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As colleges and universities strive to increase the number of students graduating in four years and prepare students for life after college, they should consider creating college experiences that include deep, experiential learning and faculty care and support. Creating an on-campus environment that harbors the necessary experiences that could increase preparedness and four-year attainment rates might also benefit students financially. Extending college beyond the "traditional" four years damages students' lifetime earnings profile, according to New York Federal Reserve researchers Jaison R. Abel and Richard Deitz. The net present value of an extra year of college is $65,319. This figure includes tuition and fees and opportunity costs of extra time in school and wage earnings.

With over 2 million college students enrolled full-time needing an extra year of college, the fifth-year effect on the economy begins to mount with every passing graduating class.

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Survey Methods

Results for this Gallup-Purdue Index study are based on Web interviews conducted Feb. 4- March 7, 2014, with a random sample of 29,560 respondents with a bachelor's degree or higher, aged 18 and older, with Internet access, living in all 50 U.S. states and the District of Columbia.

For results based on the total sample of bachelor's degree or higher respondents, the margin of sampling error is ±0.9 percentage points at the 95% confidence level.

The Gallup-Purdue Index sample was compiled from two sources; the Gallup Panel and the Gallup Daily Tracking survey.

Learn more about how the Gallup-Purdue Index works.

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Gallup-Purdue Index Inaugural National Report

Great Jobs and Great Lives

Is College Worth It? That Depends

Is a college degree worth it? The good news is that for most college graduates the answer is: "yes." But only if they made the most of it.

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Big Ten Grads More Likely to Have Useful Internships

More than one in three Big Ten alumni (36%) strongly agree they took part in internships or jobs where they applied what they were learning as undergrads, compared with three in 10 graduates of large universities.

Posterior predictive checks (PPCs) are a great way to validate a model. The idea is to generate data from the model using parameters from draws from the posterior.

Elaborating slightly, one can say that PPCs analyze the degree to which data generated from the model deviate from data generated from the true distribution. So often you will want to know if, for example, your posterior distribution is approximating your underlying distribution. The visualization aspect of this model evaluation method is also great for a ‘sense check’ or explaining your model to others and getting criticism.

Here we will implement a general routine to draw samples from the observed nodes of a model.

Lets generate a very simple model:

This function will randomly draw 500 samples of parameters from the trace. Then, for each sample, it will draw 100 random numbers from a normal distribution specified by the values of mu and std in that sample.

Now, ppc contains 500 generated data sets (containing 100 samples each), each using a different parameter setting from the posterior:

One common way to visualize is to look if the model can reproduce the patterns observed in the real data. For example, how close are the inferred means to the actual sample mean:

Comparison between PPC and other model evaluation methods. KENDALL KYLIECALEB Slipons black/white dml6So7

An excellent introduction to this was given in the Edward documentation:

PPCs are an excellent tool for revising models, simplifying or expanding the current model as one examines how well it fits the data. They are inspired by prior checks and classical hypothesis testing, under the philosophy that models should be criticized under the frequentist perspective of large sample assessment.

PPCs can also be applied to tasks such as hypothesis testing, model comparison, model selection, and model averaging. It’s important to note that while they can be applied as a form of Bayesian hypothesis testing, hypothesis testing is generally not recommended: binary decision making from a single test is not as common a use case as one might believe. We recommend performing many PPCs to get a holistic understanding of the model fit.

The same pattern can be used for prediction. Here we’re building a logistic regression model. Note that since we are dealing the full posterior, we are also getting uncertainty in our predictions for free.

Note that these are uncertainty caused by MCMC estimation, and not from the underlying model. If we had taken more samples in sample_ppc , the error bars would get smaller quite quickly.

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