Welcome back!  ​Here are the tentative plans for this semester.  I'm sorry we didn't have any zoom capabilities last week.  This week and the weeks to follow will have the following zoom link alive and connected!

Jan 30th: What the heck are you doing with AI these days?  DEMO

Feb 6th:  L.O.L.A.'s birthday!  Dog social day SOCIAL

Feb 13th:  Latest Research.  SCIENCE

Feb 20th:  Future plans DISCUSSION

Feb 27th:  Measurement Invariance:  What is it and why should we care?  SCIENCE

Mar 6th:  Look at this new technology!  DEMO

Mar 13th:  SPRING BREAK - NO MEETING

Mar 20th:  Should data be shared?  DISCUSSION

Mar 27th:  Movie suggestions SOCIAL 

Apr 3rd: Data, data, and more data SCIENCE

Apr 10th:  Modeling the unknown for the unknowing  DEMO

Apr 17th:  Tax week trivia SOCIAL

Apr 24th:  Perfectionism, conscientiousness, and science  DISCUSSION

May 1st:  International Workers Day!  So, we work.  What else?  Why? SCIENCE

May 8th:  Another odyssey over; a new one to begin. SOCIAL

​Join us.  I will try my level best to start the zoom meetings on time.  Forgive me if I forget or lose track of time.  Please ping me if the zoom link doesn't work.  Thank you.

Tentative Schedule

829:  Meet and greet.  Let's get things rolling by getting reacquainted. [Social]

9/5:  More chit chat to get acquainted.  Some quarto.  [Social]

9/12:  Being wrong in science.  [Discussion]
9/19:  Blogs, podcasts, and self-publication.  When does self-publication equal peer-review? [Intro-Discussion]

9/26:  Will our data ever be good enough?  [Discussion]

10/3:  The future of science is here!  [Demonstration]

10/10:  Casual 'coffee chat' – Just for students

10/17:  LLMs - using them to your advantage [Social]

10/24:  Publicizing your work and your thinking [Discussion]

10/31:  Learn with me & Bubba [Demonstration]

11/7:  What does probability tell us?  [Social]

11/14:  Statistical Power Primer [Intro-Discussion]

11/21:  A new textbook idea.  [Discussion]

11/28:  Thanksgiving

12/5: TBA

12/12: TBD

[Social] = Come and chat with us! Informal (no reading)

[Intro-Discussion] = We introduce something and then we discuss it (background reading optional)

[Discussion] = Come prepared to discuss your perspectives (read to be a better discussant)

[Demonstration] = Sit back and watch (get ready to gain some skills)

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.  

Tentative Schedule 

1/18: Meet and Greet.  We are back!  T-shirts and swag.  COME AND GIT IT! [Social]

1/25: Quarto.  Yes, quarto.  Let's talk.  [Intro-Discussion]

2/1: Learning by doing.  An app a week, a paper a month, etc. [Discussion]

2/8: So you think you understand AI?  [Demonstration]

2/15: Fixing what has you stuck.  Bring us your obstacles! [Social]

2/22:  Python for data analysis?  R might be a dying beast.  [Intro-Discussion]

2/29: Standards of evidence and costs to attain said evidence [Discussion]

3/7:  SPRING BREAK!!

3/14:  TBA [Demonstration]

3/21: How are you using LLMs? [Social]

3/28: Case Studies in Data Analysis:  A real world, interactive, and collaborative approach [Intro-Discussion]

4/4: Credibility of the source or the durability of the adage.  [Discussion]

4/11: TBA [Demonstration]

4/18: Human Factors in Medicine & Technology [Social]

4/25:  TBA [Intro-Discussion] 

5/2: LAST MEETING OF THE ACADEMIC YEAR!! [Social]

[Social] = Come and chat with us! Informal (no reading)

[Intro-Discussion] = We introduce something and then we discuss it (background reading optional)

[Discussion] = Come prepared to discuss your perspectives (read to be a better discussant)

[Demonstration] = Sit back and watch (get ready to gain some skills)

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.  

Tentative Schedule 

8/24:  Meet and Greet.  We are back!  T-shirts and swag.  COME AND GIT IT!

8/31: Figures figure.  What are these figures and who do they persuade?

9/7: Data Colada as a Research Methods course.

9/14: Validating Data

9/21: Workflows (additional ideas: 1, 2, 3)

9/28:  Shiny is here!

10/5: Data sources:  Are new subject pools the answer?  

10/12: Snow Day!  Woohoo!!!

10/19:  Effect sizes once again please.

10/26: Science competition (HFAC opportunities)

11/2: Experience.  Time to revisit some old ideas.

11/9:  Statistical Power (via Shiny)

11/10:  PSYC Department demonstration of 11/9 material (no pressure)

11/16:  Scientists, data analysts, or data scientists; who are we?

11/23:  THANKSGIVING - NO MEETING

11/30:  LAST MEETING OF THE FALL SEMESTER

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.  

Tentative Schedule
1/25:  Hype and outrage in science: a case study.

2/2: The role of a scientific organization (c 1969).  Have times changed?

2/9:  Trust in AI.  Do we differ in our adoption?

2/16:  Reading Science.  Join me.

2/23:  Mechanisms in science.  Do we need 'em?

3/2:  OK, how about reading Nature?  Try it.

3/9:  Back to psychology.  Do we over-hype our claims?

3/16:  SPRING BREAK!!!!!

3/23:  Replication, failure, rejection, or else?  An odd plea.

3:30:  Revisiting or preventing another time-traveling porn study.

4/6:  NO SCHOOL (for kids)

4/13:  Making sense out of survey responses.  Huh....

4/20:  Giving psychology away.  Is it really that easy?

4/27:  Student Presentations
5/4:  SUMMER - final meeting.  TBD.

Tentative Schedule 

Aug 25th:  Statistical Consulting.  A recent blog post  by Andrew Gelman reminded me that we had not discussed this paper yet.

Sept 1st:  Finalize T-shirts and have a little fun discussing options

Sept 8th:  Describe your data.  Friends of mine (EGAD members) published this very thought-provoking article.  

Sept 15th:  Machine Learning.  Read this fairly extensive article (skim if only partly interested) on MLe and how it might be applied in psychology.

Sept 22nd:  Brunswik on psychological science (c 1956).  A rather dense but insightful paper on where psychology fits within a historical lens of science.

Sept 29th:  Twenty years later - a retrospective on online dating.  Finkel and friends published this piece in 2012.  How much did they get right to date?

Oct 6th:  Paul Meehl and The Sacred Cows of Academia.  Skim this book more for the highlights than all the details.  Listen to his own words.

Oct 13th:  The Rules of the Game Called Psychological Science.  Read this fine paper so we can have a spirited exchange during our meeting.

Oct 20th:  Decolonizing academia?  I don't get it.  Help me understand this fringe view.

Oct 27th:  Student Presentations - TBA during week 1 with details.  

Nov 3rd:  Representative Design.  More Brunswik

Nov 10th:  Measuring scientific impact.  Is there more to counting citations and the h-index?  Nosek thought so in 2010..

Nov 17th:  Canceling scientists.  In this era, it happens.  Read what Paul Meehl had to say about it in 1998.

Nov 24th:  Thanksgiving

Dec 1st:  No meeting today - meet at the pub on Friday Dec 2nd to wrap up the semester.

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.

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.  

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:

https://www.sciencemag.org/careers/2016/09/how-review-paper
https://www.nature.com/articles/d41586-018-06991-0
https://www.journals.elsevier.com/applied-soft-computing/news/tips-and-advice-when-you-review-a-scientific-paper
https://authorservices.wiley.com/Reviewers/journal-reviewers/how-to-perform-a-peer-review/step-by-step-guide-to-reviewing-a-manuscript.html 

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:

https://www.khanacademy.org/math/cc-seventh-grade-math/cc-7th-probability-statistics#cc-7th-basic-prob 

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:
https://www.pnas.org/content/pnas/111/Supplement_4/13664.full.pdf