How America Pays for College

Washington, DC, June 23, 2019 — For the twelfth year in a row, nine in 10 families with a student enrolled in college agree college is an investment in the student’s future. This year, 80% of families feel confident in their paying-for-college decisions, according to "How America Pays for College 2019," the national study from Sallie Mae, the nation's saving, planning, and paying for college company, and Ipsos, an independent global market research company.

The approach used in making the goal of college a reality, however, varies from family to family. There are many commonalities in the path families follow to college, but distinct differences emerge from among some population groups when choosing a school and when figuring out how to pay for the investment of college. Importantly, attitudes toward the value of college, criteria used in choosing a school, level of planning and research prior to enrolling, and the resources used to pay for college all have an impact on satisfaction with the experience and perception of the value relative to the price paid.

About the Study

Ipsos conducted the How America Pays for College survey online between Friday, April 5, 2019 and Monday, May 6, 2019. Ipsos interviewed 2,000 individuals: 1,000 parents of 18 to 24-year-old undergraduate students, and 1,000 18 to 24-year-old undergraduate students.

Sample Design

The sample for this study was randomly drawn from Ipsos’ online panel, partner online panel sources, and “river” sampling and does not rely on a population frame in the traditional sense. Ipsos uses fixed sample targets, unique to the study, in drawing sample. This sample design was a disproportionate stratified sample of parents of college students and college students. The sample was designed to over-represent African Americans and Hispanics, with a minimum of 150 responses from each group. After a sample has been obtained, Ipsos calibrates respondent characteristics to be representative of the U.S. population using standard procedures such as raking-ratio adjustments. The source of these population targets is 2016 American Community Survey data. The sample was stratified by additional variables, such as region and student enrollment status.

Weighting

To correct for the disproportionate stratified sample, both samples were weighted using a statistical technique called raking, in which all of the population marginal profiles of interest are replicated in the sample. The sample of parents was weighted by gender, age, race/ethnicity, region, education and by college information (region, size and type). The sample of students was weighted by gender, age, race/ethnicity, region, and by college information (region, size and type). All of the demographic profiles used for both parents and students in the weights were sourced from the Current Population Survey (CPS). The National Center for Educational Statistics provided additional data for the college information weights.

Bayesian Credibility Intervals

The calculation of credibility intervals assumes that Y has a binomial distribution conditioned on the parameter θ\, i.e., Y|θ~Bin(n,θ), where n is the size of our sample. In this setting, Y counts the number of “yes”, or “1”, observed in the sample, so that the sample mean (y ̅) is a natural estimate of the true population proportion θ. This model is often called the likelihood function, and it is a standard concept in both the Bayesian and the Classical framework. The Bayesian 1 statistics combines both the prior distribution and the likelihood function to create a posterior distribution. The posterior distribution represents our opinion about which are the plausible values for θ adjusted after observing the sample data. In reality, the posterior distribution is one’s knowledge base updated using the latest survey information. For the prior and likelihood functions specified here, the posterior distribution is also a beta distribution (π(θ/y)~β(y+a,n-y+b)), but with updated hyper-parameters.

Our credibility interval for θ is based on this posterior distribution. As mentioned above, these intervals represent our belief about which are the most plausible values for θ given our updated knowledge base. There are different ways to calculate these intervals based on π(θ/y). Since we want only one measure of precision for all variables in the survey, analogous to what is done within the Classical framework, we will compute the largest possible credibility interval for any observed sample. The worst case occurs when we assume that a=1 and b=1 and y=n/2. Using a simple approximation of the posterior by the normal distribution, the 95% credibility interval is given by, approximately 2.5.

For more information on this news release, please contact:

Jennifer Berg
Senior Account Manager, U.S.
Ipsos Public Affairs
+1 312 526 4224
[email protected]

About Ipsos

Ipsos is an independent market research company controlled and managed by research professionals. Our team of 18,000 across 90 countries serves 5,000 clients and undertakes 70,000 different projects each year. Our polling practice is a non-partisan, objective, survey-based research practice made up of seasoned professionals. We conduct strategic research initiatives for a diverse number of American and international organizations, based not only on public opinion research, but elite stakeholder, corporate, and media opinion research.

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