We estimate changes in the rates of five FBI Part 1 crimes—homicide, auto theft, burglary, robbery, and larceny—during the COVID-19 pandemic from March through December 2020. Using publicly available weekly crime count data from 29 of the 70 largest cities in the U.S. from January 2018 through December 2020, three different linear regression model specifications are used to detect changes. One detects whether crime trends in four 2020 pre- and post-pandemic periods differ from those in 2018 and 2019. A second looks in more detail at the spring 2020 lockdowns to detect whether crime trends changed over successive biweekly periods into the lockdown. The third uses a city-level openness index that we created for the purpose of examining whether the degree of openness was associated with changing crime rates. For homicide and auto theft, we find significant increases during all or most of the pandemic. By contrast, we find significant declines in robbery and larceny during all or part of the pandemic and no significant changes in burglary over the course of the pandemic. Only larceny rates fluctuated with the degree of each city’s lockdown.

We describe think-aloud interviews with students as a powerful tool to ensure that draft questions fulfill their intended purpose, uncover unexpected misconceptions or surprising readings of questions, and suggest new questions or further pedagogical research. We have conducted more than 40 hour-long think-aloud interviews to develop over 50 assessment questions, and have collected pre- and post-test assessment data from hundreds of introductory statistics students at two institutions.

We empirically examine the degree to which best-estimates of coastal Louisiana floodplains have changed over time and consider implications for risk management policies. We generate variation in estimated 100-year flood depths by truncating a historical data set of observed tropical cyclones to end in years ranging from 1980 to 2016, adopting three procedures for updating various inputs to an existing flood risk model using the truncated data set to identify which factors are most important in driving variation in risk estimates over time. Our findings indicate that the 100-year floodplain extent has substantially expanded in populated areas since 1980 due to these effects.

Think-aloud interviews with students can be used to detect specific misconceptions and understand how students reason about statistical questions. Data from think-aloud interviews can then be used to develop conceptual assessments, design new teaching strategies, or suggest further experiments to learn how students think about statistics. In this webinar, we will discuss the benefits of using think-aloud interviews to develop conceptual assessments and the experience we have had using think-aloud interviews in two introductory-level statistics courses.