CAMeL

CAMeL (Community for Advanced Methodological Learning)

What is CAMeL: CAMeL is a student-run group based in the Psychology department, focused on broadening and deepening our knowledge about advanced quantitative methodology. (Faculty mentors: Aidan Wright, PhD and Rebecca Reed, PhD. Student organizers: Molly Bowdring, Brenden Tervo-Clemmens, Kirsten McKone, and Delainey Wescott)

Who is CAMeL: We encourage scholars of all levels (including students, post-docs, staff, and faculty) and departments to join and contribute as often as desired.

Contact: Please feel free to contact Rebecca Reed (rebecca.reed@pitt.edu), Kirsten McKone (kirstenmckone@pitt.edu) or Delainey Wescott (dlw92@pitt.edu) with any questions! If you have an area of methodological interest or expertise on which you’d like to present, we’d love to hear from you!

 

Spring 2020 Schedule

Speaker – Aidan Wright (Associate Professor, Psychology, University of Pittsburgh)

Date and Location – February 6, 12-1, Sennott Square 4125

Topic – Department of Psychology Quantitative Minor for graduate students majoring in psychology

 

Speaker – Julia Feldman, BA (Graduate Student, Clinical-Developmental Psychology, University of Pittsburgh)

Date and Location – March 5, 12-1, Sennott Square 4117

Topic – Path analysis with moderation

 

Speaker –  Traci Kennedy, PhD (Postdoctoral Scholar, Department of Psychiatry, University of Pittsburgh)

Date and Location – March 26, 12-1, Sennott Square 4125

Topic –  Parallel process models

 

Speaker – Matt Lehrer, PhD (Postdoctoral Research Fellow, University of Pittsburgh)

Date and Location – April 9, 12-1, Sennott Square 4125

Topic –  3-level multilevel models

 

Speaker – Charles Judd, PhD (College Professor of Distinction Emeritus, University of Colorado Boulder)

Date and Location – April 16, 12-1, Sennott Square 4125

Topic –  Experiments in Which Samples of Participants Respond to Samples of Stimuli: Designs, Analytic Models, and Statistical Power

Abstract – Many psychological experiments ask participants to judge faces, memorize words, or solve analytic problems under different experimental conditions. The interest is in the mean condition differences in participants’ responses. Most typically differences due to the specific stimuli (i.e., faces, words, and problems) are ignored in the analysis of the resulting data. I will show that this can result in serious bias if the goal is to generalize conclusions to other samples of participants and other samples of stimuli that might have been used. I will then provide an introduction of the use of linear mixed models for analyzing data from designs involving two random factors, participants and stimuli. I will briefly discuss a range of such designs and then discuss issues of statistical power. I will argue that many failures to replicate experimental results may be due to the failure to treat stimuli as a random factor in the analysis of data from experiments involving samples of both participants and stimuli.