunemployment, single motherhood, welfare receipt,and mobility as more distal risks associated with motherstransactions with people or systems; both types ofmaternal risks have been shown to relate to childrenspreschool skills. However, there are far fewer studiesthat utilize a variable-centered, multivariate approach,

investigating multiple maternal risks in the same modeland their relative relations with both academic andbehavioral school readiness outcomes.

In their study of single Black mothers of preschoo-lers, Jackson, Brooks-Gunn, Huang, and Glassman(2000) found that maternal risks were associated withchildrens behavior problems and school readiness skills.

Specifically, they found that maternal education andmaternal depression were related to childrens behaviorproblems, with more highly educated mothers havingchildren with lower levels of behavior problems andmore depressed mothers having children with higherlevels of behavior problems. In their study of poor,adolescent mothers, Almgren, Yamashiro, and Ferguson(2002) also found that childrens problem behaviors werea function of their mothers depression status, and not ofany employment or welfare characteristics.

Using the National Longitudinal Survey of Youth(NLSY), Harvey (1999) examined the effects of earlyparental employment on childrens cognitive and recep-

tive language skills, as well as on behavior problems. Shefound that marital status interacted with employmentduring the first three years in predicting child behaviorproblems at age five; this relationship was more positivefor married mothers than for single mothers, in thatmarried mothers reported higher levels of behaviorproblems. However, this relationship was not significantwhen looking at married or single mothers separately.Employment during the first three years also interactedwith marital status in predicting receptive vocabularyscores in three year olds; this association was morepositive for single mothers than for married mothers.

In a study examining the effects of maternal employ-

ment and prematurity on preschool childrens cognitiveand social emotional outcomes in single parent families,findings indicated that children of employed mothersscored higher on a measure of achievement and loweron a measure of externalizing behavior problems(Youngblut et al., 2001). Further, the more hours thatthe mother worked per week, the higher her childsachievement and mental processing skills were. Whencontrol variables such as income and maternal edu-cation were added to the regression models, however,the associations between employment and child out-comes were lost, indicating that it was not employmentper se that was contributing to childrens outcomes, butthose characteristics often associated with maternalemployment.

While these multivariate studies help us better under-stand the relationship between multiple maternal risksand childrens preschool outcomes, there are severalconceptual and methodological limitations to this bodyof research. For example, the samples in these studiesare often constricted. The Jackson et al. study (2000)was based on single, Black mothers, and the Almgren

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et al. study (2002) was based on adolescent mothers.Studies based on such samples provide limited infor-mation in terms of generalizing to other groups.Another limitation is that these studies are typicallycross-sectional in nature. Maternal risks are measuredat one point in time, instead of looking at the consist-ency (or inconsistency) of those risks across a childs

first few years.While the majority of studies focused on maternal

risks include low-income populations, poverty is oftenused as a risk factor itself. Since so many maternalrisks are so highly correlated with poverty, and becausedevelopmental scholars are calling for research examin-ing variability within low-income populations (GarciaColl et al., 1996; McGroder, 2000; Slaughter, 1988;Spencer, 1990), it is important to investigate maternalrisks within the context of poverty. Further, to date,there are no studies that have sought to find patternsof maternal risks and how those patterns relate topreschool skills.

EXTENDING THE KNOWLEDGE BASE FORLOW-INCOME FAMILIES

The purpose of the present study was to understand theunique impact of multiple maternal risks across time onethnically diverse, low-income childrens preschoolskills. This study was designed in response to theaforementioned critiques within the context of adevelopmental-ecological framework. We sought toextend the knowledge base by looking at multiplematernal risks in two different, but complementary,

ways: (1) a variable-centered approach and (2) a person-centered approach. A variable-centered approach willallow us to understand the impact of maternal risks asa set, AND as individual risks. A multivariate variable-centered approach, such as a hierarchical analyticstrategy, allows for the simultaneous comparison ofmultiple maternal risk variables while controlling thepotential influence of other relevant characteristics, suchas child sex, race=ethnicity, and birth risks and concur-rently controlling for the effects of the other risks(Cicchetti, 1993). A person-centered approach allowsus to better understand the variability of low-incomemothers. Such an approach identifies and describesgroups of individual cases defined by similarities amongmultiple dimensions of interest, in this case, maternalrisks (Henry, Tolan, & Gorman-Smith, 2005). There-fore, in the case of the present study, while a variable-centered approach can offer information about whichspecific maternal risks relate to poor preschool outcomes,a person-centered approach can yield information onmothers who manifest specific patterns of risk factorsand, thus, provide valuable information regarding

interventions and policies aimed toward low-incomewomen and children.

The following research questions, reflecting variable-centered and person-centered approaches, are presentedin order to meet the objectives of this study: (1) What isthe unique impact of multiple maternal risks across timeon preschool childrens cognitive ability, social skills,

and behavior problems, controlling for child sex, race=ethnicity, and biological risks? (2) Do maternal riskscombine to form distinct profiles? (3) Do maternal riskprofiles relate differentially to childrens cognitive abil-ity, social skills, and behavior problems?

METHOD

Participants

The dataset from the national impact evaluation of theComprehensive Child Development Program (CCDP)

was used to address the research questions for thepresent study. The CCDP was a large federally fundeddemonstration (19911996) that was designed to delivercomprehensive services to low-income families withyoung children with the aim of enhancing childdevelopment and increasing economic self-sufficiency(St. Pierre, Layzer, Goodson, & Bernstein, 1997). Tomeet the objectives of the current study, only familieswhere the mother (biological or adoptive) was therespondent for each of the interviews from baseline tothe childs assessments at age four were included.Additionally, because the numbers of Asian and NativeAmerican participants were too low to discern meaning-

ful group differences (less than three percent wereNative American, less than two percent were Asian),only Black, Hispanic, and White children were included.The resulting sample was comprised of 3,852 mothersand their children. Forty-two percent of the childrenwere Black, 30% were Hispanic, and 28% were White.Half of the children were male. Eighty-three percent ofthe families spoke English as their primary language inthe home, 16% spoke Spanish, and 1% spoke anotherlanguage. The average age of mothers when they gavebirth to the focus child was twenty-four (SD 5.7years). However, only 35% of the children were the firstborn to their mother; the average age of mothers whenthey gave birth to their first child was 20 (SD 3.8years). The average education level of mothers whenthey gave birth to the focus child was 10th grade(SD 2.2 years).

The original CCDP impact evaluation involved alongitudinal study of 4,410 low-income families across21 national sites. These families were randomly assignedto CCDP program and control conditions. The CCDPimpact evaluation found no significant differences

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across any of the major constructs under study forCCDP versus control families.1 Because participantswere randomly assigned at the start of the study, andthere were no significant differences in any major con-structs, the entire dataset could be used to support theresearch aims of the current investigation. Details aboutthe CCDP project can be found in St. Pierre et al.

(1997).

Procedures

Recruitment of Participants

The CCDP demonstration was administered by theAdministration for Children, Youth, and Families(ACYF) within the U.S. Department of Health andHuman Services. The CCDP grantees included universi-ties, school districts, hospitals, and public and privatenon-profit organizations. Grants were awarded througha competitive process that emphasized selection of the

most qualified applicants, with the strongest staff, andthe best history of providing comprehensive services.Of the 24 projects initially funded, 21 participated inthe impact evaluation (see St. Pierre et al., 1997, formore details).

Data Collection

Collection of data involved the training and monitor-ing of approximately 50 staff members in the 21 sites,who were responsible for interviewing participatingmothers and children for the duration of the study.On-site teams consisted of an On-Site Researcher and

a Child Tester; all evaluation data were collected by thisteam. All data on children and families were collectedthrough tests of children and in-person interviews withmothers. Most data collection took place in the familyshome; when that was not possible, arrangements weremade to collect the data elsewhere. Visits to administertests and interviews lasted between one and a half tothree hours, depending on the age of the child and thelanguage used (Spanish language interviews and tests

took longer). Interviews took place once during thechilds first year of life and approximately every sixmonths thereafter (St. Pierre et al., 1997). The age fourchild outcomes utilized in this study were assessedwithin two months of a childs fourth birthday.

Measures

The following is a list of the measures used in terms ofchild characteristics (birth risks), child outcomes (cogni-tive and social-emotional skills), risks associated withmaternal characteristics (age, education level, chronicityof depression), and risks associated with maternal trans-actions with people and systems (single motherhood,unemployment, welfare receipt, mobility).

Birth Risks Index

The birth risks index is a count of the number ofprenatal or antenatal risks that children faced, accord-

ing to maternal report. The risks included the following:problems during pregnancy, maternal use of alcohol,maternal use of cigarettes, maternal use of drugs, childborn prematurely, child in special care unit, motherreceived late prenatal care, and child was of low- or verylow-birthweight (less than 2,500 grams or 1,500 grams,respectively). The average number of birth risks for thissample was 1.2 (SD 1.2).

Age at Birth of Focus Child

Maternal age at the birth of the focus child wasderived from the childs and mothers dates of birth,

which were both obtained at the baseline interview.Maternal age was rounded to the nearest year.

Maternal Education Level

The mothers highest level of education was recordedat the baseline interview. It was recorded as the numberof years of school the mother had completed, includingthose beyond high school.

Mobility

Mobility was defined as the total number of movesthe family made in the childs first four years of life,based on maternal report at each interview.

Four periodicity variables were created in order tomeasure how long mothers had experienced particularrisks over the childs first four years; these includedmaternal depression, single motherhood, unemployment,and welfare receipt. Details for each are explained undertheir respective headings, but the process of the creationof the variables was as follows. Binary variables werecreated for each interview (e.g., single vs. not single).

1Specifically, CCDP had no effect on maternal employment,household income, receipt of public assistance, home environment,parenting beliefs, parent-child interactions, or parents pregnancy

behaviors. Moreover, even though children enrolled in CCDP were

more likely to be enrolled in center-based care, CCDP had no effecton childrens level of cognitive functioning, social-emotional problems,or adaptive social behavior. Neither were there significant site or

sub-group effects; CCDP had no effect on any of 36 different outcomemeasures in almost all of the projects in the evaluation (with the excep-tion of one site) and had no consistent differential effects on any sub-

groups of parents or children, including children of low birthweight,children of teenage mothers, children of employed versus unemployed

mothers, children of mothers with at least a high school versus lessthan a high school education, and children whose mothers weredepressed versus not depressed (St. Pierre et al., 1997).



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A variable reflecting the constancy of single mother-

hood, for example, was created by dividing the

number of times the mother reported being single by

the number of interviews in which she participated; in

other words, the percent of time she reported being

single over the course of the project. This variable was

then only included if the mother participated in at least

four interviews, and if they occurred at baseline andwithin two months of the childs 2nd, 3rd, and 4th

birthdays. This was to ensure that the variable captured

her status for that risk at least once during each of the

childs first four years.

Maternal Depression

Depressive symptoms were assessed by the Center for

Epidemiologic Studies Depression scale (CES-D; Radloff,

1977). The CES-D is used for initial screening of symp-

toms related to depression or psychological distress and

has been used extensively for research purposes to investi-

gate levels of depression among non-psychiatric popula-tions (Administration for Children and Families [ACF],

2005). Respondents indicate the frequency or duration

of time (in the past week) during which they have experi-

enced certain feelings and=or situations for 20 items.Possible range of scores is from 060, with higher scores

indicating greater distress. The author of the scale suggests

that a total score of 16 be used as the cut-off to indicate

depression. The CES-D has shown adequate reliability

and validity with low-income samples (ACF, 2005).

Internal consistency for the present sample was .90.

Binary categories were created for each of the interviews:

either the mother exceeded a score of 16 on the CES-D (1)

or she did not (0). Presence of depressive symptoms wasthus used as a proxy for maternal depression.

Single Motherhood

Mothers reported their living arrangements and

marital status at each interview. Mothers were con-

sidered to be single mothers if they reported not being

married, in a common-law marriage, or co-habitating

with a partner.

Maternal Unemployment

Mothers reported their employment status at each

interview. Binary categories were created for each of

the interviews: either the mother was unemployed (1)

or reported at least some part-time employment (0).

Welfare Receipt

Mothers reported on whether or not they were

receiving AFDC funds at each interview. Binary

categories were created for each of the interviews:

either the mother was receiving AFDC (1) or she

was not (0).

Receptive Vocabulary Skills

The Peabody Picture Vocabulary TestRevised

(PPVT-R) (Dunn & Dunn, 1981) was used to assesschildrens receptive vocabulary at four years of age.

The PPVT-R is an individually administered measure

of receptive language or vocabulary in individuals aged

2.5 years through adulthood and provides a quick

estimate of verbal ability and literacy-related skills.

The test consists of 175 vocabulary items of increasing

difficulty. For Spanish-speaking children, the Spanish

version of the PPVTthe Test de Vocabulario en

Imagenes Peabody, or TVIPwas used (Dunn, Padilla,

Lugo, & Dunn, 1986). The PPVT-R was nationally

standardized on a stratified normative sample of

4,200 children and adolescents (aged 2.5 through 18

years) and 828 adults. Raw scores can be convertedto age-referenced standard scores with a mean of 100

and a standard deviation of 15. The PPVT-R has

demonstrated good psychometric properties (Sattler,

1992; Umberger, 1985).

Cognitive Development

Childrens cognitive development at four years of

age was assessed with the Kaufman Assessment Battery

for Children (K-ABC; Kaufman & Kaufman, 1983).

The K-ABC yields two separate scales: Mental Proces-

sing and Achievement. The Mental Processing scale

was designed to measure problem solving skills;

whereas, the Achievement scale is intended to measure

knowledge acquisition (Lamp & Krohn, 2001). The

K-ABC was standardized on a national sample of

2,000 children (aged 26 to 125), stratified within

half-year groups for sex, geographic region, parental

education, race, and community size. The K-ABC has

strong psychometric properties (Anastasi, 1985; Sattler,

1992). Analyses by ethnic groups yielded similar

validity coefficients for Blacks, Hispanics, and Whites

(Anastasi, 1985).

Problem Behaviors

Childrens emotional and behavioral problems

at age four were assessed using the Child Behavior

Checklist (CBCL; Achenbach, 1991). The CBCL

consists of 113 items and provides scores on eight sub-

scales: Withdrawn, Somatic Complaints, Anxious=Depressed, Social Problems, Thought Problems, Attention

Problems, Delinquent Behavior, and Aggressive Beha-

vior. In addition, scores on two overarching composites

MATERNAL RISKS 5


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can be determined: Internalizing and Externalizing.

The Internalizing composite consists of items on the

Withdrawn, Somatic Complaints, and Anxious=Depressed subscales, while the Externalizing composite

is comprised of the items from the Delinquent

Behavior and Aggressive Behavior subscales. Items

are rated on a three-point scale (Sometimes True, Very

True, or Often True). Raw scores are converted to stan-dardized T scores with a mean of 50 and a standard

deviation of 10 (Achenbach, 1991). The CBCL has been

used in a number of large-scale evaluation studies,

including those for Early Head Start and the Nurse

Home Visitation Program; however, some questions

have been raised about its appropriateness with

low-income and minority preschool populations.2 The

Adaptive Skills Behavior Inventory (see as follows)

was also, therefore, included as a social-emotional

outcome measure.

Pro-Social Skills

The Adaptive Social Behavior Inventory (ASBI;

Hogan, Scott, & Bauer, 1992) was used to measure

childrens social-emotional competence at age four.

The ASBI consists of 30 items and yields three dimen-

sions: Express, Comply, and Disrupt. The Express

dimension consists of 13 items and reflects pro-social

behaviors. Examples of items include understands

others feelings, like when they are happy, sad, or

mad and is open and direct about what he=shewants. The Comply dimension (10 items) describes

cooperative behaviors such as is helpful to other chil-

dren and shares toys or possessions. Items such asgets upset when you dont pay enough attention and

is bossy, needs to have his=her way comprise the Dis-rupt dimension (7 items). Each item is rated on a three

point scale (Rarely or Never, Sometimes, or Almost

Always). Originally developed for the Infant Health

and Development Program (IHDP) as an outcome

measure, the ASBI was created for use with 36-month

old at-risk infants (Infant Health and Development

Program, 1990). However, research by Greenfield and

colleagues (Greenfield, Iruka, & Munis, 1997; Greenfield,

Wasserstein, Gold, & Jorden, 2004) has demonstrated its

reliability and validity with three-, four-, and five-year old

Head Start children. Additionally, the ASBI was used inthe NICHD Study of Early Child Care to supplement

the CBCL.

Data Analytic Strategies

Variable-Centered Approach

Hierarchical setwise multiple regression was used to

assess the ability of the maternal risk variables to predict

to measures of childrens cognitive ability, social

skills, and behavior problems (PPVT-R, K-ABC, ASBI,

and CBCL) at age four. Hierarchical setwise multipleregression was chosen as the most conservative analytic

procedure because each set of variables is introduced to

test an a priori hypothesis and the influence of random

error is minimized (Cohen & Cohen, 1983). Also, as

each set is entered, a significance test is conducted that

signals violation of Type I Error.

Person-Centered Approach

To supplement the variable-centered analyses

described previously, person-centered analytic techni-

ques were used to identify distinct patterns of maternal

risk variables across individuals (Magnusson & Berg-mann, 1988). Similarities among individual CCDP

mothers across the seven maternal risk variables (age

at birth of child, level of education, dependence on

welfare, persistence of unemployment, chronicity of

depression, constancy of single motherhood, mobility)

were explored using multistage hierarchical cluster

analysis with replications and relocation (McDermott,

1998). Next, maternal risk profiles were inspected with

respect to child sex, race=ethnicity, and number of bio-logical risks to determine if any significant differences

existed among the profiles. Finally, contrasts between

profile types were assessed to determine whether

patterns of maternal risks were differentially related tomeasures of childrens cognitive ability, social skills,

and behavior problems using one-way ANOVA (for

the PPVT-R) and MANOVA (for K-ABC, ASBI, and

CBCL) with Tukeys post hoc comparisons.

Sample Size Adequacy for AddressingResearch Questions

For the first research question, a sample of 3,852 was

employed. In multiple regression, an N of 850 is

required to detect a small effect with 11 explanatory

variables, with power set at .80 and alpha set at a .05 sig-

nificance level (Cohen, 1992). For the second and third

research questions, a sample of 2,019 was employed

(i.e., the number of mothers who have data for each risk

variable). In conducting ANOVA procedures with 8

groups (i.e., the number of clusters resulting from pre-

liminary analyses), an N of 1,448 is required to detect

a small effect, with power set at .80 and alpha set at

the .05 significance level (Cohen, 1992). For the present

study, the sample sizes of 3,852 and 2,019 are sufficient

2See, for example, Gross, Sambrook, and Fogg (1999), Konold,

Hamre, and Pianta (2003), and Ngo (2007).



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to provide adequate power for examining the unique

contribution of maternal risks to variation in the cri-

terion variables, and differences between maternal risk

profiles3.

RESULTS

Prediction of Childrens Cognitive Skills by Maternal

Risk Variables

Table 1 displays the results from the multivariate hier-

archical setwise regression models indicating the amount

of variation in childrens cognitive skills explained by

the set of maternal risk variables (after accounting for

variance associated with the set of child characteristics

[sex, race=ethnicity, and biological risks]). The overallWilks Lambda (K) was significant4 for the K-ABC

(Wilks K .75, F[22, 2982] 21.22), permitting inspec-tion of the two dependent models for the K-ABC scales:

Achievement and Mental Processing. Both models were

significant (F[11, 1492] 38.44, and F[11, 1492] 9.99,

respectively). As indicated by the partial r2, the maternal

risk dimensions as a set accounted for 8.0% of the vari-

ance in Achievement scores (F 21.90), and 5.2% of

variance in Mental Processing scores (F 11.78). Stan-

dardized Beta () coefficients for each of the maternalrisks demonstrated several different prediction patterns.

Maternal education was strongly associated with both

Achievement and Mental Processing scores at age four

( 1.2 and 0.9, respectively). Depression was inver-

sely related to Mental Processing (0.1). Unemploy-

ment was inversely related to Achievement (0.1)

and Mental Processing (0.2).

For the PPVT-R, the Wilks Lambda was significant

(Wilks K .78, F[11, 1478] 38.25). The set of

maternal risk variables accounted for 5.6% of the vari-

ance in receptive language scores (F 15.12). Again,

maternal education was related to scores at age four

( 1.2). Maternal age was also related to PPVT-R

scores ( 0.3), and unemployment was inversely

related to PPVT-R scores (0.1).

In order to further interpret the beta coefficients,

squared semi-partial correlation coefficients for maternal

risks were converted into percentages in order to demon-

strate how much of the variance each risk accounts for

(see Table 1). For all three areas of cognitive skills,

maternal education accounted for the most variance

3To ensure that the two samples did not differ in important ways,

the regression analyses were also run on the smaller sample used for

the cluster analyses. Results were identical for both samples, and the

two samples did not differ significantly on any of the maternal or child

variables.

TABLE 1

Prediction of Cognitive Skills by Maternal Risk Variables

PPVT-Ra K-AB C Achievement K-ABC Mental Processing

Child characteristics

Sex (male) 0.1 (0.9) 0.0 (0.4) 0.2 (1.4)

Black 10.7 (14.7) 5.3 (6.6) 0.0 (0.6)

Hispanic 2.5 (7.8) 5.8 (7.6) 0.2 (2.0)

Biological risks 0.1 (0.4) 0.1 (0.3) 0.0 (0.0)Maternal Risk Characteristics

Age at birth of child 0.7 (0.3) 0.2 (0.1) 0.0 (0.1)

Education level 2.0 (1.2) 4.3 (1.2) 1.5 (0.9)

Chronicity of depression 0.0 (0.1) 0.1 (0.1) 0.4 (0.1)

Dependence on welfare 0.0 (0.0) 0.0 (0.0) 0.0 (0.0)

Constancy of single motherhood 0.1 (0.1) 0.0 (0.0) 0.1 (0.1)

Persistence of unemployment 0.2 (0.1) 0.5 (0.1) 1.0 (0.2)

Mobility 0.2 (0.4) 0.1 (0.2) 0.0 (0.2)

% Variance Explained by:

Child characteristics 16.6 14.1 1.7

Maternal risk characteristicsb 5.6 8.0 5.2

Overall modelc 22.2 22.1 6.9

Note: N1490 (PPVT-R), 1504 (K-ABC Mental Processing and Achievement).aNon-parenthetical entries correspond to squared semi-partial correlations converted into percentages by multiplying values by 100. Parenthetical

entries are standardized parameter estimates derived in hierarchical multiple regression of PPVT-R or K-ABC dimensions on maternal risk variables.

Values reflect the relative contribution of each dimension as covaried by child sex, race=ethnicity, and number of biological risks. Tests assess thedeviation of each parameter estimate from zero, where p< .05, p< .01, p< .001, p< .0001.

bValues equal the partial r2 (100) for prediction of PPVT-R or K-ABC dimensions by all maternal risk variables. All values are covaried for child

sex, race=ethnicity, and number of biological risks.cValues equal the Multiple R2 (100) for prediction of PPVT-R or K-ABC dimensions for the entire model.

4All results presented were significant at least the .05 level.

MATERNAL RISKS 7


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(ranging from 1.5% to 4.3%). Maternal unemployment

accounted for a lesser percentage of the variance (1%

or less).

Prediction of Childrens Pro-Social Skills byMaternal Risk Variables

Table 2 displays the amount of variation in the twopro-social ASBI dimensions explained by the set of

maternal risk variables after applying child characteris-

tics (sex, race=ethnicity, and biological risks) as covari-ates. The overall Wilks Lambda (K) was significant

(Wilks K .83, F[33, 5310] 10.39), allowing inspec-

tion of the two dependent models for the ASBI

pro-social dimensions: Comply and Express. Both mod-

els were significant (F[11, 1804] 12.67 and F[11,

1804] 18.07, respectively). As indicated by the partial

r2, the maternal risk dimensions as a set accounted for

4.5% of the variance in Comply scores (F 12.48) and

4.9% of variance in Express scores (F 14.05). Standar-

dized Beta () coefficients for each of the maternal risksdemonstrated several different prediction patterns.

Maternal depression was associated with both pro-social

ASBI dimensions: Comply (0.2), and Express

(0.2). Maternal education was also related to both

pro-social dimensions ( 0.3 and 0.5, respectively).

Age at birth of child was related to Comply ( 0.1).

Unemployment was inversely related to Express

(0.1).

Squared semi-partial correlation coefficients for

maternal risks (see Table 2) showed that maternal

depression accounted for the most variance (approxi-

mately 1.5%) for each of the pro-social behaviors. Eachof the other significant maternal risks accounted for less

than 1% of the variance in these dimensions.

Prediction of Childrens Problem Behaviors byMaternal Risk Variables

Table 3 displays the amount of variation in the Disrupt

dimension of the ASBI explained by the set of maternal

risk variables after applying child characteristics (sex,

race=ethnicity, and biological risks) as covariates. Themodel for Disrupt was significant: F [11, 1804] 11.82.

Maternal risks accounted for 6.2% of the variance in

childrens Disrupt scores at age four (F17.06). Interms of beta weights, maternal depression was

associated with childrens disruptive behaviors

( 0.2). Maternal education and maternal age were

inversely related to childrens disruptive behaviors

TABLE 2

Prediction of Pro-Social Skills by Maternal Risk Variables

Adaptive Social Behavior Inventory (ASBI)a Dimensions

Comply Express

Child CharacteristicsSex (male) 1.4 (2.4) 0.6 (1.5)

Black 0.3 (1.3) 2.2 (3.9)

Hispanic 0.0 (0.2) 1.0 (2.9)

Biological risks 0.0 (0.1) 0.0 (0.0)

Maternal Risk Characteristics

Age at birth of child 0.5 (0.1) 0.1 (0.1)

Education level 0.3 (0.3) 0.9 (0.5)

Chronicity of depression 1.5 (0.2) 1.6 (0.2)

Dependence on welfare 0.1 (0.1) 0.0 (0.0)

Constancy of single motherhood 0.0 (0.0) 0.1 (0.0)

Persistence of unemployment 0.2 (0.1) 0.7 (0.1)

Mobility 0.0 (0.0) 0.0 (0.0)


Child characteristics 2.7 5.0

Maternal risk characteristicsb 4.5 4.9

Overall modelc 7.2 9.9

Note: N1816.aNon-parenthetical entries correspond to squared semi-partial correlations converted into percentages by multiplying values

by 100. Parenthetical entries are standardized parameter estimates derived in hierarchical multiple regression of the ASBI

dimensions on the maternal risk variables. Values reflect the relative contribution of each dimension as covaried by child

sex, race=ethnicity, and number of biological risks. Tests assess the deviation of each parameter estimate from zero, wherep< .05, p< .01, p< .001, p< .0001.

bValues equal the partial r2 (100) for prediction of ASBI dimensions by all maternal risk variables. All values are covaried

for child sex, race=ethnicity, and number of biological risks.cValues equal the Multiple R2 (100) for prediction of ASBI dimensions for the entire model.



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(0.3 and 0.1, respectively). Dependence on

welfare was also related to childrens disruptive

behaviors ( 0.1). Squared semi-partial correlation

coefficients showed that maternal depression accounted

for the most variance in Disrupt (see Table 3; 2.7%).Maternal age and education level accounted for 0.5%

or less of the variance.

Table 3 also displays the amount of variation in

CBCL dimensions explained by the set of maternal risk

variables after applying child characteristics (sex, race=ethnicity, and biological risks) as covariates. The overall

Wilks Lambda (K) was significant for the CBCL

(Wilks K .84, F [22, 3606] 14.46), permitting inspec-

tion of the dependent models for the two CBCL dimen-

sions: Externalizing and Internalizing. Both models were

significant (F[11, 1804] 17.51 and F[11, 1804] 19.81,

respectively). As indicated by the partial r2, the maternal

risk dimensions as a set accounted for 8.0% of variance

in Externalizing scores (F 22.74), and 8.9% of the vari-

ance in Internalizing scores (F 25.67). Standardized

Beta () coefficients for each of the maternal risks

demonstrated several different prediction patterns.

Maternal depression was associated both CBCL dimen-

sions: Externalizing (0.3), and Internalizing ( 0.3).

Maternal age was inversely related to both dimensions

(0.2 and 0.2, respectively). Dependence on

welfare was related to Externalizing ( 0.1), and

unemployment was associated with Internalizing scores

( 0.1). In terms of child characteristics, increased

biological risks were associated with higher scores on

both dimensions.Squared semi-partial correlation coefficients again

showed that maternal depression accounted for the

greatest amount of variance (see Table 3); 4.2% for

Externalizing and 5.7% for Internalizing. Maternal age

accounted for just over 1% of the variance in Externaliz-

ing and less than 1% for Internalizing. Other maternal

risks accounted for less than 0.5% of the variance of

these dimensions.

Typological Analyses of Maternal Risk Variables

Profile Types

The primary goal of person-centered analyses was to

determine a reliable and meaningful typology of distinct

maternal risk patterns. Multi-stage, hierarchical cluster

analyses produced eight distinct maternal risk profiles.

These profiles replicated an average of 87.5 percent over

first- through third-stage clustering and demonstrated

strong psychometric properties. Homogeneity coeffi-

cients (H), which measure the internal cohesion of each

TABLE 3

Prediction of Problem Behaviors by Maternal Risk Variables

Adaptive Skills Behavior Inventory (ASBI) Dimension Child Behavior Checklist (CBCL)a Dimensions

Disrupt Externalizing Internalizing

Child Characteristics

Sex (male) 0.2 (0.8) 0.0 (0.3) 0.5 (1.3)

Black 0.1 (

0.9) 0.8 (

2.4)

0.1 (

0.7)Hispanic 0.0 (0.6) 0.2 (1.4) 0.2 (1.1)

Biological risks 0.0 (0.0) 0.7 (0.8) 0.5 (0.6)

Maternal Risk Characteristics

Age at birth of child 0.5 (0.1) 1.1 (0.2) 0.7 (0.2)

Education level 0.4 (0.3) 0.1 (0.2) 0.0 (0.1)

Chronicity of depression 2.7 (0.2) 4.2 (0.3) 5.7 (0.3)

Dependence on welfare 0.2 (0.1) 0.3 (0.1) 0.0 (0.0)

Constancy of single motherhood 0.0 (0.0) 0.0 (0.0) 0.0 (0.0)

Persistence of unemployment 0.1 (0.0) 0.1 (0.0) 0.5 (0.1)

Mobility 0.0 (0.1) 0.0 (0.1) 0.0 (0.1)


Child characteristics 0.5 1.7 1.9

Maternal risk characteristicsb 6.2 8.0 8.9

Overall model c 6.7 9.7 10.8

Note: N1816.aNon-parenthetical entries correspond to squared semi-partial correlations converted into percentages by multiplying values by 100. Parenthetical

entries are standardized parameter estimates derived in hierarchical multiple regression of ASBI Disrupt dimension or CBCL dimensions on the

maternal risk variables. Values reflect the relative contribution of each dimension as covaried by child sex, race=ethnicity, and number of biological

risks. Tests assess the deviation of each parameter estimate from zero, where p< .05, p< .01, p< .001, p< .0001.bValues equal the partial r2 (100) for prediction of CBCL dimensions by all maternal risk variables. All values are covaried for child sex, race=

ethnicity, and number of biological risks.cValues equal the Multiple R2 (100) for prediction of CBCL dimensions for the entire model.

MATERNAL RISKS 9


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profile, ranged from .71 to .82 with an overall average of

.78. Calculations of external isolation indicated a high

level of separation among profiles, with average simi-larity coefficients (rp) ranging from .07 to .31. Table 4

shows the mean T-scores for each type, and Figure 1

presents a pictorial representation of the dimensions

that comprise the eight distinctive profiles.

Type 1 (Low risk; 10.4% of the sample) and Type 2

(Employed; 13.3% of sample) are both characterized by

low levels of unemployment. However, Type 1 is also

characterized by low levels of welfare receipt and con-

sists of slightly older mothers with relatively high levels

of education; whereas, Type 2 shows average levels of

the other maternal risks. Type 3 (Low education, resident

partner; 8.5%) consists of mothers with low levels of

education and single motherhood; this type also demon-

strates relatively low rates of welfare dependence andconsists of slightly older mothers. Type 4 (Relatively

depressed; 12.2%) and Type 7 (Depressed, high mobility;

14.6%) are both comprised of mothers with high or rela-

tively high levels of depression. However, Type 7 is also

characterized by high levels of mobility. Type 6 (Young

mothers; 10.5%) consists of mothers who gave birth to

their focus child at young ages. Type 8 (Resident partner,

low welfare receipt; 11.3%) is characterized by mothers

with low levels of single motherhood and with relatively

low levels of welfare receipt. Type 5 (Average; 19.2%) is

marked by average levels of all seven maternal risks.

TABLE 4

Means of Maternal Risks across Profiles (N 2019)

Profiles

Variable M (SD) 1 2 3 4 5 6 7 8

Maternal age 49.5 (9.9) 58.8 44.4 57.1 53.3 52.0 34.4 45.2 52.6

Education level 49.6 (10.0) 59.3 52.8 32.1 48.3 53.6 41.2 47.1 55.8

Depression 49.4 (8.2) 46.5 46.9 47.6 57.3 43.6 47.7 58.4 47.8Number of moves 50.4 (8.7) 47.3 54.8 45.7 43.9 48.5 51.5 60.9 47.1

Unemployment 48.4 (8.7) 35.2 38.4 50.6 53.8 52.8 52.5 52.5 47.8

Single motherhood 49.3 (8.1) 46.4 49.3 39.9 53.9 54.1 53.2 51.5 38.8

Welfare receipt 49.1 (8.3) 40.5 43.8 41.4 55.9 55.8 51.2 54.1 41.0

(Profile n) (210) (269) (171) (247) (388) (211) (294) (229)

Note: Reported means of profiles are expressed as T scores, based on area conversion of precision-weighted factor scores (in standard z-score

form). To assist with interpretation, T scores one standard deviation above or below the mean for that particular variable are underlined and in

boldface type. T scores within one point of a standard deviation above or below the mean for that particular variable are underlined.

FIGURE 1 Mean T-scores for maternal risks across the eight profiles .



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Significant Differences Between Profiles withRespect to Child Characteristics

Divergence in the expected cluster prevalence of childsex, race=ethnicity, English as a primary language, andnumber of biological risks in comparison to the largersample of children was examined through two-tailedtests of the standard error of proportional differences(Ferguson & Takane, 1989). For all pairwise compari-sons, the Bonferroni correction was applied to limitType I error. Results showed that there were no signifi-cant differences in terms of child sex with respect to clus-ter prevalence, indicating relatively equal distributionsof boys and girls across each of the clusters. In termsof race=ethnicity, Low Risk (Type 1), Employed(Type 2),Low Education, Resident Partner (Type 3), and ResidentPartner, Low Welfare Receipt (Type 8) encompassedmore Whites than expected based on the overall sample.Relatively Depressed (Type 4), Average (Type 5), andYoung Mothers (Type 6) were comprised of more Blacks

than expected. Low Education, Resident Partner (Type 3)and Relatively Depressed (Type 4) both encompassedmore Hispanics than expected. There were significantdifferences in terms of prevalence of English speakersin some of the clusters. Low Education, Resident Partner(Type 3) had more mothers whose primary language wasnot English than would be expected given the prevalencein the overall sample. Employed(Type 2), Average (Type5), Depressed, High Mobility (Type 7), and ResidentPartner, Low Welfare Receipt (Type 8) were all com-prised of more mothers whose primary language wasEnglish than would be expected. In terms of biological

risks, Low Education, Resident Partner (Type 3) encom-passed more children with no biological risks thanwould be expected, and Relatively depressed (Type 4)was comprised of more children who had two or morebiological risks than would be expected given theprevalence in the overall sample.

Significant Differences Between Profiles withRespect to Preschool Skills

The MANOVA models were examined to determine ifmaternal risk profiles differentially related to preschoolcognitive skills, pro-social behaviors, and behaviorproblems (in the case of the PPVT-R, an ANOVA wasused). In terms of cognitive skills, the overall ANOVAmodel was significant for the PPVT-R with F (7,1529) 14.15, g2p .061. The overall MANOVA wasalso significant for the K-ABC, with Wilks Lambda.89, F (14, 3084) 11.98. Significant main effects were

detected for both K-ABC subscales with F(7, 1543)

9.00, g2p .039 (Mental Processing) and F(7, 1543)22.44, g2p .092 (Achievement). Table 5 presents PPVT-Rand K-ABC means by type along with significant differ-ences. For all three measures of cognitive skills, childrenin Low Risk (Type 1) families demonstrated the highestlevels, followed by children in Resident Partner, Low Wel-

fare Receipt (Type 8) families and Employed(Type 2) fam-ilies. Children in Depressed, High Mobility (Type 7)families and in families headed by Young Mothers(Type 6) performed at significantly lower levels onmeasures of cognitive skills.

TABLE 5

Group Differences in Cognitive Skills across Maternal Risk Profiles

K-ABCa

Profile PPVT-Rb Achievement Mental Processing

1. Low risk 86.66 91.42 94.63c

2. Employed 79.36 86.99 90.203. Low education, Resident partner 78.41 78.69 85.534. Relatively depressed 75.35 83.38 86.07

5. Average 79.56 86.07 89.576. Young mothers 73.16 82.11 86.49

7. Depressed, High mobility 75.27 83.21 85.078. Resident partner, Low welfare receipt 85.25 87.59 90.22

Significant differences among typesd 1, 8> 2,3,4,5,6,7 1> 2,3,4,5,6,7 1>2,3,4,5,6,7,82,5> 6 2,5,8> 7 2,8> 3,4,6,7

4,7> 35> 3,6

Note: a K-ABC 2019.bPPVT-R 2019.cReported means are least mean squares calculated for MANOVA models due to unequal cell sizes across the eight profiles.

Means are expressed as standard scores (M 100, SD 15).dStatistically significant differences between profiles with respect to each multivariate model were determined based on

Tukey-Cramers post hoc comparison.

MATERNAL RISKS 11


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In looking at pro-social behaviors, the overall MAN-OVA model was significant for the ASBI with WilksLambda .93, F(21, 5347) 6.91. Significant maineffects were found for both pro-social subscales withF(7, 1864) 7.70, g2p .028 (Comply) and F (7, 1864)10.45, g2p .038 (Express). Table 6 presents the meansfor the Comply and Express subscales of the ASBI bytype along with significant differences. Children in

Low Risk (Type 1) families again scored significantlyhigher than children in most other family types.Children in Employed (Type 2) families also fared wellin terms of the Express subscale of the ASBI. Childrenfrom Depressed, Welfare Dependent, and Low Mobility(Type 4) families again performed poorly, along withchildren in Depressed, High Mobility (Type 7) and Low

Education, Resident Partner (Type 3) families.In terms of behavior problems, there was also a

significant main effect for the Disrupt subscale of theASBI: F (7, 1864) 9.37, g2p .034. The overall modelfor the CBCL was significant, with Wilks Lambda.93, F (14, 3732) 10.14. There were significant maineffects for both subscales: F (7, 1867) 14.20, g2p.051 (Externalizing), and F (7, 1867) 11.69, g2p .042(Internalizing). Table 7 shows the CBCL means andthe means for the Disrupt subscale of the ASBI, by type,along with significant differences. Children in Low Risk(Type 1) families again fared best, showing significantlylower levels of problem behaviors than children in

several other types of families. Children in Low Edu-cation, Resident Partner (Type 3) families also exhibitedlower levels of problem behaviors, while children inDepressed, Welfare Dependent, and Low Mobility (Type4) and Depressed, High Mobility (Type 7) demonstratedthe highest levels of problem behaviors.

DISCUSSION

National concerns about low-income preschool childrensschool readiness skills call for a look at the unique

TABLE 6

Group Differences in Pro-Social Skills Across Maternal Risk Profiles

ASBIa

Profile Comply Express

1. Low risk 53.08b 53.442. Employed 50.29 52.56

3. Low education, Resident partner 50.85 48.094. Relatively depressed 47.56 47.545. Average 49.69 50.616. Young mothers 48.76 48.70

7. Depressed, High mobility 47.69 49.088. Resident partner, Low welfare receipt 51.14 51.33

Significant differences among typesc 1> 4,5,6,7 1> 3,4,5,6,73,8> 4,7 2>3,4,6,7

5> 4

8>3,4

Note: aASBI 2019.bReported means are least mean squares calculated for MANOVA

models due to unequal cell sizes across the eight profiles. Means are

expressed as T-scores (M 50, SD 10).c

Statistically significant differences between profiles with respect toeach multivariate model were determined based on Tukey-Cramers

post hoc comparison.

TABLE 7

Group Differences in Problem Behaviors Across Maternal Risk Profiles

ASBIa CBCLb

Profile Disrupt Externalizing Internalizing

1. Low risk 47.18c 51.59 46.22

2. Employed 49.87 54.98 47.933. Low education, Resident partner 50.11 50.82 48.98

4. Relatively depressed 52.44 57.66 51.525. Average 48.73 53.17 47.846. Young mothers 50.80 54.59 50.21

7. Depressed, High mobility 52.69 58.29 52.958. Resident partner, Low welfare receipt 48.51 53.91 48.78

Significant differences among typesd 7> 1,2,5,8 7>1,2,3,5,6,8 7> 1,2,3,5,84> 1,5,8 4> 1,3,5,6,8 4> 1,2,5

6> 1 2>1,3 6> 16> 3

Note: aASBI2019.bCBCL 2019.cReported means are least mean squares calculated for MANOVA models due to unequal cell sizes across the eight profiles.

Means are expressed as T-scores (M 50, SD 10).dStatistically significant differences between profiles with respect to each multivariate model were determined based on

Tukey-Cramers post hoc comparison.



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relations between multiple maternal risks across timewith the cognitive, pro-social, and behavior problemoutcomes of ethnically diverse, low-income preschoolchildren, controlling for relevant child characteristics.Using a developmental-ecological framework, thepresent study conducted secondary analyses of theComprehensive Child Development Program (CCDP)

dataset using variable-centered and person-centeredapproaches to extend our understanding of the associ-ation between these maternal risks and preschool out-comes for a low-income population of White, Black,and Hispanic children.

Consideration of Variable-Centered Findings

Cognitive Skills

Analyses indicated that maternal educationaccounted for the most variance in childrens receptivevocabulary, achievement, and mental processing skills.

Specifically, mothers with low levels of education hadchildren who performed poorly on all three measuresof cognitive skills, controlling for child characteristicsand other maternal risks. This finding supports the fewstudies that have examined the role of maternal edu-cation as it relates to preschool childrens cognitiveskills. Dollaghan and colleagues (1999) found that lowlevels of maternal education were associated with lowscores on a measure of receptive vocabulary in her studyof White and Black children. In her study ofwelfare-receiving mothers, Magnuson (2003) found thatmothers with higher levels of education had childrenwith higher levels of academic school readiness skills.

Pro-Social Behaviors

Chronicity of maternal depression accounted for themost variance in childrens compliant and expressivebehaviors. Mothers exhibiting higher degrees of de-pression had children demonstrating low levels ofcompliant and expressive behaviors. The majority ofthe literature on maternal depression and childrensoutcomes investigates the presence of behavior problemsin children, not necessarily the absence of pro-socialbehaviors (see for example, Alpern & Lyons-Ruth,1993, LaRoche, Turner, & Kalick, 1995). An exceptionto this, however, is a study by the NICHD Early ChildCare Research Network (1999), which demonstratedthat three-year-old children of depressed mothersexhibited lower levels of cooperative behaviors thandid same age peers of non-depressed mothers. Althoughthis study did look at the chronicity of maternaldepression over childrens first three years, it did notaccount for childrens birth risks or other maternal risks,other than maternal education.

Problem Behaviors

Chronicity of maternal depression accounted for themost variance in childrens externalizing, disruptive,and internalizing behaviors. Children of mothers withhigh levels of depression showed the highest levels ofall three behavior problem constructs. This finding isconsistent with what the empirical literature has shownin terms of the relation of maternal depression to prob-lem behaviors in low-income, preschool children.Maternal depression has been most frequently linkedto disruptive, aggressive, and oppositional behaviors inyoung children (Almgren et al., 2002; Black et al.,2002; Hubbs-Tait et al., 1996; Leadbeater, Bishop, &Raver, 1996; Spieker, Larson, Lewis, Keller, & Gilchrist,1999). Fewer studies include childrens internalizingbehaviors as a separate outcome variable, with somefinding a relationship between maternal depression andinternalizing behaviors (Black et al., 2002; Hubbs-Taitet al., 1996; Hubbs-Tait, Osofsky, Hann, & Culp,

1994), and at least one study showing only a relationshipwith externalizing behaviors, but not internalizingbehaviors (Alpern & Lyons-Ruth, 1993).

The variable-centered findings from the present studyextend the literature by demonstrating that maternaleducation and chronicity of maternal depressionaccount for the most variance in childrens preschoolcompetencies, even when controlling for child character-istics and other maternal risks. Further, it is of interestto note that both education and depression are moreproximal (microsystem) maternal risks, as opposed tomore distal (exosystem) risks associated with mothersrelationships or transactions with systems. This finding

has important implications for policy and targetedinterventions.

Consideration of Person-Centered Findings

Person-centered analyses revealed eight distinctmaternal risk profiles. Three profiles were characterizedby high levels of maternal risks, including Depressed,High Mobility (Type 7), Young Mothers (Type 6), andRelatively Depressed (Type 4). One profile was charac-terized by low levels of multiple risksmothers in LowRisk(Type 1) demonstrated low levels of unemploymentand welfare receipt and were relatively older and moreeducated than other mothers in the sample. The remain-ing profiles were characterized by varying levels of risks.These included Low Education, Resident Partner (Type3), Average (Type 5), Employed (Type 2), and ResidentPartner, Low Welfare Receipt (Type 8).

The results of these person-centered analyses demon-strate heterogeneity among low-income families, evenheterogeneity within risks (i.e., there is more than onetype characterized by high levels of depression or

MATERNAL RISKS 13


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employment). Ramey, Ramey, and Lanzi (1998) alsofound heterogeneity within low-income families usinga person-centered approach. However, their study waslimited in several ways. Their sample was limited to onlyformer Head Start families, and some of their profileswere defined by only one attribute (e.g., there was onechronic health problem profile, which encompassed

all the families with a primary caregiver with a chronichealth problem that interfered with parenting duties).Further, their study used a cross-sectional design, mea-suring risks at only one point in time, and their risk vari-ables were often measured dichotomously (i.e., did themother receive welfare benefits?), which is inadequatefor many types of risks that are often episodic in nature.Finally, they utilized less robust cluster analytic techni-ques than were used in the present study. For example,instead of splitting their sample into multiple groups inorder to maximize replicability of the cluster solution,their sample was only split into two groups.

Multivariate analyses of variance demonstrated that

the eight maternal risk profiles related differentially tochildrens preschool skills. First, these analyses confirmedfindings from the variable approach in that maternaldepression is related to behavior problems for preschoo-lers. Types 4 and 7 (Relatively Depressed and Depressed,High Mobility, respectively) are both characterized byhigher than average levels of maternal depression, andboth were consistently related to high levels of disruptive,externalizing, and internalizing behaviors.

Additionally, a significant contribution of the presentstudy is the finding of a low risk profile (Type 1; LowRisk). Despite the high prevalence of these maternalrisks within a low-income population, there is a sub-

group of families who are experiencing low levels ofseveral of these risks and are doing well. Children ofmothers in this low risk profile are showing the best out-comes, both in terms of cognitive skills and in terms ofsocial-emotional competence.

Implications for Research, Policy, and Practice

By employing secondary analyses of data from theComprehensive Child Development Program (CCDP)to investigate the relationship of maternal risks acrosstime with young childrens preschool skills, the currentstudy was limited by the maternal risk variablesavailable within the CCDP dataset. Although this studyinvestigated a comprehensive set of maternal risksgrounded in empirical literature, it is missing someimportant familial variables that the literature hasidentified. For example, family discord and maternalrelationship stress have been shown to be correlatedwith maternal depression (Black et al., 2002), as hasdomestic violence (Bonomi et al., 2006). These additionalfamilial risks have also been shown to have negative

sequelae for young children (Black et al., 2002; Mohr,Lutz, Fantuzzo, & Perry, 2000; National ResearchCouncil, 2000). As such, future studies could explorehow these kinds of related familial risks mediate therelationship of maternal depression to childrens beha-vioral outcomes.

Similarly, the present study revealed maternal

education as an important maternal risk relating tochildrens cognitive outcomes. Future work could inves-tigate correlates of low educational attainment, such asIQ or special education status (Alexander, Entwisle, &Kabbani, 2001; Magnuson, 2003) to determine if andhow they add to what we know about the relationshipbetween maternal education and childrens cognitiveoutcomes. Additionally, educational disengagementhas been shown to be a significant predictor for drop-ping out of school (Alexander et al., 2001; Magnuson,2003; Slaughter-Defoe, Addae, & Bell, 2002); motherswho exhibit educational disengagement during theirown school years may pass along their negative attitudes

toward school to their children and=or may show lessinterest in educational activities for their children (e.g.,reading books together), resulting in poorer cognitiveoutcomes for their children. Future studies coulddisentangle beliefs and attitudes about educationfrom educational attainment to further explain thisrelationship between maternal education and childrenscognitive outcomes.

It also cannot be ignored that parent-child relation-ships are reciprocal in nature, and although the presentstudy does not indicate causality, it is focused onmaternal risks predicting to child outcomes. However,the literature has also noted that child characteristics

are related to maternal risks, such as depression (see,for example, Hammen, Burge, & Stansbury, 1990).Future work could examine more closely the potentialpathways of both child and maternal risks and theirinfluence on each other.

The current study was focused on the presence (orabsence) and chronicity of maternal risk factors. Futureresearch could explore more carefully the unique impactof protective factors on childrens preschool skills andtheir potential ability to mitigate risks. The presence ofa large social support network, for example, has beenshown to be negatively correlated with maternaldepression (MacPhee, Fritz, & Miller-Heyl, 1996;Melson, Ladd, & Hsu, 1993). Similarly, research hasdemonstrated that feelings of efficacy and satisfactiontoward ones job are related both to lower levels ofdepression and higher rates of consistent employment(Goldberg, Greenberger, Hamill, & ONeil, 1992; Parcel& Menaghan, 1997; Zaslow & Emig, 1997). Inclusion ofprotective factors such as these can further improve ourknowledge base about the impact of maternal risks onpreschool childrens outcomes by highlighting other



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important contexts for low-income mothers and theiryoung children.

The policy context at the time of CCDP qualifies thefindings of the present study. Welfare receipt duringCCDP was under the regulations of Assistance toFamilies with Dependent Children (AFDC); findingsmay differ under regulations of the Personal Responsi-

bility and Work Opportunity Reconciliation Act(PRWORA; Public Law 104-193, 110 Statute 2105).With PRWORA posing time limitations on welfarereceipt, dependence on welfare may show a differentassociation with the development of childrens preschoolskills. Further, PRWORA now mandates employment,so maternal employment may play even more of a rolein childrens outcomes, especially important processissues such as job satisfaction or efficacy. Studies arebeginning to disentangle these complicated aspects ofwelfare reform and their impact on childrens develop-ment, but they often do not include other maternal risks.Thus, future research should continue to investigate this

important issue, being sure to examine welfare reform inthe context of concurrent maternal risks.

Findings from the present study have importantimplications for two-generation programs, which arecharged with enhancing the well-being of children andfamilies living in poverty and ideally suited for amelior-ating the different combinations of risks that familiesface. Early childhood programs that have this dual focusare optimal mechanisms to improve the school readinessskills of young children living in poverty, both by teach-ing those skills to children directly, and by helping tomitigate familial risks that impact the development ofthose skills. Head Start is the nations largest

two-generation program, serving more than 900,000low-income children and families nationwide (U.S.Department of Health and Human Services, Office ofHead Start, 2006). Partnering with families and com-munities is a central part of Head Starts mission (U.S.Department of Health and Human Services, 1999); itis through these partnerships that Head Start canconnect families with appropriate services, such asmental health programs or GED classes.

Despite these mandates for partnerships, HeadStarts family involvement component has been criti-cized as insufficient, especially following welfare reformrules that require parents to work, thus not allowingthem to participate during a typical Head Start day(Zigler & Styfco, 1993). This is disheartening givenresearch that shows that the simple act of participatingin Head Start, whether in their childrens classroomsor in support groups with other parents, lessens feelingsof social isolation and depression in parents (Parker,Piotrkowski, & Peay, 1987; Fantuzzo, Stevenson,Abdul-Kabir, & Perry, 2006), especially considering thefindings about maternal depression in the present study.

Further, parent involvement in their childrens edu-cation is associated with improved school readinessskills (Fantuzzo, McWayne, Perry, & Childs, 2004; Fan-tuzzo, Tighe, & Perry, 1999). Another area of deficiencyin Head Starts parent programming relates to theself-sufficiency goals for parents, such as educationalor literacy services. Programs generally do not have

the infrastructure to help parents attain these goals,nor do they have sufficient staff or funding (Parkeret al., 1995). These issues make the mandates for com-munity collaborations even more important, as theycould potentially connect parent with other necessaryservices, such as GED classes or mental health services.However, such programs can be cumbersome and costlyto fund, and current mechanisms fall short of theneed (Knitzer, Theberge, & Johnson, 2008). Two-generational programs such as Head Start are on theright track in terms of meeting the disparate needs ofpoor families; however, the nation needs to recognizethat poverty brings multifaceted needs and invest more

resources in programs that promote protection againstthese multiple risks.

REFERENCES

Achenbach, T. M. (1991). Manual for the child behavior check-list=418and 1991 profile. , Burlington, VT: University of Vermont Depart-

ment of Psychiatry.

Administration for Children and Families. (2005). Center for epidemio-logic studies depression scale (CES-D). ACF OPRE: Performance

measures for Head Start programs serving infants and children.

Retrieved on October 20, 2005, from http://www.acf.dhhs.gov/

programs/opre/ehs/perf_measures/reports/resourcesAlexander, K. L., Entwisle, D. R., & Kabbani, N. S. (2001). The drop-

out process in life course perspective: Early risk factors at home andschool. Teachers College Record, 103, 760822.

Almgren, G., Yamashiro, G., & Ferguson, M. (2002). Beyond welfare

or work: Teen mothers, household subsistence strategies, and childdevelopment outcomes. Journal of Sociology and Social Welfare,

29, 125149.

Alpern, L., & Lyons-Ruth, K. (1993). Preschool children at social risk:

Chronicity and timing of maternal depressive symptoms and childbehavior problems at school and at home. Development and Psycho-

pathology, 5, 371387.

Anastasi, A. (1985). Review of the K-ABC. 9th Mental MeasurementsYearbook. Lincoln, NE: University of Nebraska Press.

Black, M. M., Papas, M. A., Hussey, J. M., Dubowitz, H., Kotch, J. B.,

& Starr, R. H. (2002). Behavior problems among children born to

adolescent mothers: Effects of maternal depression and perceptionsof partner relationships. Journal of Clinical Child and Adolescent

Psychology, 31, 1626.

Bonomi, A. E., Thompson, R. S., Anderson, M., Reid, R. J., Carrell, D.,Dimer, J. A., & Rivara, F. P. (2006). Intimate partner violence andwomens physical, mental, and social functioning. American Journal

of Preventive Medicine, 30(6), 458466.

Bronfenbrenner, U. (1986). Ecology of the family as a context for

human development. Developmental Psychology, 22, 723742.Cicchetti, D. (1993). Developmental psychopathology: Reactions,

reflections, projections. Developmental Review, 13, 471502.

MATERNAL RISKS 15


17/18

Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1),155159.

Cohen, J., & Cohen, P. (1983). Applied multiple regression=correlationanalysis for the behavioral sciences. Hillsdale, NJ: Lawrence

Erlbaum Associates, Inc.

Dollaghan, C. A., Campbell, T. F., Paradise, J. L., Feldman, H. M.,Janosky, J. E., Pitcairn, D. N., & Kurs-Lasky, M. (1999). Maternal

education and measures of early speech and language. Journal of

Speech, Language, and Hearing Research, 42, 14321443.

Dunn, L., & Dunn, L. (1981). The Peabody picture vocabulary

test-revised. Circle Pines, MN: American Guidance Service.

Dunn, L. M., Padilla, E. R., Lugo, D. E., & Dunn, L. M. (1986). Test

de Vocabulario en Imagenes Peabody. Circle Pines, MN: AmericanGuidance Service.

Fantuzzo, J., McWayne, C., Perry, M. A., & Childs, S. (2004).

Multiple dimensions of family involvement and their relations tobehavioral and learning competencies for urban, low-income

children. School Psychology Review, 33, 467480.Fantuzzo, J., Stevenson, H., Abdul-Kabir, S., & Perry, M. A. (2006).

An investigation of a community-based intervention for sociallyisolated parents with a history of child maltreatment. Journal of

Family Violence, 22, 8189.

Fantuzzo, J. W., Tighe, E., & Perry, M. (1999). Relationships betweenfamily involvement in Head Start and childrens interactive peer

play. National Head Start Association Dialog, 3(1), 6067.Ferguson, G. A., & Takane, Y. (1989). Statistical analysis in

psychology and education (6th ed.). New York, NY: McGraw-Hill.

Garbarino, J., & Ganzel, B. (2000). The human ecology of early risk.

In J. P. Shonkoff, & S. J. Meisels (Eds.), Handbook of early child-

hood intervention (2nd ed., 7693). NY: Cambridge University Press.Garca Coll, C. T., Lamberty, G., Jenkins, R., McAdoo, H. P., Crnic,

K., Wasik, B. H., & Vazquez Garca, H. (1996). An integrativemodel for the study of developmental competencies in minoritychildren. Child Development, 67(5), 18911914.

Goldberg, W. A., Greenberger, E., Hamill, S., & ONeil, R. (1992).Role demands in the lives of employed single mothers with

preschoolers. Journal of Family Issues, 13(3), 312333.Greenfield, D. B., Iruka, I. U., & Munis, P. (2004). Assessment of

social competence in high-risk preschoolers: Evaluation of the

Adaptive Social Behavior Inventory (ASBI) across home and schoolsettings. Journal of Psychoeducational Assessment, 22, 220232.

Greenfield, D. B., Wasserstein, S. B., Gold, S., & Jorden, B. (1997).The Adaptive Social Behavior Inventory (ASBI): Evaluation with

high-risk preschoolers. Journal of Psychoeducational Assessment,15, 322333.

Gross, D., Sambrook, A., & Fogg, L. (1999). Behavior problemsamong young children in low-income urban day care centers.Research in Nursing and Health, 22, 1525.

Hammen, C., Burge, D., & Stansbury, K. (1990). Relationship ofmother and child variables to child outcomes in a high-risk sample:

A cause modeling analysis. Developmental Psychology, 26, 2430.Harvey, E. (1999). Short-term and long-term effects of early parental

employment on children of the National Longitudinal Survey of

Youth. Developmental Psychology, 35, 445459.Henry, D. B., Tolan, P. H., & Gorman-Smith, D. (2005). Cluster

analysis in family psychology research. Journal of Family

Psychology, 19, 121132.

Hogan, A. E., Scott, K. G., & Bauer, C. R. (1992). The AdaptiveSocial Behavior Inventory (ASBI): A new assessment of socialcompetence in high-risk three-year-olds. Journal of Psychoeduca-

tional Assessment, 10, 230239.

Hubbs-Tait, L., Hughes, K. P., Culp, A., Osofsky, J., Hann, D.,Eberhart-Wright, A., & Ware, L. (1996). Children of adolescent

mothers: Attachment representation, maternal depression, and later

behavior problems. American Journal of Orthopsychiatry, 66, 416426.

Hubbs-Tait, L., Osofsky, J. D., Hann, D. M., & Culp, A. M. (1994).Predicting behavior problems and social competence in children of

adolescent mothers. Family Relations, 43, 439446.Infant Health and Development Program. (1990). Enhancing the out-

comes of low-birth-weight, premature infants: A multisite, rando-

mized trial. American Medical Association Journal, 263, 30353042.http://www.promisingpractices.com/

Jackson, A. P., Brooks-Gunn, J., Huang, C., & Glassman, M. (2000).Single mothers in low-wage jobs: Financial strain, parenting, and

preschoolers outcomes. Child Development, 71, 14091423.Kaufman, A. S., & Kaufman, N. L. (1983). Kaufman assessment bat-

tery for children. Circle Pines, MN: American Guidance Service.

Knitzer, J., Theberge, S., & Johnson, K. (2008). Reducing maternaldepression and its impact on young children: Toward a responsive

early childhood policy framework. Project Thrive Issue Brief No. 2.

New York: National Center for Children in Poverty.

Konold, T. R., Hamre, B. K., & Pianta, R. C. (2003). Measuring prob-

lem behaviors in young children. Behavioral Disorders, 28, 111123.Lamp, R., & Krohn, E. (2001). A longitudinal predictive validity inves-

tigation of the SB: FE and K-ABC with at-risk children. Journal of

Psychoeducational Assessment, 19, 334349.

LaRoche, M. J., Turner, C., & Kalick, S. M. (1995). Latina mothersand their toddlers behavioral difficulties. Hispanic Journal of Beha-vioral Sciences, 17, 375384.

Leadbeater, B. J., Bishop, S. J., & Raver, C. C. (1996). Quality ofmother-toddler interactions, maternal depressive symptoms, and

behavior problems in preschoolers of adolescent mothers. Develop-

mental Psychology, 32, 280288.

MacPhee, D., Fritz, J., & Miller-Heyl, J. (1996). Ethnic variations inpersonal social networks and parenting. Child Development, 67,32783295.

Magnuson, K. (2003). The effect of increases in welfare motherseducation on their young childrens academic and behavioral

outcomes: Evidence from the National Evaluation of Welfare-to-

Work Strategies Child Outcomes Study. Institute for Research onPoverty. Discussion Paper No. 1274-03.

Magnusson, D., & Bergmann, L. R. (1988). Individual andvariable-based approaches to longitudinal research on early risk

factors. In M. Rutter (Ed.), Studies of psychosocial risk: The power

of longitudinal data (pp. 4561). NY: Cambridge University Press.McDermott, P. A. (1998). MEG: Megacluster analytic strategy for

multistage hierarchical grouping with relocations and replications.Educational and Psychological Measurement, 58, 677686.

McGroder, S. M. (2000). Parenting among low-income, African Americansingle mothers with preschool age children: Patterns, predictors, anddevelopmental correlates. Child Development, 71, 752771.

McLoyd, V. C. (1990). The impact of economic hardship on Black

families and development. Child Development, 61, 311346.

Melson, G. F., Ladd, G. W., & Hsu, H. (1993). Maternal supportnetworks, maternal cognitions, and young childrens social and

cognitive development. Child Development, 64, 14011417.Mohr, W. K., Lutz, M. J., Fantuzzo, J. W., & Perry, M. A. (2000).

Children exposed to family violence: A review of empirical research

from a developmental-ecological perspective. Trauma, Violence, &Abuse, 1(3), 264283.

National Research Council. (2001). Eager to learn. Washington, DC:

Author.

National Research Council. (2002). Neurons to neighborhoods.Washington, DC: Author.

Ngo, V. K. (2007). Contextual analysis of measurement bias in the

Child Behavior Checklist for African American children (Doctoraldissertation, Vanderbilt University, 2007). Dissertation AbstractsInternational, 67(10-B), 6070.

NICHD Early Child Care Research Network. (1999). Chronicity ofmaternal depressive symptoms, maternal sensitivity, and child



18/18

functioning at 36 months. Developmental Psychology, 35, 12971310.

Parcel, T. L., & Menaghan, E. G. (1997). Effects of low-wageemployment on family well-being. The Future of Children, 7(1),116121.

Parker, F. L., Piotrokowski, C. S., Horn, W. F., & Greene, S. M.(1995). The challenge for Head Start: Realizing its vision as a

two-generation program. In S. Smith (Ed.), Two generation pro-

grams for children in poverty: A new intervention strategy. Advances

in applied developmental psychology, Vol. 9 (pp. 135159). Norwood,

NJ: Ablex Publishing Corporation.

Parker, F. L., Piotrkowski, C. S., & Peay, L. (1987). Head Start as a

social support for mothers: The psychological benefits of involve-ment. American Journal of Orthopsychiatry, 57, 220233.

Parker, S., Greer, S., & Zuckerman, B. (1988). Double jeopardy: The

impact of poverty on early child development. Pediatric Clinics ofNorth America, 35, 12271240.

Radloff, L. S. (1977). The CES-D scale: A self-report depression scalefor research in the general population. Applied PsychologicalMeasurement, 1, 385401.

Ramey, C. T., Ramey, S. L., & Lanzi, R. G. (1998). Differentiating

developmental risk levels for families in poverty: Creating a familytypology. In M. Lewis, & C. Feiring (Eds.), Families, risk, & com-

petence (pp. 187205). Mahwah, NJ: Lawrence Erlbaum Associates,

Publishers.Sattler, J. M. (1992). Assessment of children: Revised and updated third

edition. San Diego: Jerome M. Sattler Publisher, Inc.

Shonkoff, J. P., & Marshall, P. C. (2000). The biology of developmen-

tal vulnerability. In J. P. Shonkoff, & S. J. Meisels (Eds.), Handbook

of early childhood intervention (2nd ed., pp. 3553). Cambridge, UK:Cambridge University Press.

Slaughter, D. (1988).Black children in poverty: A developmentalperspective. San Francisco, CA: Jossey-Bass.

Slaughter-Defoe, D. T., Addae, W. A., & Bell, C. (2002). Toward the

future schooling of girls: Global status, issues, and prospects.Human Development, 45, 3453.

Spencer, M. (1990). Development of minoritychildren: An introduction:Minority children [Special issue]. Child Development, 61, 267269.

Spieker, S. J., Larson, N. C., Lewis, S. M., Keller, T. E., & Gilchrist, L.(1999). Developmental trajectories of disruptive behavior problemsin preschool children of adolescent mothers. Child Development, 70,

443458.

St. Pierre, R., Layzer, J., Goodson, B., & Bernstein, L. (June 1997).National impact evaluation of the Comprehensive Child Development

Program: Final report. Cambridge, MA: Abt Associates, Inc.

Umberger, F. G. (1985). Peabody picture vocabulary test Revised.In D. J. Keyser, & R. C. Sweetland (Eds.), Test Critiques, Vol. III.(pp. 488495). Kansas City, MO: Library of Congress Cataloging

in Publication Data.

U.S. Department of Education, National Center for Education Stat-istics. (2006). The condition of education 2006 (NCES 2006-071).

Washington, DC: U.S. Government Printing Office.

U.S. Department of Health and Human Services. (1999). Head Start

Program Performance Standards and Other Regulations, Revised

[Performance Standards 1304.40 Family Partnerships and 1304.41

Community Partnerships]. Washington, DC: Author.

U.S. Department of Health and Human Services, Office of Head Start.

(2006). Head Start program fact sheet. Retrieved July 17, 2006 fromhttp://www.acf.hhs.gov/programs/hsb/research/2006.htm

Wirt, J., Choy, S., Rooney, P., Provasnik, S., Sen, A., & Tobin, R.

(2004). The condition of education 2004 (NCES 2004-077). U.S.Department of Education, National Center for Education Statistics.

Washington, DC: U.S. Government Printing Office.

Youngblut, J. M., Brooten, D., Singer, L. T., Standing, T., Lee, H., &

Rodgers, W. L. (2001). Effects of maternal employment and prema-turity on child outcomes in single parent families. Nursing Research,50, 346355.

Zaslow, M. J., & Emig, C. A. (1997). When low-income mothers go towork: Implications for children. The Future of Children,

perry y fantuzzo a multivariate investigation of maternal risks and their relationship to income,...

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