Create a simple experiment in Psycho
Assignment: Create a simple experiment in PsychoPy and analyze the data using R or Python. You can recreate an existing experiment (e.g., the Implicit Association Task, the n back task, the mental rotation task), extend an experiment we have programmed in class, or design your own experiment. Requirements: 1. Program an experiment in PsychoPy. Your experiment should be ready to give to participants. That is, it should have: ○ A dialogue box to gather basic demographics data from the participant (e.g., participant ID, age, etc.). You can use the dialogue box to also assign people to conditions. ○ Instructions on how to complete the task. ○ An experimental manipulation of some kind. I am leaving this somewhat vague, but essentially your experiment needs to test the relationship between at least two variables. ○ A way of logging participant responses. E.g., your participants need to press a key or the mouse, and the response they make for a given trial needs to be logged. ○ An output file that you can later analyze. ○ Appropriate presentation of stimuli. If your task is a perceptual task or a reaction time task, display a fixation target before displaying the stimuli. Have appropriate blank screens between trials. ○ Feedback on participant responses. Not every experiment provides feedback, but in this case, practice how to programmatically give your participants feedback for correct or incorrect responses. (If there is another form of feedback you can think to give that isn’t “correct/incorrect”, that’s ok, too!). ○ Keep your task short. Your task should take participants no longer than 10 – 15 minutes to complete. It’s ok if it takes less time. 2. Collect data. Get a few friends or some classmates to do your experiment or run through your own experiment several times. Ideally, you’d have data from at least 10 participants. 3. Prepare your data. You can use either Python or R. You will need to import your data files onto Posit (if you don’t have a local version of Python or R on your computer). Get your data into tidy format or otherwise prepare your data to be analyzed. 4. Analyze your data. Again, you can use either Python or R. Conduct an appropriate analysis for your data (e.g., t-test, ANOVA, correlations, regression, etc.). Tip: when designing your experiment, consider the type of analysis you would need to conduct to analyze your data. You’ll want to design an experiment that you can analyze given your statistical background. If you design an experiment that requires an analysis you are not familiar with (e.g., say your data requires an ordinal logistic regression because your dependent variable is ordinal), you can opt for another type of analysis that closely approximates the one you can’t do (e.g., linear regression) and explain in your write-up why your analysis isn’t optimal. 5. Create a visualization of your data. Create a plot that demonstrates the patterns in your data. In most cases, this means showing both the individual patterns of data as well as the average across all participants. 6. Write a short report on your experiment. Your report should include: ○ A brief introduction: What’s your research question? What are your hypotheses? If your experiment is a replication or extension of a well-known study, what is that study? ○ A description of the methods: What did participants do in your study? What are the conditions? What does the stimuli look like? ○ A description of your results: How did you analyze your data? What did you find? Include both descriptive statistics (e.g., means and standard deviations) as well as the results from your inferential statistics. ○ A short conclusion: What can you infer from your study? What are the limitations of your study? I don’t care about exact length (quality is far more important than quantity), but as a rough guide, your report will likely be around 2 – 3 pages double spaced. Please format your paper in roughly APA format. We won’t be grading strictly on APA format (so don’t fret too much on it…), but having a consistent format makes it easier to grade. 7. Include your final code files. Your code should be easy to follow. Please leave comments to indicate what your code is doing. Your code should only contain relevant code. Grading: It will be broken down into three components: 1. The experiment (37.5%) 2. The data coding / analyses (37.5%) 3. The report (25%)
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