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Copy file name to clipboardExpand all lines: 12-wrap-up.qmd
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# Wrap-up
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Congratulations! You've made it to the final week of the course! I hope you are proud of your efforts and all you've accomplished throughout this term! You engaged in industry-standard best practices in open research, from experiment pre-registration to programming an experiment, to analyzing data. You also got to explore a research topic of your own choosing! You might, for example, want to take the pre-registration from this course and use it to guide an independent or thesis project next semester or academic year.
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🎉 Congratulations! You've made it to the final week of the course! I hope you are proud of your efforts and all you've accomplished throughout this term! You engaged in industry-standard best practices in open research, from experiment pre-registration to programming an experiment, to analyzing data. You also got to explore a research topic of your own choosing! You might, for example, want to take the pre-registration from this course and use it to guide an independent or thesis project next semester or academic year.
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This week you get to show off all you've learned about your topic and your experiment and data analysis plan through your [final poster presentation](assignment_poster.qmd). We all look forward to seeing them!
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## Feedback
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We continually seek to improve this course! If there are aspects of this book or any of the assignments that can be improved, please feel free to suggests changes via a pull request or issue on GitHub or directly to Dr. Fink via email.
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We continually seek to improve this course! If there are aspects of this book or any of the assignments that can be improved, please feel free to suggests changes via a [pull request](https://github.com/Perception-Lab-PNB3EE3/courseBook/pulls) or [issue](https://github.com/Perception-Lab-PNB3EE3/courseBook/issues) on [GitHub](https://github.com/Perception-Lab-PNB3EE3/courseBook) or directly to Dr. Fink via email.
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## Reflection
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If you would like to recieve 1 bonus percentage point towards your final grade, you can include a reflection in your individual repository. Further instructions are [here](assignment_reflection.qmd).
cover-image-alt: Book cover image that says Perception Lab Course, generated by chatGPT4o. Black and white offsets and subtle text distortions make one take a second look at the image, which is reminiscent of perceptual illusions.
Copy file name to clipboardExpand all lines: assignment_analysis_code.qmd
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For the sake of this course, the easiest way to accomplish the goal of creating understandable, reproducible code is to use the 'notebook' format, which allows you to intersperse chunks of code with chunks of text (markdown). There are many notebooks to choose from: [Jupyter Notebooks](https://jupyter.org), [Google Collab Notebooks](https://colab.research.google.com/?utm_source=scs-index#scrollTo=GJBs_flRovLc), [R Notebooks](https://bookdown.org/yihui/rmarkdown/notebook.html), etc. You are free to use whichever tool is most comfortable for you.
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Below are some examples of final analysis repositories from my own research:
None of these are perfect (I am still learning too!); they are here to provide some examples/inspiration.
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## What to do
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> SIDENOTE: it is a bit beyond the scope of this course, but worth pointing out that one reason code intended to be reproducible might no longer be is due to changes in, e.g., function, package, language, or operating system versions, compared to when the code was originally written and run. Tools like [Docker](https://www.docker.com/) and [Code Ocean](https://codeocean.com/) provide solutions to this very real problem! They "containerize" your code in the computational environment you developed it in (e.g., with specific operating system, python verison, package versions, etc.).
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## What to do for this assignment
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Your analysis code should follow your pre-registered data analysis plan. Be sure to write the code for all the analyses you proposed, including any exploratory analyses (as registered or now desired). Also be sure to include visualizations. In addition to visualizing your main findings, you might also want to produce visuals to ensure your data meet certain assumptions or that your experimental conditions were balanced as intended.
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You should organize your analysis notebook logically, providing heading and descriptive text, notes, and comments where necessary. Someone unfamiliar with your analysis plan should be able to look at your notebook and understand what is going on and what it means.
Copy file name to clipboardExpand all lines: assignment_dataVis.qmd
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In this assignment, you should use the programming language of your choice (e.g., R, Python, etc.) to do the following:
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1. Create a new notebook, in which you will conduct all of your analyses. Name your file `dataVisualisation_analyses.[file ext here]`
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2. Load in the [`assignments_dataVis_dataset.csv`](resources/assignments_dataVis_dataset.csv) file.
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2. Load in the [`assignments_dataVis_dataset.csv`](resources/assignment_dataVis_dataset.csv) file.
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3. Print summary statistics for each condition in the data set (e.g., mean, standard deviation)
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4. Visualize the results of each condition. At very least, you should plot y as a function of x for each condition. You are welcome to create as many plots as you like. Try to apply the visualiztion best practices presented during lecture. Please create your plots in line in your notebook.
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4. Visualize the results of each condition. At very least, you should plot y as a function of x for each condition. You are welcome to create as many plots as you like. Try to apply the visualization best practices presented during lecture. Please create your plots in line in your notebook.
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5. In a text cell in your code notebook, answer the following questions:
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## Tips & Tricks
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Use this as an opportunity to get more familiar with your programming language of choice and to explore the fundamentals of data visualization! It is my understanding that your PNB coursework already involves R. I also know there is a Python for PNB course some of you may have taken. If you find yourself needing a refresher on either of these languages, there are so many great online resources! Here is a resource on [R essentials](https://tellingstorieswithdata.com/20-r_essentials.html) and here is one on [python essentials](https://lukas-snoek.com/introPy/week_1/python.html).
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Use this as an opportunity to get more familiar with your programming language of choice and to explore the fundamentals of data visualization! It is my understanding that your PNB coursework already involves R. I also know there is a Python for PNB course some of you may have taken. If you find yourself needing a refresher on either of these languages, there are so many great online resources! Here is a resource on [R essentials](https://tellingstorieswithdata.com/20-r_essentials.html) and here is one on [python essentials](https://lukas-snoek.com/introPy/week_1/python.html). There are even more helpful links listed on the course [Resources page](resources.qmd).
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I highly recommend doing all of your coding in R markdown, Quarto, or a jupyter notebook. Such notebooks allow you to combine natural language with code. Why would you want to do that? Well, imagine you need to communicate your findings to someone else, or you want someone else to be able to reproduce the exact analyses you did. By writing code, creating detailed descriptions, and plotting figures in line, everything you need to do can all be in one place! In research jargon, we sometimes refer to this as a "reproducible workflow" -- if you're interested in learning about such workflows, [here is a primer](https://tellingstorieswithdata.com/03-workflow.html). Note that there are many types of notebooks to choose from, for example, [Jupyter Notebooks](https://jupyter.org), [Google Collab Notebooks](https://colab.research.google.com/?utm_source=scs-index#scrollTo=GJBs_flRovLc), and [R Notebooks](https://bookdown.org/yihui/rmarkdown/notebook). Choose whichever one you are familiar with or which seems most intuitive to you!
Copy file name to clipboardExpand all lines: assignment_jsPsych_1.qmd
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### Read these 2 journal articles:
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This article provides context for why web-based research is important in psychology.
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> Reips, U. D. (2021). Web-based research in psychology. Zeitschrift für Psychologie. Available from: [https://psycnet.apa.org/fulltext/2022-17584-002.pdf](https://psycnet.apa.org/fulltext/2022-17584-002.pdf)
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This article introduces jsPsych--a tool for web-based research.
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> De Leeuw, J. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a Web browser. Behavior research methods, 47, 1-12. Available from: [https://link.springer.com/article/10.3758/s13428-014-0458-y](https://link.springer.com/article/10.3758/s13428-014-0458-y)
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> NOTE: You do not need to go through the tutorial included in the text (We will do a more up to date tutorial later!)
Perception Lab will culminate with each student giving a 6 minute poster presentation (4 minute presentation + 2 mins for discussion). The poster is your chance to integrate everything you have learned throughout the course. It should include background motivation for your pre-registered experiment, your proposed methods, you statistical results and data visualizations from your simulated data, and a discussion about the implications of your research.
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Perception Lab will culminate with each student giving a 6 minute poster presentation (4 minute presentation + 2 mins for discussion). The poster is your chance to integrate everything you have learned throughout the course. It should include background motivation for your pre-registered experiment, your proposed methods, your statistical results + data visualizations from your simulated data, and a discussion about the implications of your research.
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## What software to use
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You are free to use whatever software tool you like for this assignment. If you have gotten really into coding in R and reproducible workflows throughout this course, you could use [posterdown](https://github.com/brentthorne/posterdown); this [blog post](https://www.shilaan.com/post/academic-conference-posters-using-posterdown/) will give you a quick intro. Or, if you are really sick of code and dealing with unfamiliar tools, you can use powerpoint to make your poster. McMaster library provides free [powerpoint poster templates](https://brand-resources.mcmaster.ca/asset-bank/action/browseItems?cachedCriteria=1&categoryId=1773&categoryTypeId=2); note that you will need to be logged in via VPN and macid. You are also welcome to explore other online resources and find something that suites your own aesthetic preferences, or to develop your own poster from scratch. Just be sure that the visual design is intuitive, fonts and font sizes are readable, etc. The software you use does not factor into your grade for this assignment. Just be sure your visual design, content, and spoken presentation meet the assessment criteria in the provided rubric.
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## Assessment
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A full rubric for the poster presentation is in the [course outline](course_outline.pdf) (page 3).
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A full rubric for the poster presentation is in the [course outline](course_outline.qmd) (page 3).
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## Examples
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Here are some recent examples from the BEAT Lab. Your poster by no means needs to look like these! Feel free to choose whatever color schemes / templates you like! You will see that even within the same lab, there are very different styles of presenting a poster!
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