Application Form for the Fourth Summer School on Statistical Methods for Linguistics and Psychology (SMLP 2020), University of Potsdam, Germany
The Fourth Summer School on Statistical Methods for Linguistics and Psychology will be held in Potsdam, Germany, September 7-11, 2020. Like the second and third editions of the summer school, this fourth edition will have both a frequentist and a Bayesian stream, with foundational and advanced versions for each stream. Participants must choose *only one* of the four streams. Please *do not* apply multiple times for more than one stream; multiple applications will lead to disqualification. Please read the information relating to the content of the summer school before filling out this form:

https://vasishth.github.io/smlp2020/

To apply for this summer school, please complete the form provided below.

The application process opens Jan 15, 2020 and closes April 1, 2020. We will announce the decisions on April 15, 2020.

NOTE: You will receive a confirmation email once you complete this form. It may appear in your Spam folder. If your email server is not set up to display html-formatted output, you may not see all your responses in your confirmation email. Rest assured that we have all your responses.
Email address *
Full name *
Your answer
Your home page (if you have one)
Your answer
Mailing address (fill this in only if you need a physical letter of invitation for a visa)
Your answer
Affiliation *
Your answer
At what stage in your career are you currently?
How comfortable are you with R programming? Note: Response 1 means that you have never used R; Response 3 means that you can copy and paste code from a textbook or some online resource and can understand more or less what this code means; Response 5 means you can explain your code in detail; Response 7 means that you have developed at least one R package. *
No knowledge
Expert
How much have you used R in your past projects? Note: Response 1 means that you have never used R; Response 4 means that you have conducted at least three research projects that involve(d) coding in R; Response 7 means that you have used R for many different projects and for many different purposes (e.g., statistical models, creation of tables, creation of datasets, graphs, simulations, etc.) *
No previous experience
Frequent user
Have you already used for- and while-loops in R? *
Required
How familiar are you with linear mixed modeling using the lme4 package in R? Note: Response 1 implies you have never used lme4; Response 4 implies that you have at least three research projects that use lme4; and Response 7 means you could edit the source code of lme4 for your specific needs. *
No knowledge
Expert
How familiar are you with the underlying (theoretical) principles of linear mixed models? Response 1 means that you have never heard of linear mixed models or have heard the term but do not know what this means; Response 4 means that you can explain the fixed and random effects components of linear mixed-effects models; Response 7 means that you could teach an introductory course on linear mixed-effects models. *
No knowledge
Expert
How familiar are you with the underlying principles of multiple linear regression? Note: Response 1 means that you have never run or interpreted a multiple regression model; Response 4 means that you have used multiple regression in one or several projects, and would be able to explain the conditions in which multiple regression is appropriate, as well as the output of the model in some detail; Response 7 means that you could teach this topic to undergraduate students. *
No knowledge
Expert
How familiar are you with contrast coding? Response 1 means that you have never heard of contrasts; Response 4 means that you can name and explain at least two types of contrast coding schemes; Response 7 means that you can define your own custom contrasts. *
No knowledge
Expert
How familiar are you with the notion of statistical power? Response 1 means that you have never heard of power; Response 4 means that you can explain the concept of power and compute power for t-tests or ANOVA models using built-in functions in R; Response 7 means that you can compute power for repeated measures designs using simulations. *
No knowledge
Expert
Do you have one or several datasets that you could analyze with a linear mixed model? *
If accepted to the summer school, are you willing to send us (by 1 August 2020) your own dataset (or simulated data), with a preliminary analysis of this dataset? *
If accepted for the summer school, will you be willing to do some background reading, and learn about using R Markdown, before the start of the summer school? *
Have you ever used Bayesian methods for data analysis (e.g., using WinBUGS, JAGS, JASP, or Stan)? Note: Response 1 means you have never used any of these programs; 4 means you have several published journal articles using one of these programs. *
No experience at all
Expert
Which stream would you like to participate in? See course descriptions on the home page to decide. Please do not submit multiple applications for different streams; multiple applications will lead to disqualification. *
What do you hope to learn in this summer school?
Your answer
Have you attended a previous edition or editions of the summer school? If yes, please give the years and streams attended. If you haven't attended any previous edition, just write "No". *
Your answer
Please confirm that, if accepted to this summer school, you agree to pay 30 Euros before or during registration as a nominal fee for covering costs for things like coffee and snacks. *
Required
A copy of your responses will be emailed to the address you provided.
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