James Soland & Yeow Meng Thum

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Introduction

Interim achievement tests are often used to monitor student and school performance over time. Unlike end-of-year achievement tests used for accountability, interim tests are administered multiple times per year (e.g., Fall, Winter, and Spring) and vary across schools in terms of when in the school year students take them. As a result, scores reflect seasonal patterns in achievement, including summer learning loss. Despite the prevalence of interim tests, few statistical models are designed to answer questions commonly asked with interim test data (e.g., Do students whose achievement grows the most over several years, tend to experience below-average summer loss?). In this study we compare the properties of three growth models that can be used to examine interim test data.

Summary by: Lily An

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Traditional methods of estimating the returns to community college remain imprecise.

Historically, to estimate the labor market returns to a community college degree, researchers have compared the earnings of students who completed a degree to those who did not, at a single point in time, while controlling for background characteristics. With the expansion of longitudinal data sets, researchers have begun to consider how earnings before and during community college can affect returns to community college. However, even improved econometric analyses overlook some temporal influences on predicted earnings growth, such as the time between graduation and measured earnings, instead estimating averaged returns over time. These influences are particularly salient for community college students, who vary in their time-to-degree completion and often enter college with pre-existing or concurrent work experiences.