Course: DATA 755, Fall 2019
Instructor: Dr. Sophia Catsambis
Assignment: Research Proposal
Submitted by: Kanwal Latif
Submission Date: 12-12-2019
The Relationship Between Maternal Education
& Student's 8th Grade Academic in the U.S.
Introduction
The academic success and life chances of American children can be influenced by
maternal education as the amount of education a mother attains can predict her children's success
in school. Throughout elementary, middle, and high school, meta-analytic findings demonstrate
strong and consistent associations between maternal education and children’s academic
achievement, including students’ grade-point averages and SAT scores (Sirin, 2005). Another
aspect that contributes to child success is parental involvement. Parental involvement plays an
important role in a child’s success in school. Parental activities with the child at home can be
instrumental in boosting academic performance (Finn, 1998). The proposed project will examine
the degree to which a mother’s education plays a role in a child’s math achievement and various
academic aspects of parental involvement will be studied as an intervening factor. This study
uses secondary analysis of the Early Childhood Educational Study-Kindergarten Cohort of
1998. It will use students’ eighth grade mathematics scores to explore the relationship between a
mother’s education and their eighth grade child’s mathematical achievement. The Early
Childhood Longitudinal Study-Kindergarten Cohort [ECLS-K], combines extensive information
of children and their parents, family structures, SES, etc. and permits a baseline examination of
links to a mother’s education to a student’s math achievement. In this research, the particular
population of interest is eighth grade students in the United States because at this age a child has
much exposure to their family characteristics and its effects, including a parent’s education and
involvement. The reason for choosing to investigate the specific subject of mathematics is
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because mastering the concepts and logic of mathematics can be greatly facilitated when parents,
especially mothers, are educated and they are involved in their child’s academic activities.
Background
A child’s academic achievement is related to a complex set of factors, such as learning
environment, family cultivation, and personal learning habits. The purpose of this research
proposal is to highlight what we already know about the effects of maternal education and
parenting on a child’s achievement in Math scores and discuss the findings of other studies that
have attempted to explain the relatively high math performance of those who have educated
mothers and parental involvement.
Mathematics Subject: The importance of Mathematics transcends all definitions and the
prosperity of any country depends on the volume and quality of Mathematics offered in its
school system, Obe (1996) conceptualizes mathematics as the master and servant of most
disciplines and thus, a source of enlightenment and an understanding of the universe. He further
opines that without it, the understanding of national problems would be superficial. Graeber and
Weisman (1995) agree that Mathematics helps the individual to understand his/her environment
and to give accurate accounts of the physical phenomena around him/her. To this end, Setidisho
(2001) submits that no other subject forms a strong binding force among various branches of
science as Mathematics, and without it, knowledge of the sciences often remains superficial.
The importance of a mother’s education in child’s academic achievement: Even though a
fathers’ educational level has considerable impact on children’s academic achievement, research
suggests a mothers’ educational level is a more potent predictor of a child’s achievement than a
fathers’ educational level (Milne, 1989). A mothers’ educational level was found to be
significantly related to children’s performance in schools compared to children whose mothers
were less educated (Garasky, 1995). Studies also show that a mothers' level of education
influences adolescent educational outcome expectancy beliefs (Rhea & Otto, 2001). There are
many benefits of having an educated mother; an educated mother will have high expectations for
her children's educational success and will continuously encourage them to develop high
expectations of their own. Education is one of the most critical areas of empowerment for
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mothers and this education leads to social change and advantages for her children. Through
education, mothers develop a set of skills, including cognitive flexibility (e.g., learning to think
about concepts simultaneously and in complex ways), problem-solving ability (e.g., hypothesis
testing), language skills (e.g., vocabulary), and skills for gathering information and applying this
information to novel situations, e.g., research skills and how to use these skills in her child’s
development and success in school (Mirowsky & Ross, 2003). Investigations have found that
maternal education is positively related to child academic outcomes (Supplee et al., 2004), and
math achievement (Vagi, 2008). Smith and colleagues found that maternal education was a
statistically significant predictor of children’s math achievement even after controlling for family
income (Smith, Brooks-Gunn & Klebanov, 1997). In another study, Roberts, Bellinger, and
McCormick (2006) found that lower maternal education was predictive of lower math scores, as
measured by the Woodcock-Johnson tests of academic achievement.
Parental involvement and child academic achievement: Research provides evidence of the
importance of parental involvement with homework in students’ school achievement (Gutman &
Eccles, 1999). Parental involvement is found to positively predict a child’s mathematics
achievement (Gonzalez & Wolters, 2006; Reynolds, 1992; Yinsqiu, Gauvain, Zhengkui, & Li,
2006). It has been well documented that family plays a meaningful role in a child's academic
performance and development (Cornell & Grossberg, 1987; Thompson, Alexander, & Entwisle,
1988; Tucker, Harris, Brady, & Herman, 1996). As parents have come to be seen as important
partners in the formal education of their children, there is no doubt that parental at-home activity
can be instrumental in boosting academic performance (Finn, 1998). Parents who provide
structure, monitor the child's use of time, help teach and explain concepts, review homework and
provide support are most likely contributing to their child's success at school (Hoover-Dempsey,
1995). Whether construed as home-based behaviors (e.g., helping with homework), school-based
activities (e.g., attending school events), or parent-teacher communication (e.g., talking with the
teacher about homework), parental involvement has been positively linked to indicators of
student achievement, including teacher ratings of student competence, student grades, and
achievement test scores ( Deslandes, Royer, Potvin, & Leclerc, 1999; Epstein & Van Voorhis,
2001; Fan & Chen, 1999; Grolnick & Slowiaczek, 1994; Henderson & Mapp, 2002; Hill &
Craft, 2003; Miedel & Reynolds, 1999; Okagaki & Frensch, 1998; Shaver & Walls, 1998; Sui-
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Chu & Willms, 1996; Wang, Wildman, & Calhoun, 1996). Others (Sheldon, 2005) revealed that
a higher level of proficiency in math test is associated with a higher level of support from family
members toward their children’s math learning. Rumberger’s (1995) research supported the
findings of earlier researchers who argued that the home has a major influence on student school
success (Swick & Duff, 1978) and that it is the quality of relationships within students’ home
environments that has an important effect on school performance (Neisser, 1986; Selden, 1990;
Caldas, 1993). Although the amount of parental involvement seems to vary between fathers and
mothers or among parents with varied levels of formal education, parents everywhere care for
their children and want to be involved in all aspects of their children's development including
their homework activities (Epstein and Sanders, 1998). Studies on the effects of parental
involvement on academic performance have indicated that parental encouragement and help in
homework are important factors in the improvement of student achievement (Bracey, 1996;
Gorges and Elliott, 1995; Keith, Reimers, Fehrmann, Pottebaum and Aubey, 1986; Radencich
and Schumm, 1997; Sui-Chu and Willms, 1996; Wang and Wildman, 1995). Children also
believe they do better in school when their parents are involved with their homework (Balli,
1998). An analysis using the National Education Longitudinal Studies (NELS88) data indicated
that extra time spent on mathematics homework increases students’ mathematics test scores
(Aksoy & Link, 2000).
Parental involvement and maternal education: The relationship between parents’ education
and parent involvement continues to interest researchers. Kohl et al. (2000) found lower that
levels of parent education were associated with lower levels of active parent involvement. They
reported that parent education had a positive relationship with parent involvement at school,
parent-teacher contact, teacher perception of parent’s value of education, and parent involvement
at home (2000). Bowen and Lee (2006) found that parents with a 2-year college degree or higher
report more parent-child discussion about education at home, more involvement at school, and
higher expectations for their child’s education. Eccles (2005) asserted that parents with more
education are more likely to enroll their children in extracurricular programs like music lessons,
educational camps or clubs, and math and computer programs. Lareau and Weininger (2008)
found that while parents share some of the responsibility of children’s activities, mothers had a
more active role in planning activities for children, such as sports or performing arts than fathers.
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Individual student characteristics and child academic achievement:
Eighth Grade: There is a connection among factors like a child’s age, cognitive development,
family atmosphere, and maternal education. If the child has a strong maternal education
background and a good family atmosphere, he/she can develop cognitive skills and enrich as
years pass. According to Yazıcı (2002), the more highly educated the mother is, the more the
child matures in school, starting from preschool. Below I will discuss works of research of my
controlled variables in relation to success in school.
SES: The Socio-Economic Status (SES) of families is an important determinant of a child’s
learning outcomes. Socio-Economic Status (SES) contributes to the physical, economic, and
social well-being of individuals and families (Sirin, 2005). Children born into poor families face
an educational disadvantage both before they enter school and throughout their education, such
that SES to a large extent determines educational outcomes. This, in turn, determine the SES of
the next generation (Willms, 2002; Willingham, 2012). Parental income reflects the potential for
social and economic resources that are available for a child’s education (Mayer, 2002;
Willingham, 2012). According to Mayer (2002), children of rich parents can be healthier, better
behaved, better educated during childhood, while children from lower-income families have
worse cognitive, social-behavioral, and health outcomes. While wealthier parents have the
resources to provide more and better opportunities for their children, children of poor parents are
subject to chronic stress, which is destructive to learning (Willingham, 2012).
Gender: Koller, Baumert, and Schnabel (2001) studied gender differences in mathematics
achievement, which favored males in achievement, interest, and placement in advanced
mathematics courses. Literature on gender differences in mathematics suggests that the number
of female students pursuing mathematics up to the higher level reduces (Eisenberg, Martin, &
Fabes, 1996) but various researchers report that gender differences in the mathematics attitudes
of American and European students may still be prevalent. Benbow and Stanley (1980, 1983)
found, that among talented junior high school mathematics students, boys’ outperformed girls on
the quantitative SAT, a test that was obviously advanced for this age group. One can clearly state
that a general pattern has begun to emerge: women perform roughly the same as men except
when the test material is quite advanced; then, often, they do worse. Though globally, the issue
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of gender inequality in Science, Technology, and Mathematics Education (STME) has produced
inconclusive results, one meta-analysis covering the period 1974 – 1987 on mathematics and
gender led to two conclusions: the average gender gap is very small (statistically not significant),
and the fact that the differences tend to decline with time (Friedman, 1989).
Race: Racial and ethnic disparities in children’s academic outcomes are a pervasive reality in
U.S. schools. Black and Latino children, on average, earn lower scores than White children (Lee
& Bowen, 2006; Cross, Woods, & Schweingruber, 2009). For Hispanics, many studies have
shown an early onset of the gap in math (Phillips 1998, 2000).
Disability: Many students in primary and secondary education experience problems with
mathematics (Geary, 2004). The disability can be highly selective, affecting learners with normal
intelligence (Landerl, 2004). Disabled children don't show medical evidence of brain damage
(Boshes & Myklebust, 1964). The average size of a secondary school class in the United States is
approximately 23 students (Education at a Glance 2010), meaning there may be four or five
adolescents in every classroom who are struggling with serious mental illness. Mental illness
does not affect emotional health in isolation; it is known to influence and co-occur with problems
in many domains of students’ lives, including their social interactions and educational
achievements (DeSocio).
Therapy/counseling by a mental health professional: For mental illness, studies show the
positive impact of counseling interventions in a school setting (Whiston and R.F. Quiby).
According to an article (Empirical Research Studies Supporting the Value of School Counseling)
from the American School Counselor Association, a 2013 study from authors K. Wilkerson, R.
Perusse, and A. Hughes found that elementary school students tend to perform better
academically when there are counseling programs in place.
Family structure and child academic achievement.
Mother employment status: Overall, the extensive literature demonstrates that maternal
employment has a detrimental effect on children's cognitive development when it occurs during a
child’s first year of life (Baum 2003; Baydar and Brooks-Gunn 1991; Bernal 2008; Hill et al.
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2005; James-Burdumy 2005; Ruhm 2004; Waldfogel et al. 2002) and that impacts the child’s
later school years. The effects of later maternal employment are less conclusive, but negative
effects have been found for children's cognitive outcomes (Bogenschneider and Steinberg 1994;
Ruhm 2008) and educational attainment (Baum 2003; Ermisch and Francesconi 2001). Maternal
work reduces the amount of time children spend with their parents, and time spent together
positively influences child development. (Butcher).
Mother’s age: Children born to younger mothers have cognitive disadvantage and educational
underachievement (Brooks- Gunn, Guo, & Furstenberg, 1993; Fergusson & Lynskey, 1993;
Hardy, Welcher, Standley, & Dallasm, 1978; Luster & Dubow, 1990; Wadsworth,1984).
Although most research in this area has focused on developmental outcomes during early or
middle childhood, there is growing evidence to suggest that the disadvantages experienced by the
offspring of younger mothers are likely to persist into adolescence and early adulthood (BrooksGunn & Furstenberg, 1986; Card & Wise, 1981; Nagin, Pogarsky, & Farrington, 1997).
Fergusson and Lynskey (1993) examined the association between maternal age and educational
achievement and conduct problem outcomes in a birth conform to New Zealand children studied
to the age of 13 years (eighth grade). Results from this analysis suggested the presence of
continuous relations between maternal age and childhood outcomes, with childhood risks
showing a clear tendency to decline with increasing maternal age.
Family type categories: With regard to family type, it is generally concluded that in the United
States children from divorced families show poorer educational outcomes than from children
from intact families. (Amato, 2000; Jeynes, 2002; 13-18). Children from reconstituted families,
that is, families with step-parent(s) tend to have lower educational attainments than children from
two-parent families, and, in quite often, single-parent families (Biblarz and Raferty, 1999,
Jeynes,1999).
Sibling Configuration: Well established literature has focused on sibling configurations and
child outcomes, including academic achievement. There is an extensive body of research on
characteristics of sibships on academic achievement, such as sibship size. (Steelman et al., 2002).
Sibship size is generally considered to have the most robust effect, with larger sibships
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producing negative effects on achievement (Blake, 1989). Particularly, U.S. studies provide
evidence that a child’s number of siblings exert influence on various educational outcomes, such
as intelligence, school attainment, competence achievement (Hauser/Sewell 1985; Downey
1995; Conley 2000; Steelman et al. 2002; Wolter 2003; Black et al. 2005; Ca- ceres-Delpiano
2005; Kantarevic/Mechoulan 2006; Buckles/Munnich 2012; Nguyen 2013).
School, teacher and classroom characteristics.
Public or Private school: About 46 million students are currently enrolled in the Nation’s public
schools in kindergarten through grade 12, and another 6 million are enrolled in private schools.
(U.S. Department of Education). Because private schools are often perceived to be more
successful in teaching students, although, with a weak empirical basis, many reform proposals
for public schools have looked to the private sector for models to emulate. Students in private
schools have higher scores than students in public schools primarily because private school
students are of higher SES than public school students. Only a few studies have indicated that
some private schools may be more effective because of their “academic press” that is, the strong
commitment they have to provide a strong academic environment and have high expectations for
their students (A.S. Bryk, V.E. Lee, & P.B. Holland).
Urbanicity, Free Lunch: Many U.S. education reform efforts have focused on the performance
of students in large, urban school districts. Compared with their suburban and rural counterparts,
urban school districts enroll larger proportions of students of color, and more of their students are
eligible for free and reduced-price lunch (Sable, Plotts, and Mitchell 2010). Rural schools,
however, do not always have access to the same level of federal funding as urban and suburban
schools, which can limit the opportunity students have for learning mathematics. (K. Patterson
2010). Nine percent of rural school district budgets are covered by federal funds, compared with
eleven percent of budgets in urban school districts (S. Provasnik et al.2007). Low salaries, threats
of consolidation, and the geographic isolation of many rural areas make it a challenge for rural
districts to attract and retain highly qualified teachers, particularly in high-need subjects such as
mathematics (M. S. Waters, 2005). Despite these challenges, many rural schools offer unique
factors that are associated with mathematics achievement, such as smaller class sizes and
community cohesiveness (E. Bouck, 2004). In the study using Program for International Student
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Assessment (PISA) 2000 data, Williams (2005) examined cross-national variation in rural
mathematics achievement in 24 industrialized nations. He found that in 14 of the 24 countries,
mathematic scores for students in rural schools were significantly lower than scores for students
attending schools in urban and medium-size communities.
Percent minority students: Two decades ago, Lee Stiff and William Harvey (1988) noted that
the mathematics classroom is one of the most segregated places in the United States. Despite
some improvement, upper-level mathematics classes are still populated with relatively few Black
and Latino students. In fact, several studies document that Black and Latino students sometimes
have more positive attitudes toward mathematics and higher educational aspirations than their
white counterparts, especially in the early years of secondary school (Goldsmith, 2004;
Strutchens & Silver, 2000). Yet students from these minority groups are less likely than Asian
American and White students to complete advanced high school mathematics classes (National
Center for Education Statistics, 2004; Teitelbaum, 2003), classes that are crucial prerequisites for
admission to competitive colleges and for career success. Although schools have achieved
greater parity for some college-preparatory courses, for example, algebra and geometry, there are
still ethnicity-related gaps in enrollments in courses like trigonometry and calculus. These gaps
have profound implications for students' achievement (Teitelbaum, 2003). Despite the curricular
reforms of the 1980s, the “algebra for all” movements of the 1990s, and the advent of No Child
Left Behind in the 2000s, there is still great variability in opportunities to learn higher
mathematics in schools across the United States. Students attending predominantly minority
schools still receive fewer opportunities to learn rigorous mathematics (Darling-Hammond,
2004; Tate, 1997).
Statement of the Problem:
This project explores the contribution of individual Maternal education and parent’s
involvement on children’s school readiness. The following research questions are examined:
1- Is a mother’s education related to a student’s eighth grade Math test scores?
2- If so, to what degree is this mediated or explained by the level of parental
involvement?
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Data:
The project will use data from the Early Childhood Longitudinal Study-Kindergarten
(ECLS-K). Sponsored by NCES, U.S. Department of Education (NCES, 1999), this data set
includes extensive information at multiple points of time from the time children entered
kindergarten, in 1998 to the time they reached eighth grade. A national sample of 21,260
kindergartners from about 1,000 schools and from diverse racial/ethnic and SES backgrounds
participated in the first wave of data collection, in the fall of 1998. Teachers and parents were
interviewed and children's cognitive skills in mathematics were assessed on a one-to-one basis.
Data were collected again at multiple points in time through children’s eighth grade year. Even
though data was collected more than 10 years ago, the ECLS-K remains the only available
national longitudinal data set of students entering U.S. schools and interviewed till their eighth
grade. Below is a list of variables collected for eighth grade in the year 2007, which will be used.
Data was weighted by the appropriate sampling weight to ensure data is nationally represented.
Independent Variables:
Individual Level Demographic Characteristics: Social and demographic background (SES,
maternal education, race/ethnicity, gender, child disability, counseling by a mental health
professional), family structure (single-parent home and number of siblings, mother’s
employment status, age of a mother) and school-level characteristics (public or private school,
Urbanicity, percent eligible for free lunch and percent minority).
Individual Level Family Processes: The variables for the individual level family processes are
the following:
1- Family practices such as learning resources at home (helping in math); 2-Parent’s
expectations from a child; 3- Parental Involvement in school. A composite variable is created as
“Number of attended school meetings/events by parents” from six parental involvement
variables (PTA meetings, parent-teacher conferences, attending a school or class event, open
house, school volunteer, participating in fundraising). A factor analysis with varimax rotation
was conducted to collapse parental involvement variables into a new variable. 4- Parental rules
for child’s leisure time. Created as a composite variable from five parental educational practices
at home (hours/time spent watching TV daily/weekly/weekdays, selection of TV programs,
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schedule for computer or playing video games). 5- Academic Rules. It is a combination of two
variables (rules for maintaining certain grade point average and rule for doing math homework).
The factor analysis results for composite variables are presented in Appendix 2.
Dependent Variable: Mathematics cognitive assessment scores (T-scores); Standardized Tscore will be used because this project is comparing scores among peers. Standardized scores (Tscores) report children’s performance relative to their peers. They provide norm-referenced
measurements of achievement, that is, cross-sectional estimates of achievement relative to the
population as a whole. A high mean T-score for a particular subgroup indicates that the group’s
performance is high in comparison with other groups (NCES 2001-029).
Sample Weight: Data analyses are based on a sample weighted by the weight variable, C7CWO
i.e. Child Weight Full Sample. This weighted variable was chosen because it is a cross-sectional
weight to be used for child direct assessment data. The weight is normalized by dividing the
weight variable by its mean. The purpose of using the normalized weight is to allow for
significance testing, as well as to address the issue of less accurate standard errors.
Method of Analysis: Univariate and Multivariate Analyses are conducted. Univariate analysis is
an important first step for conducting statistical analyses. It gives an idea of the distribution of
the data and helps to detect outliers. Descriptive statistics will be used to describe the data for
maternal educational status distribution and Students ‘achievement in Mathematics. An account
of the Mathematics Test Scores of eighth grade for maternal educational status and the level of
parental involvement will be conducted through multivariate analysis.
Results:
Univariate Analysis: The main focus of this research is to investigate potential associations
between a mother’s education and a child’s mathematics achievement of eighth graders and if so,
to what degree is this mediated or explained by the level of parental involvement. All variables
used in the analysis together with their descriptive statistics are presented in Appendix 1. I begin
by presenting the frequency distribution of different measures of mother’s educational levels
(Table1), parent's involvement in different school activities (Table 2), helping in math homework
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by parents (Table3), at home rules for a child’s leisure time set by parents (Table 4), academic
rules (Table 5), and parent's expectation from a child to attain levels of degree (Table 6).
The sample size of eighth grade mathematics T-test scores is 9,653. The average
mathematics T-test score is 50 with a standard deviation of 10.00. This indicates that the
majority of students (68% of them) have received scores anywhere between 40 and 60 (Figure
1). The minimum score is 24 and the maximum score is approximately 75. The distribution of
mathematics test scores is greatly approximating a normal distribution.
Figure 1: Math Standardized Test Score Distribution of U.S. Eighth Graders in 2006 (N=9,653)
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Mother’s educational levels were evaluated using the frequency distribution of the amount of
education attained by the mothers of 8th grade students.
Table 1. Mother’s Educational level of Eighth Graders
Grade
VOC/ Tech
Graduates/MS/MA/
Twelve
High
Program/ Some
Bachelor’s Professional School/
&
School
Postsecondary
Degree
Doctorate W/O
Below
Education
Degree
Percent of
Educational
11%
24%
37%
18%
9%
Level
N
897
1982
3052
1479
942
Table 1 shows that a plurality of mothers in the ECLS-K sample have completed vocational/
technical postsecondary program or have completed some postsecondary education but have not
attained a bachelor’s degree, comprising 37% of the total sample. The next largest group is
mothers who have completed a high school degree, comprising approximately 24% of the
sample, followed by mothers with bachelor degrees, comprising 18% of the sample.
The number of attended school meetings/events by parents were evaluated using the mean of
composite variable, parent’s involvement in various school activities of 8th grade students.
Table 2. Descriptive Statistics of Parental Involvement for Eighth Graders
N
Minimum
Maximum
Mean
Standard Deviation
8,465
0
6
2.53
1.64
Table 2 shows the average number of school meetings/events attended by parents of a child in
the Spring of eighth grade was approximately 3, with a standard deviation of approximately 2.
This indicates that the majority of parents attended between 1 and 5 school meetings/events
during the semester.
Help in Math homework of a child was evaluated using the frequency distribution of parent’s
efforts in helping in math homework of 8th grade students.
Table 3. Help in Math Homework by Parents of Eighth Graders
Mother / Father
Other source / No Help
Percent of Guidance
N
71%
4838
28%
1908
Table 3 shows the majority of parents help their child in math homework, comprising 71% of the
total sample.
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The number of at-home rules for a child’s leisure time set by parents were evaluated using the
mean of the composite variable, a child’s leisure time for various at-home activities of 8th grade
students.
Table 4. Descriptive Statistics of a Child’s Leisure Time of Eighth Graders
N
Minimum
Maximum
Mean
Standard Deviation
8,528
0
5
3.58
1.47
Table 4 shows the average number of rules set by parents of a child in the Spring of eighth grade
was approximately 4, with a standard deviation of approximately 1. This indicates that the
majority of parents have set rules between 3 and 5 different types of activities during the
semester.
Academic rules of a child were evaluated using the mean of academic rules of 8th grade
students.
Table 5. Academic rules of Eighth Graders
N
Minimum
Maximum
Mean
Standard Deviation
8,526
1.72
0.53
0
2
Table 5 shows the average number of academics rules of a child in the Spring of eighth grade
was 1.72, with a standard deviation of 0.53. This indicates that the majority of parents (68% of
them) have set between approximately 1 and 2 of academic rules during the semester.
The Parent’s expectation of a child was evaluated using the frequency distribution of levels of
degree attainment expected of 8th grade students.
Table 6. Parent's Expectation from a Child of Eighth Graders
Percent of
Educational
Level
Expected
N
14
Receive
Less than
a High
School
Diploma
Grad.
from
High
Schoo
l
Attend
Two/
More
Years of
College
Finish a
Four/ Five
Years of
College
Degree
7%
8%
17%
46%
15%
13%
60
723
1437
3977
1313
1076
Earn a
Master's
Degree/
Equivalent
Finish a
PH.D./ MD/
Other
Advanced
Degree
Kanwal Latif
Table 7 shows that almost half of the parents in the ECLS-K sample expect their child to finish
four or five years of college (46%). The next largest group is to attend two/more years of college,
comprising 17% of the sample. Expectations to earn a Master’s degree comprises 15%, followed
by to finish PH.D./MD or other Advanced degree comprising 13% of the sample.
From the above univariate analysis, it has seen that the average mathematics T-test score
is 50 of eighth graders’ and the majority of students have received scores anywhere between 40
and 60. A plurality of students’ mothers have completed vocational/ technical postsecondary
program or have completed some postsecondary education but have not attained a bachelor’s
degree, comprising 37% of the total sample and the lowest group is mothers who have the
highest level of degree (Graduates/MS/MA/ Professional School/ Doctorate W/O Degree) among
all groups defined by mother’s educational level, comprising only 9%. The above analysis also
showing that parents attended an average number of 3 school meetings/events during the Spring
of eighth grade. An average number of leisure time rules are 4 and an average number of
academic rules are 2 set by parents of their child, and the majority of parents help their child in
math homework. Almost half of the parents in the ECLS-K sample expect their child to finish
four or five years of college and the lowest group is parents who expect to earn the lowest
degree, that is, less than a high school diploma, comprising only 7%.
I begin bivariate analyses by presenting one-way ANOVAs (Table7) to examine the
relationship between a child’s math achievement and a mother’s educational level, and parent’s
educational expectations for their child, Pearson Correlations (Table 8) for the relationship
between math achievement and parent's involvement in different school activities, leisure time
and academic rules set by parents, and, Independent Samples T-test (Table 9) for the relationship
between math achievement and help in math homework by parents.
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Kanwal Latif
Table 7. Average Math Test Scores by Mother’s Educational Levels, and Parent’s Educational
Expectations for the Child (Spring of Eighth Grade)
Mother’s Level of
Education/Parent’s
expectations
Grade Twelve & Below
High School
VOC/TECH Program / Some
Postsecondary Education;
Attend Two/More Years Of
College
Bachelor’s Degree; Finish A
Four/Five Years of College
Degree
Graduates/ MA/ MS/
Professional School/ Doctorate
W/O Degree; Earn Master’s /
PH.D./MD/Other Advanced
Degree
Overall Mean
(Overall Standard Deviation)
P Value for F Test 0
Mathematics T- Test Score;
Mother’s Level of Education
(mean values)
43.15
47.34
Mathematics T- Test Score;
Parent’s expectations (mean
values)
38.84
41.94
50.18
46.3
55.6
51.67
56.45
53.73
50.3
9.989
50.47
10.006
The table 7 indicates that, on average, there seems to be a linear relationship between eight
graders’ math test scores and mother’s educational levels, and parents’ educational expectations
for their child. Specifically, eighth graders whose mothers have the highest educational levels
have the highest math test scores, 56 and 54 respectively compared to all other groups defined by
mother’s educational level and parents’ educational expectations. Mothers with the lowest
educational level and parent’s expectations for their child to attain the lowest level of degree i.e.
grade twelve and below, their child has the lowest math test scores with an average of 43 and 39
respectively. The difference in math test scores between a child whose mother has the highest
educational level/parents’ expectations to attain the highest levels of degree and a child whose
mother has the lowest educational level/parents’ expectations to attain the lowest levels of degree
is quite large. This difference is 13 points, which is above one standard deviation of this
variable.
The F test of both analyses of variance indicates that these differences are statistically
significant at the same alpha level of 0.0001. This indicates that the observed relationship
between mother’s educational levels of a child/parents’ expectations to attain levels of degree
and math test scores are highly likely to be found in the entire population of the 1998
kindergarten cohort of students to the time they reached eighth grade in the U.S.
16
Kanwal Latif
Table 8. Pearson Correlation Coefficients for Student's Math T- Scores, Parental Involvement,
Leisure Time Rules for the Child & Academic Rules for the Child (Spring of 8th Grade)
Math TScore
** p<=.01
Math TScore
Number of School
Meetings/Events Attended
by Parents
Leisure Time
Rules
A Child’s
Academic Rules
1
.116**
-0.048**
-0.112**
Table 8 shows that there is a negative weak relationship between students’ math achievement in
eighth grade and parental leisure time rules, and academic rules for the child. However, the
relationship between math test scores and the number of school meetings/events attended by
parents is positive and slightly stronger than leisure time and academic rules set by parents.
The Pearson correlation coefficient between math test scores in the Spring of eighth
grade and parental involvement is positive and has a value of .116, whereas the Pearson
correlation coefficient between math test scores in the spring of eighth grade and leisure time and
academics rules are negative and have values of -0.048 and -0.122 respectively, showing that a
child whose parents impose a high number of rules regarding leisure time and academic has only
slightly lower math test scores than a child whose parents have few such rules. Higher levels of
parental involvement in school is linked with slightly higher math test scores of the child. These
correlations are also statistically significant at the same alpha .01, indicating that these
relationships are highly likely to be found in the entire population of the 1998 kindergarten
cohort of students in the U.S.
Table 9. Mean Differences in Eighth Grader’s Math Test Scores by Parent’s Help in Math
Guidance in Math
Mean Value
Help by Parents
51.53***
No Help by Parents
47.67
*** Mean Difference significant at p<=.001
Standard Deviation
9.923
9.771
N
4,809
1892
Table 9 shows that students with help in math by parents have slightly higher math test scores
than students with no help in math (test scores of 51.53 and 47.67 respectively). This association
is highly statistically significant at p<.000.
17
Kanwal Latif
From the above bivariate analysis, it has seen that there is a linear relationship between a
child’s math achievement and a mother’s educational level, and parent’s educational
expectations for their child. Higher the mother’s educational level and higher the parent’s
educational expectations for their child, the higher the child’s math achievement. Also, I have
found that a child whose parents impose a high number of rules regarding leisure time and
academics has only slightly lower math test scores than a child whose parents have few such
rules. Higher levels of parental involvement in school is linked with slightly higher math test
scores of the child. Students with help in math by parents have slightly higher math test scores
than students with no help in math.
Table 10 provides multivariate statistics of all variables used in the analysis. Model 1
shows that there is a positive relationship between mothers’ educational levels and students’
math achievement in eighth grade. The unstandardized coefficient of the mother’s educational
level variable shows that for each additional unit increase in mother’s educational level, students’
math test scores increases by 3.553 points.
When the student’s demographic characteristics are considered in model 2, the b
coefficient of mother’s educational level drops to more than one third of its value, from the
original 3.553 to 0.985. This shows that when we compare students who have the same
demographics, students whose mothers have higher educational levels have only slightly higher
math achievement than students whose mothers have lower educational levels. It seems that
demographic characteristics largely explain the original relationship between mother’s education
and student’s test scores in math. Two variables, Race and SES are particularly important in
explaining the original relationship between mother’s educational levels and math test scores.
Analysis showing that there is a strong positive association between high SES and mother’s high
educational background, i.e. higher educated mothers come from high SES backgrounds. Also,
there is a strong negative association between Black race and mothers’ education, i.e. Black
mothers have lower educational levels as compared to White mothers. Therefore, the biggest part
of the relationship between a mother’s educational level and her child’s math achievement can be
attributed to the demographics of students whose mothers have educational levels. The
coefficient of Black race shows that Black students score 5.889 points lower in math
achievement than white students (This is the reference category for the variable “Race).
18
Kanwal Latif
Comparing the beta coefficients of all independent variables shows that, SES has the strongest
relationship with eighth graders’ math achievement, followed by race Black.
The multiple regression in model 3 introduces parental involvement aimed at developing
students’ math skills into the equation. Beta coefficients of two variables Socioeconomics status
and Black race are the most important in explaining the drop in the coefficient of maternal
education. When students with the same demographics and parental involvement are compared,
students with higher maternal education still have slightly higher achievement in math than
students of lower maternal education. When the variable of parental involvement is introduced
in the equation, the coefficient of mother’s education level changes (from 0.985 to 0.923) but
remains positive with a small decrease in value. Therefore, the higher math achievement of
students from higher mother’s educational backgrounds cannot be explained much by more
frequent parental involvement experienced by children with higher maternal education. Results
showing that families that have strong rules about academic and leisure time, may have students
not doing as well in school. Parents’ expectation is the only important variable while the
coefficients of the other variables introduced in this model are a week and many are negative.
Comparing the beta coefficients of all independent variables, still being Black and SES continue
to be strongest but parental educational expectations for students seem to lower further the
coefficient of mother’s educational level. It seems that these new control variables do not affect
the coefficient of mother’s educational level.
The multiple regression in model 4 introduces school level characteristics aimed at
developing students’ math skills into the equation. SES and Black are the biggest factors that
explain the important relationship between mother’s educational levels and test scores. It shows
that mothers with high education comes from high Socioeconomics status, and also black
mothers are less educated. When students with the same demographics, parental involvement,
and school characteristics are compared, students of higher mother’s educational level still have
higher achievement in math than students of lower mother’s educational level. When the school
level characteristics are introduced in the equation, the coefficient of the mother’s educational
level remains almost the same (from 0.923 to 0.922). Therefore, the higher math achievement of
students from higher mother’s education backgrounds cannot be explained by more frequent
school characteristics experienced by students from higher mother’s education backgrounds. It
seems that these new control variables do not affect the coefficient of mother’s education.
19
Kanwal Latif
Table 10. Linear Regression Models Predicting Student Math Achievement in Spring of Eighth
Grade and Mother’s Educational Levels (N = 9,720)
Model 1
Model 3
Model 4
Unstd
.
Std. Coeff.
Coeff.
39.673
---
Unstd.
Coeff.
Std. Coeff.
Unstd.
Coeff.
Std. Coeff.
Unstd.
Coeff.
Std. Coeff.
45.501
---
39.947
---
41.314
---
0.362
0.985***
0.100
0.923***
0.094
0.922***
0.094
-----------
-5.889***
-1.121***
3.400***
-0.393
0.858 ***
-0.223
-0.044
0.058
-0.008
0.043
-6.045***
-1.955***
2.623***
-0.551
1.281***
-0.229
-0.076
0.045
-0.11
0.064
-5.053***
-1.451***
2.898 ***
0.068
1.241***
-0.191
-0.056
0.049
0.001
0.062
---
3.930***
0.297
2.954***
0.223
2.497***
0.189
---
0.053***
0.034
0.048***
0.031
0.043**
0.028
---
0.655**
0.026
0.426*
0.018
0.319
0.013
---
---
---
-0.896***
-0.044
-0.871***
-0.043
---
---
---
1.960 ***
0.204
1.965 ***
0.205
---
---
---
-0.356***
-0.049
-0.319***
-0.044
---
---
---
0.066
0.010
0.100
0.015
---
---
---
1.015 ***
0.038
0.947***
0.036
---
---
---
---
---
-0.164
-0.005
---
---
---
---
---
-0.020
-0.003
---
---
---
---
---
-0.850***
-0.036
---
---
---
---
---
-0.045***
-0.110
-----
--0.255
-----
--0.294
-----
0.533*
0.305
0.021
---
Intercept
Mother’s
Educational
3.553***
Levels
Black
--Hispanic
--Asian
--Other Races
--Gender
--W8 Cont. SES
--Measure
Mother’s Age
--Parent Type
--Structure
Academic Rules
--Parent’s
--Expectations
Leisure Time
--Rules
Parents’ Activities
--at School
Help in Math by
--Parents
School Type
--Percent Minority
--Students
Takeaway Child
--Allowance
Imputed % Free
--Lunch Eligible
Location
--R Square
0.131
*** p<=.001, ** p<=.01, * p<=.05
20
Model 2
Kanwal Latif
Summary & Conclusion:
The proposed research follows the long tradition of studies on the critical role that
maternal education plays on children’s learning. It adds parental involvement to support
children’s success in school. There is a wealth of evidence on the positive relationship between
parental education, especially mother’s and her offspring’s education (Behrman, 1997). There is
much less information, however, on the degree to which maternal education is related to levels of
parental involvement in children’s education, and the potential consequences of this relationship
for students’ achievement. The question remains, to what degree is the well documented
relationship between maternal education and student achievement accounted for by higher levels
of parental involvement among mothers with high levels of education? The proposed research
expands knowledge on this issue by using data from a large national data set to examine the
relationship between maternal education and eighth graders’ mathematics achievement while
considering various aspects of parental involvement as intervening factors. This project results
elucidate the significance of a perspective that includes both maternal education and parent
involvement for understanding children’s learning. The study aimed to establish basic findings,
constituting the “first step” of further research, which will show maternal education effects for
eighth grade. Findings from this study are of great significance to families and societies aimed at
improving the life chances of students with an awareness of the importance of a mother’s
education and parental involvement.
The original multivariate models showed a strong positive relationship between the
maternal educational levels and students’ math achievement in eighth grade. Students whose
mothers have higher educational levels tend to have higher math test scores than students whose
mothers have lower educational levels. However, when SES and race were controlled in the
multivariate analyses, the relationship between the mother’s educational level and student’s math
test score seems to be spurious. It seems that demographic characteristics largely explain the
original relationship between mother’s education and student’s test scores in math. Regarding
parental involvement, I have found that parental involvement does not explain the relationship
between mother’s education and student’s math achievement. The most significant indicator of
parental involvement is only parent’s educational expectations for the child. Comparing all
independent variables show that, Socioeconomic Status has the strongest relationship with eighth
graders’ math achievement, followed by Black race. SES and Black are two very important
21
Kanwal Latif
predictors that explain the original relationship between mother’s education and student’s math
test scores. The most part of the relationship with mothers who have a high educational level and
students’ math achievement can be attributed to demographics. Even though prior research has
found a mothers’ educational level to be significantly related to student’s performance in schools
compared to students whose mothers were less educated (Garasky, 1995), my analysis does not
support this conclusion.
Finally, students’ academic achievement is related to a complex set of factors, such as
mother’s education, race, SES and parent’s expectations.
22
Kanwal Latif
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Appendix 1. Weighted Descriptive Statistics for Variables Used in the Analyses of Students’
Math Test Scores in Eighth Grade.
Descriptive Statistics
Variables
N
Mean
Std. Deviation
-Dependent VariableC7 RC4 MATH T-SCORE
9653
50
10.001
-Main Independent VariableMother’s Educational Levels
8177
2.91
1.108
-Parental Involvement Level VariablesParents’ Activities at School
Leisure Time Rules
Parent’s Expectations
Academic Rules
Help in Math by Parents
W8 Categorical SES
Measure
W8 Continuous SES
Measure
Mother’s Age
Parent Type Structure
Gender
Race White
Race Black
Race Hispanic
Race Asian
Race Other
School Type
Percent Minority Students
Takeaway Child Allowance
Location
Imputed % Free Lunch
Eligible
31
8205
2.53
Range
24.00-75.00
1.00-5.00
1.639
0.00-6.00
8528
3.58
8586
4.05
8526
1.72
6746
0.72
-Family Level Variables-
1.473
1.104
0.526
0.45
0.00-5.00
1.00-6.00
0.00-2.00
0.00-1.00
8599
3.05
1.412
1.00-5.00
8599
-0.09
0.801
-3.00-2.00
6.863
0.445
19.00-88.00
0.00-1.00
0.5
0.49526
0.37737
0.3877
0.16905
0.20288
0.00-1.00
0.00-1.00
0.00-1.00
0.00-1.00
0.00-1.00
0.00-1.00
0.30183
1.479
0.452
0.43
0.00-1.00
1.00-5.00
0.00-1.00
0.00-1.00
25.727
0.00-95.00
8348
41.34
8599
0.73
-Student Level Variables9725
0.52
9725
0.5688
9725
0.172
9725
0.1842
9725
0.0294
9725
0.043
-School Level Variables9651
0.8986
9606
2.92
8481
0.29
8423
0.76
8672
35.68
Kanwal Latif
Appendix 2: Factor Analysis for Parental Involvement Indicators.
Factor 1
(At School Activities)
Variable
Name
P7ATTENP
P7PARGRP
P7ATTENS
P7ATTENB
P7VOLUNT
P7FUNDRS
32
Factor
Loading
0.66
0.639
0.653
0.665
0.734
0.664
Factor 2
(Child Leisure Time
Rules)
Variable
Factor
Name
Loading
P7TVRULE
0.571
P7TVRUL2
0.611
P7FRNUMH
0.832
P7FRHRWK
0.77
P7VIDHRS
0.689
Factor 3
(Academic Rules)
Variable
Name
P7GPARUL
P7HWKRUL
Factor
Loading
0.8
0.8
Kanwal Latif