Below are all of my presentations, including slides and video when available. You might also be interested in my peer-reviewed publications.
Using think-aloud interviews to assess student understanding of statistics concepts (with P Burckhardt, P W Elliott, C Evans, K Lin, A Luby, M Meyer, J Orellana, R Yurko, G Weinberg, J Wieczorek, and R Nugent).
USCOTS 2019, May 16-18, 2019. Breakout session.
Assessing student understanding of statistics concepts is quite difficult: conceptual questions are difficult to write clearly, students often interpret questions in unexpected ways, and students may choose answers (even the correct answer) for unexpected reasons. This makes it difficult to assess student learning of concepts, but as we continuously improve our introductory statistics courses, we need tools to understand what students understand.
In this breakout session, we will report on a year-long exploratory project to build an assessment using a powerful tool: think-aloud interviews. Audience members will learn to use think-aloud interviews to elicit student misconceptions and revise assessment questions, providing a practical method they can use in their own courses and research to better assess student learning. We will then share surprising misconceptions discovered during our own round of 36 student interviews, and summarize our assessment’s results from several hundred students in several introductory courses.
Identifying misconceptions of introductory data science using a think-aloud protocol (with S Hyun, P Burckhardt, P Elliott, C Evans, K Lin, A Luby, C P Makris, J Orellana, J Wieczorek, R Yurko, G Weinberg, and R Nugent).
eCOTS 2018, May 23, 2018. Video poster presentation.
Think-aloud interviews can provide assessment designers with insights into student thinking that may not be clear from test responses alone. We present results from preliminary rounds of think-aloud interviews with introductory students and describe surprising misconceptions we have identified, along with insights from our experience designing assessments and performing think-aloud interviews.
A Spatio-Temporal Statistical Model of Crime Hotspots (with Daniel S. Nagin).
American Society of Criminology Annual Meeting 2017, Philadelphia, PA, November 17, 2017.
The concentration of crime into small “hotspots” has been widely observed across many different cities and types of crimes. Current tools to understand the causes and dynamics of crime hotspots are limited. A variety of mapping tools, for example, have been proposed to detect hotspots in crime data, but these tools cannot correlate clusters to events or covariates which may have caused them. Separately, methods such as Risk Terrain Modeling attempt to identify spatial features that predict crime rates, such as gang territories, bars, or poverty, but consider only chronic hotspots, not accounting for temporary flare-ups.
We propose a statistical model which accounts for spatial and temporal variation in crime by modeling both spatial features and near-repeat and retaliatory crimes, allowing it to model the birth and death of crime hotspots and the reasons they appear, and to statistically test hypotheses about each predictive variable. We demonstrate the model on a large dataset of crimes in Pittsburgh, Pennsylvania, showing its utility in understanding the dynamics of crime.
Point process modeling with spatiotemporal covariates for predicting crime (with Joel Greenhouse and Xizhen Cai).
JSM 2016, Chicago, IL, August 3, 2016.
Extensive research has shown that crime tends to be concentrated in hotspots: small pockets with above-average rates of crime. Criminologists and law enforcement agencies want to better predict crime hotspots and understand the factors that cause them, in order to target interventions. Prior research suggests that past crime hotspots, spatial features (like bus stops or bars), and leading indicators (like 911 calls) are all predictive of future crime, but no proposed predictive policing model can account for all of these factors. We have adapted a previous self-exciting point process model to incorporate past crime data, leading indicators, spatial features and spatial covariates (like population density or zoning data), and developed new tools to evaluate the performance of the model and select variables. We show the basic model and demonstrate its application to seven years of Pittsburgh crime data, comparing its fits to previous hotspot models. These results can be used to better guide crime prevention programs and police patrols.
Statistics Done Wrong: Pitfalls in Experimentation.
LASER Workshop, Washington, DC, October 16, 2014. Video.
Most research relies on statistical hypothesis testing to report its conclusions, but the seeming precision of statistical significance actually hides many possible biases and errors. The prevalence of these errors suggests that most published results are exaggerated or false. I will explain some of these errors, such as inadequate sample sizes, multiple comparisons, and the keep-looking bias, and their impact on published results. Finally, I will suggest solutions to these problems, including statistical improvements and changes to scientific funding and publication practices.