online course: intro to stats with R

This Summer, Felicia Zhang and I are developing an online course with the Princeton McGraw Center for Teaching and Learning. Below is an overview of the course, which will be accessible in Fall, 2018:

Two of the biggest challenges for undergraduates in psychology are understanding key concepts in statistics and applying those concepts to analyze data and interpret findings. These challenges not only make it difficult for students to understand material in lectures and labs, but also difficult for instructors to help the students because students feel defeated and become unwilling to engage with the course. Therefore, we are designing an online course to introduce statistics and R programming. Integrating statistics and R programming in one course is ideal for learning: The former is essential for students to understand research more broadly and the latter is an important tool for students to engage with research directly. For example, a psychology student must interpret statistical results from prior experiments as well as analyze their own data for their senior thesis. In sum, our course is designed to help students who have minimal prior experience to understand key concepts in statistics and to apply those concepts to realistic problems in psychology research.

The course will include 6 modules (i.e., getting started with statistics; getting started with R; descriptive statistics; correlation; one-sample t-test and binomial test; two-sample t-test) and each module will have the same basic structure: The first portion helps students to understand key concepts. Students will watch narrated text, live drawings, or videos. To assess students’ understanding, students will complete multiple choice questions. The next portion of the module helps students to apply key concepts via R programming. First, we will pose realistic psychology research questions (e.g., Do toddlers who hear more language from caregivers tend to have larger vocabularies?). Students will observe how we answer each question with the appropriate statistical test (e.g., correlation) and R syntax (e.g., cor.test(data$language, data$vocabulary)). Next, we will pose new, similar questions (e.g., Do toddlers who have more books at home tend to hear more language from caregivers?) and students will attempt to answer these questions. To assess students’ understanding, students will input their answers and receive feedback. The last portion of the module helps students to review key concepts. Students will watch narrated text, live drawings, or videos. Finally, to assess students’ overall understanding, students will complete a module quiz.

After participating in our course, students will have fundamental knowledge of statistics and R programming. Although both are extremely important for students in psychology, students need more resources to understand key concepts in statistics and to apply those concepts to real research (e.g., their senior thesis). Our course will provide these resources, with two notable strengths: First, unlike other online R programming courses, we will use realistic, psychology-specific examples. This design enables direct connections between what students learn in lecture, in lab, and in our online course. Second, although our course is tailored to the needs of psychology students, having basic knowledge of statistics and R programming is applicable to a growing number of fields (e.g., sociology, politics, etc.). In sum, our online course will support learning among undergraduates in psychology and could have wide-reaching impact among undergraduates in science, more broadly.

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CUNY 2018

Here are my abstract and poster for CUNY 2018.

Our study evaluated whether listeners can use spatial deixis (e.g., this, that) to predict a speaker’s likely referent. Adults and 5-year-olds viewed scenes while listening to deictic sentences (e.g., Look at that beautiful baby) and neutral sentences (e.g., Look at the beautiful baby). We found that both adults and children used deixis to predict the plurality of the referent, but only adults used deixis to predict the proximity of the referent (e.g., using this to anticipate a referents proximal to the speaker). In sum, our findings reveal specific developmental changes in how prediction occurs during language processing.

Looking forward to next year!

cognition paper in press

Reuter, T., Emberson, L. L., Romberg, A. R., & Lew-Williams, C. (in press). Individual differences in nonverbal prediction and vocabulary size in infancy. Cognition.

Big thanks to my co-authors, Fernanda Fernandez and Jean Bellamy, and all our participant families!

what teachers make

While prepping teaching materials for Developmental Psychology this semester, I suddenly remembered Taylor Mali. It’s been years since I listened to his poetry, but it’s still as poignant now as it was back then. (If you’re not familiar with his work, I strongly encourage you to check out his website.)

In particular, I’ve been reflecting on his poem ‘What Teachers Make‘ and how I feel about teaching. Being a teacher – making a difference! – is genuinely important to me. As is hopefully evident from my posts on teaching and mentorship, I enjoy working with students both in the lab and in the classroom. In sum, my intrinsic motivation to teach is one of the reasons that I decided to pursue a career in academia.

Yet it’s rare that teaching is openly celebrated. To the contrary, graduate students are often warned not to invest ‘too much time’ in developing our pedagogical skills, lest this distract from our research. How much time is ‘too much’ undoubtedly depends on whomever is giving the advice on the matter, but there are also more systematic cues to put teaching on the back burner. For example, in my department, if a graduate student receives outside funding, their teaching responsibilities are vastly reduced. In behaviorist terms, funding is therefore both a positive reinforcement (i.e., increasing funding for your research is good) and a negative reinforcement (i.e., decreasing your time spent on teaching is good). In sum, the overall message to graduate students is clear: Focus on your research.

This is not to say that advisors who counsel their graduate students to focus on research are necessarily doing the wrong thing. It makes perfect sense to focus on research if you intend to pursue a professorship at a research-focused institution! And this is not to say that I personally enjoy or value teaching more than research. What I mean to say is that the strict dichotomy of research and teaching may be a false one. Research and teaching can be combined in ways that benefit both the undergraduate students and the graduate instructors, and I’m grateful that my advisors have encouraged me to develop skills in both research and teaching.

Princeton supports grad students

Statement from President Christopher L. Eisgruber (12/4/17):

We share the concerns expressed by our graduate students regarding the provision in H.R.1 that would subject tuition waivers to taxation.  This provision is not in the bill that passed the Senate, but if it were enacted into law, it would impose a tax that graduate students cannot afford to pay and would severely harm graduate education throughout the United States.

Preserving the tuition remission exemption is a top priority for Princeton.  We have been and will continue to work vigorously and in concert with other universities and educational associations to achieve that goal.

If the tax were to be enacted, we would take steps to ensure the well-being of Princeton’s graduate students and to minimize the damage to graduate education at Princeton.  Our graduate students are important to us, and we will support them so that they can pursue their studies, careers, and aspirations successfully.  There can be no doubt, however, that this ill-conceived and counter-productive tax would not only impose significant costs on graduate programs here and elsewhere, but also do serious damage to the scope and quality of graduate education in our nation.