An Empirical Investigation of Relationships between Junior

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1　Background of the study

　U.S. studies on parental involvement (PI) indicate that parenting practices vary by families’ socioeconomic status (SES) (e.g., Lareau 2003) and that different degrees of PI differentiate students’ academic achievement (e.g., Hill and Tyson 2009). PI differences based on parents’ SES are considered

one source of the achievement gap. While some scholars (e.g., Honda 2008) address this critical topic in

Japanese society, existing studies using regional and/or retrospective data without a rigorous indicator

of students’ academic abilities fall short of investigating relationships between students’ family SES,

Article

An Empirical Investigation of Relationships between Junior High School Students’ Family Socioeconomic Status,

Parental Involvement, and Academic Performance in Japan

理論と方法 (Sociological Theory and Methods） 2014, Vol.29, No.1:147-165

数理社会学を社会調査の授業に埋め込む？

Abstract

　U.S. studies on parental involvement (PI) indicate that parenting practices vary by families’ socioeconomic

status (SES) (e.g., Lareau 2003) and that different degrees of PI differentiate students’ academic achievement

(e.g., Hill and Tyson 2009); PI differences based on parents’ SES are considered one source of the achievement

gap. While some scholars (e.g., Honda 2008) address this critical topic in Japanese society, existing studies

using regional and/or retrospective data without a rigorous indicator of students’ academic abilities fall short

of investigating relationships between students’ family SES, the degree of PI, and their achievement at one of

the most important stages of education: compulsory education. This study is therefore intended to empirically

investigate these relationships by analyzing nationally representative data of Japanese eighth-grade students.

　This study’s results indicate that (1) higher SES parents tend to more frequently ask their children what they

study in school; (2) the school-level PI indicator is not equally distributed socioeconomically, and School SES

relates to the degree of PI in school activities; and (3) the degree of PI and school PI in school activities are

associated with students’ mathematics achievement. Contrary to expectations, however, PI mediates small parts

of SES effects, especially at the student level; only some of the relationships between SES, PI, and achievement

are verified empirically.

Keywords and phrases: Parental involvement, concerted cultivation, compulsory education, multilevel, TIMSS

Ryoji MATSUOKA

Waseda University

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the degree of PI, and their achievement at one of the most important stages of education: compulsory

education. Since students’ academic performance in the ninth grade influences their subsequent

educational and occupational achievement (e.g., Honda 2008), it is important to empirically test whether

PI varies with families’ SES and if its disparity relates to students’ academic performance. This study is

therefore intended to empirically investigate these relationships by analyzing nationally representative

data of Japanese eighth-grade students.

1.1　The relationship between family SES and parental involvement

Arguably, “Unequal Childhoods” by Lareau (2003, 2011) is one of the most influential studies in PI.

In this qualitative study, she coins the term “concerted cultivation,” which is a cultural logic of middle-

class mothers’ parenting practices. These mothers structure children’s daily lives (e.g., by scheduling

extracurricular activities for their time outside school), emphasize the importance of language use by

reasoning and negotiating with their children, and actively interact with social institutions (e.g., school)

to develop their children’s cognitive and social abilities. Meanwhile, working-class mothers follow the

“accomplishment of natural growth” logic, which emphasizes children’s development without rigorous

guides. Disadvantaged mothers do not structure children’s time as much as middle-class mothers do, use

directive and restricted language codes when talking to their children, and tend to avoid interacting with

social institutions. Studies using longitudinal U.S. data empirically support Lareau’s qualitative findings.

In fact, strong relationships between parents' social class and concerted cultivation were found for

elementary school years (e.g., Cheadle and Amato 2011).

Building on Lareau’s study, Bennett, Lutz, and Jayaram (2012) interviewed parents at two urban

middle schools, finding that middle-class and working-class youth participate in different types of

school activities. Specifically, middle-class parents attempt to customize their children’s participation in

extracurricular activities to develop their talents and interests, while working-class parents emphasize

the importance of safety. In addition, working-class children are involved in fewer non-school activities.

These class differences stem from financial and institutional constraints (i.e., less access to institutions

other than school and church) (Bennett, Lutz, and Jayaram 2012). This argument is consistent with Chin

and Phillips (2004), who, based on ethnographic data, report social class differences in the quality and

quantity of fourth-grade children’s involvement in activities during summer stemming from parents' different levels of access to various resources (e.g., money and networks).

Studies conducted in Japanese society also identified relationships between social class and parental

educational involvement/strategies (e.g., Kataoka 2001) and between social class and child rearing (e.g.,

Kanbara and Takata 2000). While these studies focus on each specific relation (i.e., if parenting style

differs by SES group), following Lareau’s study, Honda (2008) assesses the relationships between SES,

parenting practices, and educational outcomes. Specifically, she interviewed 39 mothers whose children

were elementary school students (from fourth–sixth grades), then analyzed survey data on youth (aged

15–29 years) and their mothers (1890 pairs) in Japan. Honda’s (2008) interviews revealed that mothers

with college degrees have higher expectations, actively intervene in home education, and intensively

An Empirical Investigation of Relationships between Junior High School Students’ Family Socioeconomic Status, Parental Involvement, and Academic Performance in Japan

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use shadow education services (e.g., lessons at juku that should be paid for). In contrast, other mothers

seem to implement home education “naturally.” Honda (2008) contends that these different parenting

practices are aligned to Lareau’s observations of the “concerted cultivation” practiced by middle-class

mothers and “accomplishment of natural growth” by working-class mothers. Then, Honda’s (2008)

quantitative analysis of mothers’ retrospective responses about their child-rearing styles when their child

was attending elementary school showed that mothers in higher SES families tend to engage in various

aspects of parenting practices: both “rigorous” child rearing (i.e., enforcing discipline at home, high

expectations for children’s academic performance, and using shadow education services) and “natural” parenting (i.e., listening to children’s desires, allowing children to play outside, and letting them to have

various sorts of experiences). However, in contrast to their lower SES counterparts, higher SES mothers

practice the former style more often.

Worth mentioning is that Sugihara (2011), based on data from four cities collected from parents of

fifth-grade students, also reports differences in mothers’ parenting (e.g., giving picture books to their

children) based on education qualifications. She found differences in children’s enrollment in enrichment

lessons offered by shadow education institutions, children’s learning time, and education expenses

according to both parents’ education backgrounds.

In the Japanese education context, it is important to point out that Honda (2008) considers the use of

shadow education services as part of the “rigorous” parenting style, which is similar to Lareau’s “concerted

cultivation” in terms of organizing extracurricular activities for children. A number of studies (e.g.,

Holloway et al. 2008; Matsuoka 2013; Yamamoto and Brinton 2010) found that family SES influences

children’s participation in additional lessons at shadow education institutions (e.g., juku and yobiko) in both

elementary and secondary education. Furthermore, studies have also indicated that parental school choice

differs according to family SES (e.g., Oshio 2012).

1.2　The relationship between parental involvement and students’ achievement

Lareau (2003; 2011) contends that parenting differences between middle-class and working-class

mothers contribute to students’ levels of engagement in structured activities, and these differences result

in an achievement gap and different life trajectories, reproducing social class advantages for the middle

class and disadvantages for the working class. Furthermore, a series of quantitative studies show that

PI positively relates to various aspects of educational outcomes,1） while these outcomes are differently

defined and measured: academic achievement (e.g., Hill and Tyson 2009), reduction of problem

behaviors (e.g., Domina 2005), middle-school students’ placement in ability groups (Useem 1992), self-

efficacy and intrinsic motivation toward English and mathematics (Fan and Williams 2010), school

persistence at upper secondary education level (e.g., McNeal 1999), or choice of majors in college (Ma

2009). As for PI in shadow education, a recent study by Park and his colleagues (Park, Byun and Kim

2011), who analyzed longitudinal data collected in South Korea over two years, found that parents’ efforts with regard to choosing and monitoring private tutoring services are related to middle-school

students’ performance in math and English.

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Likewise, studies conducted in Japan show that PI is associated with educational outcomes. For

example, Uzuki (2004) shows that mothers’ attitudes toward education influence elementary school

students’ learning hours, and mothers’ expectations for their children’s education shape whether their

children desire college education; parents’ daily approaches and high expectations for their children are

meaningful. Moreover, Honda (2008) tests relationships between the two parenting styles and various

aspects of educational outcomes by analyzing mother–child pair data.2） The results of a series of multiple

regression analyses indicate that “rigorous" parenting relates to children’s academic performance in the

ninth grade. Importantly, ninth-grade academic performance is associated with whether children received

four-year college education or higher, which in turn determined if they were employed full time, which

afforded them a higher income (Honda 2008). Honda (2008) concludes that the “rigorous” parenting

style is critical in shaping children’s academic performance at the ninth-grade level, which subsequently

influences their academic background, employment status, and income level. As such, parenting style

during children’s elementary school years directly influences their achievement at the compulsory

education level and indirectly affects their subsequent educational and occupational accomplishments.

Other studies (e.g., Katase and Hirasawa 2008) also indicate that parental strategies (i.e., using shadow

education in the ninth grade) relate to subsequent educational achievement.

　Some U.S. studies (e.g., Desimone 1999) show that PI effects also vary with SES group. For

instance, lower SES students obtain less benefits from discussions with their parents, even when they

have the same level of parent-child discussion as higher SES counterparts do. Conversely, higher SES

students are less likely to drop out of high school because of discussions with parents, but this effect

was not observed among low SES students (McNeal 1999). Furthermore, equal PI does not produce

identical education results (e.g., Dumais, Kessinger and Ghosh 2012). Park (2008), based on a large-

scale international examination (PISA2000) of 14 countries, reported a positive association between

PI and high school freshmen’s reading literacy in Japan. In addition, Park (2008) identified a negative

statistically significant interaction between an index of child–parent communication and students’ SES

(p < 0.1). Essentially, when PI is equal, the effect of PI is stronger for low SES parents. However, for

more specific child–parent communication regarding schooling, while it remains negative, the interaction

between PI and students’ SES is not significant statistically (Park 2008). This result seems to indicate no

difference in SES effects on specific PI in students’ educational outcomes in Japan.

2　Rationale of the study

　The literature indicates that parenting practices are likely to vary according to family SES, and

differences in PI contribute to the achievement gap. These relationships, however, have not been

rigorously studied in lower secondary education, the last stage of Japanese compulsory education

before students are sorted into different school-based tracks that shape their education trajectories.

In addition, previous research uses regional and/or retrospective data that do not include rigorous

information on students’ academic performance. Furthermore, the studies rely on single-level analyses,


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even though students are nested in schools; student characteristics in one school tend to be similar, likely

producing spurious significant estimates (e.g., Hox 2010). To overcome these issues, this study analyzes

nationally representative large-scale data that include detailed information regarding students’ academic

performance by employing multilevel techniques that consider its nested structure.

It should also be highlighted that this is the first empirical attempt to address school-level PI and its

relation to students' academic performance in Japanese society. This is critically important because

using multilevel techniques and assessing school-level factors could clarify the achievement gap between

schools and the school factors related to the gap in compulsory education. Certainly, previous studies

do address school differences in Japanese compulsory education, but these compare private/national

schools with public counterparts; higher SES students tend to attend private junior high schools (e.g.,

Kataoka 2009), and those attending non-public schools demonstrate higher academic performance (Taki

2012). Since most students attend neighborhood public junior high schools in Japan and lower SES

students who cannot afford shadow education institutions depend on public schooling (Kariya 2004),

it is imperative to investigate whether school factors relate to or explain the achievement gap between

schools, while controlling for types of schools (i.e., public and private/national schools). If PI at the

school level is unequally distributed and this partly explains the achievement gap between schools, it

could be considered as a mechanism of how the gap emerges and persists.

3　Research Questions

　This study attempts to unravel the relationships between family SES, PI, and student achievement by

addressing the following four research questions.

(1)　Does the degree of PI vary according to family SES?

　Following the literature (e.g., Honda 2008) based on regional and/or retrospective data without

rigorous information regarding students’ academic skills, this study hypothesizes that higher SES

students receive a higher degree of PI.

(2)　Does the degree of PI at the school level differ by school SES?

Lareau (2003) shows disparities between PI at schools (e.g., middle-class mothers tend to actively

interact with schools); PI in school activities is another aspect of “concerted cultivation.”3） Based on her

study, disparities are expected between schools in terms of the degree of PI at the school level in Japan as

well. A hypothesis for this question is that school SES shapes the degree of school-level PI; higher SES

schools tend to have a higher degree of PI at the school level.

(3)　Are the two PI indicators related to students’ academic performance?

Student-level PI is hypothesized to relate to students’ achievement in accordance with the literature (e.g.,

Honda 2008) indicating this association. Likewise, this is the case for the school level as well, since U.S.

studies show that school-based PI moderately influences students’ achievement (Hill and Tyson 2009).

Specifically, students receiving a higher degree of PI at home and attending schools with a higher degree

of PI in school activities tend to demonstrate higher academic achievement.

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(4)　Are the effects of family and school SES mediated by the two PI indicators?

This question clarifies the associations between SES, PI, and achievement. It is hypothesized that the

effects of SES are mediated by PI at each level; students’ achievement is shaped by both student- and

school-level degrees of PI that vary by Student/School SES. If so, this implies that the effect of different

parental practices should be considered as a source of inequality. The frequency of PI in their children’s

school issues differs by SES and differentiates students’ academic performance, which creates, maintains,

and widens the achievement gap.

4　Method

4.1　Data

This study uses the Japanese sample of the Trends in International Mathematics and Science Study

2011 (TIMSS 2011), which was designed and conducted by the International Association for the

Evaluation of Educational Achievement (IEA). TIMSS employed a two-stage sampling process: schools

were randomly chosen for each category (i.e., by area and type of school), and a class was selected from

the sampled schools. The sampled students completed a test and a student questionnaire. This nationally

representative data include 4414 second-year junior high school students in 138 schools (National

Institute for Educational Policy Research 2012).

　The test was administrated in March̶the end of the Japanese academic year̶meaning that the

sampled second-year junior high school students were about to become third-year junior high school

students (ninth-grade students). In less than a year, these students will take high-stakes high school

entrance examinations. As high schools are hierarchically ranked and function as academic tracks that

shape students’ academic trajectories (e.g., Kariya 2011), students’ academic performance at the time of

TIMSS-test is important.

4.2　Variables

Student-Level Variables

Student SES. Second-year junior high school students were asked to report (a) the number of books

in their homes and (b) any specific items at home (e.g., own room and Internet connection). First, 11

items were added to develop a “home possessions” index.4） Then, the number of books at home and the

“home possessions” index were combined through a principal component analysis to create a continuous

variable. This independent variable should indicate students’ SES.5）

Student Score/Standardized Math Score. Five plausible values were used to represent students’ performance in mathematics, while standardized scores were also created and utilized in the first

set of the analyses. Of the two subjects tested in TIMSS, mathematics was selected over science, as

mathematics is the most popular subject taken by junior high school students in the shadow education

industry (MEXT 2008). It is likely that PI and parental encouragement shape students’ achievements in

this most studied subject.


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Female (Gender). Female students were indicated as 1. Male counterparts were coded as 0.

PI. Among many PI aspects, this study focuses on parent–child communication/discussion on academic

issues, which U.S. literature indicates as being effective (e.g., Hill and Tyson 2009; Sui-Chu and Willms

1996). More specifically, this study tests if students’ (family) SES relates to the frequency of parent–

child communication about schoolwork and investigates if the communication frequency is related to

students’ achievement. Therefore, “PI” was created from students’ responses to Q11 of the IEA Student

Questionnaire (2011b: 9): “11. How often do the following things happen at home? a) My parents ask me

what I am learning in school.” Sampled students were asked to select one of the following four options:

“Every day or almost every day,” “Once or twice a week,” “Once or twice a month,” and “Never or almost

never.” These responses were recoded from 3 (Every day or almost every day) to 0 (Never or almost

never). This variable represents the degree of parent–child communication on academic matters. Through

communication, parents can notice if their child begins to disengage in school, and “the importance of

schooling and education is conveyed to the child” (McNeal 1999:124). This variable also likely works

as a proxy for parents’ daily approaches and the high expectations of their children, which Uzuki (2004)

contends as meaningful.

School-Level Variables

School SES. Student SES was averaged at each school to create this variable to determine a school

socioeconomic compositional effect (e.g., Raudenbush and Bryk 2002) on students’ performance.

School PI in School Activities (School PI in School Activities). This variable was based on school

principals’ or department heads’ responses to the IEA School Questionnaire (2011a: 6): “11. How

would you characterize each of the following within your school?” “f) Parental involvement in school

activities.” Responses were coded as “Very high” (4), “high” (3), “medium” (2), “low” (1), and “very low” (0).

Private/National. Using TIMSS classifications of schools, private and national schools were coded as

1, and public schools as 0. This needs to be controlled because private/national schooling is a possible

mechanism for the SES-based achievement gap in Japan (Taki 2012).

Urban. This variable was based on responses to the IEA School Questionnaire (2011a: 2): “5. B. Which

best describes the immediate area in which your school is located?” (2) “Urban–Densely populated” was

coded as 1 and others (e.g., medium-size city and small town) as 0.

　School Score. Each plausible value was used at each school to indicate school performance.

4.3　Analysis

To test the first research question on the relationship between students’ SES and the degree of PI, a

multilevel ordinal regression analysis was conducted. An ordinal regression analysis was then employed

to assess whether School SES is associated with School PI in School Activities. Finally, multilevel

regression analyses were carried out to empirically test whether student- and school-level PI indicators

relate to students’ performance, and if the effect of SES are mediated by PI at each level. For the first

and last part of the analyses, multilevel modeling techniques were applied as the data with student and

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school levels being better suited to multilevel investigations, which may more accurately capture the

school effects embedded in school settings on students’ performance.6） For each multilevel analysis,

a model including both student- and school-level predictors is presented in this study.7） In addition,

to observe if the two PI indicators mediate the effects of Student and School SES, two models were

created (with/without the PI indicators) for the last part of the analyses. The following is an example of

modeling showing the last multilevel analysis: Level-1 (Student level) Model: Student Math Score ij = β0j

+ β1j(Student SES ij) + β2j(Female ij) + β3j(PI ij) + rij Level-2 (School level) Model: β0j = γ00 + γ01(School

SES j) + γ02(Private j) + γ03(Urban j) + γ04(School PI in School Activities j) + u0j, β1j = γ10 + u1j, β2j =

γ20, β3j = γ30

In the first set of the multilevel analyses, random intercept models are specified as all slopes do not

vary, indicating that the effects of Student SES, Female (gender), and Math Score are the same across

schools. Meanwhile, all models of the last analysis are random intercept and slope models. The variation

in the levels of the intercepts was estimated to reflect between-school differences in students’ math

performance, while the slope of Student SES varies between schools, which indicates that the effect of

Student SES differs between schools.8） The other within slopes were fixed, given the results that random

slopes of the other variables became insignificant, suggesting no between-school differences in the

effects of Female and PI.

5　Results

5.1　Descriptive Statistics

Descriptive statistics of three student-level and two school-level continuous variables are shown

in Table 1.9） Table 2 is a frequency table of the two student-level and three school-level categorical

variables.10） These results clarify that the PI is unequally distributed at each level.

Table 1. Descriptive Statistics for Continuous Variables


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5.2　Correlation matrices

The correlation matrix of the variables is shown in Table 3.11） Student SES significantly correlates to

PI (.220), which means that the parents of higher SES students often ask what they learn in schools. At

the school level, School SES correlates with School PI in School Activities (.413), indicating that school

principals in higher SES schools perceive a higher degree of PI in school activities.

Table 2. Frequency Distribution of Categorical Variables

Table 3. Correlations between Variables at Each Level

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5.3　Assessing the relationship between Student SES and PI

Table 4 shows the results of a multilevel ordinal regression analysis.12） Due to space limitations, only a

final model is presented (not only for this analysis but for all the analyses). The ordinal outcome ranges

from 0 to 3; the four categories are ordered from the lowest (never or almost never) to the highest (every

day or almost every day). Table 4 provides an estimation of the log odds that a student reports receiving

PI (the highest category versus combined lower categories). Intercept log odds are negative because it is

more likely that most students report lower categories of PI.

Table 4. Factors Differentiating the Degree of PI at Home

　The results show that, while there are differences in the degree of PI between schools, most

differences are at the student level; only School Score appears to be significant (p < 0.1). In fact, intra-

class correlation (ICC) of Model 1 (without any predictors) is 0.032 (3.2%) and that of Model 3 is

0.022 (2.2%); only 3.2% of the total student differences is at the school level, while the remainder is

at the student level where Student SES, Standardized Math Score, and Female are significant positive

predictors of the PI degree. As Table 4 shows, Student SES influences the PI degree that students receive

at home. More specifically, when holding other variables constant, 1-standard deviation (1-SD) increase

in Student SES increases the odds of being at the highest category of receiving PI versus the combined

lower categories by a factor of 1.470 (or 47.0%), compared to their average SES peers.


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5.4　Testing School-level relationships between SES and PI

The results of the school-level ordinal regression analysis in Table 5 indicate that all independent

variables including School SES are significant predictors of School PI in School Activities. These results

seem consistent with those in Table 4: higher performing schools likely have a higher PI degree in school

activities, while private/national schools and those in urban areas are less likely to have a higher PI

degree in school activities.

Table 5. Relationship between SES and PI at the School Level

5.5　Investigating the Relationships between SES, PI, and Score

For this analysis, two sets of results are presented in Table 6. The left side of the table shows model’s

results without two PI indicators, while the right side presents findings of the final model with two PIs to

investigate if these PI indicators mediate effects of Student SES and School SES.13）

For relationships between PIs and achievement, all student-level predictors, namely Student SES,

Female, and PI, significantly relate to students’ achievement in mathematics. For Student SES, when

other variables are held constant, students with an SES 1-SD above the mean score around 20.9 points

higher than the grand mean. Female students are likely to obtain a slightly lower score (approximately

－5.6 points) than male counterparts. Results for PI indicate that students whose parents ask them about

schoolwork tend to demonstrate higher math ability. As this variable ranges from 0 to 3, students whose

parents ask them what they learn in school every day or almost every day score an additional 12.57

points (4.190 × 3) compared to those whose parents “never or almost never” do so, while the other

variables including Student SES are controlled. Although its effect is relatively weak, this result shows

the significant relationship between the degree of PI and students’ math achievement.

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In addition, three school-level variables appear to be significant predictors of students’ achievement:

School PI in School Activities, Private/National, and Urban. The results indicate that students who

attend schools with a higher PI degree in school activities demonstrate higher academic ability. As the

variable ranges from 0 (very low) to 4 (very high), students attending schools with “very high” PI in

school activities have 34.08 points (8.520 ×4), compared to those who go to schools with “very low” PI

regarding school activities. Attending private and national schools also significantly relates to students’ math achievement; students in private/national schools score 46.5 points higher than their counterparts

in public schools when other variables are held constant. ICC is 0.128 (12.8%) for Model 1 (without

any predictors) and 0.042 (4.2%) for the final model. These mean that 12.8% of the variance in math

performance is at the school level, and it decreased to 4.2% when (primarily) school-level explanatory

variables were added to the model.15）

Comparing the two models indicates that the effects of Student SES and School SES weaken with PI

indicators, implying that PIs mediate some SES effects. Specifically, because of School PI in school

activities, the effect of School SES decreases (approximately 61.12% = (6.009- 2.336)/6.009), although

the effect of School SES itself is small. With regard to Student level, only 3.3% of Student SES effect

(=(21.564‒20.847)/ 21.564) is mediated by the degree of PI.

Table 6. Association between PI and Score/Mediated Effect of SES


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6　Discussion and implications

6.1　Discussion

This study’s results support the first three hypotheses: (1) higher SES parents tend to more frequently

ask what their children study in school, (2) the school-level PI indicator is not equally distributed

according to socioeconomic lines, and (3) the degrees of PI and school PI in school activities are

associated with students’ mathematics achievement. Contrary to the fourth hypothesis, however, PIs mediate

small parts of SES effects, especially at the student level. Only some relationships between SES, PI, and

achievement are verified empirically.

Although there is not much support for the last hypothesis, the study still provides evidence of each

relation between SES and PI and between PI and achievement based on nationally representative data.

Students with lower family SES experience not only low SES but also less PI about their schoolwork.

This seems to be a mechanism for inequality; higher SES students tend to have greater academic ability

facilitated through early family socialization and then continuously receive attention from their parents

with regard to schoolwork when they have less than one year before taking high-stakes examinations for

upper secondary education. In addition, School SES differentiates School PI in School Activities. This

result is consistent with Lareau’s observation of “concerted cultivation.” By being involved in school

activities, parents get to know teachers and other parents, helping them monitor their children’s schooling

(McNeal 1999). Furthermore, by having opportunities to communicate with other parents, mothers might

obtain information about shadow education institutions to boost their children’s likelihood of gaining

admission to selective high schools, as Park, Byun, and Kim (2011) found that selecting and monitoring

shadow education services may improve academic performance in South Korea.

While PIs mediate only small parts of SES effects, the study empirically demonstrates the relationship

between PIs and achievement; students receiving PI demonstrate higher achievement. It should be

noted that, since interaction terms (e.g., between PI and student/school SES) were insignificant, no

difference is evident between SES groups in terms of PI effects on students’ math achievement.16） This

finding parallels results obtained by Park (2008), who assessed specific child–parent communication

regarding schooling of Japanese high school freshmen. Considering that Park’s results are similar to the

PI definition in this study, the results of his study and those obtained in this study indicate that the PI

effect (i.e., the specific type of child–parent communication) on students’ achievement does not vary with

family SES for both lower and upper secondary education in Japan.

6.2　Policy Implications

Parents should be informed that the PI degree significantly relates to students’ achievement. However,

it is likely that higher SES parents will respond to any recommendation as they tend to value academic

achievement. More specifically, policy emphasis on home education and social pressures could widen

disparities, as advantaged parents are already active in their children’s education and can employ more

resources to maximize their children’s potential (Honda 2008). Moreover, this study finds no interaction

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effect between the PI degree and student SES; the effect of PI does not vary with family SES in Japan.

When PI uniformly benefits students’ achievement regardless of family SES, encouraging parents to

get involved in their children’s education may not reduce the achievement gap (Park 2008) because

compared to lower SES counterparts, higher SES parents are more likely to (afford to) respond to the

importance of home education advocated by policies and the media.

The importance of academic performance at the time of TIMSS administration must be emphasized;

tested students were about to enter ninth grade. Since academic achievement in the ninth grade is

associated with long-term educational and occupational achievement (Honda 2008), policies should

focus on narrowing the achievement gap in compulsory education. Honda (2008) proposes that public

school education be improved (e.g., by lowering student–teacher ratios) and learning opportunities

outside schools be increased (e.g., by allowing students to have various experiences and distributing

vouchers that can be used for enrichment courses outside schools).

Essentially, simply promoting more PI may expand differences in parenting practices along with

socioeconomic lines, reinforcing the unequal distribution of PI. To rectify the trend and avoid victim

blaming, students from low SES families and their parents need support; it is unrealistic to expect the

new involvement of these parents in their children’s education without any support from schools or

other social institutions. Consequently, as the literature (e.g., Chin and Phillips 2004; Bennett, Lutz and

Jayaram 2012; Honda 2008; Lareau 2003) indicates, their relatively lower degree of involvement in

children’s education stems from fewer resources including economic means and networks.

6.3　Research Implications

If variables had represented various aspects of students’ SES and PI indicating “concerted cultivation” in more detail, it would have been more rigorous to test the relationship between students’ family SES

and PI to assess if PI mediates SES effects on students’ achievement. There may be other unobserved

factors that change the relationship between the degree of PI and student achievement. For example,

if the data had indicated student enrollment in shadow education institutions (juku) or private tutoring

services at the time of TIMSS administration, the PI degree (i.e., parent–child communication on

academic matters and PI in school activities) could have accounted for less variance in students’ academic

performance, and SES effects on math scores could be mediated by shadow education participation. This

is a limitation also faced by Honda (2008). In her study, shadow education and other types of lessons

during a child’s elementary school years are significant predictors of students’ academic performance at

the ninth-grade level, while “rigorous rules at home” is weakly related to achievement and the other PI (i.e.,

passionately leading a child to have better academic performance) is insignificant. These results could

indicate that using shadow education services substantially mediates SES effects on students’ academic

performance, and specific parenting styles may not matter much, at least, in Japanese society. If this is

the case, it would be necessary to test if the cultural logic of “accomplishment of natural growth” (Lareau

2003) relates to the use of shadow education services; following this logic, lower SES parents may avoid

structuring their child’s time using shadow education services. It is also important to investigate the


161

effect of cultural logic when parents decide to apply or not apply for private elementary, junior high, and

high schools, since attending top private high schools significantly increases the likelihood of gaining

admission to competitive universities (Kariya 2011). Finally, further studies, ideally using longitudinal

data, should be conducted to assess relationships between SES, PI, and different aspects of educational

outcomes (e.g., students’ behavior problems), as McNeal (1999) found, with U.S. students, that parent–

child communication might reduce the likelihood of behavioral issues.

*The author gratefully acknowledges the valuable suggestions and comments from anonymous

reviewers.

*This study was supported by JSPS KAKENHI Grant Number 24830009.

Notes

1)　 While cited studies show significant relationships between some types of PI and educational outcomes, the PI

effects are not conclusive. Domina (2005) argues that findings of studies on effects of PI have been inconsistent

at middle and high school levels, while his longitudinal data analysis shows that some aspects of PI (i.e., parents

volunteering at school and helping/checking their child’s homework) prevent children’s behavioral problems at the

elementary school level. PI effects seem to significantly vary on the basis of how PI and educational outcomes are

measured. In relation to this study, which assesses junior high school students, Hill and Tyson (2009) find positive

associations between PI and achievement through meta-analysis of 50 studies.

2)　 It should be noted that a small number of pairs were used in some of her analyses.

3)　 As TIMSS 2011 did not ask students about the degree of PI in school activities, this aspect of PI was assessed

with variables obtained from the school questionnaire completed by school principals.

4)　 The 11 items are from BSBG05 A to K, including 6 country-specific items.

5)　 In total, 25.8% of students chose “I don’t know” with regard to their father’s highest education level, while

33.3% did not know their mother’s education background. In addition, there is no reasonable way of conducting

multiple imputation analyses because of data limitations (student questionnaire items). Thus, to avoid a large

number of missing values, parents’ education backgrounds were not included in Student SES. See note 15 for

more detail in this regard.

6)　 It should be noted that TIMSS 2011 is a cross-sectional examination. Had it been designed as a longitudinal

study, alternative explanations for the results of the study could have been excluded more rigorously. All

multilevel models were created on the basis of the major literature in the field (e.g, Hox 2010; Raudenbush and

Bryk 2002; Raudenbush, Bryk, Cheong, Congdon and Mathilda 2011; Heck and Thomas 2009).

7)　 While interaction terms (e.g., PI and Student/School SES) were created and tested, they were all insignificant.

The models in this study include only primary effects. These results show no differential effect of PI by SES

groups on eighth-grade students’ achievement in Japan.

8)　 No school variable was found to explain the variation in the SES-achievement slope at the individual level.

Thus, why the relationship between Student SES and Student Math Performance varies between schools is

unknown. See Hox (2010) for detailed explanations of fixed and random effects.

9)　 All continuous variables, except Math Score, were standardized to facilitate the interpretation of results. IEA

IDB Analyzer produced the descriptive statistics for these variables with Total Student Weight (TOTWGT) for

the student level and School Level Weight (SCHWGT) for the school level. For Math Score, five “Plausible

Value Mathematics” (BSMMAT01 to 05) were employed and then averaged. See Olson, Martin and Mullis

理論と方法

162

(2008) for a detailed discussion on plausible value. N in the table was not weighted to show the number of

original cases. Only cases used for all analyses are included; 110 students (2.49%) and 1 school (0.72%) are

missing because of lack of data.

10)　These variables were not weighted to show the original distributions of variables. Valid percentages are shown

in this table. The number of private/national schools is only 11 in the dataset; this is an accurate reflection

of the population. MEXT (2012) shows that there were 73 national and 763 private schools of 10751 junior

high schools in Japan: 0.7% national and 7.1% private schools in the academic year 2011–12 (the year of

TIMSS2011 administration).

11)　IEA IDB Analyzer was used to analyze five “Plausible Value Mathematics” with Total Student Weight. In

the correlation matrices, non-continuous variables were also included to indicate a sense of the strength of

relationships between the variables used in the analyses.

12)　HLM7.01 was utilized for all multilevel analyses. Five “Plausible Value Mathematics” were separately included

in each model, while Student Weight Adjustment (WGTADJ3) and School Level Weight (SCHWGT) were

applied. Then, five sets of results were averaged. In an ordinal model, thresholds (or cutpoints) are used to

determine which response category is observed, with one less threshold than the number of ordered categories

(C ‒ 1) in the outcome required to specify estimated probabilities (Hox 2010). It is common to treat the first

threshold (δ0) as the intercept, and the second and third thresholds can be designated as δ1 and δ2, respectively.

The model is specified such that the intercept can vary as a random parameter at the school level of the model.

All other thresholds are fixed to 0 between groups for model identification purposes. While the thresholds are

useful in determining predicted probabilities, they have no substantive meaning because they are unaffected by

the levels of X predictors for individual cases in this study.

13)　The two ordinal PI indicators are treated as continuous in the last analyses, since the outcomes (five PVs)

increase over the ordinal categories. More specifically, the means of PI are 552.28 (never or almost never),

570.12 (once or twice a month), 576.68 (once or twice a month), and 589.18 (every day or almost every

day). For School PI in School Activities, a standardized school score (mean = 0, SD = 1) was used to obtain

the means: ‒ 0.48 (very low), ‒ 0.22 (low), ‒ 0.10 (medium), 0.11 (high), and 0.24 (very high). As the means

increase over the categories of the ordinal variables, they were included in the models as if they were

continuous. See Agresti (2013) for a detailed discussion on defining ordinal predictors as intervals.

14)　A deviance of Model 1 is 50483.207 and that of Model 2 is 49290.587. The final model’s deviance is

49211.057, as shown in Table 6 (a smaller deviance means a better model fit). As for the multilevel ordinal

regression model presented in Table 4, HLM7.01 provides no model-fit indicator including a deviance. When

the dependent variable was treated as continuous, a deviance of each model is 12050.229 (Model 1), 11805.874

(Model 2), and 11801.129 (Model 3), while the results did not change: a better model fit as variables were

added.

15)　Ad-hoc multilevel analyses were conducted to verify this study’s results. First, parents’ highest education level

(BSDGEDUP with missing value of 22.2%), number of books at home, and “home possessions” were combined

with a principal component factor analysis. This newly created Student SES index and its averaged school-level

variable were included in the final set of the multilevel models instead of the originally coded SES variables.

PI at both student and school levels became insignificant with the analysis (i.e., t-value of PI at the student

level is 1.519). A total of 22.2% of students omitted from the analysis did not know either parent’s education

background. Many of these were from low SES families, did not demonstrate high mathematical ability, and

indicated a lower degree of PI. In fact, means for Math Score, (originally coded) Student SES, and PI of these

students are lower than those for the entire sample. Moreover, in preliminary analyses, a missing variable flag

was created to indicate that students who reported parents’ education background scored significantly higher in


163

math performance than those with missing values (p < .001). This implies that the missing data may be missing

not at random (MNAR). To further examine this possibility, preliminary analyses of a number of different

hypothetical distributions of missing information on parental education were also examined (i.e., from 100%

low education to 0% low education), and in each case, except the most extreme (i.e., 100% low education), the

results did not change the effect of PI on the outcome. These preliminary results indicate that in the extreme

case where all missing cases were from low education families, the analysis likely produces a downward bias,

resulting in the non-significance of PI variables. In any other case, however, results showing the significance of

PI could be considered valid.

16)　Results of tested interaction terms are not included in the paper because of space limitations.

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　　　　　　　　　　　　　　　　　　　　（Received March 8, 2013 / Accepted November 7, 2013）

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