Temperamental, parental and contextual contributors to early

Temperamental, Parental, and ContextualContributors to Early-emergingInternalizing Problems: A New IntegrativeAnalysis Approachsode_629 229..253

Rosemary S. L. Mills, University of Manitoba, Paul D. Hastings andJonathan Helm, University of California Davis and Lisa A. Serbin,Jamshid Etezadi, Dale M. Stack, Alex E. Schwartzman andHai Hong Li, Concordia University

Abstract

This study evaluated a comprehensive model of factors associated with internalizingproblems (IP) in early childhood, hypothesizing direct, mediated, and moderatedpathways linking child temperamental inhibition, maternal overcontrol and rejection,and contextual stressors to IP. In a novel approach, three samples were integrated toform a large sample (N = 500) of Canadian children (2–6 years; M = 3.95 years;SD = .80). Items tapping into the same constructs across samples were used to createparallel measures of inhibited temperament, maternal positive, critical, and punitiveparenting, maternal negative emotionality, family socioeconomic and structural stres-sors, and child’s IP. Multiple-groups structural equation modeling indicated that asso-ciations were invariant across samples and did not differ for boys and girls. Childinhibition, less positive and more critical parenting, maternal negative emotionality,and family socioeconomic disadvantage were found to have direct associations with IP.In addition, maternal negative emotionality was associated with IP through morecritical parenting, and both maternal negative emotionality and socioeconomic stresswere associated with IP through less positive parenting. Results highlight the multiple

This research was supported by Canadian Institutes of Health Research grant MOP-74642 awardedto the team, and by grants from Health Canada (Child and Youth Division), awarded to Lisa Serbinand colleagues from the Concordia Longitudinal Risk Project, from Social Sciences and Humani-ties Research Council of Canada and the Fonds de la Recherche en Santé du Québec grantsawarded to Paul Hastings for the Daycare and Preschool Adjustment Study, and from CanadianInstitutes of Health Research grant MOP-57670 to Rosemary Mills for the Shame in ChildhoodStudy. We thank the children and parents who made this research possible and gratefully acknowl-edge the assistance of Farriola Ladha, Claude Senneville, Nadine Girouard, and Bobbi Walling. Wealso thank Keith Widaman for statistical consultation.Correspondence should be addressed to Rosemary S. L. Mills, Department of Family Social Sci-ences, University of Manitoba, 35 Chancellor’s Circle, Winnipeg, Manitoba R3T 2N2, Canada.Email: [email protected]

doi: 10.1111/j.1467-9507.2011.00629.x

© Blackwell Publishing Ltd. 2011. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street,Malden, MA 02148, USA.

independent and cumulative risk factors for early IP and demonstrate the power ofintegrating data across developmental studies.

Keywords: internalizing; temperament; parenting; early childhood

Introduction

Internalizing problems (IP) are a common type of maladjustment in childhood, affecting14–18 percent of young people (Zahn-Waxler, Klimes-Dougan, & Slattery, 2000). Earlychildhood is a common period of onset, with anxiety problems in particular becomingevident during the transition to childcare or school. Early-emerging IP are moderatelystable from childhood onward (Majcher & Pollack, 1996), and are likely to be detri-mental to development (Hammen & Rudolph, 2003), underscoring the importance ofearly identification and improved understanding of how these problems develop toadvance prevention and treatment (Hirshfeld-Becker & Biederman, 2002). Numerousfactors in children and their environment contribute to these problems, includinginhibited temperament, parental overcontrol and negativity, and environmental stressors(e.g., Rapee, 2001). How these factors interact to increase the risk of IP and the processesthrough which these problems develop are not well understood, however, due in part tothe lack of research in which multiple factors have been examined simultaneously inearly childhood. Such research requires studies in which children are assessed compre-hensively on factors both internal and external to the child. The present study addressedthis need using a novel cross-study method in which three independent samples wereintegrated at early childhood (e.g., Curran & Hussong, 2009).

Bioecological models suggest that children’s development occurs in nested multi-level contexts, from biological to cultural, with risk factors occurring across levels(e.g., Bronfenbrenner & Ceci, 1994). IP most likely originate from the interactiveeffects of inhibited temperament reflecting high physiological reactivity attributableto predisposing genetic vulnerabilities, experiences of parental overcontrol and nega-tivity, and exposure to stressful familial, economic, and social disadvantages(Zahn-Waxler et al., 2000). Children are increasingly likely to develop IP when moreof these multilevel risk factors are present and less likely to develop them if risk factorsare offset by the presence of protective factors (Ashford, Smit, van Lier, Cuijpers, &Koot, 2008). In the present study, we tested a model postulating pathways to IP fromchild temperamental inhibition; from parenting practices that are likely to increasechildren’s anxiety, especially if they are highly inhibited and hence more susceptible toanxiety; and from parental negative emotionality and family stressors that are likely tointerfere with effective parenting.

Twin and family studies have revealed shared genetic risk for temperamental inhi-bition, anxiety, and depression (e.g., Bolton et al., 2006; Boomsma, van Beijsterveldt,& Hudziak, 2005). Evidence linking physiological reactivity to anxiety problemssupports the idea that inhibition may contribute to the development of IP by affectingthe ability to cope with anxiety. Some children are physiologically prone to states ofanxiety due to over-activation of brain systems involved in stress responses. At thebehavioral level, these brain systems activate regulatory processes designed to lowerarousal, evidenced by distress in the face of novelty and avoidance of unfamiliarsituations (e.g., Kagan & Snidman, 1999). Distress decreases after withdrawal, rein-forcing avoidant coping. However, withdrawal also decreases opportunities to learn orto practice more effective coping, increasing the likelihood of future anxious reactions

230 Rosemary S. L. Mills, Paul D. Hastings, Jonathan Helm et al.

© Blackwell Publishing Ltd. 2011 Social Development, 21, 2, 2012

through a positive feedback loop in which anxious avoidance maintains or increasesarousal and the sense of low control. Thus, more inhibited children may show more IPin early childhood.

Factors external to the child may contribute also to children’s IP. Parents are likelyto contribute by responding to the child in ways that reinforce the child’s distress. Bothinappropriate behavioral control (e.g., overdirective and punitive) and psychologicalcontrol (e.g., rejection and criticism) might contribute to the development of IP byundermining the child’s sense of self-agency and self-efficacy and increasing anxietyand distress (e.g., Barber, 1996; Rapee, 2001). Meta-analyses of research on relationsbetween parenting and childhood IP (McLeod, Weisz, & Wood, 2007; McLeod, Wood,& Weisz, 2007), indicated that child depression was associated with parental rejectionand to a lesser extent with parental overcontrol (overinvolvement) whereas anxiety wasassociated with parental overcontrol and to a lesser extent with parental rejection. Ahandful of prospective longitudinal studies suggest the same conclusion (e.g., Bayer,Sanson, & Hemphill, 2006; Letcher, Smart, Sanson, & Toumbourou, 2009), althoughyoung children’s inhibition also contributes reciprocally to these negative parentingdimensions (Rubin, Nelson, Hastings, & Asendorpf, 1999). Studies also point tosubstantial inverse relations between positive parenting (warm engagement) and bothIP and negative parenting (Bayer et al., 2006; Coplan, Arbeau, & Armer, 2008).However, it is unclear whether the presence of negative parenting and the absence ofpositive parenting are independently associated with children’s IP or whether they areat opposite ends of a single continuum. Less positive, more critical, and/or morepunitively controlling parents may have children with more IP. Given inhibited chil-dren’s greater susceptibility to parental influences (e.g., Gallagher, 2002), the linksbetween IP and parenting dimensions may be stronger among more inhibited children.

Parental behavior may be the final common pathway through which other riskfactors contribute to children’s IP. Parents’ personal characteristics may be an impor-tant risk factor for IP (Rubin & Mills, 1991), that operates through the mechanism ofparent behavior. Psychologically vulnerable parents are more likely to resort to inef-fective parenting practices during challenging childcare interactions and may directfeelings of frustration or distress toward their children. Emotionally negative parentsare more likely to engage in overcontrolling and critical or rejecting parenting (Coplanet al., 2008; Mills et al., 2007). Further, parenting behavior likely mediates relationsbetween parental emotionality and children’s IP (Elgar, Mills, Waschbusch, Brown-ridge, & McGrath, 2007). Thus, parental negative emotionality may be associated withchildren’s IP, and less positive, more critical, and/or more punitive parenting maymediate the association.

Parental behavior may also be the pathway through which family risk factorscontribute to children’s IP. Family socioeconomic disadvantages (e.g., low income andlow level of education) and challenging structural characteristics (e.g., single parent-hood and large family size) are well-established family risk factors in children’sdevelopment (Conger & Dogan, 2007; Weinraub, Horvath, & Gringlas, 2002). Theireffects on children have been attributed at least partly to the stresses they put uponparents leading to effects on the quality of parenting (Bronfenbrenner & Ceci, 1994).Economic hardship undermines parents’ ability to respond nurturantly to their children(Conger & Conger, 2002). Single-parent and blended families are at higher riskof parent–child conflict, leading to deterioration in the quality of parenting (e.g.,Weinraub et al., 2002). The more children there are in the family, the more parents’financial, psychological, and time resources are likely to be strained (Henderson,

Early-emerging Internalizing Problems 231


Hetherington, Mekos, & Reiss, 1996), and larger family size has been linked to lessindividual attention to and more differential treatment of children, less positive parent-ing, and more autocratic discipline (e.g., Jenkins, Rasbash, & O’Connor, 2003). Themore young children there are in the family, the greater may be the strain on resources(e.g., Harris, Raley, & Rindfuss, 2002). Thus, contexts of greater family socioecon-omic stress (lower socioeconomic status) and structural stress (single-parent family,less than two biological parents, and more young children in the family) may beassociated with children’s IP, and low positive, more critical, and/or more punitiveparenting may mediate these relations.

In summary, the present study evaluated a comprehensive, ecological modelhypothesizing direct, mediated, and moderated pathways linking child temperamentalinhibition, maternal overcontrol and rejection, and contextual stressors to IP in earlychildhood. We predicted that: (1) more inhibited children would show more IP inearly childhood; (2) less positive, more critical, and/or more punitively controllingparents would have children with more IP; (3) the links between IP and parentingdimensions would be stronger among more inhibited children; (4) parental negativeemotionality would be associated with children’s IP, and less positive, more critical,and/or more punitive parenting would mediate the association; and (5) contexts ofgreater family socioeconomic and structural stress would be associated with children’sIP, and low positive, more critical, and/or more punitive parenting would mediate theserelations.

We evaluated the model using integrative data analysis (e.g., Curran & Hussong,2009), a new technique in which raw data from two or more existing independentsamples are integrated, and models are fit directly to the integrated dataset. Theadvantages of the technique include increased statistical power, greater heterogeneity insample demographic characteristics, the ability to test more complex models than wouldbe possible within any single sample, and the potential to replicate findings across thesamples. There are threats to parsimony associated with pooling datasets with differentsample characteristics, measurements, and response scales. However, the examinationof sample differences allows a richer understanding of the generalizability of the resultsas well as the accuracy of current theoretical notions. The present study is the first to usethis approach to evaluate a comprehensive model of the factors associated with IP inearly childhood. A large and regionally, socioeconomically, and linguistically diversesample of young Canadian children was obtained by integrating three samples: a sampleat familial and sociodemographic risk (sample 1; Campisi, Serbin, Stack, Ledingham, &Schwartzman, 2009), a sample of children varying in risk for anxiety problems (sample2; Daycare and PreschoolAdjustment Study, e.g., Hastings et al., 2008), and a sample offamilies recruited from a representative community group (sample 3; Shame in Child-hood Study, e.g., Mills, Imm, Walling, & Weiler, 2008). To maximize the size of theintegrated sample, assessments of parenting focused on mothers.

Method

Participants

Participants were 523 Canadian children ranging from 2.1 to 6.1 years of age (M = 3.95years; SD = .80) obtained by integrating three samples. Given that there is no evidencefor age differences in risk factors associated with IP in early childhood, the samplecomprised all children up to kindergarten age, including seven kindergarten children in



sample 1 who turned six before being assessed late in the school year. Twenty-threechildren with missing values on most of the key variables were excluded (7 in sample1 and 16 in sample 3), leaving a final integrated sample of 500. Demographic charac-teristics are shown in Table 1. On average, children in samples 1 and 3 were 7 monthsolder than those in sample 2, t(257) = 4.82, p < .001, and t(372) = 11.23, p < .001,respectively; children in samples 1 and 3 did not differ in age, t(365) = .09, NS.Mothers in sample 1 were significantly younger than those in sample 2, t(255) = 8.59,p < .001, and in sample 3, t(364) = 8.39, p < .001; mothers in samples 2 and 3 did notdiffer in age, t(369) = .03. Proportionately, more mothers had a low level of education(high school or less) in sample 1 (67 percent) than in sample 2 (12 percent) or sample3 (16 percent), c2(8, N = 500) = 213.87, p < .001, and proportionately more motherswere in a low income family (below $30 000/year) in sample 1 (40 percent) than insample 2 (11 percent) or in sample 3 (16 percent), c2(12, N = 487) = 73.01, p < .001.Details on the three samples follow.

Sample 1 (N = 126). The Concordia Longitudinal Risk Project is a study of long-termtrajectories of childhood social withdrawal and aggression. The original sampleincluded over 1770 elementary-school Francophone (French-speaking) children livingin predominantly lower income neighborhoods of Montreal in 1976–1978. As theoriginal participants (now in their thirties and forties) became parents, assessments oftheir children were conducted. Of over 700 representative participants in the mostrecent phase of the ongoing Concordia project, 126 had children of an appropriate agefor the current study and agreed to participate with their child (acceptance rate wasapproximately 78 percent of eligible families). Participating parents did not differsignificantly from either the ongoing longitudinal sample or the original Concordiasample of 1770 in their sociodemographic or behavioral characteristics. The 126participants involved in the present study consisted of those second-generation chil-dren for whom the target variables were assessed in early childhood and their mothers.Families were 95 percent White and 5 percent other ethnicities (Hispanic and African).Assessments were done in home when the children were 4–6 years old, at which time,mothers completed instruments used in the present study.

Sample 2 (N = 133). The Daycare and Preschool Adjustment Study comprised 133families in greater metropolitan Montreal. Targeted advertisements were used torecruit children ranging from ‘low risk’ to ‘at risk’ for early anxiety problems. Bymother report of behavior problems (Achenbach & Rescorla, 2001), 42 children werein the borderline-clinical to clinical range for IP for their age whereas 48 were less than1 SD above, and 43 were at or below the norm for their age. Children ranged from 2.1to 4.9 years old. The sample was 70 percent White and 30 percent other ethnicities(Asian, Caribbean, African, Indian, Hispanic, and North African). Half the familieswere Anglophone (first language is English), one or both parents were Francophone(first language is French) in 37 families, and one or both parents were Allophone (firstlanguage is neither English nor French) in 33 families. Mothers completed instrumentsused for the current study during four assessments over approximately 7 months,involving a telephone interview, a home visit, a mailed questionnaire, and a laboratoryvisit.

Sample 3 (N = 241). The Shame in Childhood Study is a community sample of 253English-speaking from three- to four-year-old children and their parents. Families with



Tab

le1.

Dem

ogra

phic

Cha

ract

eris

tics

ofth

eIn

divi

dual

and

Inte

grat

edSa

mpl

es

Cha

ract

eris

tics

Sam

ple

1(N

=12

6)S

ampl

e2

(N=

133)

Sam

ple

3(N

=24

1)In

tegr

ated

(N=

500)

Perc

ent

orM

Nor

SDPe

rcen

tor

MN

orSD

Perc

ent

orM

Nor

SDPe

rcen

tor

MN

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Chi

ldag

e(y

ears

)4.

111.

233.

50.7

64.

10.2

63.

95.8

0C

hild

gend

erB

oys

48.4

%61

45.9

%61

54.4

%13

150

.6%

253

Gir

ls51

.6%

6554

.1%

7245

.6%

110

49.4

%24

7M

ater

nal

age

30.6

93.

3135

.32

5.11

34.9

85.

6234

.14

5.40

Mat

erna

led

ucat

ion

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owhi

ghsc

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27.0

%34

.0%

0.4

%1

7.0%

35C

ompl

eted

high

scho

ol39

.7%

5012

.0%

1615

.4%

3720

.6%

103

Com

mun

ity

coll

ege

24.6

%31

26.3

%35

48.1

%11

636

.4%

182

Und

ergr

adua

tede

gree

7.9%

1042

.1%

5632

.8%

7929

.0%

145

Gra

duat

e/pr

ofes

sion

alde

gree

.8%

119

.5%

263.

3%8

7.0%

35Fa

mily

inco

me

($)

0–10

000

7.1%

9.0

%0

.9%

22.

3%11

1000

1–20

000

16.7

%21

6.2%

88.

6%20

10.1

%49

2000

1–30

000

15.9

%20

4.7%

67.

3%17

8.8%

4330

001–

4000

010

.3%

139.

3%12

10.3

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10.1

%49

4000

1–60

000

28.6

%36

17.8

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26.3

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24.6

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060

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7499

912

.7%

1614

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1814

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3313

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6775

000

orm

ore

8.7%

1148

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6232

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7530

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148



young children born between June 1, 1999 and May 31, 2000 were recruited througha letter of invitation sent to 3500 families drawn randomly from all 6358 familiesresiding in Winnipeg, a mid-size city (population 600 000) in the geographic center ofCanada. Details were provided about the study, and families were invited to return anenclosed stamped return postcard if they had a healthy child and wished to participate.Of 257 families volunteering to participate, four withdrew shortly thereafter. Parentspredominantly were White (94 percent), were of European ancestry (74 percent), wereat least second-generation Canadian (78 percent), and reported feeling very Canadian(94 percent rated themselves 8 or higher on a 10-point scale). Mothers completedinstruments used for the current study during an assessment that involved a mailedquestionnaire followed shortly by a laboratory visit.

Between-sample Heterogeneity. The samples varied considerably in method of recruit-ment, with the result that they differed in children’s risk for IP. Children in sample 1were at elevated risk of internalizing and/or externalizing problems by virtue of havingparents at high risk, children in sample 2 ranged from low to at risk of anxiety basedon screening scores, and children in sample 3 were recruited from the community andthus were at low risk of adjustment problems. By increasing the heterogeneity of thepooled sample, these differences increased the potential generalizability of the results,but it was also possible that the higher risk for IP in samples 1 and 2 would contributedisproportionately to the findings. For this reason, in the analyses described below, weexamined sample differences before pooling across samples and tested the measure-ment and structural models with a multiple-groups approach.

Measures

A central challenge in integrative data analysis, given that the measures are likely todiffer across samples, is to develop appropriate measures of the constructs and toestablish measurement invariance across samples. In the present study, in all samples,the same instrument was used to assess IP, and the same information was gatheredabout family socioeconomic and structural characteristics. In two of the three samples,the same instruments were used to assess child temperament and maternal emotion-ality. The three samples used different instruments to measure parenting, all of whichhave been used and validated in previous research. Where the instruments differedacross samples, parallel measures were created by selecting items that, although notidentical across samples, reflected the same construct and had common scales. Wherethe items were on different scales (4- or 5-point scale vs. 7-point scale), values of thesmaller scale were converted to those of the larger scale using graduated constants topreserve distributional properties (1 = 1, 2 = 2.5, 3 = 4, 4 = 5.5, 5 = 7; 0 = 1, 1 = 3,2 = 5, and 3 = 7). The use of rescaled original units was preferable to a transformation(e.g., z-transformation) because the latter can change distributions of data, and thus,relations among variables can be altered (Cudeck, 1989).

For child temperament, parenting, and maternal emotionality, we (five of the coau-thors with cumulatively many decades of experience in measurement and assessmentof child and parent characteristics) began by identifying candidate parallel items in theinstruments used with each sample on the basis of item content. Item and scalestatistics were examined in iterative processes to identify sets of relevant items, withthe highest possible reliability indices, computed for the integrated sample usingdata centered within each constituent sample. Centering served to control for



between-group variance by ensuring that variances would range around zero. Thismethod of item selection produced subsets of items with the highest reliability acrossthe samples while accounting for between-sample differences. Selected items werethen rigorously tested with their original sample means included [multiple-groupconfirmatory factor analysis (CFA)] to ensure unidimensionality across the samples, asdescribed in the Results section. Thus, the parallel measures were sets of items thatreflected the same construct as indicated by acceptable reliability in the integratedsample. The instruments and procedures used to create parallel measures acrosssamples are described below. The items selected for each measure are shown in Table 2(see Table 3 for alpha coefficients in the three samples and the integrated sample).

Child Temperament. To assess child temperament, parents completed the children’sbehavior questionnaire (CBQ; Rothbart, Ahadi, Hershey, & Fisher, 2001), in samples2 and 3, and the emotionality, activity, and sociability (EAS) temperament survey forchildren (Buss & Plomin, 1984), in sample 1. For the CBQ, parents rate descriptionsof children’s reactions to situations on a 7-point scale from 1 (extremely untrue) to 7(extremely true). For the EAS, parents rate items using a 5-point scale from 1 (notcharacteristic or typical) to 5 (very characteristic or typical). Six items equivalent inmeaning across instruments (6 of 12 shyness items from the CBQ; all 5 shyness and 1of 5 sociability items from the EAS) were used to create a parallel measure of inhibitedtemperament [e.g., prefers to watch than join, at ease with almost anyone (reversed),acts shy around new people]. EAS items were rescaled converting 5-point to 7-pointscales.

Parenting. Mothers’ parenting styles and practices were assessed using the instru-ments and items shown in Table 2. Mothers rated the likelihood or frequency of usinga parenting strategy or indicated how descriptive the item was of their parentingpractices. To increase similarity, some items within an instrument for one sample wereaveraged to form a single composite item that cohered more than any of its constituentitems with the items on the instruments for the other samples. Individual and compos-ite items were used to create three-item parallel measures of positive, critical, andpunitive parenting. Items originally scored on 4- or 5-point scales were rescaled to7-point scales. Parenting scores were computed as the mean of item ratings. Therelatively low alphas (see Table 3) are likely due to the small number of items in eachmeasure necessitated by rigorous item selection. The composites reflect parcels, andthey are very beneficial empirically. Their use allows for the inclusion of informationfrom multiple items without requiring extra parameters within the model (Bagozzi &Edwards, 1998), a greater ratio of common-to-unique variance (Little, Cunningham,Shahar, & Widaman, 2002), and are most likely to capture the true relations amongconstructs of interest (Rushton, Brainerd, & Pressley, 1983).

Maternal Negative Emotionality. Two self-report scales were used to assess mothers’emotionality: the EAS temperament survey for adults (Buss & Plomin, 1984) insamples 1 and 3, and the positive and negative affect scales (PANAS; Watson, Clark,& Tellegen, 1988) in sample 2. The EAS for adults required mothers to rate itemsdescribing emotions and behaviors on scales ranging from 1 (not characteristic ortypical of you) to 5 (very characteristic or typical of you). The PANAS included a listof affect terms (e.g., bashful, nervous, and happy) that parents rated as describing theirown feelings in recent weeks on scales from 1 (not at all) to 5 (extremely). Eight items



Tab

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and

Item

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sure

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edE

asily

frig

hten

ed

Not

es:

Pare

ntin

gw

asas

sess

edin

sam

ple

1m

ainl

yby

the

pare

ntin

gsc

ale

(PS

;A

rnol

d,O

’Lea

ry,W

olff

,&

Ack

er,

1993

),in

sam

ple

2,m

ainl

yby

the

chil

dre

arin

gpr

acti

ces

repo

rt(C

RP

R)

Q-s

ort

(Blo

ck,1

981)

,and

insa

mpl

e3,

mai

nly

byth

epa

rent

ing

styl

esan

ddi

men

sion

squ

esti

onna

ire

(PS

DQ

;R

obin

son,

Man

dlec

o,O

lsen

,&H

art,

2001

).W

here

asp

ecifi

cit

emw

asdr

awn

from

one

ofth

efo

llow

ing

mea

sure

s,th

eso

urce

isin

dica

ted

besi

deth

eit

em:

RC

E=

resp

onse

sto

chil

dren

’sem

otio

ns(H

asti

ngs

&D

e,20

08);

PC

S=

psyc

holo

gica

lco

ntro

lsc

ale

(Ols

enet

al.,

2002

);P

DI

=pa

rent

ing

dim

ensi

ons

inve

ntor

y(S

late

r&

Pow

er,1

987)

;PS

I=

pare

ntin

gst

ress

inde

x-sh

ortf

orm

(Abi

din,

1995

).R

espo

nse

scal

esra

nged

from

1to

7fo

rth

eP

San

dC

RP

R,f

rom

1to

5fo

rth

eP

SI,

RC

E,P

SD

Q,a

ndP

CS

,and

from

0to

3fo

rth

eP

DI.

Com

posi

teit

ems

that

wer

ecr

eate

dth

roug

hav

erag

ing

are

encl

osed

inpa

rent

hese

s,w

ith

com

pone

ntit

ems

inth

eco

mpo

site

sepa

rate

dby

slas

hes.

R=

reve

rse-

scor

ed.I

tem

sw

ith

para

llel

cont

ent

acro

ssth

ein

stru

men

tsw

ere

used

tocr

eate

mea

sure

sof

Posi

tive

Pare

ntin

g(i

nsa

mpl

e1,

3of

12pa

rent

–chi

lddy

sfun

ctio

nal

inte

ract

ion

item

sfr

omth

eP

SI

and

all

five

disc

uss

item

sfr

omth

eP

DI;

insa

mpl

e2,

2of

7ac

cept

item

sfr

omth

eC

RP

Ran

d2

of3

rew

ard

item

sfr

omth

eR

CE

;in

sam

ple

3,2

of5

war

mth

/sup

port

and

2of

5re

ason

ing/

indu

ctio

nit

ems

from

the

PS

DQ

),C

riti

calP

aren

ting

(in

sam

ple

1,5

of10

over

reac

tivit

yit

ems

from

the

PS

;in

sam

ple

2,2

of5

reje

ctan

d2

of16

auth

orit

aria

nit

ems

from

the

CR

PR

and

1of

3pu

nish

and

1of

3m

agni

fyit

ems

from

the

RC

E;i

nsa

mpl

e3,

2of

4ve

rbal

host

ilit

yit

ems

from

the

PS

DQ

and

3lo

vew

ithd

raw

al/g

uilt

indu

ctio

nit

ems

from

the

PC

S),

and

Pun

itiv

ePa

rent

ing

(in

sam

ple

1,3

of10

over

reac

tivit

yit

ems

from

the

PS

and

all

5ph

ysic

alit

ems

from

the

PD

I;in

sam

ple

2,3

of16

auth

orit

aria

nit

ems

from

the

CR

PR

and

1of

3pu

nish

item

san

d1

of3

mag

nify

item

sfr

omth

eR

CE

;in

sam

ple

3,2

of4

verb

alho

stil

ity

and

all

four

phys

ical

coer

cion

item

sfr

omth

eP

SD

Q).



Tab

le3.

Des

crip

tive

Stat

isti

csan

dR

elia

bilit

ies

ofth

eM

easu

res

bySa

mpl

e

Var

iabl

es

Sam

ple

1S

ampl

e2

Sam

ple

3In

tegr

ated

sam

ple

Mor

%SD or

Na

Mor

%SD or

Na

Mor

%SD or

Na

Mor

%SD or

Na

Chi

ldte

mpe

ram

ent

Inhi

biti

on2.

921.

03.7

53.

591.

37.8

53.

401.

37.8

53.

331.

31.8

3M

ater

nal

pare

ntin

gPo

sitiv

e6.

181.

01.6

66.

29.6

6.4

75.

93.6

9.4

56.

09.7

9.5

4C

riti

cal

2.03

.60

.43

1.55

.65

.58

2.39

.79

.52

2.08

.79

.51

Pun

itiv

e3.

261.

01.5

33.

27.8

7.5

42.

48.7

7.7

12.

89.9

5.6

1M

ater

nal

emot

iona

lity

Neg

ativ

eem

otio

nali

ty2.

19.6

9.7

92.

23.7

0.8

22.

33.7

0.7

82.

27.7

0.7

7Fa

mily

soci

oeco

nom

icst

ress

Mot

her’

sed

ucat

ion

2.16

.94

3.69

.92

3.23

.76

3.08

1.03

Fath

er’s

educ

atio

n2.

14.8

53.

58.9

93.

10.8

93.

001.

05O

ccup

atio

nal

pres

tige

40.9

412

.09

54.0

712

.15

51.9

311

.31

49.7

812

.79

Fam

ilyin

com

e4.

101.

755.

731.

545.

251.

645.

081.

75Fa

mily

soci

oeco

nom

icst

ress

(z)

-.05

.75

.67

-.02

.75

.68

-.01

.79

.78

-.02

.77

.74

Fam

ilyst

ruct

ural

stre

ssD

ual-

vs.

sing

le-p

aren

tfa

mily

(81.

0)(1

02)

(82.

7)(1

10)

(89.

6)(2

16)

(85.

6)(4

28)

Two

vs.

few

erbi

olog

ical

pare

nts

(73.

8)(9

3)(7

8.2)

(104

)(8

3.4)

(201

)(7

9.6)

(398

)N

umbe

rof

chil

dren

unde

rth

eag

eof

seve

n1.

65.6

51.

60.6

51.

97.7

31.

79.7

1

Chi

ldin

tern

aliz

ing

prob

lem

sIn

tern

aliz

ing

prob

lem

sa52

.40

8.49

.85/

.70

54.4

710

.21

.85

51.1

19.

15.8

152

.33

9.37

Not

es:

For

the

inte

grat

edsa

mpl

e,C

ronb

ach’

sal

pha

reli

abil

itie

sw

ere

com

pute

dus

ing

cent

ered

data

toco

ntro

lfo

rbe

twee

n-gr

oup

vari

ance

.a

Mea

nsan

dst

anda

rdde

viat

ions

are

for

the

Tsc

ores

;sa

mpl

e1

alph

asar

efo

rth

e2–

3an

d4–

8ve

rsio

ns,

resp

ecti

vely

,of

the

chil

dbe

havi

orch

eckl

ist.



with parallel content across the two instruments (3 of 4 distress, 3 of 4 fear, and 2 of4 anger items from the EAS; eight matching emotion words from the PANAS) wereused to create a measure of negative emotionality (e.g., many things annoy me and getemotionally upset easily).

Family Socioeconomic Stress. Family stress associated with socioeconomic status wasassessed from four components: mother’s education, father’s education, highest occu-pational prestige in the family, and family income. Occupational prestige was scoredusing a recently updated version of the standard international occupational prestigescale (SIOPS; Treiman, 1977). The SIOPS was originally constructed with reference tothe International Standard Classification of Occupation (ISCO68), an internationalmeasure of occupational codes and prestige criteria, to obtain a cross-national com-parative measure of occupational status. The original SIOPS has been updated to referto the ISCO88 (Hakim, 1998). The occupations listed in the SIOPS range from thehighest scores given to professionals (e.g., lawyers, physicians, and chief executiveofficers) to the lowest given to those who hold such occupations as domestic laborers,manufacturers, and farmhands.

In all three samples, mothers were asked to report the highest levels of educationcompleted by themselves and their children’s fathers (in two-parent households).Education levels were converted into 5-point scales ranging from 1 (did not completehigh school) to 5 (attained graduate or professional degree, e.g., MD and PhD).Mothers were also asked to report the annual total family income before taxes. Familyincome was converted into a 7-point scale ranging from 1 ($0–$10 000) to 7 ($75 000or more). A principal components analysis indicated that a single-factor solution wasappropriate for the four component measures (mother’s education, father’s education,highest SIOPS score in the family, and family income), eigenvalue = 2.23, varianceaccounted for = 56 percent. An overall measure of family socioeconomic stress for eachsample was created by averaging the four measures after transforming each of theminto a standard score (z) and reversing each scale so that higher values would reflectgreater stress.

Family Structural Stress. Family stress associated with structural characteristics wasassessed by three indices: dual- vs. single-parent family, two vs. fewer biologicalparents, and number of children under the age of seven. Preliminary analyses indicatedthat number of children under the age of seven did not cohere with either of the othertwo indices; therefore, rather than form a single index of structural stress, the threevariables were examined separately in the model.

Child IP. IP were assessed by two versions of the Achenbach child behavior check-list, the child behavior checklist (CBCL) and the Achenbach system of empiricallybased assessment (ASEBA CBCL) using the age 2–3 and 4–18 versions in samples2 and 3 (ASEBA CBCL; Achenbach & Rescorla, 2001). The CBCL is the mostwidely used tool for assessing IP. It has good reliability and validity. Across CBCLversions, the IP scale comprised more than 30 items rated from 0 (not true as far asyou know) to 2 (very true or often true). Total scores were obtained by summingmothers’ ratings of the items; to account for changes in measurement over time, Tscores derived from population norms were used. To check whether the use of Tscores would obscure gender differences, we performed analyses using mean itemscores. No significant gender differences within or across samples were found.



Results

Preliminary Analyses

An examination of missing values revealed that for the integrated sample about 2percent of data had missing values with the exception of one variable, maternalnegative emotionality, which had 9 percent missing values due to a delay in adding itto the sample 3 protocol. Given the moderate amount of missing data for maternalnegative emotionality, it was decided to perform the main analyses using an imputationapproach (Tabachnick & Fidell, 2007). Before imputation, 58 of the 500 cases had oneor two missing values, and these data were assumed to be missing at random. Toinvestigate this assumption, we used expectation maximization (EM) imputation withthe complete dataset, as recommended by Widaman (2006) for small amounts ofmissingness. The imputation method uses all available data to estimate a new datasetwith imputed values for those that originally had missing values. Next, the model ofinterest is estimated with the newly imputed dataset. Maximum likelihood (ML)estimation simply uses all available data from the original dataset to estimate the modelof interest (Allison, 2002). We compared the results using EM with those obtainedfrom ML, and we found that the parameter estimates under the two approaches werealmost identical. The similarity of the results suggested that the more rigorous treat-ment of the missingness via EM was not necessary. Therefore, we used ML to estimatethe final model and assumed that the missing values were missing at random.

For the most part, variables were normally distributed. Although some variablesshowed skewness or kurtosis, multivariate normality was not substantially violated anddid not affect the analysis. However, one case in sample 2 was excluded from thestructural analysis owing to an unusual impact on multivariate kurtosis. Therefore, N= 499 in the final, reported model.

Test of the Measurement Model

To assess construct validity of the measures, a CFA model was fitted to each sample(Anderson & Gerbing, 1988). This measurement model constructs the latent variablesfrom their corresponding manifest variable indicators and then correlates all of thelatent variables. Good fit of the measurement model within each sample indicates agood construction of latent variables for each sample (Anderson & Gerbing, 1988). Weevaluated model fit with the robust method (Byrne, 2006) using the comparative fitindex (CFI) and root mean square error of approximation (RMSEA), which is notoverly sensitive to sample size (Bentler, 2004). By convention, CFI values of .95 orabove and RMSEA values of .06 or below are indicative of a good empirical fit(Tabachnick & Fidell, 2007). The analyses yielded CFI values of .969, .984, and .924and RMSEA values of .023, .019, and .040 for samples 1, 2, and 3, respectively. Theseindices were within acceptable ranges, indicating reasonably good fit and providingevidence for factorial validity of the measures.

Next, we performed a multiple-group CFA model with the integrated sample toevaluate the extent to which the measures functioned similarly in relation to theirunderlying constructs across samples. Typically, multi-group comparisons require con-straints among the factor loadings as well as the correlations among the latent variables(Meredith, 1993). However, in the present context, in which the latent variables for thethree samples were derived from different sets of items, invariance of the factor



loadings would not be expected and was not required (Bentler, 2004; Byrne, Shavel-son, & Muthén, 1989). Because the latent variables were the constructs of interest, wewere concerned with invariance of the correlations among the latent variables but notwith invariance of the factor loadings. If the correlation pattern for the latent variablesis invariant across the samples, then the latent variables can be assumed to represent thesame constructs for each of the three samples (Nesselroade, Gerstorf, Hardy, & Ram,2007). It should be noted that two of the latent variable correlations were found to beproblematic. Firstly, a significant difference was found for the correlation betweenparent emotionality and punitive parenting for samples 2 and 3 (c2 = 5.03, df = 1,p = .025). Secondly, a marginally significant difference was found for the correla-tion between family socioeconomic stress and critical parenting for samples 2 and 3(c2 = 3.661, df = 1, p = .056). However, given the large sample size, there was a highamount of power for detecting differences among these correlations. Thus, the fit of themeasurement model was used to assess the invariance of the correlations among thelatent variables. The model with the constraints across all correlations yielded a CFI of.952 and a RMSEA of .031 across the three samples (c2 = 55.09, df = 51, p = .32),indicating a very good fit of the model. The fit of the model with all correlations freelyestimated across the samples was very poor (c2 = 529.904, df = 129, p < .0001). Thefit for the restricted model was much better than the fit of the free model (Dc2 =507.814, df = 78, p < .0001). This finding suggests that it is reasonable to assumethat the differences in the two problematic correlations are negligible and that theindicators within all samples are modeling the same latent variables (Nesselroadeet al., 2007). Furthermore, when correlations were constrained to be direct effects inthe hypothesized model, no significant differences were detected across the samples.

Preparation for the Structural Model

Given that each set of manifest variables loaded onto one latent factor, and these latentfactors had a similar correlation pattern across samples, we next focused our attentionon the reliability of the manifest variables across the samples. Because each set ofvariables loaded onto a single factor, we created a composite score for each set ofvariables. Simple averaging was used to create the composite score rather than aweighted score, which would be sample-dependent (e.g., Cohen, 1990; Wainer, 1976).The use of single composite indicators for the latent variables within the measurementmodel allowed for a simple method of accounting for unreliability in the set ofindicators (Kline, 2005). In this method, the uniqueness is defined as the total vari-ability of the manifest variable X (or sx

2) multiplied by the proportion of unreliabilityof the manifest variables (1 - rxx) for the particular construct of interest (where rxx

represents Cronbach’s alpha for variable X). Using these constraints, x becomes theremaining proportion of variability in X, or the part of the variability in X that iscommon across all the manifest variables.

In essence, this approach extracts the part variability in the set of items that isestimated to be reliable and models only this reliable portion of the variance at thestructural level. To incorporate this within our model, we defined the residual variancefor each of the composites as the proportion of variance in the composite that wasestimated to be unreliable (one minus Cronbach’s alpha). X can be thought of as oneparticular composite within our model, e represents the proportion of the variability ofthe manifest variables that is estimated to be unreliable, and x represents the proportionthat is reliable. Therefore, the unreliability of each set of manifest variables from each



sample can be accounted for in the measurement model. Because only the reliable partin each sample will go to the structural level, we know that the low reliabilities of thesets of manifest variables will not adversely affect the robustness of the model.

Cronbach’s alpha was used to estimate the reliability for each set of manifestvariables (Bedeian, Day, & Kelloway, 1997). Cronbach’s alpha was chosen because itcan be used to estimate the reliability of a composite score for a set of variables(McDonald, 1999). Once Cronbach’s alpha had been calculated for each set of mani-fest variables, the composites that summarized each set were decomposed within themeasurement model into common and uncommon variance as described above. Thisdecomposition ensured that relations at the structural level were based on reliablevariance rather than random noise within the data.

In summary, our approach to estimating the measurement model allowed differentlevels of reliability across each of the samples, as well as a direct specification ofreliability based on Cronbach’s alpha. Given that the sets of manifest variables wereeach unidimensional (good fit of a one-factor CFA for each set of manifest variables),we knew that each set could be summarized via a composite score. To account forunreliability of the composite score, we extracted the proportion of variance that wasestimated to be reliable and used only that portion at the structural level. This methodensures that the relations at the structural level reflect only reliable measurements ofthe manifest variables rather than a mixture of reliable and unreliable portions ofvariability.

Sample Differences

Prior to investigating the structural model, sample differences, as well as correlationsamong the measured variables, computed as composite indicators, were investigated.Descriptive statistics and coefficient alpha reliabilities for the variables are shown inTable 3. To examine mean differences among the three samples, a series of one-wayanalyses of variance was performed; with Bonferroni’s correction applied to follow-upcomparisons, only sample differences at p < .001 are reported. The analyses revealednumerous differences among the samples. Children in sample 1 scored lower ontemperamental inhibition than those in samples 2 and 3, t(253) = -4.37, t(362) = -3.40,respectively; the latter two samples did not differ significantly. Mothers in sample 3rated themselves as less punitive than those in samples 1 and 2, t(365) = -8.27, t(372)= -9.06, respectively; the latter two samples did not differ significantly on punitiveparenting. Mothers in sample 3 rated themselves more critical than those in sample 1,t(365) = 4.37, who in turn rated themselves more critical than those in sample 2, t(257)= 6.19. Mothers in sample 2 rated themselves more positive than those in sample 3,t(372) = 4.90, but not those in sample 1, t(257) = 1.01, NS; there was no significantdifference between mothers in samples 1 and 3, t(365) = 3.02, p = .003 (below theBonferroni criterion). Family socioeconomic stress was higher in sample 1 than insample 3, as indicated by lower scores on all four components, smallest t(356) = 6.19,and higher in sample 3 than in sample 2 on mother’s and father’s education, smallert(344) = 4.60. Family structural stress did not differ significantly across samples exceptwith respect to the number of children under the age of seven, which was higher insample 3 than in samples 1 and 2, t(365) = 4.13, t(372) = 4.85, respectively, which didnot differ significantly. Finally, mothers in sample 2 reported more IP in their childrenthan did mothers in sample 3, t(371) = 3.26; sample 1 did not differ significantly fromsamples 2 or 3. In summary, the samples differed on several of the measured variables.



Levels of IP were highest in sample 2 and lowest in sample 3, possibly due to sampledifferences in the recruitment of children at risk for IP. Sample 2 mothers showed morepositive and less critical parenting than mothers in the other samples, possibly relatedto lower family stress in sample 2.

Correlations among Variables

Zero-order correlations among the composite variables are summarized in Table 4 forthe integrated sample. Due to the age range of the sample and research indicating thatchild age is negatively associated with positive parenting (Brody, Stoneman, & Burke,1987; Stevenson, Leavitt, Thompson, & Roach, 1988), child age was included todetermine whether it should be considered as a covariate in the analysis. As shown inTable 4, children with more IP were significantly more inhibited, r = .27, p < .001. Theyhad mothers whose parenting was less positive, r = -.13, p < .01, more critical, r = .16,p < .001, and more punitive, r = .14, p < .01, and whose emotionality was morenegative, r = .25, p < .001; and they lived in families under greater socioeconomicstress, r = .09, p < .05, with fewer than two parents, rs = -.10, p < .05, or with fewerthan two biological parents, rs = -.11, p < .01. Child inhibition was not related tomaternal parenting or negative emotionality. Mothers who reported more negativeemotionality used parenting practices that were less positive, r = -.20, and morecritical, r = .23, or more punitive, r = .33, all p < .001. Mothers experiencing moresocioeconomic stress were less positive, r = -.09, or more punitive, r = .11, p < .05.Positive parenting was negatively related to both critical parenting, r = -.10, p < .05,and punitive parenting, r = -.28, p < .001, which were positively related to each other,r = .33, p < .001. Child age was not substantially related to other variables.

Test of the Hypothesized Model

The model postulated pathways to IP from inhibition; from parenting practices that arelikely to increase children’s difficulty regulating anxiety reactions, especially if theyare highly inhibited and hence more prone to anxiety reactions; from negative emo-tionality in the parent that interferes with effective parenting; and from family socio-economic disadvantage and structural stressors (single-parent family, fewer than twobiological parents, and having more young children) that are likely to interfere witheffective parenting. The model was tested with EQS 6.1 (Multivariate Software,Encino, CA, USA). To avoid potential bias caused by violations of normality inassessing model adequacy, we used maximum-likelihood estimation with and withoutthe robust option. The robust option corrects for violations of the assumption ofnormality (Chou, Bentler, & Satorra, 1991). The two methods yielded similar results.We evaluated model fit with the robust method using model chi-square, CFI, andRMSEA.

We tested the model using path analysis with a multiple-groups approach thatevaluated the equality of the model structure across samples. Although child age didnot have significant zero-order correlations with other variables, given its knownassociation with less positive parenting, it was examined as a covariate in the modeland retained due to its relations with parenting. Firstly, the model was tested in each ofthe three samples without any constraints on the parameters (sample 1: c2(48) = 63.9,p = .06, CFI = .969, RMSEA = .023; sample 2: c2(48) = 61.04, p = .10, CFI = .984,



Tab

le4.

Zer

o-or

der

Cor

rela

tion

sam

ong

Var

iabl

esin

the

Inte

grat

edSa

mpl

e(N

=50

0)

1.2.

3.4.

5.6.

7.8.

9.10

.

1.C

hild

age

2.In

hibi

ted

-.02

3.Po

sitiv

e-.

07-.

014.

Cri

tica

l.0

2.0

1-.

10*

5.P

unit

ive

.04

-.05

-.28

***

.34*

**6.

Neg

ativ

e-.

02-.

01-.

20**

*.2

3***

.33*

**7.

Fam

ilyso

cioe

cono

mic

stre

ss.0

0-.

11*

-.09

*-.

03.1

1*.0

58.

Dua

l-vs

.si

ngle

-par

ent

fam

ilya

.00

.06

.07

.07

-.04

-.03

-.33

***

9.Tw

ovs

.fe

wer

biol

ogic

alpa

rent

sa-.

03.0

5.0

8.0

1-.

03-.

01-.

36**

*.8

1***

10.

Num

ber

ofch

ildr

enun

der

the

age

ofse

ven

.04

.06

-.08

.08

.12*

*-.

02-.

13**

.14*

**.1

6***

11.

Inte

rnal

izin

gpr

oble

ms

.03

.27*

**-.

13**

.16*

**.1

4**

.25*

**.0

9*-.

10*

-.11

**-.

02

Not

es:

aA

ssoc

iati

ons

wit

hth

ese

vari

able

sw

ere

exam

ined

usin

gS

pear

man

rank

-ord

erco

rrel

atio

ns.

*p

<.0

5,**

p<

.01,

***

p<

.001

.



RMSEA = .019; and sample 3: c2(88) = 131.82, p = .002, CFI = .924, RMSEA = .04).These models included the specifications for the residual variance as described earlier(Kline, 2005). Because all CFI values were larger than .95 and all RMSEA values weresmaller than .05, these models were judged to be very good. Next, we constrained thepath coefficients to be equal across the three groups. This model showed equally goodfit, c2(51) = 55.09, p = .32, CFI = .991, RMSEA = .022. The hypothesis that associa-tions between IP and the three parenting indices would be stronger for more inhibitedchildren was tested by including parenting ¥ temperament interaction terms in themodel. All were nonsignificant, indicating no moderation effects by inhibition, b = .47,.11, and 1.23 for the interaction with positive, critical, and punitive parenting, respec-tively, all p > .40. Fit indices for the non-constrained model were c2(96) = 192.92, p <.01, CFI = .96, RMSEA = .08, and those for the constrained model (all interaction pathcoefficients constrained to zero) were c2(105) = 180.54, p < .01, CFI = .96, RMSEA =.10. No significant difference in model fit could be detected between the constrainedand non-constrained models (Dc2(9) = 12.37, p = .19). These results suggest thateffects of parenting on IP were not moderated by temperamental inhibition; therefore,these moderating effects were excluded from the final model. To examine reciprocalrelations between critical parenting and IP and between positive parenting and IP, weperformed two separate analyses including the inverse instead of the hypothesized pathin the model. The coefficient for the reverse path from IP to critical parenting wasnonsignificant [b = .01, p = .15; c2(38) = 46.14, p = .17, CFI = .98, RMSEA = .03], andthe coefficient for the path from IP to positive parenting was also nonsignificant [b =-.01, p = .10; c2(38) = 45.17, p = .20, CFI = .98, RMSEA = .03]. On the basis of thesefindings, reciprocal relations between parenting dimensions and IP were excluded fromthe final model. Finally, additional multiple-group analyses revealed no path coefficientdifferences as a function of dual- vs. single-parent family [Dc2(18) = 15.24, p = .65],two vs. fewer biological parents [Dc2(18) = 17.97, p = .46] or child gender [Dc2(18) =16.14, p = .58]. For each of these three analyses, constraints were placed on all of thepath coefficients across the two specified groups. The large p values (i.e., >.05) indicateno significant difference while constraining these paths across the groups; therefore,there was no detectable difference among the relationships of the latent variables as afunction of group membership. Fit statistics for the non-constrained models werec2(10) = 6.64, p = .78, CFI = 1.0 and RMSEA = .001 for dual- vs. single-parent family;c2(10) = 12.04, p = .36, CFI = .99 and RMSEA = .035 for two vs. fewer biologicalparents; and c2(10) = 11.78, p = .78, CFI = .99 and RMSEA = .03 for boys vs. girls.Therefore, these variables were excluded from the final model.

The results of the final model are shown in Figure 1. Independent variables in themodel were allowed to freely covary. Covariances for each pair of independent vari-ables were estimated to measure the level of dependence not accounted for by the directeffects in the model. The covariances for each sample are not provided in Figure 1because they differ across samples. Because multiple-groups analysis constrains theunstandardized coefficients to be equal across groups, it does not yield a common setof standardized coefficients (Kline, 2005). For this reason, the unstandardized solutionis shown in the figure, which provides coefficients for the significant paths. Coeffi-cients reflect, for each unit increase in a predictor, the expected unit increase ordecrease in the predicted variable, given that the other parameters remain unchanged.Note that the unstandardized coefficients for prediction of parenting variables aresmaller in magnitude than those for prediction of IP due to differences in the mea-surement scales for these variables (1–7 for parenting and 29–76 for IP).



As Figure 1 shows, most hypothesized relations were significant. Unstandardizedcoefficients indicate strong direct predictive relations between IP and child inhibition(b = 2.64, p < .01), maternal low positive (b = -1.98, p = .02) and high critical (b = 3.20,p = .02) parenting, maternal negative emotionality (b = 3.22, p < .01), and familysocioeconomic stress (b = 2.25, p < .01). Maternal punitive parenting was stronglypredicted by maternal negative emotionality (b = .50, p < .01) and family socio-economic (b = .19, p < .01) and structural (children under the age of seven) (b = .17,p < .01) stress; however, there was no association between children’s IP and punitiveparenting over and above the associations with less positive and more critical parent-ing. There were pathways to IP from negative emotionality and family structural stress(having more children under the age of 7) via less positive parenting (b = -.29, p < .01,and b = -.12, p < .01, respectively) and more critical parenting (b = .31, p < .01, andb = .09, p = .02, respectively), and from socioeconomic stress via less positiveparenting (b = -.12, p < .01).

Mediation by Parenting. The hypothesis that maternal negative emotionality andfamily stress would be associated with IP via their impact on parenting was tested byexamining indirect effects estimated as the product of the path coefficients linking theindependent variable to the mediator and linking the mediator to the dependent vari-able. Indirect effects are shown in Figure 1 by the significant paths to IP from maternalnegative emotionality and from family structural stress (having more children underthe age of seven) through low positive and high critical parenting, and from familysocioeconomic stress through low positive parenting. To test the significance of

Child Age

Family StructuralStress

(Children Under the Age of Seven)

FamilySocioeconomic

Stress

MaternalNegative

Emotionality

Critical Parenting

PositiveParenting

PunitiveParenting

TemperamentalInhibition

InternalizingProblems

.08*

.09*

.31***

-.10* -.12**

-.12*

-.29***

.19** .17***

.50***

3.20*

-1.98*

2.25***

3.22***

2.64***

Figure 1. Unstandardized Path Coefficients Depicting Direct and Indirect pathwaysLinking Family, Parent, and Child Variables to Internalizing Problems in Early Child-hood. Notes: Significant paths are represented by solid lines and nonsignificant pathsby dashed lines.* p < .05, ** p < .01, *** p < .001.



mediation, we used bootstrapping procedures (MacKinnon, Lockwood, Hoffman,West, & Sheets, 2002; Shrout & Bolger, 2002), which involve taking multiple randomsamples of observations from the original sample, with replacement, and analyzingthem to provide a distribution of parameter estimates. Using EQS, we created bootstrapsamples and computed confidence intervals (CI) to assess the significance of theindirect effects. Distributions of parameter estimates tend to be skewed (Bollen &Stine, 1990; Lockwood & MacKinnon, 1998; MacKinnon et al., 2002); therefore,bias-corrected confidence intervals were computed (Efron & Tibshirani, 1993;MacKinnon, Lockwood, & Williams, 2004). Maternal negative emotionality was asso-ciated with IP through high critical parenting (95 percent CI: .121–3.308, p = .03) andthrough low positive parenting (90 percent CI: .029–1.375, p = .08). Family socioeco-nomic stress was associated with IP through low positive parenting (95 percent CI:.007–.909, p = .045).

Discussion

The purpose of this study was to test an ecological model of the development ofearly-emerging IP, postulating pathways to IP from temperamental inhibition, frommaternal parenting practices that are likely to increase children’s difficulty regulatinganxiety reactions, from maternal negative emotionality that interferes with effectiveparenting, and from family socioeconomic and structural sources of stress that arelikely to interfere with effective parenting. Using a form of integrative data analysis(e.g., Curran & Hussong, 2009), three independent samples were integrated to createa large and heterogeneous sample of children assessed on these multilevel factors.Structural analysis performed with a multiple-groups approach revealed that associa-tions were invariant across samples. Child inhibition, less positive and more criticalparenting, maternal negative emotionality, and family socioeconomic disadvantagewere found to have direct associations with IP. In addition, there was some evidence formediation by parenting. Maternal negative emotionality was associated with IPthrough more critical parenting, and both negative emotionality and socioeconomicstress were associated with IP through less positive parenting. Reciprocal relationsbetween IP and parenting dimensions were nonsignificant. These findings provide thefirst evidence in a large and heterogeneous sample that early-emerging IP may beunderstood from an ecological perspective as involving multilevel factors and that theimpact of more distal factors operates through effects on parenting.

In keeping with the model, most of the key factors hypothesized to contribute to IPwere found to be directly associated with IP. Each of these multilevel factors mayincrease the risk of IP in early childhood, and they may do so in an additive fashion.The finding of a direct link between inhibition and IP is consistent with theory andresearch identifying inhibited temperament as a vulnerability factor in the developmentof IP (e.g., Rapee, 2001). The finding that inhibition was relatively independent of theparenting and family contextual factors that were associated with IP could be inter-preted as evidence for the dispositional or genetic basis of this temperament trait(Biederman et al., 1990; Rosenbaum et al., 1993), and for the notion that inhibitedtemperament and parenting may not be strongly related in the absence of cumulativerisk (e.g., Paulussen-Hoogeboom, Stams, Hermanns, & Peetsma, 2007). Temperamen-tal inhibition is thought to reflect the behavioral manifestation of underlying biologicaldifficulties with emotion regulation (Rubin, Coplan, Fox, & Calkins, 1995). Thedevelopment of emotion regulation is fundamental to young children’s ability to



manage their stress reactions. Inhibited children’s greater physiological reactivity tonovel and social stressors and resultant avoidant coping strategies leave them prone toheightened anxiety and IP. This risk may be independent of any effect inhibitedchildren’s reactivity has on parenting, at least in the context of low cumulative risk.

Also consistent with current theory and research (Coplan et al., 2008; Hastings,Nuselovici, Rubin, & Cheah, 2010), early IP were directly associated with less positiveand more critical maternal parenting. During early childhood, children lacking externalsupport to develop emotion regulation skills due to low parental warmth or engagementor high parental rejection or negativity will be more prone to states of anxiety and lessable to manage these states. Past research has linked punitive parenting to IP (McLeod,Wood, et al., 2007), and although such was the case in the present study when consid-ering their zero-order correlation, the relation was not significant once the contributionof critical parenting was taken into account. Given the association found betweenpunitive and critical parenting, possibly owing to the element of hostility common tothese parenting dimensions, critical parenting might reflect the aspect of emotionallynegative parenting specifically salient for children’s IP more strongly than punitiveparenting. These findings support models suggesting that psychological control(behaviors such as criticism that undermine self-efficacy) is an important risk factor forIP (e.g., Barber, 1996).

The association between IP and low positive and high critical parenting is consistentwith recent research linking IP not only to negative parenting but also to a lack ofpositive parenting (Bayer et al., 2006; Coplan et al., 2008). Either or both the absenceof positive responses and the presence of negative responses may play a role in themechanisms mediating the effect of parenting on children’s risk of developing IP.Parenting practices that foster understanding of rules and show respect for childautonomy should protect against IP by contributing to the development of self-regulation and a sense of competence and self-efficacy. Conversely, parenting practicesthat convey criticism and rejection of the child should increase the risk of IP bycontributing to poor self-regulation and a sense of incompetence. Research withmultiple time points is needed to determine whether parenting does in fact have alasting influence on the development of IP and whether it occurs through theseprocesses. Such research would also permit analysis of the reciprocal influences thatmay occur between parenting and child inhibition.

Family stress and parent characteristics showed both direct and indirect associationswith IP via parenting. IP were predicted by socioeconomic stress directly and indirectlythrough less positive parenting and by maternal negative emotionality directly andindirectly through more critical parenting, associations reflecting significant mediationby parenting. These associations provide evidence that family stressors may be linkedto young children’s IP through multiple processes, in accord with ecological models(Bronfenbrenner & Ceci, 1994), that posit a chain of reciprocating influences leadingfrom socioeconomic factors at the family level to the psychological resources ofparents, the quality of parenting, and children’s psychological adjustment (Conger &Dogan, 2007; Rubin & Mills, 1991). Thus, family and parent factors may contribute tothe development of children’s IP through multiple processes, including parentingpractices, genetic transmission of emotionality from parent to child, or other learningprocesses.

In summary, child temperamental inhibition, family socioeconomic status, maternalnegative emotionality, and maternal low positive and high critical parenting appearedto be key independent factors in early-emerging IP, and maternal negative emotionality



appeared to have both direct and indirect associations with IP through critical parent-ing. Interestingly, there were no moderating effects of inhibition, possibly reflecting therelative lack of consistency in studies of the interactive contributions of temperamentand parenting to adjustment (Bates & Pettit, 2007). This study’s failure to find signifi-cant moderating effects cannot be attributed to a lack of power, inasmuch as theintegrated sample afforded excellent power to detect even small effect sizes. Rather,these results suggest that, in early childhood, separate individual factors may besufficient by themselves to increase or decrease the risk of IP. To the extent that this isso, it would appear that developmental trajectories for IP may have multiple highlyspecific starting points, in accord with the principal of equifinality. Whether problemstend to persist, worsen, or improve thereafter, however, may depend on the cumulativeand interactive effects of multiple risk and protective factors. To test this idea, researchis needed to identify the factors associated with persisting, worsening, or improvingtrajectories of IP.

There are limitations of the study that must be considered. Most of the measureswere constructed by selecting items that were parallel in content due to the use ofdifferent measurement instruments in the three separate samples. Measurement errorwas likely higher than would have been the case with identical measures, and as such,the findings may represent an underestimate of the relations that exist. Nevertheless, asdescribed in the Preparation for the Structural Model subsection of the Results section,this limitation is mitigated by the use of structural equation modeling techniquesdesigned to account for measurement error by incorporating the individual samplereliabilities in the model. The results of the analyses supported most of the hypothesesand showed equality of the model across samples, providing replication of the findingsacross three independent samples. Another limitation is the use of reports frommothers only, which may have increased the degree of correspondence between them.Although the use of a single source of information may not entirely account for thepattern of relations that were found (e.g., consistent with theory, IP were predictedmore strongly by critical than by punitive parenting), future work should replicate thefindings using multiple raters and different methods of measurement.

Also limiting the study is its cross-sectional design, which necessitates caution ininterpreting the causal directions reflected by the present results. The findings areconsistent with theory and research suggesting that temperament, parenting, andfamily context contribute to the development of IP. Moreover, reciprocal relationsbetween IP and parenting dimensions were nonsignificant. However, the possibilityremains that IP are a cause as well as a consequence of less positive or more criticalparenting. Due to their deference and appearance of vulnerability, internalizing chil-dren may elicit parental over- or under-reactions, either of which could interfere withthe development of emotion regulation skills and, therefore, contribute to anxiety.Thus, an important research direction is to examine trajectories of developmentacross childhood. Such research is also needed to determine which aspects of chil-dren’s psychological functioning serve as the mechanisms by which internal dispo-sitional vulnerabilities and external relational and contextual influences becomemanifest as IP.

Despite these limitations, the present study has important implications. Firstly, itdemonstrates the power and efficacy of integrative data analysis and the advantagesthat can be afforded to researchers by combining complementary samples to investi-gate complex developmental questions. Secondly, the findings raise the possibility thatsingle risk factors could be starting points for the development of IP. Single risk factors



may lead to the worsening of IP through ripple effects that elevate other risk factorscompounding the child’s vulnerability. Thirdly, the findings highlight the need formore investigation of the role parenting may play in the development of IP. Parentingpractices may be a key link in the chain of effects leading to or away from youngchildren’s IP, with family stressors affecting parenting practices both by decreasingpositive parenting and by increasing negative parenting. Whether the associationsfound in the present study are in fact attributable to these processes awaits longitudinalanalysis.

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Acknowledgments

This research was supported by Canadian Institutes of Health Research grant MOP-74642awarded to the team, and by grants from Health Canada (Child and Youth Division), awarded toLisa Serbin and colleagues from the Concordia Longitudinal Risk Project, from Social Sciencesand Humanities Research Council of Canada and the Fonds de la Recherche en Santé du Québecgrants awarded to Paul Hastings for the Daycare and Preschool Adjustment Study, and fromCanadian Institutes of Health Research grant MOP-57670 to Rosemary Mills for the Shame inChildhood Study. We thank the children and parents who made this research possible andgratefully acknowledge the assistance of Farriola Ladha, Claude Senneville, Nadine Girouard,and Bobbi Walling. We also thank Keith Widaman for statistical consultation.



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Temperamental, parental and contextual contributors to early