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SPSP provides online professional development to members at all career stages. Some of these opportunities are offered live and made available in recorded format, while others are only in video format.


Theory and Practice of Bayesian Inference Using JASP

Alexander Etz headshot

Alexander Etz
(email)
University of California, Irvine

Julia Haaf headshot

Julia Haaf
(email)
University of Amsterdam

Johnny van Doorn headshot

Johnny van Doorn
(email)
University of Amsterdam

Friday, June 21, 2019

10am–11:30am EDT

Format: Online Webinar

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Description:
This webinar will provide attendees with a friendly, gentle introduction to Bayesian statistics, as well as demonstrate how to perform Bayesian analyses using JASP statistical software. Attendees will come away understanding the "why" and "how" of Bayesian estimation and hypothesis testing. This workshop is relevant to any student or researcher who wishes to draw conclusions from empirical data. No background in Bayesian statistics is required.

About the Presenters: Alexander Etz is a PhD student in the Cognitive Sciences department at the University of California, Irvine. His research is all over the place at the moment, but the common thread running through it all is the theory and application of Bayesian inference. Alexander recently completed his MSc. in statistics at UC Irvine, and he has taught a number of tutorial workshops on Bayesian analysis for the social sciences.

Julia Haaf is a postdoc at the Psychological Methods Unit of the University of Amsterdam. Her main research focus is on ordinal constraints in Bayesian hierarchical models. Using these constraints she investigates individual differences in cognitive tasks, and variability in meta-analysis. Julia taught research methods for undergraduates using JASP. She is currently contributing to a new JASP module for Bayesian metaanalysis.

Johnny van Doorn is a PhD candidate at the Psychological Methods Unit of the University of Amsterdam. His research focuses on the development of Bayesian analyses for ordinal data, such as rank correlations and nonparametric t-tests. He is part of the JASP programming team, and is (ironically) responsible for maintaining and improving the frequentist analyses. In addition, he teaches various workshops on Bayesian inference, cognitive modeling, and JASP.

Intended Audience: Student, Early Career, Mid-Career, Late Career, Academic Occupation, Non-Academic Occupation

Prerequisite Knowledge required: Basic knowledge of statistical tests (e.g., correlations and t-tests)

Outcomes: At the end of this presentation, participants will be able to:

  1. Understand the basic theory behind Bayesian inference.
  2. Conduct your own Bayesian analyses in JASP.
  3. Interpret the output and report the results.


“Turning your CV into a Résumé”

David A. Richards

Tuesday, May 21, 2019

6pm - 7pm EDT

Format: Online Webinar

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Description:
This conversational webinar will lead attendees through the process of turning a curriculum vitae into a résumé suitable for seeking employment outside academia. Discussion topics will include the differences between a CV and résumé and the process of developing the former into the latter. Practical, specific tips will be provided throughout.

The intended audience for this webinar is current graduate students, as well as recent graduate students who are early in their career since earning a graduate degree, but it may be of interest to any trained academic interested in pursuing a career outside the ivory tower.

The webinar will have a workshop component, during which we will try to present feedback specific to your situation, including on your CV and/or résumé. Before the webinar, attendees should respond to the survey at the below URL. This will help the presenter tailor the webinar to your needs.

“Creating Reproducible Research Reports Using R Markdown”

Michael Frank headshot

Michael Frank
(email)

Stanford University

December 5, 2018
Format: Online Webinar

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Description:
R Markdown is a simple but very powerful way to mix R data analysis code and text. R Markdown documents are a great way to document your data analysis and create reproducible reports (e.g., that automatically render your graphs and tables and even your results section from your data). You can even use R Markdown to write your entire paper, avoiding copy-and-pasting your analyses, which can be a major source of errors in papers. The rendered documents look spiffy on the web and in print. In this workshop, we introduce R Markdown and show how it can be used as part of a reproducible writing workflow.

 

“A Practical Guide to Multilevel Modeling: Part 2”

Amie Gordon headshot

Amie M. Gordon (email)
University of California San Francisco

September 27, 2018
Time: 2:00-3:30PM ET
Format: Online Webinar

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Description:
This is the second of a two-part multilevel modeling (MLM) webinar for newbies as well as researchers who have been exposed to it through a prior class or workshop but still have lots of questions. Topics in Part 2 include: 

1. Fixed versus random effects – the difference between fixed and random effects and what changes in the analysis process when random slopes are allowed in the model.

2. Grand-mean versus group centering – what they are and when to use them, unconfounding within and between person effects.

3. Covariance matrices – cover the basics of the residual and random effects covariance matrices.

 

“A Practical Guide to Multilevel Modeling: Part 1”

Amie Gordon headshot

Amie M. Gordon (email)
University of California San Francisco

September 26, 2018
Time: 3:30-5:00PM ET

Format: Online Webinar

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Description:
This is the first of a two-part multilevel modeling (MLM) webinar for newbies as well as researchers who have been exposed to it through a prior class or workshop but still have lots of questions. Topics in Part 1 include: 

1. Identifying if MLM is necessary – the first step in MLM is figuring out whether data actually violates assumptions of independence.

2. Figuring out the nested structure of your data (including cross-classified models) – Identifying the sources of non-independence in your data, including the possibility of cross-classification.

3. Approaches to dealing with non-independence – when to deal with non-independence through random versus fixed factors.

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