Extensive Python data processing, exploration, and visualization work from my 2014 study on rhythmic synchronization timing, in Jupyter notebooks. See also: dissertation manuscript, dissertation defense slides
- Part 1 - questionnaire scales: processing, visualizing, and transforming questionnaire data
- Part 2 - dataframe setup: structuring data (indexing and attributes for each timing measurement) and setting up an outer dataframe to store all tasks' measurements.
- Part 3 - ISIP tasks: motor timing variability measurements for equal-interval performance tasks
- Part 4 - SMS structuring and cleaning: processing timing measurement data for sensorimotor synchronization tasks
- Part 5 - SMS outcome variable setup & plotting: calculating and visualizing and calculating summary outcomes
- Part 6 - output array assembly: combining processed task outcomes to output summarized data for statistical modeling
- Part 7 - analyses in Python: minor statistical calculations needed for manuscript
- Part 8 - additional analyses after modeling in SPSS and R