aat-analysis

AAT Analysis

This package helps with analyzing mobile AAT data.

Install

pip install aat_analysis

How to use

#%run utils.ipynb # Some utility functions
#%run make_condition_templates.ipynb # Defines expected data based on resources
#%run json_to_df.ipynb # Turns raw json data into dataframes and calculates responses, rts, and force

from aat_analysis.make_condition_templates import make_condition_templates
from aat_analysis.json_to_df import json_to_df
from aat_analysis.utils import merge_data

#from aat_analysis.

Define folder paths

• raw should include the raw data from your experiment
• external should include the contents of the Resources folder of your experiment app
• interim and processed can be empty

external_folder = "../data/external/"
interim_folder = "../data/interim/"
raw_data_folder = "../data/raw/"
processed_data_file = "../data/processed/data.csv"

Preprocess data

# Creates empty dataframes to define expected data for each condition
templates = make_condition_templates(external_folder)
# Preprocesses data for each participant and moves it to interim
json_to_df(raw_data_folder, external_folder, interim_folder, templates)
# Merges interim data and stores it for further analysis
data = merge_data(interim_folder, drop=['interpolated','interpolated_gyro'])
data.to_csv(processed_data_file)

100%|█████████████████████████████████████████████| 3/3 [00:27<00:00,  9.24s/it]

AAT data

The selected columns below contain all data needed to calculate approach tendencies for each session, participant, and stimulus type. The additional data in the dataframe (not shown) are answers to other questions and some additional AAT variables.

data[['participant','condition','session','trial','is_practice','stimulus_set','stimulus','correct_response','response','accuracy','rt','force']]