predictors of preschool children's compliance behavior in early childhood classroom settings

Applied Developmental Psychology 25 (2004) 439–457

Predictors of preschool children’s compliance behavior in early

childhood classroom settings

Theodore D. Wachs*, Pinar Gurkas, Susan Kontos

Department of Psychological Sciences, Purdue University, West Lafayette, IN 47907, USA

Abstract

Working within a person–process–context framework, we investigated the relation of the level of preschool

children’s compliance to child temperament, caregiver–child interaction in the child care setting, child care

quality, and contextual chaos. Participants were 86 preschoolers and their teachers. Our database included both

questionnaires and observations in child care centers. Child compliance was predicted by child temperament,

caregiver behaviors, daycare quality, and level of daycare chaos. Child difficult temperament moderated the

influence of caregiver behaviors but did not moderate the influence of environmental chaos on child

compliance. Caregiver behaviors did not act to mediate the relations between environmental chaos and

compliance. The predictive variance increased as process and context variables were added to the initial

person predictor model (additive coaction), and different sets of predictors were associated with committed,

situational, and passive noncompliance. The present results indicate that different combinations of main effects

predicted different outcomes, illustrating how multiple predictors relate to different aspects of child

compliance.

D 2004 Elsevier Inc. All rights reserved.

Keywords: Temperament; Environmental chaos; Preschool quality; Compliance; Child care

1. Introduction

The environmental context within which the child develops is a crucial component for explaining the

nature of child development. Following the theoretical guidance of Bronfenbrenner (1989, 1999), there

has been an emphasis on going beyond the family environment to understand environmental

contributions to development. Bronfenbrenner’s person–process–context approach analyzes variability

0193-3973/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.appdev.2004.06.003

* Corresponding author.

E-mail address: [email protected] (T.D. Wachs).

T.D. Wachs et al. / Applied Developmental Psychology 25 (2004) 439–457440

in developmental outcomes as a function of the characteristics of the individual and environment

(Bronfenbrenner, 1989). Within this framework, the microsystem, a face-to-face setting composed of

activities, roles, or relations experienced by the child, is the basic starting point for the contextual

analysis of child development (Bronfenbrenner, 1989).

The aim of the present study was to adopt a person–process–context framework to help illuminate

the nature of preschool children’s compliance. Child temperament, caregiver–child interaction in the

child care setting, child care quality, and child care chaos were chosen as person, process, and context

components, respectively. According to the person–process–context approach, none of the three

components in isolation will be sufficient to explain developmental outcomes. In addition, the

contributions of person, process, and context may depend on the nature of the outcome variable as

well (Bronfenbrenner & Ceci, 1994).

Children’s compliance behavior is viewed as a marker for the child’s internalization of the prevalent

norms and values of their society (Kochanska, 2002). In the model developed by Kochanska and

Aksan (1995), there are two qualitatively different types of compliance. Committed compliance,

defined as children’s wholehearted compliance with a request, is regarded as the initial step for the

internalization of behavior standards that are necessary for proper functioning of the individual within

society (Kochanska, 1993). Situational compliance, defined as complying with requests due to the

presence of an authority figure, is compliance without sincere commitment and is not associated with

internalization. In the model, there also are three different types of noncompliance: defiance, passive

noncompliance, and negotiation. Defiance is defined as noncompliance by overt refusal; the child

usually does the opposite of what is required and shows anger and negative affect. Passive

noncompliance is when the child pretends as if the request was not made; and neither complies

nor overtly refuses to comply. Negotiation is defined as a child’s asking for explanations, offering

alternative solutions, and attempting to reach a new agreement with the caregiver. To the extent that

these qualitatively different forms of compliance reflect young children’s characteristic reaction

patterns to caregiver demands, we would expect children who were high in committed compliance

to display lower levels of both situational compliance and noncompliance (Kochanska & Aksan,

1995). What is the role of person, process, and context in explaining individual variability in these

different aspects of compliance?

1.1. Person: Compliance and temperament

Theoretically, difficult temperament is regarded as a risk factor for the development of behavior

problems (e.g., Kochanska, 1991). Children who display high reactivity and low regulatory skills are

more likely to be defiant (Stifter, Spinrad, & Braungart-Rieker, 1999). However, in the NICHD Early

Child Care Research Network study, mother-rated difficult temperament in infancy was not a

significant predictor either of child compliance at 3 years of age or of caregiver-reported behavior

problems and conflict with adults at 4.5 years of age (NICHD Early Child Care Research Network,

1998, 2003). These findings may reflect the fact that mother-rated temperament may not generalize

to children’s behavior in the child care setting (NICHD Early Child Care Research Network, 1998).

The modest level of correlation between parent- and teacher-rated temperament in other studies

supports this explanation (Rothbart & Bates, 1998). In the present study, we minimize generalization

problems by relating teacher ratings of child temperament to children’s compliance in preschool

settings.

T.D. Wachs et al. / Applied Developmental Psychology 25 (2004) 439–457 441

1.2. Process: Compliance, parenting, and caregiver–child interactions in child care

The majority of the studies assessing influences on children’s compliance have focused on

parenting practices. Parental responsiveness and control have been shown to have consistent patterns

of association with child compliance (e.g., Chamberlain & Patterson, 1995; Kochanska & Aksan,

1995; Londerville & Main, 1981; Schaffer & Crook, 1980; Wahler, 1997). However, relations between

parenting and compliance have also been shown to vary, depending on what aspects of compliance are

being assessed. Committed compliance is associated with responsive parenting, and mutually

responsive relations in toddlerhood are influential in predicting children’s internalization at 4 years

of age (Kochanska, 1997; Kochanska, Aksan, & Koenig, 1995). Situational compliance is either not

associated with parenting or it is associated with negative control. Passive noncompliance is either not

related to a specific control technique or it is associated with negative control and decreased guidance

(Braungart-Rieker, Garwood, & Stifter, 1997; Kuczynski, Kochanska, Radke-Yarrow, & Girnius-

Brown, 1987). Negotiation as a type of noncompliance is associated with mutually responsive parent–

child relationships. In negotiating with his (her) parents, verbal refusals of the child foster and

necessitate self-assertion of the child, whereas this relation does not hold for defiance (Crockenberg &

Litman, 1990). In addition, the child’s temperament may moderate the relation between specific

parenting practices and specific types of compliance (Himmelfarb, Hock, & Wenar, 1984; Kochanska,

1995; Kochanska & Aksan, 1995). Committed compliance is more likely to occur when parents use

gentle control with their fearful children, whereas uninhibited children’s committed compliance is

related to attachment security (e.g., Kochanska, Tjebkes, & Forman, 1998). For temperamentally

difficult children, the use of commands without explanations is the least effective method for eliciting

compliance (Martin & Bridger, 2000).

However, parenting practices are not the only proximal processes related to children’s compliance.

Major increases in maternal employment and an increased percentage of single-parent families make

nonmaternal child care an important factor in children’s development (Clarke-Stewart & Allhusen, 2002;

Lamb, 1998). Teacher–child interactions in childcare settings have also been shown to play a role in the

development of children’s compliance behaviors. In nonparental care settings, compliance is associated

with more social exchange and positive affect between adults and children and less control of children

(Howes & Olenick, 1986). In addition, Clarke-Stewart, Gruber, and Fitzgerald (1994) have reported that

caregivers’ use of controlling commands and punishments was more important in predicting child

compliance than the level of one-to-one attention that children received from caregivers. Children who

received less controlling commands and punishments were more compliant, and the association between

caregiver’s discipline and compliance was parallel to the associations found for parents during home

observations of the same children. In summary, the research findings about children’s interaction with

their parents, as well as their teachers, show that use of commands without explanations seems to be the

least effective method in eliciting compliance from children, especially when adults are interacting with

temperamentally difficult children. One limitation to these studies is that they typically treat compliance

as a unitary construct. This study examines multiple forms of compliance.

1.3. Context: Compliance, quality of child care and environmental chaos in child care setting

Quality of child care is partially defined by proximal process interactions between children

and caregivers. In high-quality child care settings, teachers interact with children in an


informative way, they encourage verbal interaction, they are not harsh with the children, and

they show more positive affect (e.g., Bryant, Lau, & Sparling, 1994; Clarke-Stewart, 1992;

NICHD Early Child Care Research Network, 2000). However, quality of child care is also

measured by distal contextual features of the child care environment. Such features include

group size, teacher–child ratios, and furnishings (Friedman & Amadeo, 1999). High-quality

nonparental care is also defined by settings where there is a well-organized and stimulating

physical environment and where the classes are not crowded (NICHD Early Child Care Research

Network, 2000).

Nonparental care experience has a facilitating influence on the development of compliance, especially

when the quality of care is high (e.g., Howes & Olenick, 1986; Peisner-Feinberg & Burchinal, 1997;

Phillips, McCartney, & Scarr, 1987). The NICHD Early Child Care Research Network’s (1998, 2002)

examination of child care effects on children’s compliance revealed that higher quality nonmaternal care

experienced by children during the first 3 years of life was associated with higher child compliance with

maternal requests, greater temptation resistance in the laboratory, and fewer caregiver-rated behavior

problems when children were 3 years old.

Quality of care, as defined above, is not the only aspect of context that could influence children’s

compliance behaviors. A number of researchers investigating the role of environmental influences

upon development have focused on the physical environment of the child, with specific reference to

the contextual dimension of environmental chaos (Evans, 1999; Wachs, 1989; Wohlwill & Heft,

1987). Chaotic environments are characterized by high levels of noise, crowding, and environmental

traffic (many people coming and going) and by a lack of physical and temporal structure (few

regularities or routines, nothing has its place). Environmental chaos has been associated with a variety

of decrements in children’s competence, including impairments in cognitive performance, attention,

and motivation (Wachs, 1992; Wachs & Corapci, 2003). A major reason for this pattern of findings

appears to be that environmental chaos is associated with greater parental restrictiveness, as well as

with decreased verbal and emotional responsiveness of parents to their children and decreased

involvement in children’s activities (Corapci & Wachs, 2002; Evans, Maxwell, & Hart, 1999; Wachs

& Camli, 1991). In addition, interactions have also been reported in the environmental chaos

literature, with both boys and temperamentally difficult children being more reactive to environmental

chaos (Wachs, 1992).

Some dimensions of environmental chaos, such as teacher/child ratio, are included among measures

of childcare quality (Clarke-Stewart & Allhusen, 2002; Lamb, 1998). In addition, a few studies have

reported that indices of environmental chaos in daycare, such as higher noise (Hambrick-Dixon, 1988)

and crowding (Maxwell, 1996), are related to decrements in young children’s performances on measures

of visual attention. However, we have not been able to find any studies that specifically focus on the

consequences of environmental chaos in child care settings for socialization, nor that ask about the

unique predictive contribution of environmental chaos over and above the contributions of child care

quality per se.

Working within a person–process–context framework, we hypothesize that variability in young

children’s compliance behavior in child care settings will be a joint function of person (teacher-rated

child temperament), process (teacher–child interaction patterns), and context (child care quality and

child care chaos). In addition to this overall hypothesis, we also address how variability in person,

process, and context translates into variability in children’s compliance behavior. Based on our

review and discussion of the compliance literature, there are three possible ways in which person,


process, and context variables could influence child compliance, which we systematically test in this

study.

� Given interactions between parenting and child temperament (e.g., Kochanska, 1995), child

temperament may act to moderate relations between child compliance and either caregiver control

strategies or level of childcare chaos.� Given a relation between environmental chaos and developmentally relevant aspects of parental

behavior (e.g., Corapci & Wachs, 2002; Evans et al., 1999), caregiver control strategies may act to

mediate relations between childcare chaos and compliance.� Given that different aspects of parental behaviors predict different types of child compliance, there

may also be specificity with different combinations of person, process, and context, predicting

different types of child compliance (Wachs, 1992). Alternatively, person, process, and context

contributions to child compliance may follow a pattern of additive coaction (multiple independent

main effects—Rutter, 1983).

2. Method

2.1. Participants

Participants were 86 preschoolers (50 males, 36 females) and their mothers. The children’s ages

ranged from 31.3 to 78.4 months (mean = 50.3; SD = 11.6). The majority of the mothers (63.9%) had

completed a college degree or had professional training and they were employed, either full (58.3%) or

part time (16.7%). The average maternal age was 32.8 years (SD = 5.4).

The directors of 15 centers in a rural Midwest county were initially contacted by letter. Follow-up

phone calls were made with the center directors to determine which centers would agree to

participate in the study. Eight directors gave their consent to participate in the study. Letters

indicating the purpose of the study were then sent to the teachers in each classroom. All of the

teachers gave their consent to participate. Each of the teachers had a college degree that focused on

education, although not necessarily in early childhood. After teachers gave their consent, parent

consent forms were distributed in the classrooms. Among the 270 parents who received the consent

forms, 115 gave consent to participate (42.5%). This level of agreement to participate is similar to

what has been reported in other studies involving childcare populations (e.g., Holloway & Reichhart-

Erickson, 1989; Lloyd & Howe, 2003). Because of the time required to complete observer training,

we were able to test only 86 of the 115 children whose parents had given consent during the data

collection phase before the start of the summer vacation period, when preschool children were

generally unavailable.

Children were enrolled in 23 classrooms in these eight centers. With one exception, all of the centers

participating in the study were nonprofit centers. Among the nonprofit centers, three were private

nonprofit, two were affiliated with a university, one was a childcare ministry, and one was director

owned. Three of the centers provided half-day service, and the rest provided full-day care. Information

about the centers and children’s entry into and participation in the centers are presented in Table 1. As

can be seen from Tables 1 and 3, which presents data on teacher behaviors, a wide range of quality in

classrooms was captured, despite sample selectivity.

Table 1

Descriptive statistics on participants and centers

Children Mean (SD)

Age (months) at first nonparental care 20.4 (12.1)

Time spent in current child care center (months) 9.9 (5.4)

Children per classroom 14 (3)

Children/Adult ratio in the classroom 5.4 (2.7)

Centers Number of centers (classrooms) Number of children

Private nonprofit 3 (4) 31

Affiliated with University 2 (14) 38

For profit 1 (2) 6

Childcare ministry 1 (2) 5

Director-owned center 1 (1) 6


2.2. Measures and procedure

Both questionnaires and observations were used to collect data. Questionnaires concerning family

demographics and child temperament were sent to children’s homes and returned to the childcare center

after completion. Head teachers completed questionnaires about child temperament and environmental

chaos in their childcare center. Observations of child compliance and caregiver control strategies were

made during clean-up and group times in the classrooms. Observations also were used to measure

childcare quality.

2.2.1. Child compliance

On the basis of pilot observations, we modified the manual for child compliance in the laboratory

setting of Kochanska (1999) for use in the child care setting1. Child compliance was coded using a 30-s

observation, 15-s record time sampling procedure during regularly scheduled cleanup, and group times

in each center. These times were chosen on the basis of our pilot observations indicating that these were

the periods during which there were the highest level of compliance demands upon children (e.g., put

away toys during cleanup time, sit quietly during group time). The exact duration of time spent in

cleanup and group time varied across centers. The duration of cleanups ranged from 5 to 10 min, and the

duration of group times ranged from 15 to 30 min. Child compliance was coded using the five mutually

exclusive categories defined by Kochanska: committed compliance, negotiated compliance, situational

compliance, passive noncompliance, and defiance (overt refusal). The operational definitions of specific

categories used in observations and descriptive statistics of children’s compliance scores can be found in

Table 2. The total frequencies of defiance and negotiation were less than 1% of the total observation

scans, and these variables had highly skewed distributions. Because of their very low occurrence rate,

defiance and negotiation were not included in the analyses.2

1 A copy of our revised compliance manual can be obtained by writing to the first author at the address indicated.2 Defiance and overt refusal have frequencies of about 7% in studies made in laboratory settings where one child is

interacting with one adult at a time (e.g. Kochanska & Aksan, 1995). The relatively lower occurrence of defiance and

negotiation in our study may reflect contextual differences between a preschool and a laboratory environment.

Table 2

Descriptive statistics and definitions of child compliance codes for cleanup and group times

Child compliance

codes

Code definitions Mean %

(SD)

Committed

compliance

Child complies with the requests without further directives through most of the segment.

During the cleanup and group times child is on task for more than 25 s in a 30-s

observation period.

65 (15)

Situational

compliance

Child fails to comply in the absence of caregiver control. Number of caregiver prompts

do not exceed three times per segment. Child is on task for 15 s or more and the number

of reminders of the request does not exceed three times in 30 s.

16 (07)

Passive

noncompliance

Child does not comply unless prompted and when prompted the most likely response is

to ignore the directive. Child is on task for less than 15 s.

18 (12)

Negotiation Child overtly refuses to comply but does not show any anger or negative affect and the

child is noncompliant for the majority of the observation block.

Mean < 1

Defiance Child does not comply and if prompted the child response is to resist by defiance with

poorly controlled anger or overt expression of frustration in body language or voice.

Mean < 1


For compliance, interobserver reliability assessed over the course of the study (intraclass correlation)

was .92. The percentage of time spent in observations of each of the two childcare contexts was

determined according to the schedules of daycare centers. Each child was observed on at least three

different days within 2 months. During observations, observers collected up to ninety-four (range: 10–

94) 30-s observation blocks per child. The median number of observation blocks was 48. There were 30

or fewer observation blocks for eight of the participants. Our analysis indicated that there were no

significant differences between these eight children and the rest of the participants regarding the outcome

or predictor measures included in this study. Percentage scores were created using the following

procedure: The frequency of each category of compliance was divided by the total frequency of the five

categories of compliance.

2.2.2. Child temperament

The teacher form of the Revised Temperament Assessment Battery for Children (TABC-R; Martin &

Bridger, 2000) was used as our measure of child temperament. There are 29 items in the teacher form.

Items are rated on a Likert-type seven-point scale. The teacher form is composed of four scales related to

inhibition, negative emotionality, lack of task persistence, and activity level. Test–retest stabilities of the

scales of the teacher form over a year are within a range from 0.47 to 0.71. In this study, principal

components analysis of the teacher form with varimax rotation confirmed the factors previously

identified by Martin and Bridger (2000). Reliabilities (Cronbach’s alpha) of the scales of the teacher

form were within a range from .82 to .94. For each child, a temperamental difficulty score was created by

summing the negative emotionality, activity, and lack of task persistence scores. The average teacher-

rated temperamental difficulty score for the children was 67.48 (SD = 24.98). The parent form of the

TABC-R was also administered but, as noted in the Introduction, was not used as a predictor to minimize

generalization problems.

2.2.3. Caregiver control strategies

There were five mutually exclusive categories of caregiver control strategies assessed during the

cleanup and group times: no interaction, social exchange, gentle guidance, control, and forceful


negative control. Caregiver control strategies were coded if an adult caregiver (i.e., head teacher,

assistant teacher, or teachers’ aide) was within 3 ft of the target child or was directly addressing a

child. The operational definitions of the codes and relevant descriptive statistics are presented in Table

3. Forceful negative high control was not observed. Social exchange, gentle guidance, and control

scores were created by dividing the frequency of each category by the total frequency of the four

remaining categories of teacher control strategies. Preliminary analyses revealed that only control

predicted compliance behavior. Therefore, the other strategies were dropped from further analysis. For

control, interobserver reliability assessed over the course of the study (intraclass correlation) was equal

to .77.

2.2.4. Childcare quality

The Early Childhood Environment Rating Scale (ECERS, Harms, Clifford, & Cryer, 1998) was used

to measure quality of daycare. The ECERS is composed of 37 items divided into seven subscales

addressing personal care routines, furnishings, and display, language/reasoning, fine/gross motor,

creative activities, social development, and adult needs. Items are rated from 1 (inadequate) to 7

(excellent). The interobserver reliability of the scale is reported as .83 (Harms et al., 1998). Item ratings

were summed and divided by the total number of items to obtain an average item score. In this study, the

average score was 6.19 (SD = 1.03), with scores ranging from 3.74 (adequate quality) to 6.91 (excellent

quality). The observer doing all the ECERS observations was trained in classrooms to use this

instrument to a criterion of 90% interobserver consistency. The ECERS scores were obtained within

a week after observations were completed on child compliance and teacher control strategy. Information

related to number of children and number of teachers in the class was collected by the observers in each

child care center during observation times.

2.2.5. Environmental chaos

We revised the Matheny, Wachs, Ludwig, and Phillips (1995) Confusion, Hubbub, and Order Scale,

which was developed for use in the home, for use in daycare settings. Our revised scale, which we called

Life in Early Childhood Programs (LECP, Kontos & Wachs, 2000), was used as our measure of the level

of chaos in the early childhood classroom setting. The LECP is a 16-item scale, in true–false format,

assessing teachers’ perceptions of use of space, crowding, environmental traffic, and the degree of

control and organization in the classroom. The test–retest stability of this instrument is .87 (Kontos &

Table 3

Descriptive statistics and definitions of teacher behavior codes for cleanup and group times

Caregiver codes Code definitions Mean % (SD)

No interaction There is no verbal or physical overture from the mother to the child throughout the segment 13 (10)

Social

exchange

Caregiver interacts with the child (e.g., teaching and playing) but s/he does not attempt to

control the child with respect to the specific requirement that s/he made from the child

55 (16)

Gentle

guidance

No forceful verbal or physical control is present. Caregiver uses polite suggestions, hints,

playful comments, or reason to make the child comply with the request

12 (10)

Control Caregiver issues commands or prohibitions for the majority of the segment. Caregiver

gives firm directions as to how to proceed.

20 (09)

Forceful

control

Caregiver directs the child’s behavior in a forceful /power assertive manner. S/he may use

threats or negative comments

Not observed.


Wachs, 2000). The reliability (Cronbach’s alpha) of the scale was .67. Total scores on LECP in the

present study ranged from 0 to 9 (mean = 3.91, SD = 2.35), with higher scores indicating higher levels of

chaos.

3. Results

3.1. Demographic variables

Before addressing specific hypotheses, we used correlations to identify which variables should be

included as covariates in subsequent analyses. Our initial analysis focused on demographic variables.

The demographic variables of interest were age of child, age at first attendance in child care, length of

time spent in current child care, mother’s working hours, and mother’s education. None of these

variables was significantly correlated with compliance or with any of our predictor measures (child care

quality, chaos, caregiver’s use of control or child temperament). Furthermore, although the age of child is

often used as a covariate in laboratory studies of compliance, in the present study, age did not moderate

the relations between predictors and outcomes. Therefore, these variables were not included as

covariates in the rest of the analyses.

Because of differences in the quality of university affiliated versus other types of child care settings

described in Table 1, we also analyzed whether there were differences in our predictor or outcome

measures as a function of type of child care setting. With two exceptions, results indicated no differences

in our predictor or outcome measures as a function of type of child care setting. The exceptions were

chaos and quality scores. As would be expected, community child care centers were rated as

significantly more chaotic [F(1, 21) = 6.81, p < .05] and of significantly lower quality then university

based centers [F(1, 21) = 74.35, p < .01]. Because center type was unrelated to outcomes, we did not

analyze for center type as a covariate.

Because of variability in the number of times the children were observed, we also correlated the

number of observation blocs per child with our predictor and outcome measures. The number of

observation blocs was correlated with adult caregivers’ use of control (r = .31, p < .05) but was

unrelated to any of our other predictors or to any of our child compliance outcome measures. Therefore,

we did not enter the number of observation blocs as covariates in our analyses.

Two separate MANOVAs were applied to examine gender differences. A MANOVA with three

forms of compliance/noncompliance as dependent variables and child gender as the between-

subjects factor revealed no significant gender differences. A MANOVA with childcare quality, child

care chaos, adult’s use of control, and child temperament as dependent variables and child gender

as the between-subjects factor indicated a strong trend toward significant gender differences

[F(4, 76) = 4.00, p < .06].3 Breakdown of this analysis indicated that males were more likely

than females to be in lower quality [F(1, 85) = 5.39, p < .05] and more chaotic childcare centers

[F(1, 85) = 5.86, p < .05]. Not surprisingly, head teachers rated males as having a more difficult

temperament than females do [F(1, 85) = 4.04, p < .05]. Although gender was related to a number

3 Tables documenting the results of our analyses for demographic, center, and gender effects can be obtained from the first

author at the address indicated.


of our predictors, it was unrelated to our outcome variables. Thus, we did not analyze for gender as

a covariate.4

3.2. Initial hypothesis: Do person, process, and context all contribute to child compliance?

Correlations were used to test our overall hypothesis predicting the relations between child

compliance and measures of person, process, and context. Table 4 provides a summary of the

associations between child compliance, child temperament, adult caregivers’ use of control, child care

quality, and child care chaos. When basing conclusions on a correlation matrix, there is the

possibility that, while some individual correlations may be large, the overall matrix could still be

nonsignificant. To deal with this possibility, we utilized the Steiger (1980) procedure to test the null

hypothesis that the overall matrix shown in Table 4 is essentially zero order. Our analysis yielded a

significant chi square of 292.49 (p < .01, df = 21), allowing us to reject the null hypothesis and

minimizing concern that our interpretation of individual correlations was based on capitalizing on

chance findings.

As shown in Table 4, committed compliance was significantly and negatively correlated with both

situational and passive noncompliance; situational and passive noncompliances were unrelated. Both the

directionality and magnitude of this pattern of relations are remarkably close to the pattern reported by

Kochanska and Aksan (1995). To further elucidate the nature of relations between our different

compliance categories, we utilized a principal components analysis with Varimax rotation. Two factors

were identified. The first (eigenvalue = 1.99) was characterized by high loadings for committed

compliance (� .92) and passive noncompliance (.99). The smaller second factor (eigenvalue = 1.01)

included only situational compliance (.99). A similar structure emerged when oblique rotation-Promax

was utilized, and there was only a modest correlation between the two factors (r = .23). This pattern

suggests that committed and passive noncompliance represent bipolar opposites of a single dimension

that is different from what is assessed by situational compliance.

As also shown in Table 4, head teachers’ rating of the child as having a difficult temperament was

positively associated with passive noncompliance and negatively associated with committed compli-

ance.5 Adult caregivers’ use of controlling commands was positively associated with passive noncom-

4 Because gender was related to a number of our predictors, we also analyzed for possible moderating effects of gender. Our

analysis indicated that gender did not moderate either the relation of quality or control to compliance. However, gender did

moderate the pattern of relations between temperament and compliance and between chaos and compliance. For boys, higher

levels of difficult temperament were associated with decreased committed compliance and increased passive noncompliance. For

girls, while difficult temperament was unrelated to committed compliance, having a difficult temperament was associated with

lower levels of passive noncompliance [for committed compliance, change in R2 associated with the Gender�Temperament

interaction equals .17, F(3, 77) = 10.12, p < .01); for passive noncompliance, change in R2 associated with the Gender�Temperament interaction equals .18, F(3, 77) = 12.22, p < .01]. For boys, as environmental chaos increased, committed

compliance decreased and passive noncompliance increased. Environmental chaos was unrelated to compliance patterns for girls

[for committed compliance, change in R2 associated with the Gender�Chaos interaction equals .17, F(3, 82) = 6.73, p < .05; for

passive noncompliance, change in R2 associated with the Gender�Chaos interaction equals .14, F(3, 82) = 6.55, p < .01]. The

latter pattern of findings are consistent with previous research showing a greater sensitivity of males than females to

environmental chaos (Wachs, 1992).5 Although not utilized as a formal predictor, our results do confirm the findings of the NICHD Early Child Care Research

Network (1998, 2003) showing that parent-rated infant temperament is not a predictor of child compliance in child care settings.

Table 4

Associations among forms of compliance, child temperament, caregiver’s use of control, child care quality, and child care chaos

1 2 3 4 5 6 7

(1) Committed compliance � 0.45** � 0.88** � 0.31** � 0.38** 0.26* � 0.28**

(2) Situational compliance � 0.01 0.12 � 0.03 � 0.29** 0.05

(3) Passive noncompliance 0.26* 0.42** � 0.16 0.31**

(4) Difficult temperament 0.28* � 0.14 0.20

(5) Caregiver’s use of control � 0.28** 0.13

(6) Child care quality � 0.45*

(7) Child care chaos

*p < .05. **p < .01.


pliance and negatively associated with committed compliance. Child care quality was positively

associated with committed compliance and negatively associated with situational compliance. Child

care chaos was positively associated with passive noncompliance and negatively associated with

committed compliance.

3.3. Does temperament moderate the relation of child compliance to childcare chaos or caregivers’ use

of control?

We initially tested to determine whether the relation between chaos and compliance would be

stronger for children with difficult temperaments. Separate hierarchical multiple regression analyses

were used to test for multiplicative interactions for committed, situational, and passive noncom-

pliance as outcome variables. The criteria described by Jaccard, Turrisi, and Wan (1990) provided

the basis for testing the interaction effects. The predictors were centered, and the interactive effect

was included in the analyses after the inclusion of temperament and chaos. There was no

significant interaction between child care chaos and head teacher ratings of child temperament

in predicting compliance, indicating that temperament was not acting as a moderator for these

variables.

We then tested whether child temperament could act to moderate relations between adult caregiver

control and child compliance. A summary of the regressions used to test for moderating interactions

are presented in Table 5. As shown in Table 5, our analysis indicated that there were significant

interactions between adult caregivers’ use of controlling commands and temperament as predictors of

children’s compliance. To explore the nature of the interaction, the relation between compliance and

adult caregivers’ control was examined across three levels of temperamental difficulty. As

recommended by Jaccard et al. (1990), low difficulty was defined as being one or more standard

deviations below the mean level of temperamental difficulty. Average difficulty was equal to the

mean. High difficulty was one or more standard deviations above the mean. Adult caregivers’ use of

control was a significant predictor of committed compliance and passive noncompliance for children

who were highly difficult. Specifically, higher levels of adult caregiver control were related to less

committed compliance and greater passive noncompliance for children with difficult temperaments.

Adult caregivers’ use of control was not a significant predictor of committed or passive

noncompliance of children with average or nondifficult temperament.

Table 5

Temperament, caregiver’s use of control, and their interaction as predictors of compliance

R2 Fch df b

Dependent variable: Committed compliance

(Step) Predictor

(1) Difficult temperament .09 8.21** 1, 79 � .22*

(2) Caregiver’s use of control .18 8.25** 2, 78 � .17

(3) Difficult temperament�Control .31 14.24** 3, 77 � .38**

Overall F(3, 77) = 11.46, p < .001

Dependent variable: Passive noncompliance

(Step) Predictor

(1) Difficult temperament .07 5.86* 1, 79 .15

(2) Caregiver’s use of control .20 13.04** 2, 78 .25*

(3) Difficult temperament�Control .31 11.96** 3, 77 .35**

Overall F(3, 77) = 11.51, p < .001

The increase in explained variance, measured by Fch, is reported for every step. Beta weights are reported for the full model.

*p < .05. **p < .01.


3.4. Does adult caregivers’ use of control mediate the relation between child care chaos and

compliance?

The criteria described by Baron and Kenny (1986) provided the basis for testing this hypothesis.

Adult caregiver control during the cleanup and group times can act as a mediator in the chaos and

compliance relationship if three conditions are met. First, variations in adult caregiver behavior should

significantly account for variations in child compliance. Second, variations in chaos should significantly

account for variations in adult caregiver behavior. Third, when adult caregiver behavior is controlled, the

previously significant relations between chaos and compliance should become nonsignificant. The

second condition for mediation was not met, in that child care chaos was not a significant predictor of

adult caregiver control. Since this criterion was not met, our results indicate that relations between chaos

and compliance are not a function of chaos effects on adult caregivers’ use of controlling commands.

3.5. Do person, process, and context predictors of compliance involve additive coaction and specificity?

Three separate hierarchical regression analyses were applied to the data. These analyses were

designed to provide information on whether our person, process, and context predictors were uniquely

related to different types of child compliance. Adult caregivers’ use of control was included as a

predictor after the inclusion of temperament and child care quality and before the inclusion of child care

chaos. In test regressions, the significance of the predictors did not change when the entry order of these

predictors to the regression equations were varied. Committed, situational, and passive were the response

variables. A summary of the regressions is presented in Table 6.

The full models explained between 18% and 31% of variance in compliance. In all models, significant

increases in predictive variance occurred beyond the first step in each regression. Adult caregivers’ use

of control was the only unique significant predictor of committed compliance when all the predictors

Table 6

Temperament, child-care quality, teacher’s use of control, and child care chaos as predictors of compliance

R2 Fch df b

Dependent variable: Committed compliance

(Step) Predictor

(1) Difficult temperament .09 8.21** 1, 79 � .18

(2) Child care quality .14 4.43* 2, 78 .01

(3) Caregiver’s use of control .21 5.99* 3, 77 � .29**

(4) Child care chaos .24 3.06 4, 76 � .23

Overall F(4, 76) = 5.85, p < .001

Dependent variable: Situational compliance

(Step) Predictor

(1) Difficult temperament 0.01 1.14 1, 79 0.16

(2) Child care quality 0.10 7.40** 2, 78 � 0.53**

(3) Caregiver’s use of control 0.13 2.74 3, 77 � 0.21

(4) Child care chaos 0.18 4.48* 4, 76 � 0.29*

Overall F(4, 76) = 4.17, p < .01

Dependent variable: Passive noncompliance

(Step) Predictor

(1) Difficult temperament 0.07 5.86* 1, 79 0.01

(2) Child-care quality 0.08 1.17 2, 78 0.24

(3) Caregiver’s use of control 0.20 11.65** 3, 77 0.41**

(4) Child-care chaos 0.31 11.25** 4, 76 0.42**

Overall F(4, 76) = 8.38, p < .001

The increase in explained variance, measured by Fch, is reported for every step. Beta weights are reported for the full model.

*p < .05. **p < .01.


were included. Adult caregivers’ use of control was negatively associated with committed compliance.

Both quality and chaos were unique and significant predictors of situational compliance. Childcare

quality and chaos were negatively associated with situational compliance. Adult caregivers’ use of

control and childcare chaos uniquely predicted passive noncompliance, which was positively associated

with both of these predictors. The addition of predictors into regressions resulted in an approximate 10%

increase in predictive variance.

4. Discussion

The aim of this study was to examine the contribution of child temperament, caregiver–child

interactions, child care quality, and child care chaos to preschool children’s compliance behavior within

a person–process–context framework. Consistent with this framework, each predictor was related to

variability in compliance behavior. Specifically, our results also show that head teacher ratings of child

temperament in child care are associated with observer-rated committed and passive noncompliance.

Supporting the importance of the person variable, children with difficult temperaments were less likely

to be committed compliant and more likely to be passive noncompliant.


The process component, adult caregivers’ use of control, was associated with the committed and

passive noncompliance of children. Children were less likely to show committed compliance and more

likely to show passive noncompliance when adult caregivers used more controlling commands. The

adult caregiver’s use of controlling commands and hints of punishment were more important than the

other categories of caregiver–child interaction, such as social exchange and gentle guidance in

predicting compliance. It can be argued that in the context of daycare, children who do not hear the

adult caregiver’s commands could be scored as passively noncompliant, given the coding criteria for this

variable. This argument does not seem plausible, given the significant and negative correlation of

passive noncompliance with committed compliance and the significant and positive correlation of

passive noncompliance with difficult-child temperament. Given this pattern of associations, it is less

likely that passive noncompliance is simply reflecting an inability to hear a command, but rather reflects

the child’s purposefully ignoring commands.

Child care quality and chaos were the two context components in this study. Higher child care quality

was associated with increased committed compliance and decreased situational compliance of children.

Child care chaos was associated with decreased committed compliance and increased passive

noncompliance. Whereas overall child care quality, as measured by the ECERS and our measure of

chaos in daycare, are moderately correlated (see Table 4), our results shown in Table 6 also indicate that

each measure contributes unique predictive variance, even after statistically partialling out variance

associated with the other measure. These findings indicate that the LECP and ECERS are complemen-

tary measures of daycare context. The different roles played by these measures in predicting different

types of compliance necessitates the inclusion of both types of measures to have a better understanding

of how context contributes to developmental variability in children.

By documenting person, process, and context contributions to children’s compliance behavior in

daycare, our results emphasize the need to take a multidimensional approach to understanding individual

differences, rather than looking at isolated predictor components. In addition to confirming the validity

of a person–process–context framework, our results provide evidence on the nature of person, process,

and contextual contributions to variability in young children’s compliance behavior. Our results support

the hypothesis that individual differences in temperament moderate the impact of adult caregivers’ use of

control in predicting children’s compliance. Children with difficult temperaments are more likely to

display passive noncompliance and less likely to display committed compliance when adult caregivers

use more controlling commands and hints of punishment in their interactions with the children. These

results may reflect a reduced sensitivity of temperamentally difficult children to punishment cues (Martin

& Bridger, 2000). One implication of these findings is the need to find alternative ways of eliciting

committed compliance from children with difficult temperaments. Studies involving mother–child

interactions have shown that secure attachment relations, rather than parental disciplinary strategies, are

particularly salient for promoting committed compliance of uninhibited preschool children (Kochanska,

1995). However, other studies have shown that some degree of parental control is important to avoid

long-term adjustment consequences for young children with resistant temperaments (Bates, 2001). Taken

together with the results from the present study, this pattern of findings suggests that a combined strategy

of firm but gentle control, plus an emphasis on positive cooperative relations between teachers and

children, might be a pathway through which temperamentally difficult preschool children can be guided

toward committed compliance.

Our results do not support the hypothesis that individual differences in temperament moderate the

impact of environmental chaos as a predictor of children’s compliance. The fact that we were unable to


find interactions between temperament and environmental chaos does not necessarily mean that such

moderating interactions do not exist. While statistical power was sufficient in this study to detect main

effects that were of moderate size, far more power is required to detect interactions. Furthermore, as

discussed by McClelland and Judd (1993), it is easier to detect interactions at the extreme ends of a

distribution than in the middle portions. Due to the voluntary nature of participation in this study, the

participation rate was moderate, and highly chaotic centers were not included in our sample of centers.

Whether the same pattern of results would occur with a larger sample or with a wider range of daycare

chaos is a question that needs to be addressed in future research. Nonetheless, within the scope of this

study, temperamentally difficult children were not at a greater risk for the adverse affects of

environmental chaos on compliance behavior.

Our results also did not support staff behavior as a mediator of the chaos and child compliance

relation. In contrast to research done in the home environment, where quality of parenting behavior is

consistently linked to level of environmental chaos, staff behavior in daycare was not related to how

chaotic the child care environment was. One possible explanation for this discrepancy comes from

chaos-parenting studies carried out in different cultures. Results from this research suggest that when

alternative caregivers are available, the impact of home chaos upon the parenting behaviors of the

primary caregiver is attenuated (Wachs & Corapci, 2003). The same process may be occurring in

daycare centers in our culture, where the presence of multiple staff members may minimize the impact of

environmental chaos upon the behavior of any one staff member. This suggests the importance of

follow-up research with a larger sample of centers than we have in the present study, looking at whether

relations between chaos and caregiver–child interactions systematically vary as a function of contextual

factors like child/caregiver ratio.

Our findings do indicate that a model integrating additive coaction (multiple independent main

effects) with specificity (different sets of predictors associated with types of outcomes) does help our

understanding of how variability in person–process–context relates to variability in child compliance

behavior. Not only did predictive variance increase as process and context variables were added to the

initial person predictor model but different sets of predictors were associated with committed, situational,

and passive noncompliance. Although our principle components analysis indicated that committed and

passive noncompliance reflect bipolar characteristics of the same dimension, there was not complete

overlap in the factors predicting how children reacted to adult compliance demands. The sole unique

predictor of committed compliance was the caregiver’s use of control. Children were less likely to

display committed compliance when adult caregivers used commands and hints of punishments together

with requests. Whereas adult caregivers’ use of control also predicted children’s use of passive

noncompliance, it was not the sole unique predictor. We also found that level of chaos in daycare

was uniquely related to passive noncompliance. Children were more likely to display passive

noncompliance when caregivers used more commands and hints of punishment when they made

requests and when centers were more chaotic. These results suggest that something other than the

covariance between committed and passive noncompliance is the driving outcome variability for the

latter variable.

Not surprisingly, given the dimensional structure of compliance, there is less overlap in predictors

when we look at situational compliance. Situational compliance was not related to children’s

temperament or how the caregivers interacted with the children. However, situational compliance was

predicted by our context variables, child care quality, and chaos, with each adding unique predictive

variance.


Whereas our findings document relations between specific person–process–context characteristics

and specific aspects of young children’s compliance behaviors, the limitations of our findings must be

considered. Although we were able to demonstrate significant and unique predictors of variability in

young children’s compliance, our effect sizes are not large. In addition, although situational compliance

appears to be on a different dimension than were the other forms of compliance assessed in our study,

committed and passive noncompliance do appear to reflect similar underlying processes, albeit in

different directions. The high level of convergence between our intracompliance correlations taken in

preschool settings and those of Kochanska and Aksan (1995) taken in a laboratory setting supports the

assumption that these behavior patterns reflect distinctly different ways in which children react to

caregiver demands, be these from parents or teachers. However, methodologically, correlations among

variables can sometimes influence which predictors are chosen as most critical in a regression analysis.

To deal with this potential problem within our hierarchical regression framework, we also ran alternate

regressions, varying the order of entry of predictors to see if this led to a different pattern of results.

Varying the order of entry did not change our pattern of findings, thus reducing the likelihood that our

findings were a function of arbitrary choice of predictors. Furthermore, as noted above, there was only

partial overlap on the predictors of committed compliance and passive noncompliance despite that these

behaviors load on the same dimension.

There are two additional limitations to our findings. First, both our child temperament and center

chaos ratings were derived from the same source, namely, head teacher report. However, as shown in

Table 4, the correlation between head-teacher-rated child temperament and daycare chaos was

nonsignificant, suggesting that shared method variance was not a major contributor to our pattern of

findings. Second, it is important to recognize that the cross-sectional nature of our study does not allow

us to specify the directionality of observed relations. Particularly, for person and process characteristics,

it is still an open question as to whether specific person–process characteristics systematically influence

different aspects of children’s compliance behaviors or whether children with different patterns of

compliance behavior systematically elicit different temperament and chaos ratings or different behavior

patterns from their adult caregivers in daycare. In this regard, it is important to note that there was a

moderate level of congruence between head teacher and parent ratings of child temperament (r = .44, p

< .01). This degree of congruence suggests that the head teacher temperament ratings were not just a

purely subjective reaction to the child’s behavior in child care, but rather reflected a genuine

intraindividual person variable. In the case of context, unless there is a systematic assignment of less

compliant children to lower quality daycare settings, it is not likely that quality influenced the observer

ratings of child compliance. There is the possibility that having greater numbers of noncompliant

children in a daycare setting could serve to increase the head teachers’ perceptions of the environment as

more chaotic. However, it is again important to note that there was a moderate level of congruence

between the head teachers’ ratings of chaos in daycare and independent observer ratings of daycare

quality (see Table 4), again suggesting that teacher ratings had some basis in objective reality.

Fortunately, there are several ways these questions can be dealt with in future research on person–

process–context contributions to children’s behavior in daycare settings. One approach would be to

utilize more objective laboratory assessments of children’s temperament (Rothbart & Bates, 1998),

where observers coded children’s temperament with no knowledge of the child’s compliance. A similar

approach could be used to assess environmental chaos. Observers could utilize objective indicators of

environmental chaos that have been used in research on home chaos (e.g., Matheny et al., 1995). These

methods could also be combined with a longitudinal research design, assessing child compliance and


temperament prior to entry in daycare and then following up both the children, with regard to changes in

compliance, and daycare staff, with regard to changes in their treatment and perceptions of children with

different levels of compliance. The use of a longitudinal design would be more congruent with the full

Bronfenbrenner model (Bronfenbrenner & Ceci, 1994), which includes time, as well as person, process,

and context. A longitudinal design could serve to clarify the processes through which the person and

context variables influence each other over time. For example, through use of a longitudinal study

design, it would be possible to address whether children who are perceived as having difficult

temperaments by their teachers are more likely to not comply with teachers’ control efforts, or whether

teachers are more likely to use control with children whom they perceive as temperamentally difficult. A

longitudinal design could also allow us to address a question raised by Kochanska (2002), namely, the

degree to which contributions from child care moderate prior parental contributions to a child’s pattern of

compliance.

We view this study as an initial step in exploring the influence of environmental chaos in child care

centers on developmental outcomes. In addition to the questions raised above that derived from our

results, future research can also address other developmental outcomes that are influenced by person–

process–context characteristics in the child care setting and the mechanisms through which person,

process, and context are associated with individual variability in developmental outcomes.

Acknowledgements

This research was supported by a grant from the Kinley Trust to the first and third authors. The

authors gratefully acknowledge the help of the preschool centers who agreed to participate in this

research. Thanks are also due to Professor Grazyna Kochanska for generously sharing her compliance

and control manuals with us. The first and second authors are in the Department of Psychological

Sciences, Purdue University. We most regretfully note the untimely death of our valued colleague and

coauthor Susan Kontos.

References

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Con-

ceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

Bates, J. (2001). Adjustment style in childhood as a product of parenting and temperament. In T. D. Wachs, & G. Kohnstamm

(Eds.), Temperament in context (pp. 173–200). Mahwah, NJ: Erlbaum.

Braungart-Reiker, J., Garwood, M. M., & Stifter, C. A. (1997). Compliance and noncompliance: The roles of maternal control

and child temperament. Journal of Applied Developmental Psychology, 18, 411–428.

Bronfenbrenner, U. (1989). Ecological systems theory. In R. Vasta (Ed.), Annals of Child Development, (Vol. 6, pp. 187–249).

Greenwich, CT: JAI Press.

Bronfenbrenner, U. (1999). Environments in developmental perspective. In S. L. Friedman, & T. D. Wachs (Eds.), Measuring

the environment across the life span (pp. 3–30). Washington, DC: American Psychological Association.

Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualized in developmental perspective: A bioecological

model. Psychological Review, 101(4), 568–586.

Bryant, D. M., Lau, L. B., & Sparling, J. J. (1994). Family and classroom correlates of Head Start children’s outcomes. Early

Childhood Research Quarterly, 9, 289–309.

Chamberlain, P., & Patterson, G.R. (1995). Discipline and child compliance in parenting. In M.H. Bornstein (Ed.), Handbook of

parentingApplied and practical parenting, (Vol. 4, pp. 205–225). Mahwah, NJ: Erlbaum.


Clarke-Stewart, A. (1992). Consequences of child care for children’s development. In A. Booth (Ed.), Child care in the 1990s

trends and consequences (pp. 63–83). New Jersey: Erlbaum.

Clarke-Stewart, A., & Allhusen, V. (2002). Nonparental caregiving. In M.H. Bornstein (Ed.), Handbook of parenting (2nd

ed.)Being and becoming a parent, (Vol. 3, pp. 215–252). Mahwah, NJ: Erlbaum.

Clarke-Stewart, A., Gruber, C.P., & Fitzgerald, L.M. (1994). Compliance. Children at home and day care (pp. 183–196).

Mahwah, NJ: Erlbaum.

Corapci, F., & Wachs, T. D. (2002). Does parental mood or efficacy mediate the influence of environmental chaos upon

parenting behavior? Merrill-Palmer Quarterly, 48(2), 182–201.

Crockenberg, S., & Litman, C. (1990). Autonomy as competence in two-year-olds: Maternal correlates of child defiance,

compliance, and self-assertion. Developmental Psychology, 26(6), 961–971.

Evans, G. W. (1999). Measurement of the physical environment as stressor. In S. L. Friedman, & T. D. Wachs (Eds.),

Measuring environment across the life span (pp. 249–278). Washington, DC: American Psychological Association.

Evans, G. W., Maxwell, L. E., & Hart, B. (1999). Parental language and verbal responsiveness to children in crowded homes.

Developmental Psychology, 35(4), 1020–1023.

Friedman, S. L., & Amadeo, J. (1999). The child-care environment: Conceptualizations, assessments, and issues. In S. L.

Friedman, & T. D. Wachs (Eds.), Measuring environment across the life span (pp. 127–167). Washington, DC: American

Psychological Association.

Hambrick-Dixon, P. (1988). The effect of elevated subway train noise over time on Black children’s visual vigilance perfor-

mance. Journal of Environmental Psychology, 8, 299–314.

Harms, T., Clifford, R., & Cryer, D. (1998). Manual for the early childhood environment rating scale (Revised ed.). New York:

Teachers College Press.

Himmelfarb, S., Hock, E., & Wenar, C. (1984). Infant temperament and noncompliant behavior at four years: A longitudinal

study. Genetic, Social, and General Psychology Monographs, 111, 9–21.

Holloway, S., & Reichhart-Erickson, M. (1989). Child care quality, family structure and maternal expectations: Relationships to

preschool children’s peer relations. Journal of Applied Developmental Psychology, 10, 288–298.

Howes, C., & Olenick, M. (1986). Family and childcare influences on toddler’s compliance. Child Development, 57,

202–216.

Jaccard, J., Turrisi, R., & Wan, C. K. (1990). Interaction effects in multiple regression. Newbury Park, CA: Sage.

Kochanska, G. (1991). Socialization and temperament in the development of guilt and conscience. Child Development, 62(6),

1379–1392.

Kochanska, G. (1993). Toward a synthesis of parental socialization and child temperament in early development. Child

Development, 64, 325–347.

Kochanska, G. (1995). Children’s temperament, mother’s discipline, and security of attachment: Multiple pathways to emerging

internalization. Child Development, 66, 597–615.

Kochanska, G. (1997). Mutually responsive orientation between mothers and their young children: Implications for early

socialization. Child Development, 68(1), 94–112.

Kochanska, G. (1999). Coding manual for child compliance/mother discipline project. Iowa City, IA: University of Iowa.

Kochanska, G. (2002). Committed compliance, moral self and internalization: A mediational model.Developmental Psychology,

38, 339–351.

Kochanska, G., & Aksan, N. (1995). Mother–child mutually positive affect, the quality of child compliance to requests and

prohibitions, and maternal control as correlates of early internalization. Child Development, 66, 236–254.

Kochanska, G., Aksan, N., & Koenig, A. (1995). A longitudinal study of the roots of preschoolers’ conscience: Committed

compliance and emerging internalization. Child Development, 66(6), 752–769.

Kochanska, G., Tjebkes, T. L., & Forman, D. R. (1998). Children’s emerging regulation of conduct: Restraint, Compliance, and

internalization from infancy to the second year of life. Child Development, 69(5), 1378–1389.

Kontos, S., & Wachs, T.D., 2000. Life in Early Childhood Programs Scale. Unpublished manuscript. Department of Child

Development and Family Studies. Purdue University. West Lafayette, IN.

Kuczynski, L., Kochanska, G., Radke-Yarrow, M., & Girnius-Brown, O. (1987). A developmental interpretation of young

children’s noncompliance. Developmental Psychology, 23(6), 799–806.

Lamb, M.E. (1998). Nonparental child care: Context, quality, correlates, and consequences. In W. Damon, I.E. Sigel, & K.A.

Renninger (Eds.), Handbook of child psychology (5th ed.)Child Psychology in Practice, (Vol. 4, pp. 73–133). New York:

Wiley.


Lloyd, B., & Howe, N. (2003). Solitary play and convergent thinking skills in preschool children. Early Childhood Research

Quarterly, 18, 22–41.

Londerville, S., & Main, M. (1981). Security of attachment, compliance, and maternal training methods in the second year of

life. Developmental Psychology, 17(3), 289–299.

Martin, R., & Bridger, R. (2000). Manual for the temperament assessment battery for children—Revised. Athens, GA: Division

of Human Development, University of Georgia.

Matheny, A. P., Wachs, T. D., Ludwig, J. L., & Phillips, K. (1995). Bringing order out of chaos: Psychometric characteristics of

the confusion, hubbub, and order scale. Journal of Applied Developmental Psychology, 16, 429–444.

Maxwell, L. (1996). Multiple effects of home and daycare crowding. Environment and Behavior, 28, 494–511.

McClelland, G. H., & Judd, C. M. (1993). Statistical difficulties of detecting interactions and moderator effects. Psychological

Bulletin, 114(2), 376–390.

NICHD Early Child Care Research Network. (1998). Early child care and self-control, compliance, and problem behavior at

twenty-four and thirty-six months. Child Development, 69, 1145–1170.

NICHD Early Child Care Research Network. (2000). Characteristics and quality of child care for toddlers and preschoolers.

Applied Developmental Science, 4(3), 116–135.

NICHD Early Child Care Research Network. (2002). Child-care structure! process! outcome: Direct and indirect effects of

child-care quality on young children’s development. Psychological Science, 13(3), 199–206.

NICHD Early Child Care Research Network. (2003). Does amount of time spent in child care predict socioemotional adjust-

ment during the transition to kindergarten? Child Development, 74, 451–477.

Peisner-Feinberg, E. S., & Burchinal, M. R. (1997). Relations between preschool children’s child-care experiences and

concurrent development: The cost, quality, and outcomes study. Merrill-Palmer Quarterly, 43(3), 451–477.

Phillips, D., McCartney, K., & Scarr, S. (1987). Child-care quality and children’s social development. Developmental Psy-

chology, 23, 537–543.

Rothbart, M., & Bates, J. (1998). Temperament. In N. Eisenberg (Ed.), Handbook of child psychology (5th ed.) Social,

emotional, and personality development, (Vol. 3, pp. 105–176). New York: Wiley.

Rutter, M. (1983). Statistical and personal interactions: Facets and perspectives. In D. Magnusson, & V. Allen (Eds.), Human

development: An interactional perspective (pp. 296–320). New York: Academic Press.

Schaffer, H. R., & Crook, C. K. (1980). Child compliance and maternal control techniques. Developmental Psychology, 16,

54–61.

Steiger, J. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87, 245–251.

Stifter, C. A., Spinrad, T. L., & Braungart-Rieker, J. M. (1999). Toward a developmental model of child compliance: The role of

emotion regulation in infancy. Child Development, 70, 21–32.

Wachs, T. D. (1989). The nature of the physical microenvironment: An expanded classification system. Merrill-Palmer

Quarterly, 35, 399–420.

Wachs, T. D. (1992). The nature of nurture. Newbury Park, CA: Sage.

Wachs, T. D., & Camli, O. (1991). Do ecological or individual characteristics mediate influence of the physical environment

upon maternal behavior. Journal of Environmental Psychology, 11, 249–264.

Wachs, T. D., & Corapci, F. (2003). Environmental chaos, development and parenting across cultures. In C. Raeff, & J. Benson

(Eds.), Social and cognitive development in the context of individual, social and cultural processes (pp. 54–83). New York:

Routledge.

Wahler, R. G. (1997). On the origins of children’s compliance and opposition: Family context, reinforcement and rules. Journal

of Child and Family Studies, 6, 191–208.

Wohlwill, J., & Heft, H. (1987). The physical environment and the development of the child. J. Stokols, & I. Altman (Eds.),

Handbook of environmental psychology, (Vol. 1, pp. 281–329). New York: Wiley.

predictors of preschool children's compliance behavior in early childhood classroom settings

Documents