MRES Meetings

Each week, we discuss topics relevant to science including publication, review, data analysis, measurement, science policy, and more. Some weeks we have brief readings to discuss but none are required for your attendance. We hope to see you at one of our meetings!

We meet in ACGC 1320A every Thursday at noon during the academic year. If you are interested in joining us remotely, register in advance for our weekly zoom sessions. After registering, you will receive a confirmation email containing information about joining the meeting. For more information, please subscribe to our low-volume email list. Below you will find our weekly calendar, and an archive of meeting topics and associated readings from this year.

Spring 2022

Tentative Schedule

2/3: Measurement and the four sacred cows.

2/10: The Theory Crisis in Psychology You can download the pdf directly here.

2/17: LMER in practice. Check out this nice tutorial.

2/24: Active Learning: What it means and what it does.

3/3: What is so significant about the NHST debate?

3/10: Program Evaluation - a (not so) brief history.

3/17: SPRING BREAK - No Meeting

3/24: My mentors' take on the current state of program and policy evaluation

3/31: Hadley's attempt to distill the essence of data visualization

4/7: Programmatic Evaluation. How about chronic pain treatment?

4/14: Scientific standards: Are we there yet?

4/21: Personal projects: A unit of analysis for individual differences

4/28: An update on Trust and its relevance to psychological science: A demonstration

5/5: End of year celebration with t-shirts!

NB: Links may require GMU authentication. If you are NOT a member of the GMU family or you no longer have access to GMU's library archives, please let me know. I will post the article links to the papers via the Google group.

Fall 2020

Thu, September 24, 12:00pm – 1:05pm

The concept of logic models gains a ton of traction in program and policy evaluation but is almost complete absent in the psychological science literature. Why? No idea. Today, I wish to discuss what a logic model is and why we ought to be more attentive to said concept/application in our own work. Here are a few suggestions to get you started:

A brief from the CDC
A nice primer
Even more details

Use these resources to get started using logic models for your own work. Before doing so, let's chat about the why and then we can talk about the how. As a final resource, I wanted to draw your attention to the following article that, for some reason, escapes most scientist's attention:
Cook & Scriven (2010)

Thu, October 8, 12:00pm – 1:05pm

Today, we have a slight departure from our usual fare (science) and venture forth into the realm of expertise. I encourage you all to pour yourself a tankard of your favorite beverage, sit back, and read this article from the New Yorker. Read it; you won't be disappointed. I want to talk about the development of expertise and why we impatient types (myself squarely in the crosshairs of that comment) are at a loss for developing much in the way of true expertise. I'm a dabbler. I'll admit it. But, if you want greatness or at least to be recognized as a true expert, read the linked article and reflect on what you think would make you an expert at what you desire such expertise. For those extremely interested in expertise and "experience" (scare quotes intentional), feel free to read and critique this paper on the topic.

Thu, October 15, 12:00pm – 1:05pm

Today, we discuss the long-awaited article by Don Campbell (1975). The paper is actually his presidential address to APA. Please read the paper or at least skim it. Yes, the paper is long. Start early and pace yourself. There will be plenty you don't understand but I assure you if you read/skim ahead of time, you will gain more from the discussion. Here is a link to the PDF: Campbell 1975

Thu, October 22, 12:00pm – 1:05pm

Today, we discuss the ever-changing world of data collection. In years well past us, psychological science relied almost exclusively on psychology undergraduates for theoretical tests, instrument development, and a vast array of dissertation attempts (and successes). The times have a-changed and what we face now are too many options with not enough information about the options. Are we better off? How? I want you each to think carefully about the sources of data that now exist. Have you used any? What expertise is required to use said source? What are the trade-offs for the source? These questions are crucial to understanding why our science may have changed permanently and may never go back to the "good ol times" of yesteryear. From my angle, that change is a good thing. Before declaring it "good," let's come together to discuss the pros and cons of such a shift.

Thu, October 29, 12:00pm – 1:05pm

Today, we talk about scientific review. No, we are not discussing the crazy state of the review process but rather HOWTO review. Jeff and I - along with others who have participated in the review process intend to discuss how we approach the review process. The ability to carefully but systematically review a scientific contribution is essential whether you are an academic or a consumer. Come prepared to think critically and examine the process of scientific review. For those of you interested in further details, I suggest you read/peruse the following bits:

Thu, November 19, 12:00pm – 1:05pm

The probability of us meeting is vanishingly low. Still, if we do meet, I want to talk about probability (theory, application, and interpretation). Our discuss will revolve around how to model things and why to model things with respect to probability. We may even touch upon the probability density function (i.e., distributions). For those who are completely new to probability, I strongly encourage you to watch Sal Kahn's great discussion:

Thu, November 12, 12:00pm – 1:05pm

NOTE: Today, we are on Cora watch - actually as of Monday. I don't know if and when Cora will decide that she has had enough of the cozy confines of mom's womb. Based upon the uncertainty, we will discuss uncertainty. How do we measure it, how do we elicit it from people, and how do we describe it in statistics? These questions form the basis of our discussion plans. Now, should Cora decide to come earlier this week, I may shift this topic to another date.

Let's begin with this article: