Using SEM to Identify Direct and Indirect Influences

on Cognitive and Language Development of

Toddlers from Low-Income Families

Teresa M. Ober and Patricia J. Brooks

1. Introduction

Children growing up in poverty have elevated risk of cognitive and language

delays relative to their peers from more affluent families (Merz, Wiltshire, &

Noble, 2019). Poverty impacts neural development from infancy, placing infants

reared in economically disadvantaged and stressful environments at risk of

compromised neurocognitive development (Ursache & Noble, 2016). For such

children, factors such as poor prenatal care, low maternal educational attainment,

teenage pregnancy, food and housing insecurity, and parenting stress tend to co-

occur, with far-reaching impact on developmental trajectories for cognition and

language (Evans, Li, & Whipple, 2013). Cumulative risk indices may be more

predictive of children’s developmental outcomes than any single factor and

contribute to developmental cascades in abilities as they unfold over time (Cutuli

et al., 2017; Masten & Cicchetti, 2010).

The current study aimed to increase an understanding of structural relations

among factors influencing developmental trajectories of infants growing up in

poverty, with the goal of distinguishing direct and indirect influences of

contextual (home environment), maternal (educational attainment, maternal

distress), interactional (joint attention, negative interaction), and child (gestational

age, gender) factors on cognitive and language development. Building on prior

work (Poulakos, 2013), we developed a structural equation model (SEM) of

cumulative risk, using measures taken at age 14 months to predict individual

differences in cognitive and language outcomes at age 36 months. The models

examined developmental trajectories of 672 children (49.7% male) from the Early

Head Start Research and Evaluation Project (EHSRE; United States Department

of Health and Human Services Administration for Children and Families, 2011).

The EHSRE was a longitudinal randomized-control evaluation study of the

impact of enrollment in Early Head Start programs on social and cognitive

outcomes from infancy through childhood. The EHSRE Birth to Three Phase

involved administration of comprehensive assessments related to children’s

cognitive and communicative functioning, parenting stress, parent-child

* Teresa M. Ober is at the University of Notre Dame; Patricia J. Brooks is at the College of

Staten Island and The Graduate Center, CUNY. Please direct correspondence to Teresa

Ober at tober@nd.edu.

© 2020 Teresa M. Ober and Patricia J. Brooks. Proceedings of the 44th Boston University Conference on Language Development, ed. Megan M. Brown and Alexandra Kohut,

416-429. Somerville, MA: Cascadilla Press.

interactions, home environment, and family background collected when children

were 14, 24, and 36 months of age. For our study, we restricted the dataset to the

control group to avoid confounds associated with varying characteristics of Early

Head Start programs.

Our SEM included latent variables for maternal distress, joint attention, and

negative interactions involving the child. Maternal distress encompassed

variables related to maternal depressive symptoms, parenting stress, and

dysfunctional interactions with the child. Previous work indicates that children

exposed to maternal distress and depressive symptoms in infancy tend to have

lower receptive vocabulary knowledge relative to their peers (Ahun et al., 2017;

Letourneau, Tramonte, & Willms, 2013). Joint attention subsumed measures of

child sustained attention, child engagement of the parent, and parental

supportiveness in the context of semi-structured toy play protocol. Infants’ ability

to sustain attention to toys while engaged with a parent has been shown to predict

their subsequent vocabulary knowledge (Yu, Suanda, & Smith, 2019). Other

research emphasizes the importance of parental support and responsiveness to

children’s communicative bids in fostering cognitive and language development

(Tamis-LeMonda, Bornstein, & Baumwell, 2003). Negative interaction

subsumed measures of parental intrusiveness, parental negative regard, and child

negativity towards the parent. Negative parenting behaviors have been found to

be associated with diminished language development, particularly in infants

exhibiting negatively valanced affective responses (Laake & Bridgett, 2018).

In addition to the latent variables, our SEM included indices of the quality of

the home environment as the home is the primary context for development during

infancy. The extent to which the home contains age-appropriate toys and books

has been linked with language development outcomes (Melvin et al., 2017). We

included gestational age and gender in the models, as infants born prematurely are

at heightened risk of developmental delays (Bee et al., 1982). Gender was also

included as girls have been observed to outperform boys on language measures

(Bornstein, Hahn, & Haynes, 2004). Maternal education was also included as it

has been linked with children’s language development (Dollaghan et al., 1999),

and with associated factors including maternal responsiveness and the quality of

the home environment (Magnuson et al., 2009). After establishing the fit of the

SEM in predicting children’s cognitive and language outcomes at age 36 months,

we tested whether the model effectively accounted for variation in outcomes for

children of minority and non-minority parents. Determining the generalizability

of the SEM to different participant subgroups is important as parenting behaviors

associated with children’s cognitive and communicative development may vary

across racial/ethnic groups (Brooks-Gunn & Markman, 2005).

2. Method

2.1. Participants

The sample consisted of a subset of participants recruited from low-income

families for the EHSRE project (United States Department of Health and Human

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Services Administration for Children and Families, 2011). The subset consisted

of children in the control group (i.e., not enrolled in Early Head Start programs)

who did not have missing data for the 36-month receptive vocabulary outcome (N

= 672; 327 boys and 345 girls). For the sample used here family income was on

average 60.2% (SD = 49.6%) of the poverty threshold.

Several key demographic variables were provided by maternal caregivers.

Mothers self-identified their race/ethnicity was as follows: 46.4% identified as

White/Caucasian, 37.1% as Black/African-American, 11.6% as Hispanic/Latino,

and 5.0% as Other. Race/ethnicity of the child’s caregiver was re-coded as a

binary variable (1=minority, 0=non-minority). Mothers indicated their highest

educational attainment by selecting one of three possible choices: 41.3% attended

some high school, 31.9% obtained a high school degree/GED, and 26.8% obtained

a degree higher than high school or GED diploma. Mothers also reported the

child’s approximate gestational age by selecting one of four choices. The infants’

gestational age was reported by the mother, with responses indicating that most

infants (85.2%) were born on time; 1.4% were born >2 months early, 11.4% were

born >3 weeks early, and 1.9% were born >3 weeks late. Many mothers (41.7%)

had given birth to the focal child before the age of 18. The majority (58.7%) were

neither married nor living with partner during the course of the study. A subset

(9.3%) of mothers reported speaking a language other than English at home.

2.2. Measures

2.2.1. Cognitive Ability and Vocabulary Knowledge

The child’s cognitive abilities were assessed at ages 14 and 36 months using

standardized scores from the Bayley Mental Development Index (MDI), a

subscale of the Bayley Scales of Infant Development, 2nd Edition (Bayley, 1993).

The MDI measures cognitive, communicative, and social-emotional development

of children under age 41 months. The child’s language ability was assessed at age

36 months using the Peabody Picture Vocabulary Test, 3rd Edition (PPVT; Dunn

& Dunn, 1997), an untimed assessment of receptive vocabulary. Four pictures are

presented at a time with the child asked to point to the one that matches a word

spoken aloud. Standardized scores for the Bayley MDI and the PPVT were

derived for each participant based on population norms, μ = 100, σ = 15.

2.2.2. Child and Maternal Characteristics

Demographic factors related to both the child and their mother were included

in the analyses; these variables were derived from a parent questionnaire

administered when the child was 14 months old. Child characteristics included

gender, coded as a binary variable (0=female, 1=male), and approximate

gestational age. Maternal characteristics included the mother’s level of

educational attainment and a set of measures used to create a latent variable for

maternal distress. The measures encompassed the 20-item Center for

Epidemiological Studies Depression (CES-D) Scale (Radloff, 1977), a self-report

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assessment of depression symptoms experienced over the past week, and two

subscales of the short-form version of the Parental Stress Index (PSI-SF; Abidin,

1995). The 12-item Parent-Child Dysfunctional Interaction subscale indicates

whether the mother perceives her child to abuse or reject her or feels disappointed

in or alienated from her child. The 12-item Parental Distress Index indicates the

mother’s level of distress in the role of parent, stemming from personal factors,

e.g., a low sense of parenting competence, or stress due to perceived restrictions

associated with parenting, depression, or general lack of social support.

2.2.3. Home Environment

Examiners completed the Home Observation for the Measurement of the

Environment (HOME Inventory; Caldwell & Bradley, 1984), which uses a

combination of maternal self-report and observed measures to assess the quality

of cognitive stimulation and emotional support provided by the child's family at

home. For our analyses, 6p used the total score on the HOME Inventory.

2.2.4. Joint Attention and Negative Interaction

Two latent variables were derived from the 3-bag task, a semi-structured play

procedure where the parent and child are given three bags of interesting toys and

asked to play with the toys in a sequence (Early Head Start, 2005). The interaction

was videotaped with child and parent behaviors scored in accordance with the 3-

bag task coding scales (Ware et al., 1998). The coding scales focused three

positive and four negative aspects of parent-child interaction (possible scores for

each scale ranging from 1 to 7). The latent variable for joint attention was derived from scales assessing child sustained attention, child engagement of the parent,

and parent supportiveness. The latent variable for negative interaction was

derived from scales assessing parent intrusiveness, parent negative regard towards

the child, child negativity toward parent. Parent detachment was dropped from the

latent variable as its loading yielded a standardized estimate < .30.

3. Results

3.1. Descriptive Statistics

Table 1 provides descriptive statistics for measures taken at ages 14 and 36

months of age for the overall sample and subgroups; Ns vary due to missing data.

Note that six mothers did not report race/ethnicity. Standardized scores on the

Bayley MDI at 14 months approximated the normed population mean of 100.

However, at 36 months of age, standardized scores on both the Bayley MDI and

the PPVT were well below the normed population mean, suggesting delays in

cognitive and communicative development, with more than half of the children

scoring > 1 SD below the population mean of 100 on each of these tests. Scores

on the Bayley MDI and PPVT measured at 36 months of age were moderately

correlated, r(620) = .58, p < .001.

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Table 1. Descriptive Statistics for Measures at 14 and 36 Months.

Overall Sample Minority Non-Minority

N M (SD) N M (SD) N M (SD)

14-month measures

Maternal Distress

Parent Distress Inventory 606 27.2 (9.4) 321 27.8 (9.6) 280 26.5 (9.0)

Parent-Child Dysfunction 604 17.3 (5.7) 319 17.6 (6.3) 280 16.9 (4.9)

CES Depression Scale 599 13.6 (9.8) 316 13.1 (9.4) 278 14.2 (10.4)

HOME Inventory 575 26.5 (3.3) 298 25.6 (3.5) 272 27.5 (2.8)

3–bag Task

Child Sustained Attention 540 5.0 (1.0) 280 4.8 (1.0) 256 5.2 (0.9)

Child Engagement 540 3.5 (1.1) 280 3.9 (1.8) 256 3.9 (1.0)

Parent Supportiveness 540 4.0 (1.0) 280 3.7 (1.0) 256 4.3 (1.0)

Parent Intrusiveness 540 2.4 (1.2) 280 2.7 (1.2) 256 2.1 (1.1)

Parent Negative Regard 540 1.4 (0.8) 280 1.6 (0.9) 256 1.3 (0.6)

Child Negativity 540 2.1 (1.1) 280 2.3 (1.2) 256 1.9 (1.0)

Bayley MDI 518 98.4 (10.8) 260 97.5 (9.9) 253 99.3 (11.6)

36-month measures

Bayley MDI 622 90.3 (12.7) 326 94.2 (12.3) 290 88.1 (12.6)

PPVT 672 82.3 (16.3) 357 78.4 (15.6) 309 86.9 (16.0)

3.2. Regression Analysis

We used regression analyses to examine the cumulative impact of the

variables as measured at 14 months in accounting for communicative and abilities

in children at 36 months of age, see Table 2. The regression analyses were

conducted with multiple imputation (10 iterations) in R (R Core Team, 2019)

using the MICE package (Van Buuren & Groothuis-Oudshoorn, 2011). Inspection

of a missing data plot suggested that data were missing at random for the selected

variables of interest. The regression model predicting Bayley MDI scores at 36

months was statistically significant, F(14, 657) = 22.68, p < .001, R2 = .33. The

regression model predicting PPVT at 36 months was also significant, F(14, 657)

= 16.65, p < .001, R2 = .26.

The results of the regression models for the two 36-month outcomes were

similar. Both models indicated significant effects of maternal education, home

environment (HOME Inventory), child sustained attention, parent supportiveness,

and infant cognitive ability (Bayley MDI at 14 months) in predicting individual

differences in cognitive and language outcomes at 36 months. The model

predicting scores on the Bayley MDI at 36 months also showed significant effects

of child gender (favoring girls) and gestational age, as well as a significant

negative effect of parent-child dysfunction. Note that despite the non-significance

of some of the variables, we retained the full set of 14-month variables as

predictors of 36-month outcomes, as preliminary analyses indicated patterns of

co-variance that would be accounted for in the SEM.

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Table 2. Standardized regression coefficients for predictors of Bayley MDI and PPVT

scores at 36 months; all predictors were assessed at 14 months (N = 672).

Bayley MDI PPVT

Maternal Education .12 *** .13 ***

Child Gender (1 = Male) –.08 * –.06 †

Child Gestational Age .07 * .03

HOME Inventory .14 *** .16 ***

Maternal Distress Variables

Parent Distress Inventory –.03 –.08 †

Parent-Child Dysfunction –.12 ** .01

CES Depression Scale .02 .04

3–bag Task

Child Sustained Attention .09 * .20 ***

Child Engagement of Parent .03 .04

Parent Supportiveness .20 *** .10 *

Parent Intrusiveness –.01 .00

Parent Negative Regard –.04 .01

Child Negativity –.01 .02

Bayley MDI (14 months) .19 *** .18 ***

Note: ***p < .001, **p < .01, *p < .05, † p < .10

3.3. Structural Equation Modeling

As a first step in building the SEM, we used confirmatory factor analysis

(CFA) to evaluate the adequacy of the latent variables serving as indices of

maternal distress, joint attention, and negative interaction. CFA and SEM analyses

were conducted in the lavaan package (Rosseel, 2012) in R (R Core Team, 2019).

The latent variables had acceptable model fit indices. Standardized loadings for

manifest variables were > .50 for maternal distress and joint attention, and > .40

for negative interaction; see Figure 1.

Our SEM tested for direct and indirect influences of variables assessed at 14

months in predicting Bayley MDI and PPVT scores at 36 months. We used full

information maximum likelihood estimation to handle missing data (0%–24% for

variables at 14 months; 0%–7% for variables at 36 months) to reduce potential

bias resulting from listwise deletion (Acock, 2012). Model fit was assessed using

the chi-square test, comparative fit index (CFI), Tucker-Lewis index (TLI), root

mean square error of approximation (RMSEA), and standardized root mean

square residual (SRMR). After building a SEM for the full sample and trimming

non-significant paths, we conducted a measurement invariance analysis to test the

adequacy of the SEM for minority and non-minority subgroups. Measurement

invariance analysis tests assumptions of psychometric equivalence by comparing

the model fit of two groups against a theoretical model using a series of models

with increasingly constrained parameters (Putnick & Bornstein, 2017).

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Fig. 1. Standardized factor loadings for the three latent variables: maternal distress,

joint attention, and negative interaction.

The SEM used to predict Bayley MDI scores at 36 months had acceptable

model fit indices using unconstrained factor loadings and intercepts; χ2(df = 80, n

= 672) = 178.56, CFI = .94, TLI = .92, RMSEA = .04 [.03, .05], SRMR = .05, R2

Bayley MDI (36m) = .345. Direct effects of joint attention, B = .37, z = 7.18, p <

.001, 14-month cognitive ability, B = .27, z = 6.46, p < .001, maternal distress, B

= –.16, z = –3.38, p < .01, and maternal education, B = .11, z = 2.94, p < .01, on

36-month Bayley MDI scores were observed. Additionally, there were significant

indirect effects of the home environment, B = .04, z = 3.34, p < .01, gender, B =

–.02, z = –2.06, p < .05, and gestational age, B = .04, z = 3.27, p < .01, via 14-month

cognitive ability, as well as of the home environment, B = .14, z = 5.28, p < .001,

and maternal education, B = .05, z = 3.16, p < .01, via joint attention.

The same SEM used to predict PPVT scores at 36 months also had acceptable

model fit indices using unconstrained factor loadings and intercepts; χ2(df = 80, n

= 672) = 178.56, CFI = .94, TLI = .92, RMSEA = .04 [.03, .05], SRMR = .05, R2

PPVT (36m) = .247. The model indicated significant direct effects of joint

attention, B = .33, z = 6.54, p < .001, 14-month cognitive ability, B = .21, z = 5.13, p < .001, maternal education, B = .11, z = 2.84, p < .01, and maternal distress, B

= –.10, z = –2.32, p < .05, in predicting 36-month PPVT scores. Again, there were

significant indirect effects of the home environment, B = .03, z = 2.94, p < .01,

and gestational age, B = .03, z = 2.86, p < .01, via 14-month cognitive ability, and

of the home environment, B = .10, z = 4.06, p < .001, and maternal education, B

= .04, z = 2.84, p < .01, via joint attention. The indirect effect of gender via 14-month cognitive ability approached significance, B = –.02, z = –1.94, p = .05.

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Fig. 2a. SEM predicting Bayley MDI scores at 36 months in the minority subsample;

χ2(df = 194, n = 357) = 138.83, CFI = .92, TLI = .91, RMSEA = .04, SRMR = .07, R2 = .348.

Fig. 2b. SEM predicting Bayley MDI scores at 36 months in the non-minority

subsample; χ2(df = 194, n = 309) = 181.19, CFI = .92, TLI = .91, RMSEA = .04, SRMR =

.07, R2 = .321.

For each subgroup and outcome measure, we established partial invariance

after relaxing constraints on the loadings and residuals for several parameters of

the SEM. The model fit indices were acceptable for both minority and non-

minority subgroups; see Figures 2a, 2b for models predicting Bayley MDI and

Figures 3a, 3b for models predicting PPVT scores. Standardized estimates shown

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in bold in the figures indicate significant differences between subgroups; all

coefficients > .09 were statistically significant (p < .05).

Fig. 3a. SEM predicting PPVT scores at 36 months in the minority subsample; χ2(df

= 194, n = 357) = 131.50, CFI = .92, TLI = .91, RMSEA = .05, SRMR = .06, R2 = .207.

Fig. 3b. SEM predicting PPVT scores at 36 months in the non-minority subsample;

χ2(df = 194, n = 309) = 186.37, CFI = .92, TLI = .91, RMSEA = .05, SRMR = .06, R2 =

.208.

The models indicate considerable stability in relations between 14-month and

36-month measures across subgroups, and the importance of joint attention,

cognitive ability (Bayley MDI), maternal education, and maternal distress as

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direct influences on cognitive and language outcomes. Effects of other variables

were indirect.

4. Discussion

For children growing up in poverty, individual differences in developmental

outcomes have been linked to factors that originate in the first years of life

(Ursache & Noble, 2016). The current study used SEM to tease apart direct and

indirect influences of child and maternal characteristics, home environment, and

parent-infant interactional dynamics in infancy in predicting cognitive and

language outcomes of low-income minority and non-minority children at age 36

months. We conducted preliminary regression analyses to identify 14-month

variables of interest and used CFA to test the adequacy of three latent variables

serving as indices of maternal distress, joint attention, and negative interaction

between mother and infant. After testing the adequacy of the latent variables, we

established a best-fitting SEM for the full sample. We trimmed non-significant

paths from the full model, and then conducted a measurement invariance analysis

to determine whether the SEM showed acceptable fit indices for subgroups

comprising children from minority and non-minority backgrounds. Testing for

measurement invariance is important as children from minority groups experience

differential hardship and risk due to structural inequalities and societal prejudice,

which may affect psychometric equivalence (Brooks-Gunn & Markman, 2005;

Putnick & Bornstein, 2017).

The two outcome measures—Bayley MDI and PPVT scores—were

moderately correlated at 36-months. The Bayley MDI encompasses measures of

cognitive, social-emotional, and communicative skills (Bayley, 1993); hence one

would expect scores on this measure to be associated with receptive vocabulary

knowledge. Importantly, the SEMs reported in this paper yielded similar findings

across outcome measures (i.e., evidence of replication). Thus, the observed direct

and indirect relations between 14-month variables and cognitive and language

abilities at 36 months were not tied to results from a specific standardized test.

Starting with the child factors, we observed a direct effect of 14-month

cognitive ability (Bayley MDI) on 36-month cognitive and language outcomes

(Bayley MDI and PPVT), indicating considerable stability in intellectual abilities

over the first years of life (Bornstein et al., 2004). Both of the other child factors,

gestational age and child gender, had significant indirect effects on 36-month

outcomes via 14-month cognitive ability. Prior research indicates long-term

impact of prematurity on language development (Bee et al., 1982; Putnick et al.,

2017). Our findings suggest that early differences in general cognitive abilities

mediate the impact of prematurity on vocabulary development. Gender (favoring

girls over boys) has also been linked with individual differences in language

abilities at 2 to 5 year of age (Bornstein et al., 2004). Our findings again suggest

that early differences in general cognitive abilities mediate this relation.

With regard to maternal characteristics, both maternal educational attainment

had a significant direct effect in predicting children’s 36-month outcomes, while

425

the latent variable maternal distress did not appear to. The two maternal variables

exhibited significant negative covariance and contributed in opposite ways to the

quality of the home environment. Previous research has found that infants with

mothers exhibiting a great number of depressive symptoms tend to later possess

lower receptive vocabulary knowledge (Ahun et al., 2017; Letourneau, Tramonte,

& Willms, 2013). Our findings concur and provide some evidence that the

association is direct and relatively stable across subgroups, albeit somewhat weak

when other factors are taken into consideration. In line with previous research

(Christian et al., 1998; Dollaghan et al., 1999), we found maternal educational

attainment to be positively associated with children’s development outcomes at

36 months. Taken together, these results indicate the importance of supporting

maternal mental health and educational opportunities for mothers living in low

income communities as an effective means of fostering their children’s cognitive

and academic skill development.

Of all of the variables in the model, the latent variable joint attention had the

strongest direct effect on 36-month outcomes. Joint attention consisted of

measures of child’s sustained attention, child engagement of parent, and parent

supportiveness taken from the 3-bag task, an observational measure of parent-

infant interaction at 14 months. As indicated by the CFA, these three measures

were closely linked, as was expected (Adamson et al., 2019). In our preliminary

regression models, both child’s sustained attention and parent supportiveness

emerged as significant predictors of 36-month cognitive and language outcomes

(see also Brooks, Flynn, & Ober, 2018). In the context of joint attention,

supportive parents may aid children’s cognitive and linguistic development by

providing vocabulary that is relevant to the child’s focus of attention (Tamis-

LeMonda et al., 2001; Yu et al., 2019).

In addition to the longitudinal associations with cognitive and language

development, joint attention exhibited a significant concurrent association with

14-month cognitive ability. We interpreted this relation as bidirectional, given that

joint attention is thought to promote infants’ cognitive, social-emotional, and

communicative skills and vice versa (Adamson et al., 2019; Yu et al., 2019).

Notably, joint attention also had a significant negative covariance with the latent

variable negative interaction, consisting of measures of parent intrusiveness,

parent negative regard toward the child, and child negativity taken from the 3-bag

task. Previous research indicated that parental support and responsiveness to

infants were associated with advances in children’s language development

(Tamis-LeMonda et al., 2003) whereas parental negativity towards the child was

associated with decrements (Laake & Bridgett, 2018). However, in our models

there was no direct effect of negative interaction on 36-month outcomes. Thus,

intrusive parenting behaviors and negativity on the part of the parent towards the

child appeared to influence later outcomes only by diminishing opportunities for

joint attention.

Two other variables, maternal education and the quality of the home

environment, both had significant indirect effects on 36-month outcomes via joint

attention. That is, joint attention was enhanced for children of mothers with

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greater educational attainment and for children living in more emotionally

supportive and cognitively stimulating homes. The quality of the home

environment has been found to have a significant and stable association with

language development, even after accounting for socio-economic status (SES;

Melvin et al., 2016). We found this to be true in regression analyses. However,

when entered into the SEM, the quality of the home environment unexpectedly

did not have a significant direct effect on 36-month outcomes. In addition to its

indirect effect via joint attention, the home environment also had a significant

indirect effect via 14-month cognitive ability.

The observed relations were not only stable across outcome measures, but

also for both minority and non-minority subgroups. The results of the

measurement invariance analysis suggested that the relations between 14-month

factors and 36-month outcomes were largely the same across the two subgroups,

even though a few of the parameters were left unconstrained (Steenkamp &

Baumgartner, 1998) and some of the standardized estimates varied in magnitude.

4.1. Conclusions

The SEMs served to highlight pathways through which individual differences

in developmental trajectories emerge and identify targets for early intervention.

We found that after accounting for the influence of various factors related to the

child, parent, and home environment, joint attention in infancy remained the

strongest predictor of subsequent cognitive and receptive vocabulary skills. The

observed direct and indirect influences on cognitive and language development

were largely stable across minority and non-minority subgroups. Although the

SEM models indicated a number of factors influencing developmental trajectories

of children growing up in poverty, the models did not aim to be comprehensive in

accounting for effects of SES as all of the children in the EHSRE study were

raised in low-income circumstances (i.e., there was restricted range on variables

associated with income). Within this low-income sample, risk factors appeared to

be heightened for minority children, which likely reflects systemic inequality in

our society (Ursache, & Noble, 2016). Further analyses of the EHSRE dataset

should examine how the constellation of factors considered here at 14 months

continue to influence children’s later cognitive and communicative abilities

beyond pre-school entry. Additional work is needed to understand more fully how

factors associated with poverty impact children’s developmental trajectories and

evaluate strategies for helping children overcome obstacles to their success.

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Proceedings of the 44th annualBoston University Conference on Language Development

edited by Megan M. Brown and Alexandra Kohut

Cascadilla Press Somerville, MA 2020

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