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The Nature and Impact of Changes in Home Learning Environment on

Development of Language and Academic Skills in Preschool Children

Article  in  Developmental Psychology · September 2010

DOI: 10.1037/a0020065 · Source: PubMed

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The Nature and Impact of Changes in Home Learning Environment onDevelopment of Language and Academic Skills in Preschool Children

Seung-Hee SonPurdue University

Frederick J. MorrisonUniversity of Michigan

In this study, we examined changes in the early home learning environment as children approachedschool entry and whether these changes predicted the development of children’s language and academicskills. Findings from a national sample of the National Institute of Child Health and Human DevelopmentStudy of Early Child Care and Youth Development (N � 1,018) revealed an overall improvement in thehome learning environment from 36 to 54 months of children’s age, with 30.6% of parents ofpreschoolers displaying significant improvement in the home environment (i.e., changes greater than 1SD) and with only 0.6% showing a decrease. More important, the degree of change uniquely contributedto the children’s language but not to their academic skills. Home changes were more likely to be observedfrom mothers with more education and work hours and with fewer symptoms of depression.

Keywords: home environment, parenting dynamics, school readiness, academic achievement, schooltransition, change analysis

Accumulating evidence has revealed that important individualdifferences in language and reading-related literacy skills emergeearly (Hart & Risley, 1995) and that these differences are relativelystable over time (Cunningham & Stanovich, 1997). Early languageskills as well as academic skills, including early literacy andnumeracy skills, have been emphasized as precursors of lateracademic achievement (Duncan et al., 2007; National Institute ofChild Health and Human Development [NICHD] Early Child CareResearch Network, 2004, 2005). Of the many sources of influencethat impinge on preschool children’s language and academic skills,the overall home learning environment constitutes a key source ofvariability (Morrison & Cooney, 2001).

Traditionally, research has shown that the quality of the homelearning environment remains relatively stable during childhood(Bradley & Caldwell, 1982; De Temple, 2001). However, theuniversal assumption of stability has been challenged by emergingperspectives that some home practices may change over time(Holden & Miller, 1999) and that these changes can redirectchildren’s development (Belsky, 1984). The period encompassingschool transition (roughly preschool to early elementary years)represents a particularly fruitful window to view these changes in

the home environment (Cowan, Cowan, Ablow, Johnson, & Mea-selle, 2005). Given that successful transition is critical, socially andeducationally, for children, some parents may change their expecta-tions of their child as school entry approaches and alter their homelearning environment, including educational activities, interactions, orlearning materials. However, whether some parents modify theirhome environment over the school transition period—and if so,whether these shifts lead to changes in children’s development—hasnot been examined extensively. The purpose of this study is to learnmore about the nature of home learning environmental changes dur-ing school transition and how those changes impact young children’slanguage and academic skills.

Importance of the Home Learning Environment

For more than half a century, the home environment has been acentral focus of inquiry in the field of early development; aconsensus is emerging that the home environment provides animportant contribution to children’s development, learning, andschool success (Collins, Maccoby, Steinberg, Hetherington, &Bornstein, 2000; Morrison & Cooney, 2001).

Bradley and colleagues have demonstrated that the home learn-ing environment constitutes an array of characteristics, includingproximal parenting behaviors, such as providing educational inter-actions and activities, as well as distal practices of making learningmaterials available at home (Bradley & Caldwell, 1995). Whereasparticular elements of home may be more likely to drive somespecific skills development, the overall home learning environ-ment quality collectively predicts children’s language and aca-demic skills vigorously (Bradley & Caldwell, 1984b; Connor, Son,Hindman, & Morrison, 2005; Foster, Lambert, Abbott-Shim,McCarty, & Franze, 2005; Storch & Whitehurst, 2001).

Measures of home learning environment were found to beassociated with children’s language outcomes across a diverse agerange, including receptive vocabulary skills of 24-month-old chil-dren (Murray & Yingling, 2000), kindergartners (Griffin & Mor-

Seung-Hee Son, Department of Child Development and Family Studies,Purdue University; Frederick J. Morrison, Department of Psychology,University of Michigan.

This research was supported by National Institute of Child Health andHuman Development (NICHD) Grants U10 HD38121 and 5R01HD27176-08. We thank the NICHD Early Child Care Research Networkfor designing the study and collecting data. We also thank Kai CortinaSchnabel and Laura Klem of the University of Michigan for comments onstructural equation models.

Correspondence concerning this article should be addressed to Seung-Hee Son, Department of Child Development and Family Studies, PurdueUniversity, 101 Gates Road, West Lafayette, IN 47907. E-mail: seunghee@purdue.edu

Developmental Psychology © 2010 American Psychological Association2010, Vol. 46, No. 5, 1103–1118 0012-1649/10/$12.00 DOI: 10.1037/a0020065

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rison, 1997; Morrison & Cooney, 2001), and 11th graders (Cun-ningham & Stanovich, 1997). Equally compelling, the homelearning environment has been strongly associated with the read-ing skills of kindergartners (Griffin & Morrison, 1997; Morrison &Cooney, 2001) and second graders (Griffin & Morrison, 1997),and it even has been associated with the reading comprehensionskills of 11th graders (Cunningham & Stanovich, 1997). Similarly,studies reported the importance of the home learning environmentfor numeracy skills, including number concepts in preschoolers(Young-Loveridge, 1989) and kindergartners (Melhuish et al.,2008) as well as mathematics achievement at Grade 3 (Melhuish etal., 2008).

Possibility of Changes in the Home LearningEnvironment

Previous research has confirmed that the home environment andparenting practices remain relatively stable over time (Bradley &Caldwell, 1984a; Dallaire & Weinraub, 2005; Masur & Turner,2001; Roberts, Block, & Block, 1984; Sameroff, Seifer, Baldwin,& Baldwin, 1993). However, stability does not necessarily meanthat home practices are not changing; rather, stability and changecan occur simultaneously. Recent evidence, in fact, reveals thatsome parenting practices change over time (Holden & Miller,1999; Meij, Riksen-Walraven, & van Lieshout, 2000).

By changes in the home environment, we mean developmentalchanges in the home that are systematic. As Collins and Madsen(2002) put it, “[parenting or home environments are] dynamicphenomena, not only because of the give-and-take and continualadaptations of all human exchanges but also because the individ-uals involved undergo changes in their developmental status andage-graded social expectations” (p. 51). Because children’s devel-opment is ongoing and because social expectations of them arechanging, the home learning environment, as a result, may becomemore or less stimulating during certain periods of drastic expec-tation changes. The child’s entrance to school constitutes an im-portant transition (Cowan et al., 2005; Pianta & Cox, 1999) whenparents may be more likely to change their expectations and homeenvironment (Stevenson, Lee, Stigler, Hsu, & Kitamura, 1990).

From the developmental systems perspective, families that arefunctional tend to interact with other microsystems (e.g., schools)to facilitate their children’s development (Pianta & Walsh, 1996).As their preschoolers approach school entry, families may strive tomake their expectations more congruent with school systems inrespect to child learning and success at school, subsequentlychanging their home environment to include educational goals andpractices (Pianta & Walsh, 1996). The concerted efforts for cog-nitive stimulation provided by these families may influence chil-dren’s academic skills (Lareau, 2003).

In this regard, preschool years or school transition periods needa close examination because they provide a venue for parents tomodify their practices to be aligned with school-related expecta-tions, and thus one might expect changes in the home learningenvironment, for instance, into a more academically stimulatingone. Yet, this does not mean that all parents may alter their homeenvironment (see Stevenson et al., 1990; Stevenson & Stigler,1992).

The level of change may vary across families. Some familiesstruggle to discover what the school expects from children and

how to prepare their children for school (Graue, 1993); they,therefore, may not put extra efforts into changing their homeenvironment. Parents may be more likely to improve their homeenvironment when they face less stress and more resources, forexample, financial-related resources, including income (Brooks-Gunn, Klebanov, & Liaw, 1995; Garrett, Ng’andu, & Ferron,1994; Kalil & DeLeire, 2004; Votruba-Drzal, 2003) and householdsize (Garrett et al., 1994; Menaghan & Parcel, 1991; Miller &Davis, 1997). Other family factors that influence parenting in thefirst place may also influence parents to change their practices(Belsky, 1984). These factors include psychological resources ofparents, such as the mother’s level of education (Baharudin &Luster, 1998; Menaghan & Parcel, 1991; Miller & Davis, 1997),cognitive ability (Supplee, Shaw, Hailstone, & Hartmana, 2004),and depressive symptoms (NICHD Early Child Care ResearchNetwork, 1999; Patterson & Albers, 2001); contextual resourcesand stresses, such as marital status (Astone & McLanahan, 1991;Menaghan & Parcel, 1995; Miller & Davis, 1997; Votruba-Drzal,2003) and employment (Menaghan & Parcel, 1991, 1995; Vandell& Ramanan, 1992; Votruba-Drzal, 2003); and characteristics ofthe child, such as gender (Garrett et al., 1994; Miller & Davis,1997) and ethnicity (Bradley, Corwyn, McAdoo, & Coll, 2001;Menaghan & Parcel, 1991).

In other words, financial, social, and psychological resourcesand stresses, as well as demographic characteristics, may worktogether to influence parents to change, significantly or in lesserways, the home learning environment as their child approachesschool entry. This change will, in turn, be linked to children’sschool readiness.

Strengths and Limitations of Home Change Analysis

Within-family changes in the home learning environment canprovide important information about the dynamics in parenting foreducating young children during the school transition period andthe importance of parenting dynamics to young children’s schoolreadiness. However, little research has been conducted on thechanges in the home learning environment targeting the schooltransition period. One notable exception was conducted byVotruba-Drzal (2003), who reported the existence of the changesin the home learning environment during the school transitionperiod using data from the National Longitudinal Survey of Youth.The author found variability in the degree of changes in standard-ized home scores during the school transition period, which werepredicted by changes in family situations, such as income, maritalstatus, and mother’s education as well as children’s age. Thesignificant impact of children’s age suggested development-relatedparenting changes. However, the degree of change or absoluteamount of score change in the home learning environment was notdirectly examined from comparing standardized home scores; al-though the use of standardized scores was due to two differenthome measures at two time points, this led to the very low effectsize (d � –0.025). Furthermore, the author did not test the impactof changes in the home learning environment on children’s out-comes. Another study by Votruba-Drzal (2006) suggested thecontribution of home change to children’s developmental out-comes. However, the study focused on elementary years, finding aslight increase in the home composite scores between early andmiddle childhood (d � 0.03) and a significant predictability of

1104 SON AND MORRISON

both early and middle childhood home environment for readingand math development during early elementary years.

Other available studies on changes in home learning environ-ment have also not provided information about the impact ofchanges in the home learning environment on children’s develop-ment during early childhood. These studies estimated relativelyshort-term changes in the home as a result from outside factors,such as income dynamics (Dearing & Taylor, 2007) or parentoccupation or family structure changes, by comparing standardizedhome scores (Menaghan & Parcel, 1995). Thus, a moderate effectsize of d � 0.32 was obtained in Menaghan and Parcel’s (1995)study; effect size was not available for Dearing and Taylor’s(2007) study. This means that the exact nature of the changes in thehome learning environment was not directly examined other thanchanges in the relative standing within the group; furthermore,information of the full variability in the degree of changes in thehome beyond the changes led by outside factors and its links tochildren’s developmental outcome are not available, specifically inrespect to changes occurring during the school transition period.

Analyses of the impact of change in the home learning envi-ronment on children’s development not only provide informationon the contribution of parenting dynamics but also offer importantadvantages over traditional static approaches on the home envi-ronment. In static approaches, the fact that the home environmentcannot be randomly manipulated and randomly assigned to youngchildren brings up the concern of unobserved influences (i.e.,omitted variables) related to both the child outcome and the homelearning environment. With the use of a change model, in whichthe changes (i.e., development) in children’s outcomes are re-gressed on the changes in the home learning environment, theapproach improves causal inference by controlling the omittedvariables (Duncan, Yeung, Brooks-Gunn, & Smith, 1998; NationalResearch Council and Institute of Medicine, 2000). Thus, exam-ining the influence of home changes on children’s developmentaloutcomes is a statistically powerful method in demonstrating theimpact of the home learning environment.

One of the challenges in the analysis of home changes lies in themethod of measuring change. A common method is taking adifference in home scores across two time points, which is used ina simple change model that subtracts Time 1 equation from Time2 equation (Duncan & Gibson-Davis, 2006; NICHD Early ChildCare Research Network & Duncan, 2003). However, comparingcomposite scores of the home learning environment across timeand regressing children’s outcome on an absolute score differencehas weaknesses: A difference score (or change score) tends to havea great measurement error, which produces larger standard errorsand can also bias parameter estimates (Duncan & Gibson-Davis,2006). Typically, a difference score is substantially less reliablethan the original two measures when those scores are moderatelyto highly correlated (Cronbach & Furby, 1970). Moreover, adifference score is usually negatively correlated with the initialscore, especially when there is a ceiling problem in a measure(Burr & Nesselroade, 1990). With the negative correlation, thissuggests that a change analysis needs to include the “level” mea-sure as a control to estimate the unique impact of change.

Another way that is often used involves entering two separatemeasures of early and later home learning environments into oneequation (e.g., Menaghan & Parcel, 1995; Votruba-Drzal, 2006).Obtaining a unique impact of both earlier and later home environ-

ments demonstrates dissimilarities in the impact of home measuredin two time points, thus suggesting home differences/changes;however, this does not directly measure or model the degree ofchange in the home environment. Moreover, when the earliermeasure shares large portions of variance with control variables inthe equation, the estimate of change is not precise.

A third approach that has been advocated by some scholars foranalyzing change across two time points is a residualized changemodel (Cronbach & Furby, 1970). The residual is the unexplainedvariance in the later home learning environment after the effect ofthe earlier home measure has been partialled out. In other words,the variance in later home environment that could not be predictedlinearly by earlier home environment becomes a measure ofchange in the home learning environment. The residualized changemodel may be a better specification when there is a high correla-tion or causal effect between earlier and later measures (Allison,1990); the residual captures a change, if there is any, beyond anaverage change in the group that the correlation cannot explain. Ifthere is an increase (or decrease) in a sample on the whole, theoverall increasing (or decreasing) trend can be explained by anintercorrelation of the home learning environment measured re-peatedly, and the variance left unexplained is the residual. Thus, itmay be a better method to estimate the change over and beyond theaverage change in the sample, but it may not be the best way whenthe absolute amount of change is the focus of the study. With allthe measurement strengths, some scholars argue that residualizedchange models may provide biased parameter estimates whenthere is measurement error (Allison, 1990). This issue may beresolved by using methods that minimize measurement errors,such as structural equation modeling (SEM; Raykov, 1993).

Comparing different methods of measuring and analyzing homechanges suggests that selection needs to be based on statisticalcharacteristics of the home measure (i.e., measurement error,shared variance with other control variables, distribution with aceiling), changing trends in the home measure over time (i.e.,stability of the measure, correlation between an initial measure andchange), and research interests (i.e., absolute score change vs.relative change over and beyond the population’s normativechange, direct modeling of change vs. uniqueness of measures attwo time points as an indirect change measure). Researchers in-terested in the nature and level of absolute changes in the homeenvironment may utilize a simple change measure. On the otherhand, with its high stability (Dallaire & Weinraub, 2005) andexpected normative developmental changing trends (i.e., improve-ment in home environment; Stevenson et al., 1990), the impact ofhome learning environment during the school transition periodmay be analyzed more statistically appropriately with a residual-ized change model. However, previous studies have not utilizedresiduals for examining changes in the home learning environ-ment.

The Current Study: Changes During Preschool Years

Using the preschool period as a window to examine changes inthe home, we investigated the natural occurrence of changes in thehome learning environment as preschoolers approached schoolentry as well as the impact of those changes on preschoolers’language and academic skills just before school entry.

1105CHANGES IN THE HOME LEARNING ENVIRONMENT

Specifically, we examined (a) the existence and nature ofchanges in the home learning environment during the preschoolyears by comparing 36- and 54-month scores, (b) the impact of thehome learning environment changes on children’s language andacademic development using a measure of residual in the 54-month home, and (c) characteristics of parents related to changesin the home environment. We expected to find that some parentswould change their home learning environment, in spite of itsrelative stability, in a positive direction and that the enhancedhome environment would be associated with improvements inchildren’s development. Figure 1 describes our hypothesis relatedto the second question; although the home learning environmentpositively predicts the contemporaneous level of children’s lan-guage and academic skills, its history of change—specificallyimprovement over time—may have a positive and unique impacton children’s development of skills. Finally, we expected to findthat parents would be more likely to change their home environ-ment when they had more resources and less stresses in theirfinancial and psychological conditions.

The current study extends previous work on home changes in atleast three ways. First, we examined home changes using a devel-opmental lens. With increasingly high expectations of children’seducational readiness, the school transition period is the time whenwe may be more likely to find changes in parenting, and thechanges may be normative (i.e., home changes may be observed inthe overall sample, not confined to a small number of parents withspecific characteristics). In other words, preschool years or theschool transition period can be a venue when we are more likely tosuccessfully observe and estimate the nature and impact ofchanges in the home learning environment, which has not beenstudied empirically. We aimed to examine more systematic anddevelopmental home changes by targeting the school transitionperiod and by estimating its full extent of change in the homelearning environment, not the variance in the home changes asinduced by other factors.

Second, we examined the direct and unique impact of changesin the home learning environment on children’s developmentaloutcomes. We were not able to find previous studies on the impactof home changes on children during the school transition period.Only one available study estimated the impact of home changes onchildren’s outcome (e.g., Votruba-Drzal, 2006); however, thisstudy examined middle childhood and did not directly test theunique contribution of home changes apart from the level of home

environment. It is likely that the degree of change may be associ-ated with the level of home scores (i.e., families who increased alot between Time 1 and 2 will have relatively high Time 2 scores);just estimating the impact of change without separating outthe contemporaneous level of home environment would mask theconcurrent or immediate impact of home and may exaggerate theimpact of changes over time or the gradual influence of home.Related to this second contribution, we tested the impact ofchanges by estimating the unique impact of home changes bycontrolling the impact of a concurrent level of home environmentand by modeling the direct impact of changes in the home learningenvironment, and not by focusing on the mediated impact of homefrom other outside sources of change. By partialling out the impactof changes from the impact of the absolute level at the contempo-raneous 54-month home measure, we were able to address thequestion of whether the history of home improvement between 36and 54 months matters or whether only the current level of homelearning environment matters.

Third, we examined the impact of home changes using a statis-tically rigorous method of residualized change models. With po-tential normative changes and high stability in the home measuresduring preschool periods, utilizing residual as a measure of changeis a more reliable method than a true difference score (Allison,1990). Especially with the existence of normative changes, ourmeasure of a residual allowed us to examine whether childrenwould benefit when parents improved their home environmentalquality more than what the average parents did. To counter themeasurement error that may bias parameter estimation, we usedlatent factor models using SEM (Raykov, 1993).

Method

Data and Participants

Original data were gathered on 1,364 children and their homesat 36 and 54 months of age from the NICHD Study of Early ChildCare and Youth Development (NICHD-SECCYD; for additionalinformation on this study, see http://secc.rti.org/).

In 1991, NICHD-SECCYD recruited 8,986 mothers who gavebirth in hospitals at 10 geographic sites. Of the 5,416 mothers whomet eligibility requirements (i.e., mother more than 18 years ofage, mother spoke English, mother healthy, baby not multiple birthor released for adoption, lived within an hour of a research site,and neighborhood not unsafe), a conditionally random sample of3,015 was interviewed via telephone to ensure that the sample wasdiverse with regard to ethnicity, education, and family structure. Atotal of 1,526 families was eligible; of these, 1,364 enrolled in thestudy. These 1,364 families were similar to the eligible hospitalsample and were normative in terms of ethnic and economiccharacteristics of the United States.

Among the total 1,364 enrollees, 1,018 children and their par-ents compose the analysis sample of the current study, with nomissing information in their home learning environment at 36 and54 months of the children’s age. Participating mothers had anaverage of 14 years of education (SD � 2.48), with 38.5% havingat least a college degree and 8.1% having less than a high schooleducation. When the children were 36 months of age, 13% of themothers were single. The sample was economically diverse andhad a 36-month income-to-needs ratio of 3.61 (SD � 3.14), with

Figure 1. Hypothesized model of the impact of changes in home learningenvironment (HLE).

1106 SON AND MORRISON

15.4% of children at or below the poverty line with a ratio of lessthan 1.0 and with 33.4% at low income with a ratio of 2.0 or lower.Gender was distributed evenly: 50.5% were boys. The sample wasalso ethnically diverse: 20% of the children belonged to minorities,with 10.9% being African American and 5.6% being Hispanic.

Compared with the analysis sample (n � 1,018), the sampleexcluded for missing data (n � 346) showed differences in severalbaseline (i.e., 36-month) characteristics; however, the effect sizeswere low to moderate. The missing sample included a significantlylarger percentage of single mothers (29.5% vs. 21.7%; �2 � 17.68,p � .01, Cohen’s d � 0.19, CI [0.12, 0.21]) and mothers withfewer years of education, (t � 4.62, p � .001, d � –0.29, CI[–0.56, –0.14]), and it had a lower income-to-needs ratio (t �2.14, p � .05, d � –0.17, CI [–0.58, –0.03]). Children in themissing group had lower receptive (t � 3.99, p � .001, d � –0.33,CI [–2.72, 0.65]) and expressive (t � 3.99, p � .001, d � –0.35,CI [–2.57, 0.56]) language skills, and they had lower scores invariety of experiences (t � 3.37, p � .001, d � –0.28. CI [–0.52,–0.19]) and learning materials at home (t � 3.36, p � .001, d �–0.29, CI [–0.69, –0.13]). However, there were no significantdifferences in children’s academic skills and parental stimulationof children’s language and academic skills at home. Results of thecurrent study should be interpreted cautiously because the attritedsample was more likely to be from the at-risk population. How-ever, given the low effect size of group differences and the exten-sive nature of the data with the wide diversity within the analysissample, we still expected that the current analysis could provideinsights about the overall home learning environment of childrenapproaching kindergarten entry in the United States.

Measures

Home learning environment. We measured home learningenvironment using the four selective portions of the original ver-sion of Home Observation for Measurement of the Environment(HOME) inventory (Caldwell & Bradley, 1984) when childrenwere 36 and 54 months old. These four subscales (out of the totaleight subscales available) included Learning Materials, LanguageStimulation, Academic Stimulation, and Variety of Experience,which purport to measure the stimulation of cognitive aspects of achild’s development and are closely related to children’s cognitiveskills (Bradley, Corwyn, Burchinal, McAdoo, & Coll, 2001).

Learning Materials includes 11 items, such as a toy that helpchildren’s learning (e.g., “Child has toys that help teach names ofanimals”). Language Stimulation (seven items) refers to theamount and variety of verbal stimulation provided by parents (e.g.,“Parent uses complex sentence structure and vocabulary”). Aca-demic Stimulation (five items) refers to direct teaching of aca-demic skills (e.g., “Child is encouraged to learn to read a fewwords”). Variety of Experience (nine items) measures out-of-homelearning experiences (e.g., “Child is taken on an outing by afamily”).

When participating children were 36 and 54 months old, inter-viewers visited each child’s home and completed the 57-itemHOME inventory. Each item was scored as “1” if the behavior wasobserved during the visit or if the parent reported that the condi-tions or events were characteristics of the home environment. Ascore of “0” was recorded if the behavior was not observed or

reported. These scores were merged and collapsed into subscalescores.

For the current study, a composite score of Home LearningEnvironment was calculated by adding the raw scores of the fourcognitive subscales of the HOME inventory. A possible range ofthe composite score was 0–32. Cronbach’s � � .75 and .62 for36-month and 54-month measures, respectively. Change scoreswere calculated by subtracting 36-month from 54-month scores.Higher scores on the Home Learning Environment indicate betterhome quality; and higher positive scores on change indicate im-provement in the home environment or positive change.

With the dichotomous scoring of the Home Learning Environ-ment measure, the Home Learning Environment scores indicatethe existence of different kinds of learning-related parental prac-tices in the home; changes in the home environment scores, spe-cifically positive changes, indicate an increase in the aggregatednumber of practices that were observed. The composite score andits difference score were used in the descriptive analysis of thenature of change to provide clear and illustrative descriptions ofthe central tendency and dispersion of the Home Learning Envi-ronment scores.

As we analyzed the impact of changes in home environment, weused a residual score from a latent factor of Home LearningEnvironment as an underlying hypothetical construct (here, thehome learning environment) that explains the visible differences inthe indicators (here, the four subscales; Kline, 2005). The latentfactor enabled us to estimate the impact of changes in the overallhome learning environment quality rather than to capture changesin a few items in the scale with taking into account the reliabilityof the HOME inventory measurement.

Children’s language skills. The first target outcome of chil-dren’s development was 54-month language skills measured by thePreschool Language Scale—Third Edition (PLS–3; Zimmerman,Steiner, & Pond, 1992). PLS–3 assesses vocabulary, grammar,morphology, and language reasoning. PLS–3 consists of (a) theAuditory Comprehension scale that measures what children“know” and understand but may not “say” and (b) the ExpressiveCommunication scale that assesses what children can actually say.The internal consistency of the PLS–3, as reported by the pub-lisher, ranged from .81 to .97 (Zimmerman et al., 1992). Standardscores of the two scales were used as indicators to create a latentfactor of 54-month language skills. The interscale correlationbetween the two scales was .82 in the current sample.

For a control measure, we used the Reynell DevelopmentalLanguage Scale (Reynell & Gruber, 1990)—assessed when chil-dren were 36 months of age. The Reynell Developmental Lan-guage Scale has two 67-item scales: Verbal Comprehension andExpressive Language. The original reliability measures reportedby the publisher include split-half reliability coefficients of .93 forVerbal Comprehension and .86 for Expressive Language. Standardscores of the two scales were used as indicators to create a latentfactor of language skills at 36 months. The interscale correlationbetween the two scales was .73 in the current sample.

Children’s academic skills. The second child outcome was54-month academic skills in early literacy and numeracy measuredby Letter-Word Identification and Applied Problems of theWoodcock–Johnson Psycho-Educational Battery—Revised(Woodcock & Johnson, 1989). Letter-Word Identification involvessymbolic learning as well as letter and word identification skills.

1107CHANGES IN THE HOME LEARNING ENVIRONMENT

Applied Problems measures mathematical analysis and practicalproblem solving. Internal consistency reliability using the split-half method ranged from .94 to .98 for the skills cluster; test–retestreliability ranged from .80 to .87 for the individual tests, asreported by the publisher. Cronbach’s alphas for the current sam-ple were .84 for both Letter-Word Identification and AppliedProblems. Standard scores of the two scales were used as indica-tors to create a latent factor of 54-month academic skills. Theinterscale correlation of the standard scores was .72 for the currentsample.

As a 36-month control of academic skills, we used the Diag-nostic Scale of the Bracken Basic Concept Scale (Bracken, 1984/1998). Containing 258 items, the Diagnostic Scale of the BrackenBasic Concept Scale provides an in-depth assessment of a child’sbasic knowledge, including early literacy and numeracy. The totalstandard score was used as a measure of 36-month academic skillsbecause this is the one available score in the data set. The internalconsistency of the total score was .90 for the current sample; theoriginal internal consistency was reported as .97 across age levels.

Covariates. Several demographic factors were included inmodels (a) as control variables to rigorously examine the impact ofhome changes and (b) as covariates explaining the characteristicsof parents who changed their home learning environment. Thesefactors were reported to influence the level of parenting (Belsky,1984) as well as to influence parents to change their home envi-ronment (Garrett et al., 1994; Menaghan & Parcel, 1995; Votruba-Drzal, 2003).

The demographic factors include family income, a total house-hold size, and the child’s gender (a dummy variable indicatingboy) and ethnicity (dummy variables indicating Asian American,African American, Hispanic, and other race). Household and par-ent information was also considered, including the mother’s psy-chological characteristics: maternal education level measured asyears of education, maternal cognitive abilities measured by re-ceptive vocabulary from the Peabody Picture Vocabulary Test(Dunn & Dunn, 1997), and maternal depression symptoms mea-sured by the Center for Epidemiological Studies Depression Scale(Radloff, 1977). Additional contextual sources of stress/supportwere considered, such as weekly working hours and marital statusindicating single mom. These variables were measured at thebaseline of 36 months of the child’s age—excluding child gender,ethnicity, and maternal education, which were measured at 1month.

Results

Nature of Changes in Home Learning Environment

To examine whether parents are likely to change their home envi-ronment during the preschool years, we compared scores of HomeLearning Environment from 36 to 54 months of children’s age. Weconducted analyses using a composite score of Home Learning En-vironment. First, a correlation coefficient between the 36-month and54-month Home Learning Environment composite scores was calcu-lated as a measure of the stability in the Home Learning Environment.Home Learning Environment composite scores at 36 months weresignificantly and positively associated with 54-month Home LearningEnvironment composite scores (r � .67, p � .001). Examininglongitudinal auto-correlations within each individual subscale showedsome variability but demonstrated overall significant correlations (seeTable 1).

Then, paired t tests determined the existence of changes in thehome by examining the differences in Home Learning Environ-ment composite scores across 36 and 54 months. Results showthat, on average, the 54-month Home Learning Environment com-posite scores (M � 27.32, SD � 3.46) were significantly higherthan the 36-month composite scores (M � 23.46, SD � 5.10),t(1,017) � 32.43, p � .001. Each scale of Home Learning Envi-ronment showed similar patterns; 54-month measures had signif-icantly higher scores than 36-month measures. The differences ofscores over time have effect sizes of medium to large (Cohen’s dfor home composite and Learning Materials is very large, ds �0.90 and 1.08, respectively; Language Stimulation, AcademicStimulation, and Variety of Experience had a medium effect size,ds � 0.65, 0.45, and 0.42, respectively).

The distribution of difference scores in the home was checkedand found to be normal (M � 3.85, SD � 3.79; skewness � 0.46;kurtosis � 0.73). This suggests that a substantial number ofparents changed their home learning environment over time ratherthan a small number of families changed a lot. In the distribution,parents who substantially improved their home quality (i.e.,changed their 54-month home scores from their 36-month homescores by more than 1 SD of 36-month scores of the sample, whichis the score changed beyond 5.10 points) composed 30.6% of thesample (n � 311), whereas only 0.6% (n � 6) dropped in thequality of home learning environment by more than 1 SD (i.e.,score change less than –5.10 points). Overall, analyses demon-strated that a substantial number of parents increased their HomeLearning Environment score during the preschool period.

Table 1Stability and Change in the Subscales of Home Learning Environment (HLE) Across Times (N � 1,018)

HLE measure

36-month scores 54-month scores

r t Cohen’s d CIM SD M SD

HLE composite 23.46 5.10 27.32 3.46 .67� 32.43� 0.90 [0.69, 1.22]Learning Materials 7.26 2.50 9.43 1.52 .61� 34.89� 1.08 [0.99, 1.23]Language Stimulation 6.02 1.14 6.62 0.71 .40� 17.79� 0.65 [0.61, 0.72]Academic Stimulation 3.37 1.22 3.86 1.06 .39� 12.25� 0.45 [0.38, 0.52]Variety of Experience 6.81 1.49 7.40 1.33 .51� 13.41� 0.42 [0.34, 0.51]

� p � .001.

1108 SON AND MORRISON

Effects of Home Learning Environmental Changes onChildren’s Developmental Outcomes

With the existence of significant changes in the home learningenvironment during the preschool period, we examined the impactof those changes on children’s language and academic outcomes.We were interested in determining whether just the level of homeenvironment matters or whether the history of changes matters aswell.

Preliminary analyses. Table 2 presents intercorrelationsamong the variables as well as means and standard deviations ofall variables used in the study. Significant associations were foundamong every pair of variables in the current study. Home LearningEnvironment subscales were significantly and positively associ-ated with children’s language skills (Expressive Language andExpressive Comprehension) as well as with their academic skills(Basic Concept, Letter-Word Identification, and Applied Prob-lems).

SEM. To examine the impact of changes in the overall homelearning environment on the development of children’s languageand academic skills during the preschool years, we conductedthe SEM using AMOS Version 7.0.0 (Arbuckle & Wothke, 1999).The home learning environment was represented by a latent factor,composed of indicators of four cognitive subscales. To directlycompare 36-month and 54-month Home Learning Environmentlatent factors, we constrained the factor loading of each indicatorof the 36-month factor to be the same as that of the 54-monthfactor. This method of “equality constraint” of factor loadingsenabled us to have the same latent factor structure across 36-monthand 54-month Home Learning Environment measures (Finkel,1995); thus, we were able to examine the changes in the HomeLearning Environment over time, not the changes within its factorstructure.

Each outcome measure of children was also represented by alatent factor. Every latent factor was indicated by two subscaleswith the exception of children’s 36-month academic skills, whichhad only one indicator. To counter the reliability/unreliability ofthe single 36-month indicator, we fixed the error variance of theindicator with the value of “variance � (1 � �),” which is(2.89)2 � (1 � 0.97) � 0.25, following Hayduk’s (1987) recom-mendation. All the indicators loaded significantly on their respec-tive latent factor.

For a measure of change, we used the residual (R) of the54-month Home Learning Environment. The residual is the unex-plained variance in the 54-month Home Learning Environmentafter the effect of the 36-month Home Learning Environment hasbeen partialled out. Unstandardized residual obtained through re-gression had a mean of 0 and a standard deviation of 2.57 and wasclose to normal distribution with a little peaked distribution in themid range (skewness � –0.86; kurtosis � 1.76).

To test the impact of changes in home learning environment, weincluded paths from the residual (R) of the 54-month Home Learn-ing Environment to children’s 54-month outcomes. Second, theimpact of the concurrent home learning environment was tested bya path from 54-month Home Learning Environment to children’s54-month outcomes. Lastly, a path from the residual as well as apath from the 54-month Home Learning Environment were in-cluded in one model to assess the unique impact of home change.We controlled 54-month home environment measures (instead of T

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1109CHANGES IN THE HOME LEARNING ENVIRONMENT

the baseline 36-month home) because (a) our sample seemed toinclude two major groups of children, one who had consistenthigh-quality home learning environment and another with im-proved home over time, and (b) high and negative correlationsexisted between initial quality and change measure (r � –.836).

In examining the change impact on children’s 54-month out-come measures, 36-month measures of the skills were controlledso that every 54-month-child skills measure became a “develop-ment” score of the variable controlling for its initial score. Severaldemographic factors were included in the SEM, including house-hold income and size; the child’s gender and ethnicity; and themother’s marital status, years of education and abilities, workinghours, and depression symptoms. These variables were controlledby allowing paths between these variables and all other latentfactors in the model. For all the paths, standardized path coeffi-cients were reported that assess the direct effect and that can beused as effect size, r (Kline, 2005).

Home changes and language development. First, we exam-ined the impact of home learning environmental changes on chil-dren’s language development. When we included a path from theresidual, but not the path from the 54-month home scores, to childlanguage skills at 54 months, changes in Home Learning Environ-ment predicted language development significantly (� � .20, p �.001). Without a path from the residual to child’s 54-month lan-guage skills, the most current or concurrent Home Learning En-vironment also predicted language development significantly (� �.15, p � .001).

Then, paths were included from the 54-month Home LearningEnvironment and the residual in Home Learning Environment tothe children’s language skills in one model. Results demonstratethat the development of children’s language skills was still pre-dicted positively by the changes in the Home Learning Environ-ment from 36 to 54 months (� � .17, p � .01) after the effects of

the 54-month Home Learning Environment and demographic fac-tors were controlled (see Figure 2). However, the 54-month HomeLearning Environment was not a significant predictor of children’slanguage development any more (� � .04, ns). The model pro-vided a good fit, �2(148, N � 1,018) � 734.35, p � .000,comparative fit index (CFI) � .922, normed fit index (NFI) �.906, incremental fit index (IFI) � .923, root mean square error ofapproximation (RMSEA) � .062.

Home changes and academic development. The secondmodel of children’s academic skills provided a different pattern.When we included a path from the residual but not a path from54-month Home Learning Environment to child’s academic skills,changes in Home Learning Environment predicted academic de-velopment significantly (� � .14, p � .01). Without a path fromthe residual to the child’s 54-month academic skills, the concurrent54-month Home Learning Environment also predicted academicdevelopment significantly (� � .19, p � .001).

Finally, we included both paths from the residual and from theconcurrent Home Learning Environment to 54-month academicskills to examine the unique predictability (see Figure 3). Resultsshow that the development of children’s academic skills was notpredicted by the positive Home Learning Environment changesfrom 36 to 54 months (� � .00, ns) after controlling for the effectsof concurrent Home Learning Environment and demographic fac-tors. However, the concurrent 54-month Home Learning Environ-ment was still a significant predictor of academic development(� � .19, p � .001). The model fit was moderately good, �2(127,N � 1,018) � 757.46, p � .000, CFI � .903, NFI � .888, IFI �.905, RMSEA � .070.

In all models, the Home Learning Environment tended to behighly stable from 36 to 54 months (�s � .73–.86). However,some changes existed in the Home Learning Environment; further-more, the degree of improvement in the Home Learning Environ-

Figure 2. Structural equation modeling of the impact of changes in home learning environment (HLE) on thedevelopment of children’s language skills. A dotted line indicates a path with a nonsignificant coefficient. Allthe factor loadings were statistically significant at the level of p � .001. �� p � .01. ��� p � .001.

1110 SON AND MORRISON

ment was positively related to children’s language and academicskills. When we controlled the concurrent Home Learning Envi-ronment, home changes uniquely and positively predicted chil-dren’s language skills. The results were consistent even after weincluded language and academic outcomes in one SEM model andcontrolled for the covariance between the two child outcomes: asignificant and unique impact of home changes on the develop-ment of language skills (� � .18, p � .01) but not on academicskills (� � –.03, ns). This suggests the reliability of the impact ofhome changes on children’s language skills.

Additional testing of specificity within the Home LearningEnvironment. To further examine changes in which specificsubscales of the Home Learning Environment contributed to chil-dren’s developmental outcomes, we ran four additional structuralequation models for each child outcome with a Home LearningEnvironment latent factor indicated by each one cognitive sub-scale. Again, residual is the measure of change, controlling for theconcurrent home measure and the same set of background vari-ables as in the original SEMs.

Changes in Academic Stimulation significantly predicted thedevelopment of language skills (� � .18, p � .05), and changesin Language Stimulation also marginally predicted the languagedevelopment with the largest effect size (� � .33, p � .10).Changes in Learning Materials had a modest effect size butwere nonsignificant (� � .28, ns). Variety of Experience didnot predict language development (� � .00, ns). Changes in anysubscale did not predict academic skills, whereas some of theconcurrent subscales were significant predictors; the concurrentlevel of 54-month Language Stimulation (� � .15, p � .01),Academic Stimulation (� � .16, p � .01), and Learning Ma-terials (� � .19, p � .01) significantly predicted academic skillsdevelopment, but Variety of Experience (� � .07, ns) did not.The tests of specificity demonstrated that the significant impact

of changes in Home Learning Environment on language devel-opment was largely the result of Language Stimulation andAcademic Stimulation.

Covariates of Changes in Home LearningEnvironment

To examine characteristics of families and parents who im-proved their home learning environment during their child’sschool transition and thus led to an improvement in children’sdevelopmental outcomes, we regressed changes in the HomeLearning Environment on a set of family, parent, and child back-ground factors using SEM.

These factors were chosen on the basis of the previous literatureon the home learning environment and its change (e.g., Belsky,1984; Garrett et al., 1994; Menaghan & Parcel, 1995; Votruba-Drzal, 2003), and they were the control variables in the analysis ofthe impact of home changes in the previous section. Factorsincluded family resources (household total income and householdsize), child characteristics (gender indicating boys, and ethnicitydummy variables indicating Asian, African American, Hispanic,and other race), parental psychological characteristics (maternaldepression symptoms, years of education, and cognitive abilities asmeasured by receptive vocabulary), and contextual sources ofstress/support (marital status indicating single mom and workinghours per week). Before entering into the final model, each vari-able was tested as a single predictor of change (i.e., residual) inSEM. All but child gender were significant covariates of change;furthermore, the significant covariates were simultaneously in-cluded in the final model with covariance between factors and the36-month Home Learning Environment and covariance amongfactors controlled.

Figure 3. Structural equation modeling of the impact of changes in home learning environment (HLE) on thedevelopment of children’s academic skills. A dotted line indicates a path with a nonsignificant coefficient. Allthe factor loadings were statistically significant at the level of p � .001. ��� p � .001.

1111CHANGES IN THE HOME LEARNING ENVIRONMENT

Results show that 22% of the variance in changes in the HomeLearning Environment was explained by the group of covariates.Controlling for the associations between covariates and the base-line Home Learning Environment and the associations amongcovariates, mother’s years of education (� � .16, p � .05) andworking hours per week (� � .15, p � .01) were positively relatedto improvement in the Home Learning Environment; maternaldepression symptoms were negatively related to home improve-ment (� � –.22, p � .001; see Figure 4). Child ethnicity, house-hold income and size, marital status, and mother’s cognitive skillswere no longer significant factors of changes in the Home Learn-ing Environment. Most of the covariates were associated with thebaseline Home Learning Environment as expected; however,mother’s working hours was not significantly associated with thebaseline home.

These findings identify important predictors of home changes;however, they do not provide a consolidated view of the combinedeffect of these factors. In real life, these predictors do not sepa-rately work for home changes; rather all the factors would acttogether, illustrating mothers with a combination of certain char-acteristics. Thus, examining potential profiles of mothers wouldprovide a more realistic view of who were more likely to changetheir home learning environment and be responsive to the chang-

ing needs of their young children as the children approachedschool transition. Using the SEM equation of covariates of homechanges, we calculated average changes in the Home LearningEnvironment for hypothetical mothers with varying characteristicsin our significant covariates. To calculate the amount of changepredicted for the mother who is at the average on all covariates, weentered the mean value of all variables for the sample, that is, bymultiplying the unstandardized path coefficients for that variableby the variable mean and then summing averages changes associ-ated with the variables. Similarly, we estimated predicted changesin the Home Learning Environment for specific groups of mothersby including specific values (i.e., mean � 1 SD for high group,mean � 1 SD for low group for significant continuous variables ofmaternal depression symptoms, years of education, and workinghours), leaving all other covariates’ values at their means. Finally,we calculated for those who have a positive profile (i.e., who havea combination of ideal characteristics for home changes withhigher scores in the positive predictors of home changes and lowerscores in the negative predictors of home changes) by replacingmean � 1 SD for all positive continuous covariates and mean – 1SD for all negative continuous covariates, 1 for all positive cate-gorical covariates and 0 for all negative categorical covariates, andvice versa for mothers with a negative profile (i.e., who have a

Figure 4. Structural equation modeling of covariates of changes in home learning environment (HLE). Adotted line indicates a path with a nonsignificant coefficient. All the factor loadings were statistically significantat the level of p � .001. Covariance among covariates were included in modeling but omitted in the figure forvisual simplicity. � p � .05. �� p � .01. ��� p � .001.

1112 SON AND MORRISON

combination of disadvantaged characteristics for home changeswith lower scores in the positive predictors of home changes andhigher scores in the negative predictors).

Figure 5 shows the predicted change for average mothers andfor those who had high/low depression symptoms, education, andworking hours with average characteristics in other covariatesand for those who had positive/negative profiles. Average mothersmade positive changes (0.68) in their residual of Home LearningEnvironment over time. Mothers who had low depression symp-toms made more changes (0.82), which was similar to motherswith more education (0.79) and with more working hours (0.78).Mothers who had high depression symptoms were less likely tomake changes (0.54), like mothers who had less education (0.57)or worked less hours (0.58). Whereas these calculations furtherillustrate the extent of changes predicted for mothers with specificcharacteristics, these scenarios hold constant in other backgrounds;in reality, mothers with high depression symptoms are more likelyto have less education and not have a full-time regular job andwork less hours. When we considered all covariates simulta-neously, mothers with a negative profile made negative changes intheir home learning environment (�0.51); at the end of the con-tinuum, mothers with a positive profile made changes more thantwice of average mothers (1.85).

Discussion

By exploring the existence of changes in the home learningenvironment, the current research extends the developmental viewof parenting (Sameroff & Chandler, 1975; Scarr & McCartney,1983) and suggests the importance of parental changes to youngchildren’s language skills but not to academic skills over theschool transition period. By examining changes using a statisti-cally rigorous method (i.e., SEM using a residual; Raykov, 1993),we estimated in a robust way the impact of home improvementover time, unique from the impact of contemporaneous homeenvironments. In this section, we discuss the results in detail andconclude with suggestions for future research and intervention.

Did Parents Change Home Learning Environments asChildren Approached School Entry?

Most parents tended to provide a relatively stable home learningenvironment quality across the age period from 36 to 54 months.Simultaneously, the comparison of raw composite scores of the

home environment showed that, overall, parents scored higher intheir Home Learning Environment measure at 54 months than at36 months. A similar pattern was found for all subscales; parentssignificantly increased scores of each subscale with medium tohigh effect sizes. Interestingly, autocorrelations showed that thecorrelations of composites were stronger than the correlations forthe four subscales. This suggests that whereas parents tended toprovide stable home learning environment quality overall, theymay be more likely to change their relative focus on individualsubscales or aspects of home learning environments; thus, thedegree or direction of change in subscales may vary considerablyamong parents.

The results suggest that stability and change in the home learn-ing environment were related to each other, simultaneously occur-ring over time. Combined results (i.e., overall scores increased butmost parents remained in their relative ranks) imply either that asubstantial number of parents increased home scores over time ina similar fashion or that a small number of families were changinga lot. Analyses showed that most parents showed a modest changein their home learning environment between 36 and 54 months ofage (i.e., change less than 1 SD). Some, however, changed theirhome environment more substantially (about 30%), and most ofthem showed an increase (about 98% of the parents who changed).Overall, a substantial number of parents improved their homelearning environment quality as their children approached schoolentry. This confirms our hypothesis that the school transitionperiod provides a good opportunity to observe developmentalchanges in home learning environment and to examine the impactof those changes on children’s development.

Did the Home Learning Environmental ChangesUniquely Contribute to Children’s Language andAcademic Development?

In studying the preschool period, a time when some significantchanges existed in the home, we found that changes in the homelearning environment were positively related to children’s devel-opment of language and academic skills. When we controlled54-month home environments, changes in the home uniquelypredicted language development; children who experienced homeenvironment improvement showed developmental changes in lan-guage. However, the level of concurrent home environment did notadd more explanation of children’s language over and beyond the

-0.5 0 0.5 1 1.5 2

Average Mother .68

Average Mother with High Depression Symptoms .54Average Mother with Low Depression Symptoms .82

Average Mother with More Education .79Average Mother with Less Education .57

Average Mother Who Work More Hours .78Average Mother Who Work Less Hours .58

Mother with a Positive Profile 1.85Mother with a Negative Profile −.51

Figure 5. Predicted changes in home learning environment for hypothetical mothers (unstandardized coeffi-cients).

1113CHANGES IN THE HOME LEARNING ENVIRONMENT

impact of changes in home environment. Results imply that thehistory of improvement in home environments matters for lan-guage development more than just the current level of homeenvironment. Even if the contemporaneous level of stimulation ishigh, the stimulation will not make a difference in language skillsin a very short span of time; rather, consistently increasing stim-ulation would accumulate to influence children’s language devel-opment over time more than a stable amount of, or a decrease in,stimulation. Improvements in language stimulation seemed to es-pecially lead the development of children’s language skills (i.e.,biggest effect size with statistically significant at a trend level)prior to kindergarten. Improvements in academic stimulation mayimplicitly contribute to children’s language development, too(Connor, Morrison, & Slominski, 2006).

Children’s academic skills seemed to be stimulated by the homeenvironment in a different pattern. Changes in home learningenvironments were not significantly related to the development ofacademic skills above and beyond the impact of the concurrenthome learning environments. Changes in any of the home sub-scales did not lead to academic skills development significantly.Instead, children seemed to grow significantly their academicskills when their parents provided high quality of language stim-ulating and academically stimulating environments and learningmaterials in the home concurrently. Especially, concurrent learn-ing materials at home had the biggest effect size and seemed toespecially lead the development of children’s academic skills. Thissuggests that what parents are doing contemporaneously may havean immediate influence on children’s academic skills. Irrespectiveof the earlier quality of the home learning environment, if parentsprovide high level stimulation later, just before their children enterkindergarten, their efforts may support children’s academic skillsdevelopment.

The unexpected differential impact of home changes on lan-guage versus academic skills might be related to different charac-teristics of developmental domains. Language skills are broad-based skills that tend to be developing in a more longitudinal basis,that is, being influenced by parental input very early (Hoff, 2003;Huttenlocher, Haight, Bryk, Seltzer, & Lyons, 1991) and consis-tently and longitudinally from earlier practices (Dickinson &Smith, 1994; Weizman & Snow, 2001), thus they may be morelikely to be responsive to the history of home environments (i.e.,change) over time. In contrast, academic skills tended to be com-posed of a set of skills, each of which may develop in a gradedpattern with a mastery of new skills, along with improvement ofexisting skills, and may be more directly relevant for learningthrough immediate and targeted stimulations (Entwisle & Alex-ander, 1990; Pungello, Kuperschmidt, Burchinal, & Patterson,1996). This may explain current and previous findings of thesignificant impact of contemporaneous home environments onacademic skills (e.g., Bradley & Caldwell, 1987; Evans, Shaw, &Bell, 2000; Storch & Whitehurst, 2001).

Current results demonstrate the first empirical evidence of theexistence and the impact of home changes during school transition.The significance of the current results can be even more high-lighted with the rigorous test of the impact of changes using aresidual analysis with SEM (Raykov, 1993). Here, residual is avariance in a later home measure that is not explained by itsrelation with an initial measure (i.e., correlation). In other words,the residual captures a change, if there is any, beyond an average

change in the group (i.e., overall increase in the current sample)that the correlation cannot explain. Thus, current results confirmthat although there are normative changes in school transitionperiods, some parents seem to change the home learning environ-ment substantially more than an average change and that thechange matters for young children’s developmental outcomes.

Who Were the Parents Who Improved Their HomeLearning Environment at School Transition?

Among the potential covariates of home changes, maternal yearsof education, working hours, and depression symptoms were sig-nificantly associated with the degree of change in the home learn-ing environment. Maternal education has been reported as one ofthe most significant predictors of the home learning environment(Baharudin & Luster, 1998; Menaghan & Parcel, 1991; Miller &Davis, 1997) and child development and learning (Korupp, Gan-zeboom, & van der Lippe, 2002; Mistry, Jeremy, Chien, Howes, &Benner, 2008). Mothers’ ability to provide a high quality andresponsive (to children’s changing needs) home environmentseems to be predicted by their education level, which may tap intotheir own experiences and value of the importance of education,knowledge of what consists of a good home environment or theability to provide a good environment, or to have and transferparental resources (Curenton & Justice, 2008; Korupp et al., 2002).Similarly, maternal occupational status has been noted as a pre-dictor of home environment and its change (Menaghan & Parcel,1991, 1995) as well as child outcomes (Korupp et al., 2002;Vandell & Ramanan, 1992). Mothers who worked more hours orworked full-time tended to be more educated and to have higherearnings than mothers who worked less hours. However, becauseour SEM model controlled family income as well as mothers’education as covariates, our variable of mothers’ working hoursmay represent unique characteristics related to occupational statussuch as regularity of work routine or contextual stability forfull-time working mothers or irregularity or stress for part-timeworking mothers. The stress or inconsistency of out-of-familydemands may deflect parental attention from young children (Me-naghan & Parcel, 1991) and lead to less change in home learningenvironments.

With the highest effect size among covariates, maternal depres-sion symptoms were negatively related to changes in the homelearning environment. Previous research demonstrated that moth-ers with depressive symptoms were less likely to be sensitive tochildren’s needs, which led to poor development of children(NICHD Early Child Care Research Network, 1999; Patterson &Albers, 2001). It is likely that mothers with higher depressionsymptoms may not be available mentally to their young childrenand may not be responsive to their developmental needs; therefore,they may not change their home learning environment at the timeof school transition.

It is notable that we did not find family income as a uniquecovariate of home changes. Although previous research studiespresented the linkage between family income and home environ-ments (Bradley, Corwyn, McAdoo, & Coll, 2001; Menaghan &Parcel, 1991), after controlling other family and maternal charac-teristics, financial resources no longer predicted home changes. Itmay be the case that the learning materials such as books orout-of-home educational visits or experiences cost relatively little

1114 SON AND MORRISON

so that their consumption depends more on parental value or tasterather than income itself (Mayer, 1997). Thus, income alone maynot bring about substantial changes in the provision of learningmaterials or experiences. Or, it may be the case that income isrelatively volatile compared with other covariates, and thus needsto be modeled of its dynamics, which the current study did notcontrol (Dearing, McCartney, & Taylor, 2001).

Identifying the significant impact of mothers’ education, workinghours, and depression symptoms suggests that interventions targetinghome improvement may need to address mothers’ needs of resources.Programs with a multigeneration aspect (e.g., family literacy programand Head Start) that provide career education, job placement, mentalhealth services, and parenting education may be effective in boostingparents’ psychological and financial resources. This helps mothersbecome more sensitive to their children’s changing needs, thus im-proving their home stimulation. In addition, early interventions maytarget at-risk mothers with low education, part-time occupation status,and depressive symptoms. Although catch-all types of programs witha variety of parent-focused services may not be as effective as targetedprograms (St. Pierre, Games, Alamprese, Rimdzius, & Tao, 1998), aslong as programs have specific goals based on children’s needs (e.g.,early literacy or numeracy skills development; Berlin, O’Neal, &Brooks-Gunn, 1998) and directly aim home stimulation (Brooks-Gunn, Berlin, & Fuligni, 2000), they can be successful in improvinghome learning environment quality and children’s school readiness.

Implications

This study provides the empirical evidence of whether parentsare changing their home environment as children approach schoolentry and whether these changes are linked to children’s languageand academic skills development. In the study, we found thatmothers who had more education, worked full-time, and hadlow-level depression symptoms were more likely to improve theirhome environment quality. The home change has significant im-plications for children’s school readiness, specifically their lan-guage but not their academic skills.

Exploring how parents provide and modify their home learningenvironment during their child’s early childhood not only revealsthe empirical evidence of dynamics in home contexts and theimpact of variations within families but also demonstrates infer-ential evidence for early intervention. By modeling changes inhome environments, the current study may provide relevant justi-fications for early intervention programs on improving parentingby assessing the likely effects of policy-driven changes in homelearning environments. First implication includes utilizing schooltransition period as an opportune time of home change. Parents aremore likely to change their home environment with overall in-creasing patterns as children approach school entry; thus, applyingthe intervention at this time is more likely to result in parentingchanges. Second, the current results of the impact of parentalchanges suggest that intervention efforts to enhance home envi-ronments may be effective in improving children’s school readi-ness. Specifically, the differential impact on language versus ac-ademic skills suggests that a short-term home interventiontargeting school readiness may be more effective for academicskills than language skills (i.e., significant concurrent impact onacademic skills). Because academic skills may require more ex-plicit stimulation, these interventions need to be very specific. In

contrast, interventions targeting language development may notexpect a cheap instant fix. Because language development seemsto be receptive to the history of longitudinal home stimulation, along-term intervention targeting gradual improvement of homeprovides better language stimulating experiences for young chil-dren. Finally, interventions may target at-risk populations of moth-ers who have depression symptoms with low education and irreg-ular work situations. On the basis of findings, future studies maydesign evidence-based programs to improve at-risk children’shome learning experiences, which will help provide equality aschildren begin school.

On the basis of current study, future studies may need toscrutinize how much of the changes are really due to parentalresponsivity to children’s changing needs or social expectationsrelated to school readiness (i.e., developmental changes; Collins &Madsen, 2002) or how much of the changes are due to otherchanges in family life such as family income or occupationalchanges (i.e., short-term or situational change; Dearing & Taylor,2007; Menaghan & Parcel, 1995; Votruba-Drzal, 2003). We partlyaddressed this issue by capturing changes more than the averagechange by utilizing a residual analysis that may partial out possiblerandom changes due to family or work situations. However, ourgroup of covariates only explains 22% of variance in the residual.The unexplained variance may be explained by (a) changes incovariates over time and (b) covariates representing parental be-liefs or expectation. Future studies may explicitly delve into rea-sons why parents changed their home learning environment, in-cluding developmental versus situational reasons, and consider thecontribution of static as well as time-varying covariates.

Another implications for future studies include modeling recip-rocal processes (Sameroff & Chandler, 1975). Simply examiningthe impact of parenting changes on child development does notgive us the complete picture; change itself is a reciprocal processemanating from both members of the dyad (Collins & Madsen,2002). Because the parent–child relationship is dynamic, chil-dren’s behavior also influences parenting practices (Rutter, Pick-les, Murray, & Eaves, 2001). Although this means that attention tothe joint contribution of both parents and children is necessarywhen examining changes in the home, we were not able to exam-ine the reciprocal relationships with limited data points in thecurrent endeavor. Future studies may address the full dynamics ofhome and child development at the time of school transition,examining how one change leads to another. This line of researchcan also address both whether some types of children with specificcharacteristics may lead parents to change more and whetherparental changes matter more or less for children with specificcharacteristics (i.e., interactions between home learning environ-ment and children).

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Received April 17, 2008Revision received March 26, 2010

Accepted April 19, 2010 �

Call for Nominations

The Publications and Communications (P&C) Board of the American PsychologicalAssociation has opened nominations for the editorships of Journal of ExperimentalPsychology: Learning, Memory, and Cognition; Professional Psychology: Researchand Practice; Psychology, Public Policy, and Law; and School Psychology Quarterlyfor the years 2013–2018. Randi C. Martin, PhD, Michael C. Roberts, PhD, RonaldRoesch, PhD, and Randy W. Kamphaus, PhD, respectively, are the incumbent editors.

Candidates should be members of APA and should be available to start receivingmanuscripts in early 2012 to prepare for issues published in 2013. Please note that theP&C Board encourages participation by members of underrepresented groups in thepublication process and would particularly welcome such nominees. Self-nominations arealso encouraged.

Search chairs have been appointed as follows:

● Journal of Experimental Psychology: Learning, Memory, and Cognition, LeahLight, PhD, and Valerie Reyna, PhD

● Professional Psychology: Research and Practice, Bob Frank, PhD, and LillianComas-Diaz, PhD

● Psychology, Public Policy, and Law, Peter Ornstein, PhD, and Brad Hesse, PhD● School Psychology Quarterly, Neal Schmitt, PhD, and Jennifer Crocker, PhD

Candidates should be nominated by accessing APA’s EditorQuest site on the Web.Using your Web browser, go to http://editorquest.apa.org. On the Home menu on the left,find “Guests.” Next, click on the link “Submit a Nomination,” enter your nominee’sinformation, and click “Submit.”

Prepared statements of one page or less in support of a nominee can also be submittedby e-mail to Sarah Wiederkehr, P&C Board Search Liaison, at swiederkehr@apa.org.

Deadline for accepting nominations is January 10, 2011, when reviews will begin.

1118 SON AND MORRISON

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