Parental Influence on American Youth's College Decision

Parental Influence on Decisions to Attend Post-Secondary School

Parental Influence on American Youth’s Decisions to Attend Post-Secondary School

Nathan Anderson

Scott Sanders

Soc 405

Brigham Young University


Introduction

Over the past 30 years income inequality has been

increasing. The “Occupy Wall Street” movement and its anguish

toward the “1%” offers a perfect example to the ever widening

income gap and intensity around the topic. One substantial

reason for the discrepancy between incomes can be explained

through an individual’s return on their education. “In dollar

terms, 1973 college graduates earned 45 percent more than high

school graduates; by 1994 they earned 65 percent more, based on

real average hourly wages for college and high school graduates

(Baumol and Binder, 1997). As this income disparity continues to

grow, an emphasis on helping American youth attend postsecondary

school (PSS) or some form of college becomes increasingly

salient. Almost 20 years ago, President Bill Clinton, in his 1997

State of the Union address, urged Americans to enhance their

efforts in helping individuals obtain college degrees. He said,


“We must make the thirteenth and fourteenth years of education—at

least two years of college—just as universal in America by the

21st century as a high school education is today, and we must

open the doors of college to all Americans (Mathtech Inc.,

1998).” Now, 14 years into the 21st century we continue to search

for answers on how to “open the doors of college to all

Americans.” Recent research by Carnevale and Rose from the

Georgetown University Center on Education and the Workforce

(2011) found that from 1915 to 1980 the demand for college-

educated young people and the supply of college-educated young


people remained proportional to each other (3.1% growth in supply

per year to 2.9% growth in demand per year). Starting in 1990

however, the demand has begun to outpace the supply at 2.0%

growth in demand per year compared to only a 1.5% growth in

supply per year. If these trends continue, by the year 2025 the

income gap between college-educated Americans and high school

educated Americans will be 56% larger than it was in 1980 and 22%

larger than in 2010.

One of the first steps to understanding the issue of helping

Americans attend PSS is first understanding what factors

influence whether or not an individual chooses to attend college.

I propose that one factor which can have a significant effect on

an American youth’s decision to pursue PSS is the educational

achievements of their parents. Additionally, I examine the

relationship between the influence of a father attending college

versus a mother attending college can have on a child’s decision

to attend additional school.

Literature Review

Figure 1 Carnevale & Rose (2011, pg. 5-6)


Much of the research on college attendance patterns are

founded upon the “human capital” model proposed by Gary Becker.

“According to this theory, one decides to enroll in college as an

investment in future earning power (Mathtech Inc., 1998:1).” As

stated previously, individuals whom attend college have a higher

earning power which would also suggest that parents who attended

college would also have increased family incomes. This monetary

factor can aid in a young person’s college decision in a number

of ways. Cecillia Rouse (1994) found that higher family income

levels increase the probability of a high school student applying

for college. Scholars have also found that there are “persistent

patterns of stratification of college enrollments by income

(Manski, 1992, pg. 16).” Additional monetary factors such as

tuition cost, employment rates, and financial aid availability

can also have significant influences on individuals attending PSS

(Manski and Wise, 1983).

While the income in which parent’s return toward their

family may aid a child’s opportunity to continue their schooling

financially, it may be the example of increased earning power

their parents provide which makes the most significant


difference. Hossler and Maple (1993) find that parental

education levels have a stronger effect on a high school

student’s college plans than family income level. Rouse (1994)

also found that individual traits such as, parental education

levels, high school class rank, and achievement levels are of

significant importance in determining the likelihood a person

applies to college.

While most of the studies I have reviewed here provide a

correlation of a child’s college enrollment and parental

education levels, much of the research is becoming dated and may

not reflect current trends in contemporary America. Additionally,

one area of study that requires further explanation is if the

relationship of a father who attended PSS has a greater influence

on their child than if their mother attended PSS. In one study

conducted by Kohn, Manski, and Mundel (1976) they distinguish

between mother’s and father’s education levels but found

conflicting results. Utilizing two subsamples of surveys from two

different states, Kohn and team found that in one subset a

father’s education level had a greater effect on the likelihood

of college attendance, while the other subsample showed a


mother’s education level having a greater effect. I will further

explore this question by analyzing the moderating effect in which

parent’s education level, father’s education level, and mother’s

education level each play on a child’s PSS decision respectively

(See Figure 2). I hypothesize, in common with previous research

that an increase in parent’s education will result in an increase

likelihood of a high school senior choosing to attend at least

two years of college. I also hypothesize that a father who

attended college will have a greater influence on whether or not

a child attends college than if the mother attended college.

Figure 2

+High

SchoolSenior

AttendCollege

Parents/FathersCollege


Method

Sample

Because much of the research on this topic comes from sample

sets which are more than 15 years old, it was important that I

used a sample population from a recent data collection. I

accomplished this by selecting a data set from 2010, one of the

most recent made publicly available on 12th-grade students. My

sample set comes from the Monitoring the Future: A Continuing Study of

American Youth (12th-Grade Survey), 2010. This survey was conducted by a

team from the University of Michigan and is conducted on an

annual basis.

The data collection comprises of high school students during

the spring of their senior year. The surveys were distributed to

approximately 130 public and private schools and contain more

than 13,000 respondents. Schools were selected based upon

geographic location and area population density. In areas of high

population density more than one school was included in the

sampling design. 350 seniors were selected either by randomly


sampling classrooms or by some other random method that is

convenient for the school and judged unbiased.

One limitation to the data is that youth who have dropped

out of school prior to their senior year of high school were not

surveyed. This excludes a relatively small portion of the age

cohort, between 11 and 20 percent, but is of importance as this

group is likely not to pursue PSS and thus, if included could

adjust statistical conclusions.

Variables

The first variable, my dependent variable, “collegeplan”

comes from three survey questions. Each question read, “Suppose

you could do just what you’d like and nothing stood in your way.

How many of the following things would you want to do? (mark all

that apply)” Each question was then followed by one of the three

following answer choices, “Graduate from a two-year college

program”, “Graduate from a four-year college program”, “Attend

graduate or professional school after college”, asking the

respondents to either mark YES or NO. I took this data and

combined these questions into one variable in order to provide a


simple metric showing if the student “Planned to attend college,

YES or NO.” Because I was most concerned with whether or not the

student plans to attend any form of college and not how much

college they plan to receive, I created a dichotomous variable

with values of 0 (No) and 1 (Yes).

The main explanatory variables comprise from survey results

regarding parents’ education. Developed from questions asking

students to mark the highest level of education their father and

mother completed, ranging from high school dropout to graduate

school degree, I then recoded the variables in one of three ways.

The first way was to create a dichotomous variable for both

father’s education (feduc) and mother’s education level (meduc)

with values of 0 (no college) or 1 (attended college). For the

purposes of bivariate analysis I combined the father’s education

variable and the mother’s education variable into one variable

called pteduc. Again, as I explained with the variable

collegeplan, for the first research question I am concerned with

whether or not a parent attended college rather than how much

college any specific parent completed. To estimate the second

research question, the influence of a father’s education as


compared to a mother’s education on a child’s decision to attend

PSS, I created a variable separating the amount of college

education received for a mother or father individually. These

education levels ranged from “No College”, “Started College”,

“Four Year Degree”, and “Grad School”.

Finally, a number of control variables were included as a

part of the model. The first set of control variables included

demographic variables such as sex, race, (coded as separate dummy

variables White (0) Black (1) and White (0) Hispanic (1)), and

urban or rural home environment. The second set of control

variables was associated to school achievement, including the

students current G.P.A and Self-Intelligence, a variable

comprised from a question asking the student to rate how

intelligent he or she felt they were compared to their fellow

peers. One limitation to this study may be that no control for

family income is included in the model. This is because no data

was obtained through the survey on this topic.

Missing Data


Because my dataset surveyed more than 13,000 respondents

missing data is present as would be expected from a sample this

vast. However, due to the large sampling population the number of

missing cases was low, (approximately 8-15%) compared to the

total number of respondents. This allowed me to remove the

missing cases from my variables and still have a sufficient

number (12,000-11,000 cases) of valid data to run my statistical

test. In order to ensure that the results would be significant

across a generalized population, I compared missing data across

various racial groups as well as age and did not find any

systematic patterns of missing data.

Analysis

Statistical tests used to develop my conclusions involved a

series of cross tabulations with accompanying chi test and

logistic regression analysis. For my analysis of collegeplan and

pteduc I ran a cross tabulation because both variables were

categorical. This allowed me to gain percentages on the

likelihood of a student attending PSS. I also ran a chi test with


this tabulation to induce statistical significance from the

results.

The effects of the explanatory variables were estimated by

using a logistic regression. Because the dependent variable was

categorical and dichotomous, logistic regression allowed for

accurate regression outputs. The explanatory variables were

tested through a series of nested models, beginning with

demographic variables, followed by adding education achievement

variables and finally father’s college attendance and mother’s

college attendance were added over the final two nested models

respectively. Odds ratios were used to compare the effect of the

explanatory variable on the dependent variable.

When comparing father’s education level to mother’s

education level and their respective influence on a child’s

college decision, I did choose to utilizing a linear fit line to

present how an increase over the four levels of education (No

college, Started College, etc.) results in a change of mother and

fathers influence across levels. Cross tabulations as well

logistic regression we also used to help explain this question.

STAT 13 was used to calculate the statistical test.


Results

Table 1

AnticipatedPlan After High

School

Parents Attended College

No Yes TotalNo Plan to Attend

PSS or College824

11.08%327

5.57%1,1518.65%

Plan on AttendingPSS or College

6,61388.92%

5,54994.43%

12,16291.35%

Total 7,437100%

5,876100%

13,313100%

Table 1 shows the results of a cross tabulation between

collegeplan or anticipated college enrollment and pteduc or

parents actual college attendance. 11% or 824 of 13,313 student

respondents plan to follow their parent’s example and not attend

college. That contrasts with 5.5% or 327 students who plan on not

continuing their schooling even when one of their parents has

gone on to college before them. Additionally, students who do

have at least one parent who has attended college are six percent

more likely to plan on attending PSS than those students who have

no parent who has attended college. These statistics were

statistically significant according to the accompanying chi


squared test at a p value of .000 allowing for the rejection of

the null hypothesis and assuming the change is not a result of

chance.

The four nested logistic regression models are summarized in

Table 2. Each nested model was significant at a p value of .000.

Significant results were found for both primary independent

variables, father’s education and mother’s education, while

controlling for sex, race, urban/rural, G.P.A., and perceived

self-intelligence. The results indicate that a student whose

father attended at least some PSS is 1.53 times more likely to

attend PSS than a student who did not have a father attend

college. Similarly, there is a 1.51 times increased likelihood

that a student will attend college if their mother previously

attended college. These findings support the previous literature

on the topic and my first hypothesis that a student whose parents

attend college is more inclined to follow their parent’s example

and also attend college. Another significant result, which

requires additional research outside the scope of this article,

is the significantly increased likelihood of a female planning on

college than a male student.


Table 2 Odds Ration of American Youth’s College Plans

Variable Model 1 Model 2 Model 3 Model 4Demographics

Female 2.4*** 2.3*** 2.4*** 2.46***Black .71*** .91 .93 .91

Hispanic .936 1.26** 1.44** 1.56***Rural/Urban 1.13*** 1.1*** 1.1*** 1.1***

AchievementGPA 1.22*** 1.2*** 1.2***

Self-Intelligence 1.36*** 1.33*** 1.32***

ParentsEducationFather Educ. 1.77*** 1.53***Mother Educ. 1.51***

The results from nested model number four show a slight

positive association that a father’s college attendance has a

greater influence on a child’s college plan than a mother’s

college attendance. Additional tests in an effort to find

additional support for my second hypothesis, a father’s education

will have a greater influence on a youth’s decision to attend PSS

than a mother’s education, are consistent with the findings from

this regression analysis. The percentages gathered from a cross

tabulation, as presented in Figure 3 show that if a father did

not go to college than their son or daughter has an 11.73% chance


of not going to college as well, verses 11.7% change of a child

not going if their mother did not go.

Figure 3

Father Did Not Go Mother Did Not Go11.00%

11.10%

11.20%

11.30%

11.40%

11.50%

11.60%

11.70%

11.80%

11.90%

12.00%

Likelihood Child Will Not Attend

College


Figure 4

Father went to college Mother Went to college90.00%

92.00%

94.00%

96.00%

98.00%

100.00%

Like

liho

od Chi

ld W

ill

Atte

nd

Coll

ege

Figure 5


The results in Figure 4 also show a very slight change in

the likelihood a child will attend college based on a mother’s

verses father’s education. Figure 5 may help explain this slight

relationship by graphically representing the increase likelihood

of a child’s college attendance across the various levels of

education between a father and mother. The linear fit lines

remain almost even until “Four-Year Degree” in which a slight

separation is visible between mothers and fathers.

Finally, using the same control variables as in the logistic

regression models previously presented, but replacing feduc and

meduc (dichotomous variables) with variables taking into account

the level of college education obtained, the correlation

coefficients of the regression model indicate that a one unit

increase in the level of college education received for a father

results in a .18 change in the dependent variable, while a

mother’s increase in level of college education reports a

coefficient of .17. Both of these coefficients we statistically

significant at a p value of .000. The results from this and the

previous statistical tests find mild support for the second

hypothesis.


Conclusion

The scope of this paper has covered two basic questions

regarding influences that may “open the door” to American youth

in attending PSS. The first question was: How does a parent’s

college education moderate whether or not their child will

progress on after high school and pursue higher education. In

response to this question I have concluded that there is strong

statistical evidence that my hypothesis was correct in stating

that an increase in parental education has a positive effect on

the likelihood a child will attend college. This finding can lend

support to the Human Capital Theory in that youth may recognize

the benefits of a college education for their parents and choose

to similarly invest in their own human capital by attending PSS.

The second question researched analyzed whether or not a

father’s education obtainment has more influence on a child’s

education choice. Utilizing data from the 2010 Monitoring the Future

Survey, I found that there is indeed a slight positive correlation

that a father’s college attendance has more influence than a


mother’s college attendance, particularly if the father went on

to obtain a four-year degree.

The conclusions made from this study present one finding,

parental education, important in helping all Americans make

college a universal part of their educational experience. I

recommend, with support from this research on how parents’

education influences a child decision to go to college, that

parents, high school counselors, scholars, legislators, college

admissions departments, and other persons of prominent position

should provide special emphasis toward potential first generation

college students to ensure that in coming years the demand for

employees with higher education does not overly exceed the supply

of persons with a college degree.


References

Baumol, William J., and Blinder, Alan S. Economics, Principles and

Policy, 7th edition, Fort Worth: The Dryden Press, 1997.

Carnevale, A., & Rose, S. (2011, June 26). The Undereducated

American. The Undereducated American. Retrieved April 17, 2014,

from http://cew.georgetown.edu/undereducated

Hossler, Don, and Maple, Sue. “Being Undecided about

Postsecondary Education.” Review of Higher Education 16:285

(1993).

Manski, Charles F. “Income and Higher Education.” Focus 14(3):14

(1992).

Manski, Charles F., and Wise, David A. College Choice in America.

Cambridge, Mass: Harvard University Press, 1983.

Mathtech, Inc., “Financial Aid Enrollment.” Ed.gov, 1998

http://www2.ed.gov/offices/OUS/PES/finaid/enroll98.pdf

Rouse, Cecilia Elena. “What to Do after High School: The Two-Year

versus Four-Year College Enrollment Decision.” In Choices and

Consequences: Contemporary Policy Issues in Education, Ronald G.

Ehrenberg, ed. Ithaca, NY: ILR Press, 1994

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