Neil Lutsky, Psychology

Initial Publication Date: October 23, 2013

Neil Lutsky is a professor of Psychology at Carleton College, a private 4-year liberal arts institution. Information for this case study was obtained from an interview conducted on August 14, 2013. This page is part of a collection of profiles about a variety of techniques for integrating Quantitative Reasoning (QR) across the curriculum.

Jump down to Design and Implementation of QR Goals | Key QR Assignment of the Course | Challenges | Advice | Documents

Overview and Context

About the Course

Principles of Psychology is an introductory course in which I do a number of things that emphasize quantitative reasoning. The course satisfies Carleton's Quantitative Reasoning Encounter requirement (QRE). This will be my 40th year teaching introductory psychology.

The audience for the course is very broad since nearly every student at Carleton takes introductory psychology. This course will be their primary, and probably their only, exposure to the field.

The course size is normally 35 to 40 students, but this fall I'm co-teaching with a colleague and we're doing a course that has 75 students.

Key QR Assignment Description (links to section in this page)

How Quantitative Reasoning (QR) and Literacy are Approached

I was heavily involved in the development of Carleton's quantitative reasoning initiative, Quantitative Inquiry Reasoning and Knowledge (QuIRK). I was one of the primary authors of the original grant that launched the initiative. Part of the orientation of QuIRK from the start, and part of my own orientation, has been to emphasize quantitative reasoning, not quantitative literacy.

Quantitative reasoning is a voluntary, intentional habit of mind. Whereas, I think "quantitative literacy" implies that thinking quantitatively is like reading--you develop a skill and then it's there automatically. I don't think quantitative sophistication has that same character at all. Quantitative reasoning is less automatic and more voluntary. Surely there are things you need to understand and appreciate and learn, so there is background knowledge of varying degrees of sophistication that you can bring to quantitative and non-quantitative issues. But it's not the same. Quantitative reasoning is not something you achieve by mastering something.

Design and Implementation of QR Goals

Motivation to integrate QR

There was a combination of motivations. I have a personal commitment and passion for teaching quantitative reasoning. I care deeply about it and I enjoy doing it. At the same time, I think it's something that needs to be done in order to educate students broadly and to represent the field that I'm teaching.

I personally believe that quantitative reasoning is a fundamental understanding and appreciation that will benefit our students as they go through life, which is why it's one of the things that I most strongly seek to emphasize.

I address quantitative reasoning in the context of psychology because I think quantitative reasoning is important in the same way that I think writing is important. In introductory psychology, and in other courses, I emphasize writing because it's a general skill that students should be developing. I feel the same way about quantitative reasoning. And I have the opportunity to do this because psychology is so popular.

Finally, I would say that the quantitative methods and values are a very important characteristic of psychological science. Part of what I do in teaching psychology is to teach students the content of psychology, but also the way of thinking scientifically that is so essential to the field.

QR goals

What I'm primarily concerned about in my teaching is I want students to see numbers and quantitative claims and have them bring to mind the kinds of questions that might be asked about those quantitative claims. In this way, students will be in a position throughout their lives to make sense of the claims that are being made and to understand the strengths and weaknesses. I want them to understand both the power and the limitations of quantitative claims.

I also want students to appreciate the value of using quantitative information when they construct arguments, which again, is a very active orientation. It's not a literacy. I want students to develop an appreciation for the power of numbers and what numbers bring to an argument. I want them to employ that tendency when they are thinking about questions that will arise as part of many aspects of their lives--academic work, professional work, their lives as voting citizens, their experiences reading newspapers like The New York Times, and when encountering health information and health claims.

Pedagogic approaches used

My students learn quantitative reasoning within a meaningful context of using it for a purpose. They look at numbers in order to answer questions they care about and write up arguments. This is an active orientation. Learning in context is very different from having a homework assignment where you have to calculate ten correlations and tell the instructor what they mean. The focus isn't just on the thing itself. In this way, students can see the potential value associated with quantitative reasoning, and the value in being able to share what they've learned with other people.

Usually when I do this course, I'm primarily presenting information for two out of three days a week. I'm interacting with the students, but I'm primarily lecturing. The third day is mainly discussion.

Knowing the course is successful

I collect data in a course evaluation questionnaire. In this questionnaire, I ask students to rate each project that they do. And I ask them, "Tell me something you learned from the project." This is one way that I get a sense of what students are taking away from the course.

Later in the course evaluation questionnaire, I ask some questions about psychology as a science and their own sense of competence with respect to statistics. I have evaluated that data, and the results are consistently positive compared to a control group I had about ten years ago. This was a course where we didn't have the QR module, and there were differences between the two.

I also use a grading rubric that helps to reinforce the things that we've been talking about. This not only gives me grading information, but it also draws students' attention to the importance of quantitative reasoning.

Key QR Assignment of the Course

This assignment involves teaching about personality psychology. Students analyze and write about a dataset to which they have contributed. The dataset is compiled from an online survey pertaining to personality types and levels of happiness and well-being.

Introducing Introductory Psychology Students to Quantitative Analysis assignment description from the Pedagogy in Action collection of teaching activities: (links to section in this page)

In this assignment students are given a dataset to which they have contributed that includes personality variables and measures of happiness and well-being. Their task is to pose a question that involves a subset of variables in this dataset, run a data analysis, and write up a short paper reporting what they have found.

This project is generally done during the first two weeks of the term to emphasize the scientific nature of psychology early on, but it can be done at any point during the course when personality psychology is taught.

In order to collect the data, students go to a website I set up and complete a questionnaire online. The questionnaire primarily includes a number of questions that relate to a particular model of personality or individual differences. It assesses what are called the five factors of personality.

Also in this questionnaire are sets of questions related to happiness and well-being. There are a number of scales, subscales, and components. The questions are standard questions from well-known personality measures and highly reliable.

Once the students have completed the survey, I add data from the most recent cohort of students to a dataset that I have from past students, creating a fairly substantial database. I upload this database to the course page so that it is available for students to use.

Next I give the students handouts with the assignment, and we go over them in class. The handouts list all the variables that are represented in the dataset and provide instructions.

Each student must propose a research question to investigate. Before they go ahead with their analyses, I ask students to share their question with me in order to make sure the questions are not unduly complicated and they can actually be answered by the data.

I go over with students some simple data analyses and what they tell you; statistical significance; and a few other statistical tools. I only cover the basic statistics that they're going to need to do this project.

In writing up their papers, I emphasize that they should anticipate the questions that a reasonable reader would have about what they're presenting and what they're claiming. They should ask themselves, "What are the questions that someone might raise? How might I answer those questions? Do I have an answer for those questions or not? What information do I need to include in the paper so that those questions would be answered by the material that's incorporated in the paper?"


  • Time for instructor to set up the project. Some of the behind-the-scenes work is fairly time-consuming for me as an instructor, such as constructing the websites for this project. Sometimes software systems change, necessitating an overhaul. There were times when I had to redo it entirely for various reasons. I've changed the focus several times, resulting in different measures. That required a fairly substantial renovation. In addition, every year after the students enter their data I have to take that data and format it and compute subscale variables because there are ten different questions that relate to each particular personality variable.
  • Some questions are too complicated to answer with the dataset. Sometimes students will generate really smart, interesting questions that are too complicated to answer using the kinds of data analyses that I want them to use in this project. In that case, I ask them to simplify their question. We'll talk about it and try to come up with some alternative or some simplification that is, in fact, viable.


  • Just do it. Quantitative reasoning is so much a part and parcel of psychology itself. It's just a natural thing to do.
  • Obtain support. I've had the benefit of good support from the IT people at Carleton. That has been helpful. It's not something that people are likely to be able to do completely on their own. I think cultivating collaboration with academic technologists who are supporting your work--which most institutions have--is an important thing to do.
  • Emphasize the broader value of quantitative reasoning to students. Students are eager to learn. They want to develop skills and appreciations that are going to help them. Make the case for the value of quantitative reasoning.
  • Have fun. Enjoy what you are doing. I use a lot of cartoons such as from the New Yorker when I'm presenting. Especially with quantitative reasoning, presenting it in a way that makes it fun goes a long way.


Key QR Assignment online description (from the Pedagogy in Action collection of teaching activities): Introducing Introductory Psychology Students to Quantitative Analysis

Key QR Assignment description (Word doc) (from the Pedagogy in Action collection of teaching activities): Introducing Introductory Psychology Students to Quantitative Analysis

SSDAN Logo ICPSR logo Teaching with Data Logo Teaching with Data Logo