PARENTAL INVOLVMENT, FAMILY STRUCTURE,
AND ACADEMIC ACHIEVEMENT
Michael Allen Small
B.A., California State University, 2005
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
SOCIOLOGY
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2010
PARENTAL INVOLVMENT, FAMILY STRUCTURE,
AND ACADEMIC ACHIEVEMENT
A Thesis
by
Michael Allen Small
Approved by:
__________________________________, Committee Chair
Randall MacIntosh, Ph.D.
__________________________________, Second Reader
Ernest Cowles, Ph. D.
____________________________
Date
ii
Student: Michael Allen Small
I certify that this student has met the requirements for format contained in the University format
manual, and that this thesis is suitable for shelving in the Library, and credit is to be awarded for
the thesis.
__________________________, Graduate Coordinator ___________________
Amy Qiaoming Liu, Ph.D.
Date
Department of Sociology
iii
Abstract
of
PARENTAL INVOLVMENT, FAMILY STRUCTURE,
AND ACADEMIC ACHIEVEMENT
by
Michael Allen Small
Statement of the Problem
In recent years, parental involvement has become the focus of attention for many
researchers interested in investigating factors that influence academic achievement.
While there are many possible reasons for this continued interest, it is important to
continuously re-evaluate the factors that influence academic progress as a means of
reducing social inequality and narrowing the gap between rich and poor. In addition,
family structure and the factors that inhibit or enhance parental involvement are
undergoing constant change. The purpose of this study is to replicate an earlier study,
Effects of Parental Involvement and Family Structure on the Academic Achievement of
Adolescents (Jeynes, 2005), that found that many types of parental involvement are
positively correlated with the child’s academic achievement.
Sources of Data
In addition to a substantial review of the current literature on the subject of parental
involvement, family structure, and academic achievement, the main sources of data used
in the development and production of this thesis are the National Education Longitudinal
Study 1988-2000, and the Education Longitudinal Study 2002-2004.
iv
Conclusions Reached
The results of this study are consistent with the findings in the original study and
lend credence to the ideas that intact family structure and certain types of parental
involvement have positive effects on academic achievement. Moreover, the consistency
in scores may indicate that living with both biological parents — even in cases where
divorce is the eventual outcome — is associated with higher test scores in comparison to
situations where the parents are never married. Regarding specific elements of parental
involvement, parents checking-up on their students’ academic activities is associated with
statistically significant, negative effects. On the other hand, parents who discuss school
classes, activities, and things studied, and parents who attend school related events and
activities are associated with statistically significant, positive effects on academic
achievement. The largest positive effect on academic achievement is associated with
socioeconomic status (SES).
, Committee Chair
Randall MacIntosh, Ph.D.
_______________________
Date
v
DEDICATION
This thesis is dedicated to my wife, Cindy. From the moment I returned to college to
complete my Bachelor’s degree, she has been a boundless source of support and
encouragement. I really don’t know how, but she managed to maintain a positive attitude,
reminding me of the goal when I began to think there were better options than completing
the program. I doubt I would have completed my Master’s degree without her; she is the
love of my life and main reason I started the process to begin with. Although she didn’t
write it, there would be no thesis without her — this one is for Cindy.
Although Cindy provided the “real-time” motivation, my ambition to investigate the
factors that influence educational outcomes is rooted in events that took place 80 years
ago. Both my parents were born in 1914; they married in 1936 and started our family in
the midst of the Great Depression. In that era, it was customary for children to leave
school as soon as possible to help with family finances, and my father, Lewis Dwight
Small, only completed the fifth grade. My mother, Alma Regina Small, nee Paulson,
completed her formal education when she graduated at the end of the eighth grade. Of
their ten children, three have earned Bachelor’s degrees, and I am the second to have
earned an advanced degree. Both my parents were very smart, and I can only imagine
how different their lives would have been if they had been given the opportunity to
continue their education beyond the bare minimum allowed by the harsh economic
realities of the 1930s. Knowing how difficult their lives were with a limited education, I
am certain they would appreciate this thesis and the completion of a Masters degree.
vi
ACKNOWLEDGEMENTS
I would like to thank Randy MacIntosh for allowing me to use a portion of his
considerable skill and experience in academic research, data analysis, and statistics in the
completion of my thesis. His expertise was invaluable in each step of the process,
allowing me to bypass some obstacles altogether and putting me on the right track to find
solutions for other problems that are often a part of a project of this size. In addition,
accepting the role as Chairperson on any thesis involves a considerable amount of work
and the willingness to surrender substantial amounts of time reading and re-reading
countless drafts of the many chapters that go into the finished product, and I am very
thankful for his efforts.
I would also like to thank Ernest Cowles for volunteering his time and effort in
accepting the role of second reader on my thesis committee. When I asked if he would
consider being my second reader, I knew that I was asking him to temporarily dedicate
his time and make room for my thesis in his incredibly busy schedule. His time is so
valuable and so scarce, yet Ernest was very willing to take on the project, and his
experience in applied research, academic research, statistics, and the whole process of
writing a thesis was incredibly helpful. It is hard to imagine that I can effectively
communicate how much I value the effort put forth by both these men, but I can say this
thesis would be deficient in so many ways without their assistance.
Thanks also to Patty Crosby and the entire staff at the Institute for Social Research.
Their support made each new challenge in the Masters’ program much less stressful.
vii
TABLE OF CONTENTS
Dedication ................................................................................................................................... vi
Acknowledgements .................................................................................................................... vii
List of Tables ............................................................................................................................... x
List of Tables - Continued .......................................................................................................... xi
Chapter
1. STATEMENT OF THE PROBLEM ..................................................................................... 1
Overview .......................................................................................................................... 1
Family Structure and Parental Involvement..................................................................... 2
The Need to Reassess Parental Involvement ................................................................... 4
Demographics .................................................................................................................. 4
Dynamic Society .............................................................................................................. 6
Sociological Theory ......................................................................................................... 7
Research Question ........................................................................................................... 8
Notes Related to the Original Study ................................................................................ 8
2. A REVIEW OF THE LITERATURE .................................................................................. 11
Parental Involvement ..................................................................................................... 11
Family Structure............................................................................................................. 13
Meta-Analyses of Parental Involvement ........................................................................ 16
Socioeconomic Status — SES ....................................................................................... 18
Social Capital ................................................................................................................. 21
Gender ............................................................................................................................ 24
Urban, Suburban, and Rural Settings ............................................................................. 25
viii
TABLE OF CONTENTS
3. METHODOLOGY .............................................................................................................. 28
Hypotheses ..................................................................................................................... 28
Sample ........................................................................................................................... 28
Dependent Measures ...................................................................................................... 30
Independent Measures ................................................................................................... 31
Parental Involvement ..................................................................................................... 33
Demographic Variables ................................................................................................. 44
The Regression Model ................................................................................................... 45
4. RESULTS ............................................................................................................................ 47
Family Structure............................................................................................................. 47
Socioeconomic Status — SES ....................................................................................... 53
Parental Involvement ..................................................................................................... 54
Demographics ................................................................................................................ 57
5. FINDINGS AND DISCUSSION ......................................................................................... 61
6. CONCLUSION .................................................................................................................... 64
Appendix A. Core Variables ................................................................................................... 67
Appendix B. Means and Standard Deviations Tables ............................................................. 69
Appendix C. Comparison of Original Core Variables and Core Variables in
Current Study .................................................................................................... 82
References .................................................................................................................................. 84
ix
LIST OF TABLES
Table 1. Means and Standard Deviations for Various Family Structures In the
ELS 2002 – 2004 Data Set ............................................................................................... 49
Table 2. Effects (in Standard Deviation Units) for Parental Involvement in the Education of
Their Adolescents for Twelfth Graders in the ELS 2002 – 2004 Data Set ...................... 55
Table 3. Effects (Unstandardized) for Parental Involvement in the Education of Their
Adolescents for Twelfth Graders in the ELS 2002 – 2004 Data Set................................ 57
Table B1. Means and Standard Deviations for Various Family Structures Included in the
ELS 2002 – 2004 Data Set ............................................................................................ 69
Table B2. Means and Standard Deviations for Adolescents from the Gender Groupings
Included in the ELS 2002 – 2004 Data Set ................................................................... 69
Table B3. Means and Standard Deviations for Mother’s Highest Level of Education
Composite Included in the ELS 2002 – 2004 Data Set ................................................ 70
Table B4. Means and Standard Deviations for Father’s Highest Level of Education
Composite Included in the ELS 2002 – 2004 Data Set ................................................ 71
Table B5. Means and Standard Deviations for Parents’ Highest Level of Education
Composite Included in the ELS 2002 – 2004 Data Set ................................................ 72
Table B6. Means and Standard Deviations for Income Groups Included in the
ELS 2002 – 2004 Data Set ............................................................................................ 73
TableB7. Means and Standard Deviations for Adolescents from the Various Race/Ethnic
Groups Included in the ELS 2002 – 2004 Data Set ...................................................... 74
Table B8. Means and Standard Deviations for Mother’s Occupation Level Included in the
ELS 2002 – 2004 Data Set ........................................................................................... 75
Table B8. (Continued) Means and Standard Deviations for Mother’s Occupation Level
Included in the ELS 2002 – 2004 Data Set ................................................................. 76
Table B9. Means and Standard Deviations for Father’s Occupation Level Included in the
ELS 2002 – 2004 Data Set ............................................................................................ 77
Table B9. (Continued) Means and Standard Deviations for Father’s Occupation Level
Included in the ELS 2002 – 2004 Data Set ................................................................. 78
Table B10. Means and Standard Deviations for Variables for Index Variable PI2Checking
Included in the ELS 2002 – 2004 Data Set................................................................. 79
x
LIST OF TABLES - CONTINUED
Table B11. Means and Standard Deviations for Variables for Index Variable PI3Discuss
Included in the ELS 2002 – 2004 Data Set ................................................................ 80
Table B12. Means and Standard Deviations for Variables for Index Variable PI4Attend
Included in the ELS 2002 – 2004 Data Set ................................................................. 81
Table B13. Means and Standard Deviations for School Location Included in the
ELS 2002 – 2004 Data Set .......................................................................................... 81
Table C1. Dependent Measures of Academic Achievement ......................................................... 82
Table C2. Independent Measures of Parental Involvement ........................................................... 82
Table C2. (Continued) Independent Measures of Parental Involvement ....................................... 83
xi
1
Chapter 1
STATEMENT OF THE PROBLEM
Overview
In recent years, parental involvement has become the focus of attention for many
researchers interested in investigating factors that influence academic achievement.
While there are many possible reasons for this continued interest, it is important to
continuously re-evaluate the factors that influence academic progress as a means of
reducing social inequality and narrowing the gap between rich and poor. According to
Becker (1993) “Education and training are the most important investments in human
capital […] high school and college education in the United States greatly raise a
person’s income, even after netting out direct and indirect costs of schooling…” (p.17).
The numerous studies in this area have utilized a variety of different sample groups,
methodologies, and variables to evaluate the existence and strength of the relationship
between parental involvement and achievement. Because there are many different ways
in which a parent can become involved in their children’s scholastic activities,
researchers have examined a myriad of different factors under the all-encompassing term
of parental involvement. The purpose of this study is to replicate an earlier study that
found that many types of parental involvement are positively correlated with the child’s
academic achievement.
Some examples of parental involvement types investigated in these studies include:
parents discussing school events and activities with their children; parents helping their
children with class or program selection; parents knowing the parents of their child’s
2
friends; parents volunteering at school; parents attending school meetings; and parents
checking their students’ homework (Jeynes 2005, Muller 1995, Weiss 2003, and
Houtenville and Smith-Conway 2008). Due to the magnitude of parental involvement
types that have been evaluated in these studies, it is a somewhat daunting task to
generalize the overall efficacy of parental involvement as it relates to academic
achievement; however, the results of Jeynes’ (2007) meta-analysis of 52 individual
studies indicates “the influence of parental involvement overall is significant for
secondary school children” (p.82). Based on reviews of many different studies; census
bureau figures on income; changing family demographics; changing family structures;
and external economic factors, two points become clear. First, the likelihood that parents
will become involved in their children’s education at levels high enough to leverage their
potential for academic achievement depends on many different factors. Second, the
factors that impinge upon parental involvement and family structure are undergoing
constant changes which make parental involvement as a method to enhance academic
achievement more tenuous than ever.
Family Structure and Parental Involvement
There are many factors which bear upon the extent to which a parent is able to
become involved in the academic affairs of his/her children, and among those factors,
different family structure types have been shown to affect academic achievement. Studies
by Amato (2000), Jeynes (2002), and Weitoft (2004) have noted that students from
divorced parents are more likely to demonstrate lower academic achievement. Although
it is reasonable to understand that single parents simply have less available time to spend
3
with their children due to work schedules, there are other factors which can be associated
with family structure as it relates to academic progress. Attewell and Battle (1991) point
out that something as simple as student access to home computers is associated with
higher test scores (p.1). Because many single-parent families also have reduced income
levels (see below), it could be argued that single parents have limited access to the
material goods that promote greater academic achievement.
In the study being replicated here, Jeynes’ Effects of Parental Involvement and
Family Structure on the Academic Achievement of Adolescents (2005), limited access to
parental resources and the physical absence of one parent are both cited by the author as
being detrimental to academic achievement. Although family structure is not, in the
strictest sense, a parental involvement variable, it is one factor that may weigh heavily
upon a parent’s ability to be involved in their children’s academic activities, and as such,
it is included here as a parental involvement variable. In addition, the category of family
structure types is undergoing constant change. For example, in 1994 approximately 36
percent of children living in single-parent families lived with a parent who had never
been married, approximately 37 percent lived with a parent who had been divorced, and
23 percent lived with a parent who was separated from their spouse due to marital discord
(Saluter 1994). Because family structure is undergoing constant change and is likely
related to a parent’s ability to promote their children’s academic progress, it is essential
to re-evaluate its association with academic achievement.
4
The Need to Reassess Parental Involvement
As noted above, parental involvement is related to academic achievement, and
different types of family structure can directly affect the extent to which parents can be
involved in their children’s scholastic activities. The absence of one parent, reduced
income, and limited access to other parental resources can affect student academic
performance. Figures presented immediately below demonstrate that single-parent
families and minorities are likely to have lower incomes in relation to non-minorities and
households with dual incomes. The dynamic nature of external economic forces and
family structure creates an environment that can adversely affect adolescent academic
achievement. Because greater academic achievement is one key to reducing social and
economic inequality, frequent reassessments of the relationship between parental
involvement, family structure, and academic achievement are necessary and welladvised.
Demographics
To emphasize the importance of re-evaluating the relationship between parental
involvement, family structure and academic achievement, and to show the state of the
problem today, several important facts should be established. 1.) According to statistics
compiled by the United States Census Bureau, the estimated median income for family
households in the U.S. during 2007 was $62,359, and for married-couple households the
median estimated income was $72,785 (DeNavas-Walt, Proctor, and Smith 2008).
Figures taken from the same data set indicate that the median income varied widely
between non-Hispanic Whites ($54,920), Whites ($52,115), Blacks ($33,916), Asians
5
($66,103), and Hispanics ($38,679). In addition, female householders, with no husband
present, had a median income of $33,370, and male householders with no wife present
had estimated incomes of $49, 839 (DeNavas-Walt et al. 2008:7). 2.) Additional figures
from the United States Census Bureau point to dramatic changes in family structure. In
1990, the number of United States households designated as female householder, no
husband present with 1 or more children under 18 was 6,987,624, and for male
householder, no wife present, the figure was 1,618,338 (U.S. Census Bureau 1990). In the
2000 census, just 10 years later, those figures rose significantly, and for family
households with 1 or more children under 18, female householder, no husband present
there were 8,827,729 households–an increase of 26 percent. For male householder, no
wife present, the number had increased by 59 percent to 2,572,370 households (U.S.
Census Bureau 2000). 3.) The proportion of children living in single-parent situations has
been changing over the last 4 decades. Beginning in 1960, the proportion of children
living with a single-divorced parent was 23 percent. That figure increased to 30.2 percent
in 1970 and grew even larger to 42.4 percent in 1980 before decreasing to 38.6 percent in
1990. While the proportion of children living with a single divorced parent appears to
have peaked in the mid 1980s it remains relatively high at 37 percent, and the proportion
of children living with a single, never-married parent has increased steadily. In 1960, that
figure was 4.2 percent, increasing to 6.8 percent in 1970, 14.6 percent in 1980, 30.6
percent in 1990 and reaching 35.8 percent in 1994 (Saluter 1994:xi). 4.) Recent analysis
of the factors affecting academic achievement at both primary and secondary education
levels suggests that SES has a significant effect on educational outcomes which limit the
6
potential for admittance to post secondary education programs as well as future
employment and income levels (Finn 2006:iii).
Dynamic Society
Researchers often replicate prior studies to advance new theories and to update
hypotheses as they apply to circumstances which did not exist at the time the original
study was conducted. For example, the predominant family structure just a few decades
ago consisted of married biological parents and their biological children. Today, many
children grow up in single-parent homes after a divorce, in blended families with stepparents, step-siblings, or in families where the parents cohabitate rather than get married.
Moreover, many single parents, perhaps due to relaxed social attitudes, are simply
choosing to have children without ever being married. Because family structure has
previously been shown to affect academic performance, research on the factors that
influence academic performance should be revisited to evaluate whether the effects of
parental involvement and family structure have remained the same or diminished in the
recent past.
In addition to changing family structure, societal changes occurring outside the
family can also have a dramatic effect on the ability of parents to be involved in their
children’s education. A case in point is the changing demographic of America’s
workforce. According to the United States Census Bureau, children born between 1946
and 1964 constitute the group known as baby boomers. This wave of people is finally
reaching retirement age, and the aging, mostly white baby boomers will be leaving the
workforce. In light of already mentioned income disparity between different
7
races/ethnicities and gender, the need for a renewed evaluation of the extent to which
parents are involved with their children’s education takes on added significance.
Perhaps as important as the picture of changing demographics is the trend toward
greater income inequality in America. According to Morris and Western (1999) the
United States’ economy is cyclical, and over the last three decades, median income has
fallen, and “the distribution of income has grown markedly more unequal[…]reversing a
pattern of earnings growth and equalization dating back to 1929” (p.623). Although it is
still premature to evaluate the economic collapse that began in late 2008, it is not difficult
to imagine that massive mortgage foreclosures and lost jobs would have a substantial
effect on parental involvement in their children’s academic progress — more than the
expected cyclical changes already mentioned. Changes in family income due to expected
and cyclical economic factors also necessitate revisiting prior research on parental
involvement in adolescent academic achievement.
Sociological Theory
Because the original research gives considerable attention to the effect of SES on
academic achievement, this research will do the same, lending itself well to several
sociological frameworks including structural / bureaucracy theories discussed by Weber,
and theories of social capital more recently discussed by Coleman. Even though limited
access to higher education, often a product of academic achievement, could be easily
viewed from Marx’s conflict theory perspective, this replication will most frequently
refer to Coleman’s theory of social capital. In fact, some of the literature on prior
research indicates that parental involvement is one form of social capital (see below).
8
Research Question
In the broadest terms, this study will address the current state of the relationship
between parental involvement and academic achievement. Regardless of parental gender
or race/ethnicity, single-parent families are likely to have lower incomes than two-parent
families, and, race/ethnicity, gender, and SES will also be included in the evaluation. The
literature review which appears below will examine recent studies, paying particular
attention to race/ethnicity, gender, and SES. Moreover, while many studies have
established the relationship between parental involvement and academic achievement,
factors such as changing economic circumstance and changing family structure suggest
that it is prudent to continually re-evaluate other factors which influence parental
involvement. This research will investigate the relationship between variables which
measure parental attendance at school related events; parents checking-up on their
children’s friends and homework; parents discussing school related activities with their
children, and family structure as they relate to academic achievement of adolescent
students, controlling for SES, race/ethnicity, and gender.
Notes Related to the Original Study
It is especially important to note that the regression coefficients in the original Jeynes
(2005) study tell a considerably different story than the text included throughout the
article. For example, in the regression that includes SES, the coefficients for
race/ethnicity and SES quartile scores are the considerably larger than the coefficients for
family structure — which the author states is the most important factor. According to
Jeynes, “The single greatest parental involvement indicator was whether a child came
9
from an intact family” (p.112). In fact, the effect of SES was between three and six times
larger than the effect of parental involvement. In addition, with the exception of the Asian
category, the race/ethnicity coefficients indicate that, after controlling for all other
independent variables in the model, the relationship between race/ethnicity and academic
achievement is mixed. For example, after controlling for other factors, coefficients for
Asians are higher than for whites on some variables such as math and social studies,
while coefficients for Blacks and Hispanics are lower than coefficients for whites on the
same variables. One particularly interesting coefficient in the original regression indicates
that a negative and statistically significant relationship exists between academic
achievement and the variable that measures the extent to which parents know their
children’s friends and how often the parents help with or check to see their children have
completed their homework. In the original research, Jeynes speculates that “this result
might have emerged because struggling adolescents need their parents to check on these
matters more” (p.112). It is also possible that parents checking up on their children’s
friends and homework represents the only interaction the parent has with their child,
which points to the need for increased overall contact between parent and child. In light
of this finding, one hypothesis specifically addresses that relationship, and considerable
attention will be given to that relationship in this replication.
In addition to this counterintuitive result, there are structural and theoretical
limitations with the original variables utilized to create this variable. Because the variable
combines two four-part ordinal variables with three dichotomous nominal variables,
Jeynes was forced to create an arbitrary break-point in the recoding of the four-part
10
variables. After a brief correspondence with Jeynes to verify the construct of this
variable, the decision was made to replicate the study as closely as possible to the original
to provide the most accurate comparison to the original study. Theoretically, including a
variable which measures how well the parents know their children’s friends may serve to
provide some sense of overall parental involvement, even though the ideas of checking
up on children’s homework and helping with homework are much different than knowing
their children’s friends. Again, the replication will include this variable in the effort to
match the original study.
11
Chapter 2
A REVIEW OF THE LITERATURE
Parental Involvement
In addition to one variable for family structure, Jeynes’ original study (2005),
includes several variables which are used in various combinations to determine the extent
to which parental involvement includes attendance and participation at school activities,
discussing school activities with their children, and checking-up on their children’s
school related activities. While it is nearly impossible to find studies which include
variables constructed in exactly the same way, there are studies that include similar
variables. Studies that utilize a variable for parents discussing school activities with their
children include: Balli, Wedman, and Demo 1997; Bogenschneider 1997; Mau 1997;
McNeal 2001; and Muller 1998. One measure of parents attending school related events
is included in research conducted by Bogenschneider 1997; Mau 1997; and Muller 1998,
and a variable for parents checking-up on students is included in studies conducted by
Balli et al. 1997; Bogenschneider 1997; and Mau 1997.
McNeal’s (2001) study was partially motivated by the dearth of research on the
possible correlation between parental involvement and adverse student behavior,
including truancy and actually dropping out of school. Findings in McNeal’s study
indicate that parental involvement is “generally a salient factor in explaining behavioral,
but not cognitive outcomes, with greatest support for parent-child discussion and
involvement in parent-teacher organizations” (p.171). Bogenschneider (1997) conducted
research on the relationship between parental involvement and achievement with the
12
intent to investigate the possibility of transcontextual validity of parental involvement
across variables including parents’ gender or education level, as well as the children’s
gender, ethnic background or family structure. A key finding in Bogenschneider’s study
holds that parental involvement is positively correlated with academic achievement,
“compared with parents who are less involved, parents who are more involved in their
adolescents’ schooling, regardless of the parent’s gender or educational level, have
offspring who do better in school, irrespective of the child’s gender, ethnicity, or family
structure” (1997:729).
Mau (1997) was primarily concerned with illuminating possible differences in the
effect of parental involvement as it relates to three specific groups of students–Asian
immigrants, Asian Americans, and white Americans. Along with similar variables to
those in the Jeynes’ study, Mau also included variables intended to measure the extent to
which controlling behavior on the part of the parents affected academic achievement.
Overall, Mau’s study suggests that a negative relationship exists between parental
involvement and academic achievement for Asian immigrants and Asian American
students. Muller (1998) focused on the potential gender based differentials in the effect of
parental involvement on academic achievement. The results Muller’s analysis indicated
that “The relationship between parental involvement and achievement is similar for girls
and boys and diminishes over the course of high school to the point that parental
involvement has essentially no relationship to the gains in achievement made by seniors”
(1998:336). Regarding the study by Balli et al., the intent of the study was “to determine
if variations in prompting families to be involved with mathematics homework would
13
influence their level of involvement” (1997:31). The results of the study indicate that
“Higher levels of family involvement were not associated with higher student
achievement in this study; however, the telephone interviews suggested that some
families experienced other benefits from being involved with homework including
companionship and an increased awareness of what their children were learning in
mathematics” (Balli et al.1997:38).
Family Structure
Three studies, (Zimiles and Lee, 1991; Brody and Flor, 1997; and Weitoft, Hjern, and
Rosén, 2004), include evaluations of family structure and suggest that children from
single-parent households may be losing ground in the battle for academic success in a
variety of ways. Zimiles’ study is a secondary analysis of another highly regarded study,
High School and Beyond (HS&B). According to the authors of the 2002 Education
Longitudinal Study report, HS&B was an important study that captured differences
between attitudes early in high school and compared those to attitudes formed later on in
high school (Ingels et al. 2005). Zimiles’ study examines the contrast between singleparent households and that of intact, two-parent homes as well as remarried parents
(1991). The longitudinal study by Weitoft et al. (2004) examines the effect of single
parenthood and examines the educational attainment of children now 24 to 25 years old
who were living with the same single parent (widowed, non-custodial other parent living,
non-custodial other parent deceased) in both 1985 and 1990, and children who were
living with the same two parents during the same time frame. According to Weitoft et al.,
“Poorer educational performance on the part of the offspring of lone parents can be
14
explained to a large extent by socio-economic disadvantage, especially a lack of
resources” (pp.134-37). The Brody and Flor (1997) study included 156 households and
examined the psychological effects of poverty on African American, single-mother
families. In what can only be described as extreme poverty 75 percent of households in
that group had incomes of less than $3,330.00 per year and the median was $2,358
(p.1002).
Specific results of the three single-parent studies were mixed. Zimiles’ study
compared children from intact families, single-parent families, and families in which the
custodial parent had remarried. The dependent variable, academic achievement, was
established by looking at the student’s performance in 3 areas: 1.) a standardized aptitude
test; 2.) high school grade point average (GPA); and 3.) the probability that the student
would drop out of school between sophomore and senior years (1991:316). The study by
Weitoft et al. included the following groups: 1.) Children of widows/widowers; 2.)
Children of lone parents with a non-custodial biological parent; 3.) Children of lone
parents with a deceased non-custodial parent; 4.) Children living with partnered parents
(2004:135-139).
Although Brody and Flor’s (1997) sample involved single mothers in African
American families, he also addressed gender issues, and found that households with male
children were more routinized than households with female children. Males with more
routinized homes were linked with higher scholastic achievement, while social
interaction between single-mothers and daughters produced fewer internalizing problems
(p.1009). Since the sample was selected from low income households, these findings
15
appear to be similar to those in the Swedish study. In Weitoft et al. (2004), the results
indicate that single-parent families were more likely to be employed in unskilled, manual
and non-manual jobs whereas partnered parents were more likely to be upper-level, nonmanual workers (p.137). The Swedish school system has a compulsory, nine year
education requirement, and the study found that children from lone parent households
were much more likely to finish only the required nine years. In addition, 36 percent of
children from intact, two-parent homes finished at least 13 years of schooling (pp.13739).
A study on the effects of home computers on academic achievement by Attewell and
Battle (1999) also included a control variable for family structure. The study, devised to
assess the effects of home computers on academic achievement, also included measures
of cultural capital, social capital, race, region, gender, and SES. In their analysis, family
structure includes several dummy variables to represent various combinations of family
structure including: biological mother only; biological father only; biological mother and
step-father; biological father and step-mother; and living with other relatives such as
grandparents; the reference category is a student being raised by both biological parents
(p.3). The regression coefficient for academic achievement of students living with single
mothers was 0.64 (p<.001) for reading and 0.63 (p<.01) for math. In other words,
students who used computers for educational purposes at home, living with their
biological mothers had higher scores in math and reading relative to students in the
reference category–living with both biological parents after controlling for SES (p.6,
original italics).
16
Meta-Analyses of Parental Involvement
Two meta-analyses, Jeynes (2007) and Hill and Tyson (2009) explore several studies
which utilize a wide array of variables to investigate academic achievement and how
parental involvement comes into play. In their overall assessment Hill and Tyson state,
“across 50 studies, parental involvement was positively associated with achievement,
with the exception of parental help with homework” (p.740). According to the authors,
the framework cited most often among the 50 studies consists of School-based
involvement strategies such as volunteering at school, communication between parents
and teachers, and involvement in school governance; home-based involvement strategies
which include taking part in scholastic activities at home; school support for parenting
which often involves parent training programs; and involvement between schools and
other community agencies (p.741). While two components most cited in the 50 studies
reviewed by Hill and Tyson are common with Jeynes’ original study (e.g. volunteering at
school and taking part in scholastic activities at home), the other components illustrate an
ever growing spectrum of measures for parental involvement. Included in the list of
relatively uncommon parental involvement components which yielded positive
coefficients were: parents communicating career aspirations (+.60 GPA outcome
measure), and parents’ value of education (Mother = +.59 GPA outcome measure).
Among the less common parental involvement measures which resulted in negative
coefficients, communicating with teacher (-.23 math test scores), and homework
surveillance (-.49, GPA outcome measure) were noted in the regression tables (pp.74447).
17
Jeynes’ (2007) meta-analysis of 52 parental involvement studies was centered on
urban secondary school children, and “the results indicate that the influence of parental
involvement overall is significant for secondary school children…and the positive effects
of parental involvement hold for both White and minority children” (p.82). Similar to the
overall definition of parental involvement offered by Hill and Tyson, Jeynes’ metaanalysis first employs a very loose definition to parental involvement, e.g., “for the
purposes of this study, parental involvement was defined as parental participation in the
educational processes and experiences of their children” (p.83). Because Jeynes is also
the author of the original study which is being replicated here, it comes as no surprise that
many of the variables selected for analysis in the 52 studies are similar to variables used
in his original 2005 study. Those variables are: parents attendance and participation at
school functions, parents’ discussing school activities with their children, parents’
checking up on and helping with homework. The remainder of variables evaluated in
Jeynes’ meta-analysis are: an overall measure of general parental involvement, a unique,
specific measure of parental involvement, parents’ expectations for children’s
performance, and parenting style (p.89).
While Jeynes’(2007) conclusion that “parental involvement is associated with higher
student achievement outcomes” (p.90) is technically true, it is worth noting that Jeynes
separated the studies into two groups — those that included “sophisticated controls” and
those that did not (p.88). In Jeynes’ meta-analysis, the measure of parents discussing
school events with students is re-named communication, and the results indicate that
communication with students yielded coefficients of .32, p<.05 for overall academic
18
achievement, and .30, p<.01 for standardized tests when no sophisticated control
variables were included in the statistical procedure. When sophisticated controls were
included in the procedure, the coefficients for the effect of communication on overall
academic achievement dropped to .15, and the coefficient for the effect of
communication on standardized tests dropped to .14. Controls for SES, race, gender, or
previous achievement reduced these coefficients by half which is noteworthy, but it is
also important to realize the reduced coefficients were not statistically significant, and
therefore communication likely has no effect on academic achievement (p.95).
Socioeconomic Status — SES
In the Jeynes (2005) study being replicated here, SES is made up of five different
variables including, mother’s education, father’s education, father’s occupation, mother’s
occupation, and family income (p.105). Even though this particular construct of SES is
relatively common, it is important to develop a basic appreciation of the numerous factors
that are often overlooked in many evaluations of SES. A brief discussion of a few
examples of the less common factors that make up SES in other studies will also
illuminate the difficulties which arise in making comparisons between studies with
heterogeneous variable constructs.
Two other studies, Crane (1996) and Attewell and Battle (1999) operationalized a
longer list of variables to comprise SES, but there were several common components.
Crane conducted an evaluation of the relationship between students’ home environment,
SES, maternal test scores and academic achievement as measured by mathematics scores.
Although Crane’s study is somewhat different than Attewell and Battle insofar as it
19
involved pre-adolescent students aged five to nine, it is consistent with the larger body of
research which supports a link between SES and academic achievement. It is also
different in the number of SES variables that were used to evaluate SES. In the Crane
study, the eight factors that were used to operationalize SES include family income,
mother’s education, father’s education, mother’s occupational status, father’s
occupational status, household size, marital status, and the percentage of students at the
mother’s high school who were poor (p.308).
The results of Crane’s research demonstrate a clear link between mathematics test
scores and income, although the “effects of the SES variables were smaller than those of
home environment” (1996:309). In addition, the SES variable in Crane’s study was not
operationalized in composite form. Crane’s research yields coefficients for each
component of SES individually. For example, an increase of one standard deviation in
family income ($10,600) raised mathematics scores by 2.9 percentiles (p.309). In the
Attewell and Battle study, coefficients for the effect of having a home computer on
academic achievement indicate a statistically significant, positive relationship, but
“computer effects on test scores are markedly smaller after controlling for SES”
(1999: 4).
In other studies, the unit of analysis is the aggregate SES of students at the school or
the neighborhood SES, based on location of the school. The components used to evaluate
school or neighborhood SES are similar, yet they do vary in some aspects. For example,
in a study designed to evaluate the effect of SES on public high school rankings and
outcomes, Toutkoushian and Curtis (2005) selected unemployment rate for the school
20
district, percentage of adults in the district with at least a bachelor’s degree, and the
percentage of children who qualify for free or reduced price school lunches as measures
of SES. Results of the Toutkoushian and Curtis study indicate that SES did affect student
performance; however, “The SES factors did not account for approximately 40% of the
variation in student outcomes across high schools” (p.268).
A variation on neighborhood SES is noted in an important meta-analysis conducted
by Sirin (2005) in which he states, “Neighborhood SES, on the other hand, is usually
measured as the proportion of neighborhood/county residents at least 20 years old who,
according to the census data, have not completed high school” (p.419). According to
Sirin, “school SES is usually measured on the basis of the proportion of students at each
school who are eligible for reduced-price or free lunch programs at school during the
school year” (p.419). Results of Sirin’s meta-analysis indicated a “medium to strong
SES–achievement relation […] contingent upon school level, minority status, and school
location” (p.417).
21
Social Capital
Because it represents benefits that accrue from many different types of human
interaction, social capital is not easily defined in a single sentence. According to Putnam
(2000) social capital has been defined at least six different ways in the last century (p.19).
Coleman (1988) is perhaps the best known authority on the subject, and in one article he
offered the following description:
Social capital is defined by its function. It is not a single entity but a variety of
different entities, with two elements in common: they all consist of some aspect
of social structures, and they facilitate certain actions of actors-whether persons
or corporate actors-within the structure. Like other forms of capital, social capital
is productive, making possible the achievement of certain ends that in its absence
would not be possible (P. S98).
Perna and Titus (2005) “conceptualize parental involvement as a form of social
capital that provides individuals with access to resources that may facilitate college
enrollment” (p.487). For the present study, social capital, or the lack of social capital,
could be related more to family structure because low-income, single-parent families
might have less time available to participate in parent teacher organizations or volunteer
to help at other school functions. Although the Perna and Titus study is different than the
present research because of its focus on the relationship between parental involvement
and college enrollment, it is relevant to this review of literature because it addresses
parental involvement in their children’s academic progress at the high school level.
The Perna and Titus (2005) study generally supports Coleman’s “conceptualization
of parental involvement as a form of social capital that promotes college enrollment by
conveying norms and standards” (pp.507-08). For the variables that closely match those
22
used in original Jeynes study, “the odds of enrolling in either a 2-year or 4-year college,
relative to not enrolling, increase with the frequency with which the parent discusses with
the student education-related topics (odds-ratio for 2-year = 1.130; odds-ratio for 4-year =
1.164), contacts the school to volunteer (odds-ratio for 2-year = 1.120; odds-ratio for 4year = 1.143), and initiates contact with school about academics (odds-ratio for 2-year =
1.132; odds-ratio for 4-year = 1.145) (p.502). It is important to note that African
American students seem to benefit less from the interaction between parental discussion
and the African American variable, as shown by the odds-ratio coefficient of 0.788
(p.505).
The subjects of another study were minorities from low-income families, and
Smrekar and Cohen-Vogel (2001) begin by pointing out the importance of parental
involvement in the education of children as well as the importance of social capital–or the
devastating consequences of its absence. In doing so, they strengthen the argument for
the need to replicate prior research and periodically update the knowledge base regarding
factors that influence academic achievement. Smrekar and Cohen-Vogel comment,
“Widespread support for parent involvement is reflected by its inclusion in nearly every
policy proposal aimed at improving the performance of our nation's schools” (2001:76).
Although the study was an ethnography aimed at elementary school students and their
parents, it is useful insofar as it illuminates concepts that have been used in many
quantitative studies, and it reveals a disconnect between school officials and parents who
would like to become more involved but probably lack the social networking skills to do
so. According to Smrekar and Cohen-Vogel, “school officials warned that it was unsafe
23
and unwise to enter the school neighborhood and conduct interviews at the parents’
homes” (2001:85). Whether this warning was based on school officials’ prior attempts to
visit the parents’ homes that went awry is not clear, but it illustrates a divide between
parents and school officials rather than a cohesive effort to improve student performance.
When compared with schools and parents who come together to enhance the education of
students, the absence of social capital in this situation is obvious. The inability of this
group of parents to communicate effectively with school officials is clear and may be
related to low-income, undeveloped social capital, or their own limited experience with
schools.
In the Smrekar and Cohen-Vogel (2001) study, the research subjects, a group of 10
families, included three distinct ethnic groups, Black, Hispanic, and Pacific Islander
(Samoan), and although there were college graduates in the group, the average education
for parents was 6 years (p.85). In spite of the warnings from school officials, the families
were interviewed in their homes. The interviewers were “welcomed warmly and politely”
into the parents’ homes, and “9 of the 10 sets of parents interviewed responded that, if
asked, they would find ways to increase their involvement at home and at school”
(Smrekar and Cohen-Vogel 2001: 85). The fact that these parents were interested and
wanted to become more involved in their children’s education suggests that they are
clearly aware of the need for parental involvement in education, yet they lack the social
capital to make that possible. The researchers’ final conclusion suggests that school
officials’ stereotyped perception of parents as apathetic, lazy, incompetent, or too
24
preoccupied to participate in school programs prevents establishing the necessary twoway communication between the school officials and parents (pp.97-8).
Gender
Briefly mentioned above, one study designed to distinguish gender differences
related to academic achievement and parental involvement, Muller (1998) utilized
parental involvement variables similar to those used in the study being replicated here.
Jeynes’ (2005) discussion variable “was based on the extent to which a child discussed
events at school with his or her parents” (p.104). In Muller’s study “All the students
reported how frequently they discussed school activities or what they studied in class”
(p.340). In both Muller and Jeynes, the parental involvement variable included measures
of how often parents attended school meetings or school events. Muller further defined
this parental interaction by commenting, “A school meeting, in which school policy and
programs are discussed, is more likely to be formal, whereas school events may have a
more social or extracurricular content” (p.340).
In Muller’s (1998) study, the discussion of descriptive statistics provides some
insight as to the differential effect of parental involvement on boys’ and girls’ academic
achievement. The study revealed that girls discussed school with parents more frequently
than did the boys, and although boys talked about school programs more with their
fathers, both groups talked more with their mothers than their fathers. Drawing from her
own earlier research, Muller (1995) indicated that fathers’ discussions of high school
with sons may be due to a need to intervene regarding disciplinary issues, or it may be
that fathers simply take more interest in shaping their son’s lives. According to Muller,
25
parents followed stereotypical norms and restricted their daughter’s activities away from
school more than boys. However, the parents of 10th grade boys attended school meetings
more often perhaps to gather information or help set school policies, in contrast to their
attending school events in support of their 8th grade daughters (1998:343-44). For 8th
grade boys, the regression results are statistically significant at the p<.001 level,
controlling only for student grades and educational expectations, indicating that boys
scored slightly higher test scores than girls, and when several other measures of parental
involvement were included in the regression, the boys’ test scores increased slightly, in
relation to girls’ scores (Muller 1998:344-45).
Urban, Suburban, and Rural Settings
While many studies have researched whether or not there is any academic benefit
associated with where the student lives, Jeynes (2007) is the first meta-study published in
an academic journal, which centers on the relationship between parental involvement and
academic achievement of on urban adolescents (p.83.). According to Jeynes meta-study,
researchers including Bauch and Goldring (1995) have argued that parental involvement
may be of greater importance to students situated in urban areas because of “high family
dissolution rates, numerous two-parent working families, and unique sociological
pressures on children” (2007:82-3). While both these studies address the importance of
parental involvement as it relates to urban locales, the current research seeks to address
the distinction between the effect of parental involvement in different school settings–
urban, suburban, and rural.
26
As mentioned above, the consensus of studies in Jeynes (2007) meta-analysis finds
ample evidence that academic achievement and parental involvement are positively
correlated for urban students. In one study that focused on a school-community
partnership model for school renewal, Bauch (2001) states that “Urban schools, to which
much of the research on current reform efforts has been directed, are not rural schools
writ large” (p.204). Rural students, according to Bauch, “face many challenges in gaining
a sound education, but one of the advantages they have is that their schools are set in a
community context that values a sense of place and offers a unique set of conditions for
building the social capital important for helping students succeed in school” (p.204-05).
While urban and rural students each face a different set of challenges, one study by Keith
et al. (1996) suggests that “rural school attendance does not affect either parental
involvement or change in achievement, and that parental involvement has the same
effects on the achievement of students in rural schools as in urban or suburban schools”
(p.55).
In another study designed to evaluate achievement differentials between rural,
suburban, and urban schools, Fan and Chen (1999) evaluated students ranging from 8th
grade to 12th grade and “found that rural students performed as well as, if not better than
their peers in metropolitan schools” (p.31). Using similar outcome variables to the Jeynes
(2005) study, Fan and Chen evaluated reading, math, science, and social studies,
controlling for SES in all analyses (p.34). The Fan and Chen study noted very small
differences in academic achievement across locales, but “differences among ethnic
27
groups were more pronounced, with Caucasian [sic] and Asian groups performing better
than African-American and Hispanic groups, regardless of locality”(p.38).
28
Chapter 3
METHODOLOGY
Hypotheses
Hypothesis 1: Students from families that include both biological parents are likely to
exhibit greater academic achievement relative to students from nontraditional family structures.
Hypothesis 2: Academic achievement scores for students who discuss school activities
with their parents will be higher than scores for students who do not
discuss school activities with their parents.
Hypothesis 3: Students from families where parental involvement includes checking up
on their homework will likely exhibit lower academic achievement scores
relative to those students whose parents do not check up on their
homework.
Hypothesis 4: After controlling for all other variables, family SES will emerge as the
variable having the largest effect on academic achievement.
Sample
This research replicates an earlier study, Effects of Parental Involvement and Family
Structure on the Academic Achievement of Adolescents, conducted by William H. Jeynes
in 2005. Data collected for Jeynes’ original study was taken from the first and second
follow-up years of National Education Longitudinal Study’s (NELS:88) collected in 1990
and 1992. Both waves of the longitudinal study were designed and implemented so as to
include large numbers of students attending high school during those years. Every effort
29
is being made to update Jeynes’ original 2005 study with fidelity to the intent of the
original study and the variables included in that study. The variables included in both
studies and each successive follow-up wave are designed to be consistent from year-toyear so as to facilitate longitudinal comparisons. While there are methodological
differences between this study and the original, these alterations are not included to
change the intent or outcome of the study, but rather to clarify and suggest a better
understanding of the complicated relationship between the variables included in the
original study — an understanding which came to light during this research. This study
will utilize the base year and first follow-up wave of a newer data set — the 2002
Educational Longitudinal Study (ELS2002). As discussed in the literature, both studies,
NELS:88 and ELS: 2002, were conducted by the United States Department of Education
and the National Center for Education Statistics (NCES). According to the study
documentation:
Data collected in ELS:2002 used a two-stage sample selection process. First,
a national sample of schools was selected using stratified probability
proportional Sample: to size (PPS), and school contacting resulted in 1,221
eligible public, Catholic, and other private schools from a population of
approximately 27,000 schools containing 10th grade students. Of the eligible
schools 752 participated in the study. In the second stage of sample selection,
a sample of approximately 26 sophomores, from within each of the
participating public and private schools was selected. Each school was asked
to provide a list of 10th grade students, and quality assurance (QA) checks
were performed on each list that was received. A stratified systematic sample
of students was selected on a flow basis as student lists were received. The
strata were Hispanic, Asian, Black, and Other race/ethnicity. The total
expected student sample size of approximately 20,000 (approximately 800 x
25) was expanded to select additional Hispanic (if necessary) and Asian
students in order to estimate subpopulation parameters within precision
requirements. The general purpose of the weighting scheme was to
compensate for unequal probabilities of selection of schools and students into
the base (ELS 2002).
30
Dependent Measures
The outcome or dependent variables in this study are comprised of test scores
calculated in two ways. Reading and Math scores exist in the data set as individual
standardized T-scores described in the ELS 2002 Codebook as:
The standardized T score provides a norm-referenced measurement of
achievement, that is, an estimate of achievement relative to the population
(spring 2002 10th graders) as a whole. It provides information on status
compared to peers (as distinguished from the IRT-estimated number-right
score which represents status with respect to achievement on a particular
criterion set of test items). The standardized T score is a transformation of the
IRT theta (ability) estimate, rescaled to a mean of 50 and standard deviation
of 10 (P.35-9).
A third test score variable is a composite created which combines scores for Reading
and Math. According to the ELS 2002 Codebook:
The composite score is the average of the math (BYTXMSTD) and reading
(BYTXRSTD) standardized scores, re-standardized to a national mean of 50.0
and standard deviation of 10.0. Some students had scores for only the math
test or reading test, but not both. For these students who did not have both
scores, the composite is based on the single score that was available. The
standardized T score provides a norm-referenced measurement of
achievement, that is, an estimate of achievement relative to the population
(spring 2002 10th graders) as a whole. It provides information on status
compared to peers (as distinguished from the IRT-estimated number-right
score which represents status with respect to achievement on a particular
criterion set of test items) (P.32).
For the standardized Reading, standardized Math, and Composite variable, scores
range from a low of 20.91 to a high of 78.76 points. The standard deviation for all three
categories ranges from 9.96 to 9.98 (N = 15,362). In addition to these measures, this
study will include one outcome variable from the first follow-up conducted in 2004.
Although the study already has one composite math score variable, the first follow-up
math composite variable (N=15,325) is the best available variable to address the issue of
31
causality because it is representative of Math scores for 12th graders in spring 2004. One
limitation of the original study was evidenced in the use of the family structure data
collected in the same time frame (1992) as the academic achievement data. A similar
limitation occurs in the current research because data for only one outcome variable had
been released at the time this study was initiated. In this replication, family structure and
all parental involvement variables were taken from the 2002 data set, and the main
achievement measure, the standardized Math score, was selected from the first follow-up
data collected in 2004. The ELS 2002/04 Electronic Codebook offers a description of the
main achievement variable which is similar to the description of the other dependent
variables:
The F1 Math standardized T score provides a norm-referenced measurement
of achievement, that is, an estimate of achievement relative to the population
(spring 2004 12th graders) as a whole. It provides information on status
compared with peers (as distinguished from the IRT-estimated number-right
score which represents status with respect to achievement on a particular
criterion set of test items). Although the T score is reported for all F1 inschool responding students (including transfer students), regardless of grade
level, the comparison group for standardizing is the 12th grade population.
The standardized T score is a transformation of the IRT theta (ability)
estimate, and has a mean of 50 and standard deviation of 10 for the weighted
subset of 12th graders in the sample.
Independent Measures
Generally, socioeconomic status (SES) is comprised of income, parental education
levels, and parental occupation (Battle 2004; Attewell 1999; Balli 1997). This replication
will utilize an SES quartile variable with quartile weights based on the distribution of the
composite SES variable. The original composite SES variable is based on mother’s
education, father’s education, mother’s occupation, father’s occupation and combined
32
income. According to the authors of the ELS 2002/04 study, the SES variable is an,
“NLS-72/HS&B/NELS:88-comparable composite variable constructed from parent
questionnaire data when available and student substitutions when not” (Ingels et al. 2005:
18). It is worth mentioning that the ELS 2002/04 data set offers a choice between two,
slightly different, composite SES variables. One type of SES variable is based on the
Duncan Socioeconomic Index (SEI) scale for ranking various occupational titles.
Socioeconomic index scores were originally calculated by Otis Dudley Duncan based on
the National Opinion Research Center (NORC) 1947 North-Hatt prestige study and the
1950 Census (Davis, Smith, and Marsden 2007: 37). The second type of SES composite
is based on the 1989 NORC / General Social Survey (GSS) Occupational Prestige Scale.
In the ELS 2002/04 data set, the first SES variable is based on occupational codes
supplied by the respondents according to the older, 1961, Duncan SEI occupational
prestige scores. The second SES variable in the ELS 2002/04 data set is based on
respondent-supplied occupational codes derived from the 1989 NORC / General Social
Survey (GSS) occupational prestige scores. Given the similarities in the two available
SES variables, the decision was made to use the SES quartile variable which is based on
newer NORC/GSS based SES. Within the composite SES variable, occupational status
for each parent ranges from unemployed to professional, and parental education levels
range from lowest, those parents who did not finish high school, to highest, those parents
who completed degrees at the level of Ph.D. or M.D.
33
Parental Involvement
From the outset it should be understood that in Jeynes’ (2005) study as well as this
replication of that study, parental involvement is more than a single, independent
variable. Parental involvement is measured by four separate variables in both studies. The
first parental involvement variable, family structure, is a composite variable that
measures whether or not children are from intact family structures; this variable was
taken in its composite form directly from the ELS 2002 data set. While there can be little
doubt that parents play an influential role in the development of children, family structure
is not always thought of as a parental involvement variable. Its inclusion in this
replication as a parental involvement variable is best explained by the author of the
original, according to Jeynes, “for the purposes of this paper, family structure will often
be referred to as a parental involvement variable, even though it probably represents a
broader construct” (2005:104). Each of the remaining three parental involvement
variables is an index variable created by combining individual variables from the ELS
2002 data set. For example, the parental involvement variable that measures parental
attendance at school functions is created by combining individual variables that measure
1.) how often parents attend parent-teacher organization meetings; 2.) how often parents
attend school activities with 10th grader; and 3.) how often parents act as a volunteer at
the school. The parental involvement variable that measures parents checking-up on their
child’s scholastic activities is created by combining individual variables that measure 1.)
how often parents check that homework is completed; 2.) how often parents worked on
homework / school projects with 10th grader; and 3.) whether parents know 10th graders
34
first, second, and third friend. The last parental involvement variable is a measure of how
often students discuss school related topics with parents, and it is created by combining
individual variables that measure: 1.) how often the student discussed school activities
with parents; 2.) how often student discussed school courses with parents; and 3.) how
often student discussed things studied in class with parents.
It comes as no surprise that each variable in a list such as this would quite likely
have a different effect on academic achievement. For example, the regression coefficients
in the original study demonstrate that after controlling for all other variables, the effect of
parental involvement on adolescent academic achievement as measured by parents’
attending school activities is not statistically significant and not as strong as the effect of
parental involvement as measured by parents’ discussing school activities with their
children which is statistically significant, positive and stronger. In this study, the
regression coefficients will be discussed in terms of the different types of parental
involvement, and every effort will be made to make the distinction between the different
types of parental involvement. A more detailed description of how each parental
involvement variable was created occurs near the end of this methods section.
Given the distinctions just mentioned regarding the different aspects of parental
involvement, it may seem counter-intuitive to revert to a discussion of parental
involvement as a monolithic variable; however, for the purpose of discussing limitations
in Jeynes’ original study, it will be helpful to briefly disregard the distinctions between
aspects of parental involvement. These limitations relate to the way parental involvement
actually occurs within the family and the way it is conceptualized and analyzed in Jeynes’
35
regression models. In fact, due to these limitations in the original study, this research will
not include a “non-SES” regression model for reasons which will become obvious in the
following explication of the numerous ways in which relationship between inter-related
variables is conceptualized and analyzed by others who discovered similar problems in
their own research.
As previously mentioned, Jeynes (2005), suggests that SES, family structure, and
parental involvement are discreet concepts of parent-child interaction, separately
measurable. This is problematic for several reasons. The relationship is, in fact, very
complicated, and it is very difficult to determine the strength of the effect of either
variable on academic achievement due to the inter-related nature of the independent
variable, parental involvement, and SES which serves as a control variable in this
research. In a spurious relationship, “the independent and dependent variables are
influenced by a causally prior control variable, and there is no causal link between them”
(Frankfort-Nachmias and Leon-Guerrero 2002:219). According to the same authors, “the
relationship between the independent and dependent variables is said to be ‘explained
away’ by the control variable” (2002). However, replicating this study presents a
situation where the linkage between the independent, dependent, and control variables is
not so clear cut. It seems logical that SES would be a causally prior variable because
most families would be building, and hopefully, improving their SES before ever having
children, but this is not necessarily true, and it is definitely not true in all cases. To
address the question of a spurious relationship more directly, in this replication,
controlling for SES does not “explain away” the effect of parental involvement on
36
academic achievement, so the effect of the independent variable on the dependent cannot
be discredited as a spurious relationship.
The results of the original study and the hypothesized results of the regression
demonstrate yet another aspect of the complicated nature of the interrelation between the
three variables. First, because SES and parental involvement may affect each other and
are likely to change over time, it is difficult to state definitively which occurs first — an
essential step in establishing causality. For example, the first hypothesis: students from
family structure types that include both biological parents are likely to exhibit higher
academic achievement scores relative to students from non-traditional family structures,
suggests that parental involvement, as it occurs in real life, is somehow isolated from SES
or a variety of other social factors that may affect academic achievement. However, when
controlling for SES, the effect of parental involvement may increase or decrease —
depending on which aspect of parental involvement is being measured — a clear
indication that both SES and parental involvement can affect academic achievement and
that they are related to each other.
According to Allison (1999), one method of clarifying this relationship involves
establishing the chronological order of events, in this case, SES occurring before parental
involvement, which would suggest that SES affects parental involvement which then
affects academic achievement. The diagram included in Allison’s text is helpful in
understanding this point:
SES  Parental Involvement  Academic Achievement
37
In this example, SES has an indirect effect on academic achievement by way of the
effect it has on parental involvement (Allison 1999:60). Allison’s example goes on to
state that the “regression model only estimates the direct effect of each variable,
controlling for all the other variables in the model” (1999:60). This model is especially
useful in understanding the limitation in Jeynes’ operationalization of the relationship
between SES, parental involvement, and academic achievement by utilizing models with
and without SES. In reality, SES becomes the independent variable which acts as a
driver for parental involvement — parental involvement moderates the effect of SES on
academic achievement. It is important to remember that the non-SES model does not
prove that SES has no effect on academic achievement, but rather that the model is only
representative of the effect of parental involvement when SES is not used as a control
variable. In fact, it is quite likely that both SES and parental involvement influence
academic achievement, but because SES and parental involvement are related to each
other, it is difficult to ascertain the individual effects of either on academic achievement.
According to Allison, another way to look at this relationship relates back to the diagram
above, and the sum of the indirect effect variable, SES, plus the direct effect variable,
parental involvement, is equal to the total effect of the independent and the control
variable upon the dependent variable, academic achievement (1999:61).
Another, more extensive, explanation of the complicated relationship between the
three variables comes from the field of social psychological research and suggests that
the relationship could be clarified by making a distinction between mediator and
moderator variables and their effects on the dependent variable. According to Baron and
38
Kenny, “Specifically within a correlational analysis framework, a moderator is a third
variable that affects the zero-order correlation between two other variables” (1986:1174).
However, as with the other attempts to sort out the complicated relationship in the current
study, SES does not fit exactly within the Baron and Kenney’s definition of a moderator
variable. In their section dedicated to testing moderator variables, the authors point out
that, “…it is desirable that the moderator variable be uncorrelated with both the predictor
and the criterion (the dependent variable) to provide a clearly interpretable interaction
term” (1986:1174, italics mine). Although it is not true in every case, it is quite likely that
SES, parental involvement, and academic achievement are all correlated with one
another. According to Jaccard and Turrisi (2003) “An interaction effect is said to exist
when the effect of the independent variable on the dependent variable differs depending
on the value of a third variable, called the moderator” (p.3). While some studies test for
an interaction effect, this replication will not, because there was no test for an interaction
effect in the original study.
In the current research, parental involvement is a mediator of SES, yet it appears that
a preliminary analysis of the variables is necessary to make that determination.
According to their study (Baron and Kenney 1986) “to demonstrate mediation, one must
establish strong relations between (a) the predictor and the mediating variable and (b) the
mediating variable and some distal endogenous or criterion variable” (1178). In the
present study, this could be accomplished by testing (a) the relationship between parental
involvement (predictor) and SES (mediator), and (b) SES and academic achievement
(distal) variable. This evaluation takes on even more significance if we also apply the
39
concept of academic achievement as being endogenous (arising from within) the student.
This at least provides some basis for understanding the complicated nature of the
relationship between the variables, but also the concept that external forces such as SES
and parental involvement can be associated with influencing internal processes study
habits which could be associated with higher or lower academic achievement.
Although Allison (1999), Baron and Kenney (1986), and Frankfort-Nachmias and
Leon-Guerrero (2002) have all provided information which informs any understanding of
the complications arising in the analysis of inter-related independent variables, the fact
remains that SES and parental involvement both have some effect on academic
achievement. Owing to the explanations offered by these researchers and the
understanding that SES quite probably affects academic achievement whether or not it is
included in the regression model, this research will not include a “non-SES” regression
model. In this context, such a model is misspecified. Instead, the model used will focus
on parental involvement as the independent variable — controlling for SES in the
analysis.
As mentioned above, family structure and parental involvement are often thought of
as separate variables, but because there is often a very close interrelation between the two
variables, the strength of either variables’ individual influence on academic achievement
can be difficult to measure. In fact, many of the same arguments listed above to describe
the difficulty in isolating the effects of SES and parental involvement, also apply to the
difficulty in isolating the effects of family structure and parental involvement. For
example, when biological parents’ are separated or divorced, family SES and the parents’
40
ability to interact with their children’s academic endeavors is likely to have undergone
changes as well. In some cases, remarriage will improve the family’s economic structure
and the parents’ ability to become involved with their children’s school activities. In
other cases, parents remain single for significant periods of time following a divorce, and
in many instances, parents in those families will realize a significant reduction in
economic support as well as the time they have available to help with their children’s
academic progress. It is for these circumstances that family structure will be evaluated in
the current study from the perspective of single and remarried parents, as well as intact
families. Within these changing circumstances it becomes clear that establishing sound
argument for a directional, cause and effect relationship between family structure and
academic achievement is exceedingly difficult.
Given the complicated relationship between family structure and achievement, this
research will simply make the distinction between various family structure types and
evaluate academic achievement as it occurs in each family type while controlling for
SES. Because the family structure variable can be recoded to represent different family
structure types, dummy variables will be created for single mother and single father
family types as well as the intact family variable which replicates the original study. The
family structure variable which actually replicates Jeynes’ original study will be coded as
follows: intact families =1 and all other parent/spouse relationship types = 0. The other
family types will be coded single mother = 1 and single father =1. This can be easily
accomplished by recoding the composite family variable included in the ELS 2002/04
data set.
41
As mentioned above, this research will also address and attempt to rectify an
additional weakness in Jeynes original study. According to Jeynes (2005):
In order to address the issue of causality, the three parental involvement
measures were taken from the 1990 (tenth grade) data set, the academic
measures were taken from the 1992 (twelfth grade) data set, and the family
structure variable was taken from the 1992 (twelfth grade) data set. This study
uses an array of parental involvement measures and distinguishes between
each of them to an extent that conclusions can be made regarding which
aspects of parental involvement are most helpful to adolescents. (P. 104)
Because Jeynes used the family structure variable from the 1992 (twelfth grade) data
set, it would have very limited value or causality as it relates to the academic
achievement variables taken from the same year. To address the issue of cause and effect,
it is important to demonstrate that activity which constitutes the independent variable, i.e.
parental involvement, occurs in the correct time-ordered sequence relative to the activity
which constitutes the dependent variable, i.e. academic achievement. In this research,
data which represents the independent or “cause” variables was collected two years prior
to collecting the data which represents the dependent or “effect” standardized math score
variable. Specifically, the present research will include family structure variables taken
from the base year (2002) and at least one academic achievement variable available in the
first follow-up in 2004.
The current study will also include an analysis of the effect of school location on
academic achievement. School location can be related to issues of SES and race/ethnicity
as well as issues raised by especially large or sparse student populations. According to
the codebook documentation, “Urbanicity of school locale as indicated in the source data
for sampling: the Common Core of Data (CCD) 1999-2000 and the Private School
42
Survey (PSS) 1999-2000.” […] “taken from the school file and replicated across each
student belonging to that school” (ELS 2002: 60). By including a base year variable
based on the locale of the school, this study will add to the knowledge base of factors that
may or may not affect academic achievement. School locations are coded as: urban,
suburban, or rural, and two dummy variables will be created by recoding those original
categories with the reference category represented by suburban school locations (Rural =
1; Urban = 1).
The remaining independent variables are designed to measure the effect of direct
parental involvement in scholastic activities. The second parental involvement variable
measures three aspects of parental involvement as it relates to parents checking up on
their children’s scholastic activities and relationships. These components include: the
extent to which parents help 10th graders with school projects or homework, how often
they check that homework is completed, and whether or not the parents know their
children’s closest friends. The regression variable is an index variable created by
combining three separate variables included in the ELS2002 data set. The component of
this variable that measures whether or not parents help with homework is an individual
response choice included in a question designed to capture information about a several
aspects of parental oversight of their child’s day-to-day scholastic activities. The question
is posed, “How often do your parents do the following?” and response choices include:
check on whether you have done your homework, help you with your homework, give
you privileges as a reward for good grades, limit privileges because of poor grades,
require you to do work or chores, limit the amount of time watching TV/playing video
43
games, and limit the amount of time going out with friends on school nights. The second
aspect of this variable concerns the frequency with which parents check up on whether or
not their child has completed his or her homework. This variable is also an individual
response choice in a single question designed to elicit information on their child’s
scholastic activities. The original question stem is: “How often do you....” followed by
response choices which include: check that your 10th grader has completed all homework,
discuss your tenth grader’s report card with him / her, know where your tenth grader is
when he / she is not at home or in school, or make and enforce curfews for your tenth
grader on school nights. The final aspect of the parents checking up variable involves
whether or not the parents know their children’s first, second, and third closest friend.
Because there are three questions regarding the same issue, an index variable will be
created to combine the three components that constitute the variable that measures
parents’ checking up on their children’s activities; that index variable will recoded to
create a dummy variable coded YES=1.
The third parental involvement variable is another multi-part variable derived from a
single question stem which asks, “In the first semester or term of this school year, how
often have you discussed the following with either or both of your parents or guardians?”
The individual response choices are: selecting courses or programs as school, school
activities or events of particular interest to you, things you’ve studied in class, your
grades, transferring to another school, plans and preparation for ACT or SAT tests, going
to college, and community, national and world events. As with the other multi-part
variables, these variables will be recoded as index variables individually and then
44
combined in a single index variable. The last parental involvement variable looks at the
ways parents are involved in extra-curricular events at their children’s schools. Similar to
the first three independent variables, this question exists in the original questionnaire as a
single stem with several individual response choices all designed to gather information
about the parent’s involvement in various school-related social and group activities. The
survey question, “In this school year, do you or your spouse/partner do any of the
following?” is followed by: belong to the school’s parent-teacher organization, attend
meetings of the parent-teacher organization, take part in the activities of the parentteacher organization, act as a volunteer at the school, and belong to any other
organization with several parents from your tenth grader’s school (for example,
neighborhood or religious organizations). In keeping with the original study, this
regression variable will be created by recoding the original questions and then creating an
index variable from those recoded original variables. Students with parents that were
involved in the above activities will be coded YES=1.
Demographic Variables
Race/ethnicity is also included in the data set as a composite, represented by the
variable name RACE = “Student’s race/ethnicity-composite,” and includes the following
categories: American Indian / Alaska Native, non-Hispanic, Asian, Hawaii / Pacific
Islander, non-Hispanic, Black or African American, non-Hispanic, Hispanic, no race
specified, Hispanic, race specified, Multiracial, non-Hispanic, White, non-Hispanic. This
variable will be a series of dummy variables for Asian, Black, Hispanic, and Native
American with White as the reference category coded: White=0 (N = 15,362).
45
The variable that measures student gender is also a composite, and this variable is
also a dummy variable which will be used to assess the effect of one group as it relates to
the other non-selected group. In this case female = 1 will be referenced against male = 0.
(N = 15,362).
The Regression Model
Because this is a replication of an earlier study, the calculation will consist of a linear
regression (OLS) which will provide regression coefficients to compare student
achievement between students whose parents are more involved in their academic
activities and students whose parents are less involved. Because different types of
parental involvement appear to affect academic achievement in different ways, the study
will compare the effect of the different types of parental involvement. For example, in the
original study, regression coefficients for two parental involvement variables, family
structure and parents’ discussing school activities with their children, were larger and
statistically significant relative to parents’ checking up on their children’s friends and
homework and parents’ attendance at school activities.
This study will make one significant departure from the original research conducted
by Jeynes (2005). Although the original study model included one regression described
as “non-SES” which was compared to another “SES” model, this research will be based
on a regression equation that includes SES as a control variable. All hypotheses are based
on a model which includes SES as a control variable. The main reason for the elimination
of the “non-SES” model is simply a re-evaluation of the original study itself. While
Jeynes’ observation that “the addition of the SES variables lowers the absolute values for
46
most of the corresponding regression coefficients, in comparison to those found using the
No-SES Model,” that does not mean that SES was not “working” in the No-SES Model.
In actuality, the No-SES Model is simply misspecified. This fact becomes even more
significant in light of the above discussion which clearly demonstrates that SES, family
structure, and parental involvement are closely related both in discussion and in the ways
that they interact in the lives of parents and students.
47
Chapter 4
RESULTS
Family Structure
In the original study, Jeynes (2005) identified intact family status as the parental
involvement measure which had the single greatest effect on academic achievement
(p.112). Although that assertion was unsupported by Jeynes’ regression models, the
variable representing family structure produced statistically significant, positive
coefficients in the original study. In this replication of Jeynes’ study, the family structure
variable included in the ELS 2002-2004 data set produced positive, statistically
significant standardized coefficients that nearly matched those in the original study.
Although the original study did not include a model with unstandardized regression
coefficients, the ELS 2002-2004 data set variable for family structure yielded
unstandardized coefficients which are also positive and statistically significant. The
regression results from this study suggest that, relative to children with different family
structure types, children from intact families have higher test scores, as measured by the
four test variables included in the regression model — after controlling for all other
variables in the regression equation. Family structure held a prominent position in the
original analysis, and this analysis will begin by discussing the family structure variable.
As indicated in Table 1 below, children from intact families unadjusted mean score
on the standardized math test is 52.29, and the mean score for the reading variable is
51.98. These figures which were produced using the ELS 2002-2004 data set, statistically
significantly different from the mean scores Jeynes found in the NELS 1990-1992 data
48
set. While the mean scores are statistically significantly different, this is primarily due to
the large sample sizes as the absolute magnitude of the mean differences is rather small.
For example, in the original study, the intact family unadjusted mean score for the math
variable was 53.46, and the unadjusted mean reading score was 52.68. The mean score
for the math variable is 1.17 points lower in the new data set, and an independent samples
t-test indicates that the difference is significant and not due to random chance. The math
variable in the original study (M = 53.46, SD = 9.65) differed significantly (t = 11.35,
p < .000). The mean score for the reading variable is .7 lower than the original reading
variable (M = 52.68, SD = 9.56), and the difference is also significantly different
(t = 6.78, p <.000).
In the past ten years, means and standard deviations produced by the two data sets
haven’t changed all that much, and comparisons within each data set between intact
families and those parents who are divorced is also very similar. In the present study, the
unadjusted mean math score for children from intact families is 7.82 points higher than
the unadjusted mean for students from families in which the parent or parents never
married. Ten years earlier, in Jeynes’ original study, the mean math score for children
from intact families was 8.32 points higher than the mean math score for children from
families where the parents never married. For the math variable, the difference in
unadjusted mean scores between children from intact families and children whose parents
were never married in the new study is .5 points lower than the difference in unadjusted
mean scores between children from intact families and children whose parents were never
49
married in the original study A two-way ANOVA F (1, 25003) = .61, p < .5, indicates
that the difference between differences across the two studies is not significant.
Table 1. Means and Standard Deviations for Various Family Structures In the ELS 2002 – 2004
Data Set
Academic
Intact
Never
Measure*
Family
Cohabitation
Widowed
Separated
Divorced
Married
Standardized
Tests
52.29
47.10
48.83
47.14
49.70
44.47
Math
(9.73)
(9.14)
(9.34)
(10.04)
(9.72)
(9.30)
51.98
47.51
49.51
47.32
49.93
44.68
Reading
(9.88)
(9.09)
(9.74)
(9.64)
(9.76)
(8.91)
Combined
52.28
47.12
49.11
47.04
49.80
44.20
Composite
(9.75)
(9.03)
(9.48)
(9.89)
(9.69)
(8.84)
1st Wave
45.44
35.16
38.11
34.33
38.97
31.97
Math **
(21.15)
(23.92)
(23.91)
(24.93)
(24.04)
(23.23)
Sub-sample N
10,024
489
337
463
1,506
603
*All measures are composite test scores. ELS 2002 – 2004 Data Set. N=15,362
**First follow-up N=15,325
Over the past ten years, the achievement disparity between children from intact
families and children from divorced parents has also remained relatively stable. In the
current research, the mean math variable score for the adolescents from intact families is
2.59 points higher than the mean for children whose parents are divorced. In the original
study, the mean math score for children from intact families was 1.79 points higher than
the score for children from divorced parents. For the math variable, the difference in
unadjusted mean scores between children from intact families and children whose parents
are divorced in the new study is .8 points higher than the difference in unadjusted mean
scores between children from intact families and children whose parents are divorced in
the original study (2.59-1.79 = .8). A two-way ANOVA F (1, 27414) = 5.02, p < .05,
indicates that the difference between differences across the two studies is significant.
While the above comparisons deal with means scores for nearly identical variables which
50
appear in both data sets, the new data set includes an additional math variable. Although
the variables were described in the methodology section, it is helpful to restate how the
variables available in the data set create an overall limitation to the study.
As its name indicates, data for the Education Longitudinal Study (ELS 2002 2004) is
collected at regular intervals, occurring every two years. Theoretically, this practice
allows researchers to evaluate the extent to which outcomes such as academic
achievement are affected by activities or conditions such as parental involvement or
family structure which occurred two years before. The first year of data included in this
data set this study was 2002, and the follow-up, or first wave data was collected in 2004.
Data released at the time this study was initiated included only one outcome variable
which is differentiated from the other math variable by its name — the 1st wave followup math variable. The two math variables discussed in this analysis are differentiated by
name and the time in which data was collected — the 1st wave follow-up math variable
data was collected in 2004, and data for the measure identified simply as the math
variable was collected in 2002. The predictive ability of this study is limited because data
for all outcome variables other than the 1st wave follow-up math variable were collected
in the same year as the independent variables, 2002.
For the key achievement variable, the 1st wave follow-up math variable, the mean
scores are 45.44 for children from intact families, 35.16 for children whose parents
cohabitate, 38.11 for widowed parents, 34.33 for students whose parents are separated,
38.97 for children from divorced parents, and 31.97 for students whose parents never
married. For the 1st wave follow-up math variable, the disparity between married and
51
never married parents is the greatest with mean scores for children from intact families
13.47 points higher than the mean for students from divorced parents.
The data in Table 1 indicate that students from intact families have higher unadjusted
mean scores than students from all other family structure types for all academic
achievement measures. It is worth mentioning that the lowest mean scores for all
achievement variables are associated with the never married category. Also noteworthy is
the fact that unadjusted mean scores for students whose parents are divorced are slightly
lower than the scores for children from intact families for three of the four outcome
measures — reading, math, and combined math/reading variables. The largest difference
in unadjusted means across family structure types is indicated by the 1st wave follow-up
math variable in which the mean for students from intact families is 13.5 points higher
than the mean for students whose parents never married. As the name indicates, each
unadjusted mean score in Table 1 is not adjusted to take into account the effect any other
demographic characteristics. In evaluating the regression coefficients in Table 2, it is
important to remember that the regression equation produces coefficients after controlling
for all other effects in the equation. For example, the regression equation renders
coefficients for race after adjusting for any differences in parental involvement, SES,
gender etc.
In Table 2 below, family structure is shown to have a small but statistically
significant effect on academic achievement. Standardized family structure coefficients
range from a high of B =.18 (p < .001) for the 1st wave follow-up math score to a low of
B =.08 (p < .001) for the composite reading score. For the family structure variable, the
52
standardized coefficients for the math score are B = .14 (p < .001), and B = .12 (p < .001)
for the combined reading/math variable. In Jeynes’ original study, the family structure
variable for math was B = .16 (p < .001) and the score for the combined math/reading
variable was B = .13 (p < .001). Over the last ten years, the coefficients have remained
unchanged to some extent, and family structure is shown to have a relatively weak effect
on academic achievement in both studies. In this replication of Jeynes’ study, after
controlling for all other independent variables in the model, children from intact family
structure score, on average, .14 standard deviations higher on the math variable and .12
standard deviations higher on the combined math/reading variable than children from
non-intact families. In the original study, children from intact families scored, on average,
.16 standard deviations higher on the math variable and .13 standard deviations higher on
the combined math/reading score than children from non-intact families, after controlling
for all other variables in the model. Looking at the unstandardized regression coefficients
in Table 3 (below) the intact family variable is b= 2.46 (p < .001) which can be thought
of as the adjusted difference in means After controlling for all other independent
variables in the model, children from intact families score, on average, 2.46 points higher
on the 1st wave follow-up math variable, than children from other family structure types.
53
Socioeconomic Status — SES
Although the standardized regression coefficients are smaller in the present study
than those in the original Jeynes study, by a wide margin, SES still yields the highest
regression coefficients, ranging from B = .38 (p < .001) in the 4th quartile for the
combined reading/math variable to a low of B = .08 (p < .001) for 1st wave follow-up
math scores for students in the 2nd quartile. As a reminder it is worthwhile to note the
SES variable in this study is a composite score based on respondent-supplied 1989
NORC / General Social Survey (GSS) occupational prestige scores, family income and
parental education. In general terms, an increase in SES, relative to the lowest SES
quartile is associated with increased academic achievement as measured by the outcome
variables included in the regression model. The other SES coefficients for the 1st wave
follow-up math scores were B = .14 (p < .001) in the 3rd SES quartile and B = .24
(p < .001) in the 4th SES quartile, compared to reference category which is the lowest SES
quartile.
While the SES variable still yields the largest coefficients for all dependent variables
in the regression, the coefficients are much smaller than the coefficients in Jeynes’
original model. For example, 4th SES quartile standardized coefficients for the reading
and math variables are both B = .35 (p < .001) in the current study, compared to 4th SES
quartile coefficients of B = .95 (p < .001) for the reading variable and B =1.06 (p< .001)
for the math variable in the original study. In the current study, the coefficient for math
scores in the 3rd quartile is B = .18 (p < .001) compared to a 3rd quartile coefficient for
math of B = .54 (p < .001) in the original study. In the new study, the 3rd quartile
54
coefficient for reading of B = .19 (p <.001) is less than half the size of the 3rd quartile
coefficient for reading in the original study, B = .49 (p < .001). For the combined
math/reading variable the coefficient of B = .38 (p < .001) in the new study is
approximately one-third the size of the standardized coefficient for the combined
math/reading variable in the original study, B = 1.07 (p < .001). Dropping to the 2nd SES
quartile yields a standardized coefficient for reading scores of B = .11 (p < .001) in the
new study compared with a coefficient of B = .28 (p < .001) in the original study.
While none of the standardized regression coefficients in this model are particularly
strong, it is worth noting that standardized regression coefficients are reduced by half for
each progressively lower SES quartile, relative to the reference group. There is a near
linear trend for SES on math achievement. Unstandardized coefficients for the 1st wave
follow-up math score are b = 4.41, (p < .001) for the 2nd quartile, b = 7.53 (p < .001) for
the 3rd quartile, and b = 12.42 (p < .001) for the 4th quartile. For those in the 4th quartile,
each one point increase on the SES scale is associated with an increase of 12.42 points on
the 1st wave follow-up math score, after controlling for all other independent variables in
the regression model.
Parental Involvement
With standardized regression coefficients that are very similar to the original study,
parental checking-up on their students’ academic activities yields statistically significant,
negative effects. For the parental checking-up variable, standardized coefficients include
B = -.05 (p < .001) for the math score; B = -.04 (p < .001) for the combined math/reading
score; and B = -.03 (p < .001) for the reading score.
55
Table 2. Effects (in Standard Deviation Units) for Parental Involvement in the Education of
Their Adolescents for Twelfth Graders in the ELS 2002 – 2004 Data Set N = 15,362
Academic Measure
Reading
Intercept
45.09
PI1-Family Structure
.08***
Missing
-.01
PI2-Checking-Up
-.03***
PI3-Discussion
.16***
PI4-Attendance
.04***
SES Quartile 2
.11***
SES Quartile 3
.19***
SES Quartile 4
.35***
Asian
-.05***
Hispanic
-.16***
Black
-.18***
Native American
-.04***
Gender
.06***
Rural
-.01
Urban
.03***
R2 for Model
.25
*p < .05; **p < .01; ***p < .001
Math
46.88
.14***
.04
-.05***
.14***
.04***
.10***
.18***
.35***
.06***
-.16***
-.21***
-.04***
-.06***
-.01
.00
.26
Composite
45.71
.12***
.01
-.04***
.16***
.05***
.11***
.20***
.38***
.00
-.17***
-.21***
-.04***
.00
-.01
.01
.29
1st Wave Matha
31.30
.18***
.03***
.01
.10
.07***
.08***
.14***
.24***
.05***
-.07***
-.09***
-.02**
-.02
-.01
-.01
.14
a N=15,325
The regression coefficient for the 1st wave follow-up math score was not statistically
significant. The parental checking-up variable also produced negative, statistically
significant standardized coefficients in Jeynes’ original study. In the prior study, the
standardized coefficient for reading was B = -.09 (p < .01); for math the score is B = -.11
(p< .001); and the composite math/reading variable was B = -.11 (p < .001).
In the current study, standardized regression coefficients for parents who discuss
school classes, activities, and things studied are the highest of the parental involvement
set ranging from B = .16 (p < .001) for both the reading and combined composite
math/reading, and B = .14 for the math score, (p < .001). These scores are also in the
same range as the original study. Jeynes’ standardized coefficients for the parents
discussion variable were B = .14 (p < .001) for reading scores; B = .09 (p < .01) for math
56
scores; and B = .12 (p < .001) for the composite reading/math scores. For the parental
discussion variable, 1st wave follow-up math score is not statistically significant.
For the last of the four involvement variables, parental attendance, data in the ELS
2002-2004 data set rendered positive, statistically significant coefficients across all
achievement tests. Standardized coefficients ranged from B = .04 for the math and
reading scores; B = .05 for the combined math/reading score; and B = .07 for the 1st wave
follow-up math score. All standardized coefficients for the parental attendance variable
are statistically significant (p < .001). In Jeynes’ original study, standardized coefficients
for parental attendance were mixed. The coefficient for the reading score was B = -.02;
for the math score the coefficient was B = .03; and for the combined math/reading
variable the coefficient was B = .01. All coefficients for parental attendance in the
original study were not statistically significant. Worth noting is the fact that parental
attendance variable coefficients in the present study are somewhat higher and statistically
significant in relation to those produced by Jeynes’ original study. Table 3 below,
indicates that the unstandardized coefficient for parents attending school events is
b = 3.24 (p< .001). Interpreted as the adjusted difference in means, adolescents whose
parents attended school events scored 3.24 points higher on the 1st wave follow-up math
variable.
57
Table 3. Effects (Unstandardized) for Parental Involvement in the Education of Their
Adolescents for Twelfth Graders in the ELS 2002 – 2004 Data Set N=15,362
Academic Measure
Intercept
PI1-Family Structure
Missing
PI2-Checking-Up
PI3-Discussion
PI4-Attendance
SES Quartile 2
SES Quartile 3
SES Quartile 4
Asian
Hispanic
Black
Native American
Gender
Rural
Urban
R2 for Model
Reading
45.09
.47**
-.41
-.71***
3.56***
.93***
2.50***
4.40***
7.82***
-1.65***
-4.42***
-5.33***
-4.13***
1.19***
-.15
.56***
.25
Math
46.88
.83***
1.18***
-.96***
3.05***
.86***
2.45***
4.24***
7.73***
1.89***
-4.56***
-6.24***
-4.22***
-1.26***
-.36
.002
.26
Composite
45.71
.69***
.41
-.89***
3.53***
.96***
2.64***
4.61***
8.30***
.13
-4.80***
-6.18***
-4.46***
-.03
-.27
.30
.29
1st Wave Matha
31.30
2.46***
1.82***
.40
5.15
3.24***
4.41***
7.53***
12.42***
4.18***
-4.97***
-5.95***
-4.26**
-.95
-.33
-.37
.14
*p < .05; **p < .01; ***p < .001
a N=15,325
Demographics
For the set of demographic variables used in this study, standardized regression
coefficients created with data from the ELS002-2004 are predominantly negative and
statistically significant, but the standardized coefficients indicate a smaller negative effect
than was demonstrated in the original study. For example, in the original study, the
dummy variable created for Blacks produced a standardized coefficient of B = -.47
(p < .001) for reading scores; B = -.51 (p < .001) for math scores; and B = -.52 (p < .001)
for the composite math/reading variable. In the new study, the standardized regression
coefficients for Blacks are B = -.18 (p < .001) for reading; B = -.16 (p < .001) for the
math variable; and B = -.21 (p < .001). For the Black variable, the standardized
coefficient for the 1st wave follow-up math score is B = -.09 (p < .001). The group
defined as Native American also had negative, statistically significant coefficients for all
58
achievement variables, relative to the reference category of Euro-Americans.
Standardized coefficients ranged from B= -.02 (p < .01) for the 1st wave follow-up math
score to B = -.04 (p < .001) each for the math, reading, and combined math/reading
scores. These contrast sharply with the coefficients produced in the first study. Jeynes’
regression coefficients were B = -.34 (p < .001) for reading scores and B = -.36 (p < .001)
for the combined math/reading score. Again, it is important to note that the coefficients
produced in the current study are weak, but they are statistically significant and markedly
improved over the coefficients in the original study. Standardized coefficients for the
group identified as Hispanic remained relatively constant over the last ten years. In the
new study, the Hispanic variable produced coefficients for reading and math variables of
B = -.16 (p < .001); and for the combined reading/math variable scores, a coefficient of
B = -.17 (p < .001). The coefficient for the Hispanic category on the 1st wave follow-up
math score is B = -.07 (p < .001). In the original study, the coefficient for math scores
was B = -.24 (p < .001), and the coefficient for the combined math/reading score was B =
-.23 (p < 001). On the other hand, for the group identified as Asian, standardized
coefficients are predominantly significant and weaker in the current research than they
were in Jeynes’ original study. In the original study, the coefficient on the math score was
B = .26 (p < .001), and the coefficient for the reading variable was B = .06, but not
statistically significant. In the current study, the coefficient for math scores is B = .06
(p < .001), and the coefficient for the composite reading score is B = -.05 (p < .001).
In the new study, the category for race demonstrates some of the biggest changes in
comparison to the coefficients for race in the original study. While the coefficients for
59
Blacks in the new study are negative and statistically significant, the difference, relative
to Euro-Americans, is reduced by more than half for the reading and math variables, and
reduced by approximately 80 percent for the combined math/reading variable. For Native
Americans, the results of the new study also indicate that the achievement gap may be
closing. The differential between Native Americans and Euro-Americans is reduced by
approximately 80 percent for the reading and math scores and reduced by over 90 percent
for the combined math/reading variable. For the Hispanic group, the coefficients in the
new study indicate that the differential between Whites and Hispanics is also reduced, but
by a smaller margin than the reductions in the differential between Blacks and Whites or
Native Americans and Whites. For the reading variable, Hispanics scored, relative to the
White reference group, approximately 16 percent better than in the original Jeynes study
and approximately 30 percent better than the reference group for the math variable. For
the Asian group the results indicate that the achievement gap may be growing. Relative to
Euro-Americans, scores on the reading variable were 77 percent lower than in the
original study, and the combined math/reading score was approximately 71 percent
lower.
Turning to gender, a comparison between the original study and the current research
produced mixed results. In both studies, the variable for females produced standardized
coefficients for math of B = -.06 (p < .001). The coefficient for reading is B = .06
(p < .001) in the newer study, which is considerably lower than the coefficient for reading
in the original B = .23 (p < .001). The coefficient for reading in the new study is .17
standard deviations lower than the coefficient for reading in the original study,
60
representing a reduction in female reading scores of approximately 75 percent, relative to
the male reference group. In the new study, the coefficient for the 1st wave follow-up
math score is weak B = -.02, and statistically significant (p < .01).
For this research, variables for school location were included in the regression
equation for the purpose of comparing scores between two major categories of schools.
After controlling for all other independent variables in the model, data in the ELS 20022004 data set produced location coefficients for the majority of achievement variables
that were not statistically significant. The single exception was the indicator variable for
urban schools which rendered a coefficient, relative to the suburban reference group, for
the reading score which was weak B = .03 but statistically significant (p < .001).
61
Chapter 5
FINDINGS AND DISCUSSION
Rather than an attempt to propose a solution which would encourage or enable
parents to become more involved in their children’s scholastic activities, the intent of this
thesis is to build upon a significant body of existing research that suggests a correlation
between parental involvement, family structure, and academic achievement. The results
of this study are consistent with the findings in the original study and lend credence to the
ideas that intact family structure and certain types of parental involvement have positive
effects on academic achievement.
This study was accomplished by using data collected in 2002 and 2004 in the
Education Longitudinal Study (ELS) and replicating the Jeynes (2005) study which
utilized data collected in the National Education Longitudinal Survey (NELS) for the
years 1990 and 1992. Based on a substantial review of literature produced by prior
research as well as findings in the original study, four hypotheses were established.
Hypothesis 1: Students from families that include both biological parents are likely to
exhibit greater academic achievement relative to students from nontraditional family structures.
Although they are far from conclusive, the regression coefficients produced in this
study generally support Hypothesis 1. While the standardized coefficients are not as
strong as those found in the original study, student scores on three of the four
independent variables are positive and statistically significant. In addition, the dependent
62
variable which was comprised of data collected two years after the independent variable
for family structure yields a statistically significant, positive coefficient.
Hypothesis 2: Academic achievement scores for students who discuss school activities with
their parents will be higher than scores for students who do not discuss school
activities with their parents.
Hypothesis 2 also appears to gain support in the regression model created with the
more recent data set. For the reading, math and combined math/reading variables, parents
discussing school did, in fact, have higher scores, than students whose parents did not
discuss scholastic activities. Standardized coefficients for the discussion variable are
consistent with the same variables in the original Jeynes study.
Hypothesis 3: Students from families where parental involvement includes checking up on
their homework will likely exhibit lower academic achievement scores relative
to those students whose parents do not check up on their homework.
Hypothesis 3 is also supported in the current study. This hypothesis was predicated
on the results of the original study, and the current data set produced results consistent
with those found by Jeynes. Although the coefficients are relatively weak, coefficients for
parents’ checking up on their children’s homework on three of the four achievement
variables are negative and statistically significant.
Hypothesis 4: After controlling for all other variables, family SES will emerge as the
variable having the largest effect on academic achievement.
Hypothesis 4 is also supported by this study. Although Jeynes’ standardized
coefficients indicate that SES has the strongest effect on academic achievement, the
63
discussion section of the original study erroneously reported that family structure was the
strongest parental involvement indicator. In this study, SES is the strongest predictor of
academic achievement as measured by scores on the four outcome variables. In general,
an increase in a student’s family SES, relative to the lowest SES quartile, is associated
with increased academic achievement scores as measured by the achievement variables in
this regression equation.
64
Chapter 6
CONCLUSION
Taken as a whole, the current study indicates, much like the original, that specific
types of parental involvement and intact family structure may play an important role in
the academic achievement of adolescents. The new study also began by pointing out the
importance of education as a means increasing income. Based on this research, Jeynes’
original research, and countless other studies, it also becomes clear that SES — income,
mother’s education, father’s education, mother’s occupation, and father’s occupation —
also plays a very important role in students’ academic achievement. What is unclear is
how these variables actually influence academic achievement.
With few exceptions, the academic achievement scores associated with each family
structure type have remained relatively stable over time. Moreover, the consistency in
scores may indicate that living with both biological parents — even in cases where
divorce is the eventual outcome — is associated with higher test scores in comparison to
situations where the parents are never married. In both studies, unadjusted means for all
achievement variables were highest for students from intact families, and the second
highest scores were associated with students whose parents were divorced. It is unclear
whether relatively high scores for students from divorced parents were attributable to the
positive influence of both parents prior to the divorce, or whether the scores are
attributable to a reduction in the stress that occurs in families following a divorce.
Nevertheless, unadjusted mean scores for students whose parents never married are at, or
near the bottom, in both studies. The lowest unadjusted means for all achievement
65
variables in the new study were associated with students whose parents were never
married; the lowest unadjusted mean scores for the math variable in the original study
were associated with students from parents who never married.
A good starting point for future research would be an investigation of the specific
mechanism(s) by which SES, intact family structure, and parental involvement actually
affect achievement. For example, earlier research has suggested that students who have
access to home computers perform better academically which could be related to parents’
income level. However, it could also be that parents who can more easily afford home
computers also have higher education levels and place a higher importance on their
children’s academic progress. In addition, this research has suggested that parents who
check-up on their children’s homework are less likely to see improved academic
achievement, but how does this happen? It has been suggested that if parents’ single
interaction with their children consists of infrequently checking their homework, they
shouldn’t be surprised if there is no improved academic progress. On the other hand, it is
entirely possible that children have fallen behind prior to parents’ interest in checking-up
on their homework. Perhaps the parents’ checking up is a reaction to poor performance.
So the decreased academic performance may be associated with a myriad of other issues
rather than parents checking to see how their children are doing on their homework. At
the very least some of these possibilities merit further investigation.
Additionally, this research does not address specifically how race/ethnicity and
gender are associated with lower academic progress as indicated by the four outcome
variables tested here. It is possible that social capital plays a role, especially in the case of
66
ethnic groups where parents’ lack of fluency in English inhibits their access to social
networking. As noted in the results, students whose parents attend and participate in
school related events show slightly higher scores on the four outcome variables tested
here, so access to social capital for specific ethnic groups may inhibit their children’s
academic achievement. However, parents who attend school events may be involved in
their children’s lives in many other ways, so the act of attending school events may not
be as important as other types of involvement not included in this study.
Limited to the variables included in the model, the strength of the coefficients alone
suggests that SES may have the strongest influence on adolescent academic achievement.
Within cyclical economic systems, SES can change dramatically in a short period of time
which makes understanding how SES affects achievement even more important. No
single research project can be considered conclusive or the final statement on any subject.
This study has added support to the growing body of research that suggests a link
between parental involvement and academic achievement. Because academic
achievement is so vital over the course of a lifetime, it is hoped that future research will
be undertaken to better understand how these variables are related.
67
APPENDIX A
Core Variables
Dependent Variables
BYTXMSTD = Math test standardized score
BYTXRSTD = Reading test standardized score
BYTXCSTD = Standardized test composite score-math / reading
F1TXMSTD = F1 (first follow-up) Math standardized score
Independent Variables
Socioeconomic Group:
SES2 = Socio-economic status composite, v.2
SES2QU = Quartile coding of SES2 variable
Social Aspect Group
BYURBAN = Urbanicity of school locale
INCOME = Total family income from all sources 2001-composite (based on BYP85)
BYRACE = Student’s race / ethnicity-composite
BYSEX = Gender-composite
MOTHED = Mother’s highest level of education-composite
FATHED = Father’s highest level of education-composite
PARED = Parents’ highest level of education
OCCUFATH = Father / male guardian’s occupation-composite
OCCUMOTH = Mother / female guardian’s occupation-composite
68
Parental involvement series
PI1FamilyStruc
BYFCOMP = Family (parent) composition taken from parent questionnaire.
Intact family =1
PI2Checking1
Extent to which parents are directly involved
1. How often check that homework completed = BYP55A
2. Worked on homework /school projects with 10th grader = BYP57B
3. Knows 10th graders friends (checking-up) = BYP59CA (1st friend), BYP59CB (2ndfriend),
BYP59CC (3rd friend)
PI3Discuss
Extent to which child discussed events at school with parents
1. How often discussed school courses with parents = BYS86A
2. How often discussed school activities with parents = BYS86B
3. How often discuss things studied in class with parents = BYS86C
PI4Attend
Extent to which parents are involved with events at school
1. Attend parent-teacher organization meetings = BYP54B
2. Attended school activities with 10th grader = BYP57A
3. Act as a volunteer at the school = BYP54D
Inadvertant language in first sentence of Jeynes’ text describing this variable suggests that variables pertaining to knowing parents of
other children should be included. However, the next sentence clearly states that only the variables listed in this description of
PI2Checking are to be included in the regression. Nevertheless, variables pertaining to knowing parents of children’s friends =
BYP59DA (1st mother), BYP59DB (2nd mother), BYP59DC (3rd mother), BYP59EA (1st father), BYP59EB (2nd father), BYP59EC (3rd
father). The knowing parents variables have, nonetheless, been recoded as an alternate choice.
1
69
APPENDIX B
Means and Standard Deviations Tables
Table B1. Means and Standard Deviations for Various Family Structures Included in the
ELS 2002 – 2004 Data Set (BYP10)
Academic
Never
Measure*
Married Cohabitation Widowed Separated Divorced Married
Standardized
Tests
52.29
47.10
48.83
47.14
49.70
44.47
Math
(9.73)
(9.14)
(9.34)
(10.04)
(9.72)
(9.30)
51.98
47.51
49.51
47.32
49.93
44.68
Reading
(9.88)
(9.09)
(9.74)
(9.64)
(9.76)
(8.91)
Combined
52.28
47.12
49.11
47.04
49.80
44.20
Composite
(9.75)
(9.03)
(9.48)
(9.89)
(9.69)
(8.84)
F1 Math
45.44
35.16
38.11
34.33
38.97
31.97
Composite** (21.15)
(23.92)
(23.91)
(24.93)
(24.04)
(23.23)
*All measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
Table B2. Means and Standard Deviations for Adolescents from the Gender
Groupings Included in the ELS 2002 – 2004 Data Set
Academic Measure*
Male
Female
Standardized Tests
51.22
50.20
Math
(10.37)
(9.57)
49.78
51.29
Reading
(10.32)
(9.59)
50.54
50.80
Combined Composite
(10.33)
(9.58)
41.69
41.22
F1 Math Composite**
(24.06)
(22.68)
*All measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
Means and Standard Deviations Tables — Continued
Table B3. Means and Standard Deviations for Mother’s Highest Level of Education Composite Included in the
ELS 2002 – 2004 Data Set (MOTHED)
Did
Attended
Attended
Completed
not
Graduated
2-year
Graduated college,
Completed PhD, MD,
finish
from high
school,
from 2no 4Graduated
Master's
other
Academic
high
school or
no
year
year
from
degree or
advanced
Measure*
school
GED
degree
school
degree
college
equivalent
degree
Standardized
Tests
44.66
48.86
49.66
51.27
51.61
54.64
56.67
55.19
Math
(9.42)
(9.33)
(9.34)
(9.27)
(9.24)
(9.42)
(9.49)
(11.31)
44.12
48.57
49.85
51.26
51.81
54.38
56.41
55.16
Reading
(8.87)
(9.30)
(9.31)
(9.55)
(9.25)
(9.67)
(9.23)
(10.76)
Combined
44.01
48.63
49.74
51.35
51.83
54.82
56.98
55.52
Composite
(8.97)
(9.20)
(9.23)
(9.32)
(9.12)
(9.41)
(9.26)
(11.16)
F1 Math
30.71
38.15
40.85
42.73
43.81
47.67
50.91
47.70
Composite** (24.58)
(23.56)
(22.38)
(22.00)
(21.65)
(21.62)
(21.04)
(23.83)
*All measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
70
Means and Standard Deviations Tables — Continued
Table B4. Means and Standard Deviations for Father’s Highest Level of Education Composite Included in the
ELS 2002 – 2004 Data Set (FATHED)
Did
Attended
Attended
Completed
not
Graduated
2-year
Graduated college,
Completed PhD, MD,
finish
from high
school,
from 2no 4Graduated
Master's
other
Academic
high
school or
no
year
year
from
degree or
advanced
Measure*
school
GED
degree
school
degree
college
equivalent
degree
Standardized
Tests
45.19
48.46
50.02
51.06
51.30
53.90
55.83
56.94
Math
(9.02)
(9.28)
(9.48)
(9.04)
(9.26)
(9.64)
(9.54)
(10.13)
44.76
48.46
49.87
50.76
51.61
53.67
55.45
56.39
Reading
(8.78)
(9.29)
(9.35)
(9.20)
(9.57)
(9.60)
(9.86)
(9.99)
Combined
44.63
48.35
49.94
50.97
51.55
54.04
56.02
57.12
Composite
(8.68)
(9.18)
(9.34)
(9.05)
(9.30)
(9.49)
(9.64)
(9.96)
F1 Math
31.24
38.22
40.74
41.86
42.65
46.89
49.88
50.50
Composite** (24.57)
(22.89)
(22.82)
(22.40)
(22.62)
(21.82)
(21.00)
(22.15)
*All measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
71
Means and Standard Deviations Tables — Continued
Table B5. Means and Standard Deviations for Parents’ Highest Level of Education Composite Included in the
ELS 2002 – 2004 Data Set (PARED)
Did
Attended
Attended
Completed
not
Graduated
2-year
Graduated college,
Completed PhD, MD,
finish
from high
school,
from 2no 4Graduated
Master's
other
Academic
high
school or
no
year
year
from
degree or
advanced
Measure*
school
GED
degree
school
degree
college
equivalent
degree
Standardized
Tests
44.16
47.25
48.66
50.00
50.16
52.95
55.53
56.17
Math
(9.15)
(9.16)
(9.38)
(9.02)
(9.16)
(9.52)
(9.64)
(10.45)
43.49
47.04
48.75
49.85
50.42
52.75
55.21
55.76
Reading
(8.48)
(9.02)
(9.31)
(9.29)
(9.35)
(9.71)
(9.66)
(10.20)
Combined
43.40
46.95
48.61
49.92
50.31
53.04
55.73
56.36
Composite
(8.51)
(8.96)
(9.22)
(9.08)
(9.13)
(9.50)
(9.59)
(10.28)
F1 Math
29.29
35.36
38.83
40.71
41.11
45.30
49.49
49.27
Composite** (25.01)
(23.77)
(22.81)
(22.14)
(22.38)
(22.18)
(21.09)
(22.73)
*All measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
72
Means and Standard Deviations Tables — Continued
Table B6. Means and Standard Deviations for Income Groups Included in the ELS 2002 – 2004 Data Set (INCOME)
Academic
Measure*
$10,001$15,000
$15,001$20,000
$20,001$25,000
$25,001$35,000
$35,001$50,000
$50,001$75,000
$75,001$100,000
$100,001$200,000
$200,001
or more
44.90
41.93
43.72
44.59
45.83
(9.34)
(7.99)
(8.68)
(9.22)
(9.36)
44.73
42.91
42.71
44.27
45.97
Reading
(10.00) (7.76)
(8.39)
(8.42)
(9.21)
Combined
44.46
41.90
42.75
44.05
45.62
Composite
(9.51)
(7.69)
(8.40)
(8.57)
(9.18)
F1 Math
32.41
29.82
30.13
30.59
32.31
Composite** (22.90) (22.45) (23.64) (23.62) (24.24)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
46.05
(9.30)
46.16
(9.19)
45.84
(9.13)
32.89
(24.80)
46.74
(9.78)
46.34
(9.20)
46.31
(9.36)
35.74
(23.27)
48.29
(9.40)
48.08
(9.24)
48.06
(9.21)
36.99
(23.67)
49.84
(9.59)
49.80
(9.76)
49.81
(9.58)
39.84
(23.38)
51.94
(9.31)
51.87
(9.50)
52.03
(9.28)
44.36
(21.58)
53.70
(9.03)
53.49
(9.34)
53.83
(9.07)
46.16
(21.85)
56.13
(9.13)
55.74
(9.07)
56.33
(8.89)
50.13
(29.84)
57.56
(9.20)
56.99
(9.41)
57.77
(9.22)
52.42
(20.82)
None
$1,000
or less
$1,001$5,000
$5,001$10,000
Standardized
Tests
Math
73
Means and Standard Deviations Tables — Continued
TableB7. Means and Standard Deviations for Adolescents from the Various Race/Ethnic Groups Included in the ELS 2002 – 2004
Data Set (RACE)
Black or
Amer.
Asian,
African
Indian/Alaska
Hawaii/Pac.
American,
Hispanic,
Hispanic,
White,
Academic
Native, nonIslander, onnonno race
race
Multiracial,
nonMeasure*
Hispanic
Hispanic
Hispanic
specified
specified
non-Hispanic
Hispanic
Standardized
Tests
45.91
53.85
44.35
45.70
45.70
50.35
53.04
Math
(7.99)
(10.64)
(8.45)
(9.30)
(9.91)
(9.76)
(9.14)
45.81
50.26
45.36
45.63
46.03
50.86
53.03
Reading
(8.20)
(10.09)
(8.69)
(9.28)
(9.86)
(9.63)
(9.47)
Combined
45.58
52.19
44.50
45.37
45.58
50.64
53.24
Composite
(7.86)
(10.16)
(8.46)
(9.26)
(9.89)
(9.63)
(9.21)
F1 Math
34.77
45.89
33.62
34.25
34.44
37.88
44.74
Composite**
(22.64)
(23.50)
(22.41)
(23.53)
(23.99)
(25.74)
(22.34)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
74
Means and Standard Deviations Tables — Continued
Table B8. Means and Standard Deviations for Mother’s Occupation Level Included in the ELS 2002 – 2004 Data Set
(OCCUMOTH)
Farmer,
Academic
No job
farm
Manager,
Measure*
for pay Clerical Craftsperson
manager
Homemaker Laborer administrator Military
Standardized
Tests
47.32
51.17
48.42
44.41
47.09
44.99
51.83
53.09
Math
(10.68)
(9.55)
(9.62)
(8.49)
(10.29)
(9.39)
(9.60)
(8.89)
45.80
50.95
47.95
44.37
46.36
44.84
51.82
51.95
Reading
(9.64)
(9.57)
(9.19)
(9.04)
(10.19)
(9.11)
(9.57)
(8.63)
Combined
46.32
51.13
48.06
44.01
46.50
44.56
51.95
52.69
Composite
(10.06)
(9.49)
(9.27)
(8.64)
(10.19)
(9.18)
(9.49)
(8.61)
F1 Math
35.86
43.28
38.26
30.94
31.99
33.48
43.19
44.72
Composite** (24.88) (21.73)
(22.86)
(24.58)
(26.48)
(23.26)
(22.81)
(23.10)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
Operative
47.21
(8.85)
47.17
(9.26)
47.00
(8.91)
35.45
(23.60)
75
Means and Standard Deviations Tables — Continued
Table B8. (Continued) Means and Standard Deviations for Mother’s Occupation Level Included in the ELS 2002 – 2004
Data Set (OCCUMOTH)
Academic
Professional
Professional
Proprietor, Protective
School
Measure*
A
B
owner
service
Sales
teacher
Service
Technical
Standardized
Tests
53.82
54.37
53.26
47.96
52.01
55.02
48.56
49.94
Math
(9.59)
(10.12)
(10.29)
(9.62)
(9.39)
(8. 69)
(9.60)
(9.65)
53.81
54.52
52.24
48.35
52.16
55.24
48.49
49.79
Reading
(9.56)
(10.18)
(9.77)
(9.00)
(9.35)
(9.05)
(9.52)
(9.83)
Combined
54.08
54.75
52.93
48.02
52.22
55.48
48.42
49.86
Composite
(9.46)
(10.23)
(9.97)
(9.18)
(9.24)
(8.69)
(9.46)
(9.76)
F1 Math
47.02
46.32
44.18
40.37
43.08
49.89
36.73
41.27
Composite**
(21.31)
(23.33)
(24.59)
(20.50)
(23.21)
(18.67)
(24.59)
(22.01)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
76
Means and Standard Deviations Tables — Continued
Table B9. Means and Standard Deviations for Father’s Occupation Level Included in the ELS 2002 – 2004 Data Set
(OCCUFATH)
Farmer,
Academic
No job
farm
Manager,
Measure*
for pay Clerical Craftsperson
manager
Homemaker Laborer administrator Military
Standardized
Tests
44.71
49.59
48.75
48.60
43.47
46.83
52.35
49.16
Math
(9.19)
(9.61)
(9.37)
(9.99)
(9.38)
(9.42)
(9.54)
(9.29)
43.62
49.78
48.89
47.24
42.79
46.71
52.09
50.11
Reading
(8.91)
(9.44)
(9.18)
(10.15)
(9.11)
(9.41)
(9.58)
(9.64)
Combined
43.77
49.67
48.74
47.78
42.66
46.55
52.37
49.61
Composite
(8.71)
(9.50)
(9.21)
(9.99)
(9.07)
(9.33)
(9.45)
(9.34)
F1 Math
34.59
38.67
37.95
39.49
30.00
36.07
44.49
37.17
Composite** (23.03) (24.45)
(23.56)
(22.46)
(23.69)
(23.15)
(22.39)
(25.15)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
Operative
47.70
(8.90)
47.70
(9.14)
47.54
(8.91)
37.20
(22.92)
77
Means and Standard Deviations Tables — Continued
Table B9. (Continued) Means and Standard Deviations for Father’s Occupation Level Included in the ELS 2002 – 2004
Data Set (OCCUFATH)
Academic
Professional
Professional
Proprietor, Protective
School
Measure*
A
B
owner
service
Sales
teacher
Service
Technical
Standardized
Tests
54.77
57.35
53.11
49.34
52.35
54.83
49.17
52.87
Math
(9.58)
(9.25)
(9.60)
(9.39)
(9.39)
(9.54)
(9.95)
(9.67)
54.52
57.03
52.73
50.28
52.18
54.77
48.34
52.49
Reading
(9.67)
(9.20)
(9.62)
(9.42)
(9.87)
(9.43)
(9.37)
(9.63)
Combined
54.95
57.68
53.11
49.79
52.42
55.12
48.67
52.86
Composite
(9.50)
(9.02)
(9.48)
(9.33)
(9.51)
(9.45)
(9.55)
(9.60)
F1 Math
47.57
50.44
44.52
39.04
43.93
48.93
39.08
44.88
Composite**
(22.46)
(21.96)
(23.02)
(23.27)
(22.65)
(20.53)
(23.11)
(22.27)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
78
Means and Standard Deviations Tables — Continued
Table B10. Means and Standard Deviations for Variables for Index Variable PI2Checking Included in the ELS 2002 – 2004 Data Set
CHECKED HOMEWORK
HELPED WITH HOMEWORK
KNOWS CHILDS FRIENDS
BYP59CA
BYP59CB
BYP59CC
FIRST
SECOND
THIRD
FRIEND
FRIEND
FRIEND
BYP55A
BYP57B
Academic
Measure*
Never
Seldom
Usually
Always
Never
Rarely
Sometimes
Frequently
No
Yes
No
Yes
No
Yes
Standardized
Tests
Math
Reading
54.19
(10.26)
53.85
(10.01)
52.93
(10.00)
52.67
(10.02)
51.38
(9.57)
51.23
(9.76)
49.76
(9.80)
49.91
(9.79)
50.46
(10.90)
49.88
(10.70)
53.99
(9.97)
53.57
(9.87)
Combined
54.29
52.98
51.39
49.82
50.18
54.03
Composite
(10.09)
(9.99)
(9.57)
(9.83)
(10.83)
(9.82)
F1 Math
48.07
45.00
43.54
41.91
40.03
46.40
Composite** (21.33) (22.90) (21.89) (21.51) (24.96) (22.56)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
51.49
(9.62)
51.45
(9.75)
49.85
(9.57)
50.07
(9.71)
48.79
(10.82)
48.57
(10.31)
51.70
(9.78)
51.65
(9.85)
50.55
(10.17)
50.20
(9.98)
51.86
(9.73)
51.82
(9.80)
51.58
(10.28)
51.17
(10.07)
52.02
(9.63)
51.97
(9.76)
51.57
(9.67)
49.95
(9.60)
48.59
(10.56)
51.78
(9.79)
50.40
(10.01)
51.96
(9.73)
51.47
(10.05)
52.13
(9.66)
44.34
(21.29)
42.01
(21.49)
39.37
(24.24)
44.16
(21.79)
41.14
(23.94)
44.52
(21.58)
43.08
(23.12)
44.72
(21.53)
79
Means and Standard Deviations Tables — Continued
Table B11. Means and Standard Deviations for Variables for Index Variable PI3Discuss Included in the ELS 2002 – 2004 Data Set
BYP86B DISCUSS ACTIVITY
BYP86A DISCUSS CLASSES
BYP86C DISCUSS STUDIES
Academic
Measure*
Never
Sometimes
Often
Never
Sometimes
Often
Never
Sometimes
Often
Standardized
Tests
48.23
51.42
53.41
48.39
51.65
53.46
49.57
51.65
52.80
Math
(9.97)
(9.65)
(9.19)
(9.86)
(9.65)
(9.16)
(10.02)
(9.71)
(9.26)
47.75
51.12
53.89
47.87
51.56
53.79
48.91
51.46
53.40
Reading
(9.66)
(9.69)
(9.09)
(9.79)
(9.61)
(9.10)
(9.77)
(9.68)
(9.30)
Combined
47.85
51.35
53.89
48.00
51.71
53.87
49.19
51.66
53.31
Composite
(9.68)
(9.59)
(9.06)
(9.70)
(9.76)
(9.03)
(9.76)
(9.65)
(9.19)
F1 Math
34.59
42.54
46.65
36.25
43.09
46.04
38.58
42.62
45.56
Composite**
(25.70)
(22.92)
(20.97)
(24.78)
(22.70)
(21.84)
(24.58)
(23.15)
(21.50)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
80
Table B12. Means and Standard Deviations for Variables for Index Variable PI4Attend Included in the ELS 2002 – 2004 Data Set
BYP54B ATTEND
BYP54D VOLUNTEER
MEETINGS
BYP57A ATTEND SCHOOL EVENTS
SCHOOL FUNCTIONS
Academic Measure*
No
Yes
Never
Rarely
Sometimes Frequently
No
Yes
Standardized Tests
51.80
50.73
47.46
51.15
51.47
53.14
50.27
53.98
Math
(9.82)
(10.00)
(10.09)
(10.02)
(9.93)
(9.27)
(9.93)
(9.36)
51.62
50.79
47.22
50.84
51.65
52.98
50.15
53.97
Reading
(9.79)
(10.11)
(9.75)
(9.79)
(9.88)
(9.58)
(9.88)
(9.52)
51.82
50.81
47.16
51.06
51.66
53.27
50.22
54.24
Combined Composite
(9.77)
(10.86)
(9.87)
(9.84)
(9.90)
(9.35
(9.87)
(9.36)
43.81
43.37
34.30
41.05
44.39
47.97
41.43
48.65
F1 Math Composite**
(22.25)
(21.73)
(25.22)
(24.16)
(21.42)
(18.77)
(22.83)
(19.36)
*All academic measures are composite test scores N=15,362
**First follow-up (F1) N=15,325
Table B13. Means and Standard Deviations for School Location Included in
the ELS 2002 – 2004 Data Set (BYURBAN)
Academic Measure*
Urban
Suburban
Rural
Standardized Tests
50.18
51.19
50.43
Math
(10.31)
(9.94)
(9.46)
50.29
50.76
50.41
Reading
(10.24)
(9.93)
(9.67)
50.25
51.04
50.45
Combined Composite
(10.30)
(9.88)
(9.51)
40.55
42.22
41.09
F1 Math Composite**
(24.20)
(23.09)
(22.55)
*All academic measures are composite test scores N=15,362
**First Wave (F1) N=15,325
81
82
APPENDIX C
Comparison of Original Core Variables and Core Variables in Current Study
Table C1. Dependent Measures of Academic Achievement
Construct
Math
Reading
Science
Social
Studies
Math
/Reading
composite
Math
Original Study - NELS 88
BYTXMSTD - Mathematics
Standardized Score
BYTXRSTD-Reading Standardized
Score
Not Available in ELS 2004 data
F22XCOMP - Reading / Math
Standardized Test Composite
BYTXCSTD - Reading / Math
Standardized Test Composite
Not utilized in original study
F1TXMSTD - First Follow-up to 2002
data
Table C2. Independent Measures of Parental Involvement
PI1 Family
F2FCMP - F2 Family Composition
Structure
F1S100A - Parents check whether
homework is done
PI2 Checkup
F1S100B - Parents help with homework
F1S103 - R’s parents know closest
friend’s parents
PI3 Discuss
F1S105A - Discuss school courses w/
parent
F1S105B - Discuss school activities w/
parent
F1S105C - Discuss things studied in class
w/ parent
F1S106A - Parents attend school meetings
PI4 Attend
Current Study – ELS 2002/04
F22XMSTD - Mathematics Standardized
Score
F22XRSTD - Reading Standardized
Score
F22XSSTD - Science Standardized Score
F22XHSTD - HISTORY/CIT/GEOG
Standardized Score
F1S106C - Parents attend school events
F1S106D - Parents acted as volunteers at
school
Not Available in ELS 2004 data
BYFCOMP- Composite Family
Structure
BYP55A - How often check that
homework is completed
BYP57B - Worked on
homework/school projects with 10th
grader
BYP59CA - Parents know R’s 1st friend
BYP59CB - Parents know R’s 2nd friend
BYP59CC - Parents know R’s 3rd friend
BYS86A - Discuss school courses w/
parent
BYS86B - Discuss school activities w/
parent
BYS86C - Discuss things studied in
class w/ parent
BYP54B - Parents attend school
meetings
BYP57A - Parents attend school
activities w/ 10th grader
BYP54D - Parents acted as volunteers at
school
83
Table C2. (Continued) Independent Measures of Parental Involvement
Construct
SES
Gender
Race
School
Region
Original Study - NELS 88
F1SESQ - Quartile Coding of F1SES
Variable (Duncan SEI occupational scale)
F1SEX - Composite Gender
F1RACE - Composite Race
Not evaluated in original study
Current Study – ELS 2002/04
SES2QU-Quartile Coding of SES2
Variable (NORC/GSS occupational
scale)
BYSEX – Composite Gender
BYRACE – Race/ethnicity Composite
BYURBAN - Urbanicity of school
locale
84
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Rainbow Project Collaborators: (in alphabetical order): Damian Bimey, Brent Bridgeman, Anna Cianciolo, Wayne Camara,
Michael Drebot, Sarah Duman, Richard Duran, Howard Everson, Ann Ewing, Edward Friedman, Elena L. Grigorenko, Diane
Halpem, P. J. Henry, Charles Huffman, Linda Jarvin, Smaragda Kazi, Donna Macomber, Laura Maitland, Jack McArdle,
Carol Rashotte, Jerry Rudmann, Amy Schmidt, Karen Schmidt, Brent Slife, Mary Spilis, Steven Stemler, Robert J. Stemberg, Carlos
Torre, and Richard Wagner.
University of Michigan Business School Collaborators: (in order of authorship on the original report of the data): Jennifer
Hedlund, Jeanne Wilt, Kristina Nebel, and Robert J. Stemberg. Other contributors to this project: Kevin Plamondon, Andrea
Sacerdote, Eric Goodrich, Weihua Niu, Melissa Droller, Evonne Plantinga, Mengdan Chu, Kathryn Rado, Julie Goodrich,
Lisa Morgan, Donna Vann, and Robert Silaghi, Former Dean Joseph White and former Senior Associate Dean Susan Ashford of the
University of Michigan.